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Making Earth Observation Work (MEOW) for UK Biodiversity Monitoring and Surveillance, Phase 4: Testing applications in habitat condition assessment Final Report

29/04/2016

Making Earth Observation Work (MEOW) for UK Biodiversity Monitoring and Surveillance, Phase 4: Testing applications in habitat condition assessment

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A report to the Department for Environment, Food and Rural Affairs, prepared by:

Dr. Johanna Breyer Samuel Pike AFRSPSoc Dr. Katie Medcalf, CIEEM Environment Systems Ltd. 11 Cefn Llan Science Park Aberystwyth Ceredigion SY23 3AH Tel: +44 (0)1970 626688 www.envsys.co.uk and Jacqueline Parker Team Projects Ltd. 6 Holly Meadows Salters Lane Winchester SO22 5FQ When referring to this report please use the following citation:

Breyer, J., Pike, S., Medcalf, K. and Parker J. (2016) Making Earth Observation Work (MEOW) for UK Biodiversity Monitoring and Surveillance, Phase 4: Testing applications in habitat condition assessment. A report to the Department for Environment, Food and Rural Affairs, prepared by Environment Systems, Ltd..


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Acknowledgements

The project team would like to thank Paul Robinson (JNCC) and Helen Pontier (Defra) and the rest of the Steering Group for all their input and support during the running of the project. Our thanks also go to staff of Natural England for collating field survey data.


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Executive summary

Making Earth Observation Work (MEOW) for UK Biodiversity, Phase 4: Testing applications in habitat condition assessment is the fourth in a series of projects under the MEOW umbrella, commissioned by Defra and the JNCC. This project examines the usefulness of Earth observation (EO) for the assessment of habitat condition and change in condition, focusing on grassland habitats, which are a major element of the new Countryside Stewardship Scheme and European Commission (EC) Habitats Directive. The project has concentrated on scoping which EO techniques can be developed into a workable system for aspects of condition assessment, taking into account:

the increasing availability of EO, in particular from the Copernicus programme;

the wider context of the use of EO within Defra, including how delivery of EO indices of habitat condition can be developed in conjunction with other mapping initiatives, in particular the Living Map and EODIP activities;

the current status of field condition monitoring approaches.

The Crick approach, developed as part of MEOW, has been used to compile and extend our understanding of the EO requirements for mapping habitat condition of specific grassland habitats. There is scope to further develop the Crick tables for habitat condition to identify for any given habitat which EO indices are of relevance and why.

Four specific condition measures were examined for grasslands which can be identified by EO (‘extent of woody vegetation’, ‘presence of dead material’, ‘presence of bare ground’ and ‘seasonal productivity’). The study also considered which EO techniques could be used to distinguish the main condition measures within a given grassland type. We concluded that spectral reflectance values, the use of EO indices (photosynthetic vegetation and non-photosynthetic vegetation) and NDVI could all play an important part in a future monitoring scheme. There is also the potential of satellite radar imagery to give useful information on structure and heterogeneity within an individual grassland parcel.

The appropriate EO measures to use are very much dependent on the specific grassland type and further development of the Crick framework has begun to show how the specific grasslands and their condition relate to EO features.

The way in which an EO based system could be developed, structured and used is described, and illustrative examples are provided to demonstrate how user needs would be translated to mapped EO outputs relevant at the scale of individual sites but also informative at landscape scales.

The importance of drawing on, and working in conjunction with other data-driven initiatives is highlighted, in particular, using the Living Map for its content on habitat extent and location will be essential. It is suggested therefore, that there is co-ordination of effort with products being developed alongside each other (e.g., region by region).

A roadmap identifying the next steps in the development of EO tools, systems and approaches to support habitat condition assessment and monitoring has been produced and recommendations made on how the interface between EO and fieldwork can be strengthened by further structured investigations.


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Table of contents

Executive summary .................................................................................................................................. 3

Table of contents ..................................................................................................................................... 4

Table of figures ......................................................................................................................................... 6

List of tables ............................................................................................................................................... 8

1 Introduction ....................................................................................................................................... 9 1.1 Policy context ........................................................................................................................ 10

1.2 Aims and objectives .............................................................................................................. 11

1.3 Headline questions ................................................................................................................ 12

2 Earth Observation and condition monitoring ............................................................................. 12 2.1 How is habitat condition monitored? ..................................................................................... 12

2.2 Key EO concepts .................................................................................................................. 13

2.3 Optical remote sensing ......................................................................................................... 14

2.4 Radar remote sensing ........................................................................................................... 14

2.5 Key EO techniques ................................................................................................................ 14

2.6 The Copernicus programme ................................................................................................. 17

2.7 EO Data Integration Pilot (EODIP) ......................................................................................... 17

3 Selection of measures, test sites and data acquisition ............................................................. 18 3.1 Building on previous research ............................................................................................... 18

3.2 Test sites and data acquisition .............................................................................................. 20

3.3 Preliminary analysis of CSM data .......................................................................................... 30

4 EO case studies ............................................................................................................................... 33 4.1 Boundaries and extent of features ......................................................................................... 33

4.2 Productivity characteristics of features (NDVI) ...................................................................... 35

4.1 Basis of evaluation ................................................................................................................ 35

4.2 SAR radar .............................................................................................................................. 46

5 Using the Crick approach to consider EO needs for condition monitoring ........................... 49 5.1 The Crick Framework ............................................................................................................ 49

5.2 Additional complexity of an EO approach to habitat condition monitoring ............................ 50

5.3 Summarising EO needs ......................................................................................................... 51

6 Assessment of practical applications: ......................................................................................... 55 6.1 Limitations encountered by the project ................................................................................. 55

6.2 Addressing the headline questions ....................................................................................... 56

6.3 Considerations of scale ......................................................................................................... 65

6.4 EO condition concept prerequisites ...................................................................................... 68

6.5 Generating products ............................................................................................................. 68

6.6 Conceptual System ............................................................................................................... 68


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6.7 Next steps ............................................................................................................................. 69

7 Glossary of terms and acronyms .................................................................................................. 71

8 References ....................................................................................................................................... 74

EO to produce indices of habitat condition and change (Gerard et al, 2015)

76 Summary .......................................................................................................................................... 76

Bibliography..................................................................................................................................... 78

Generic benefits of EO based approaches ............................................................. 86

Optical remote sensing ............................................................................................ 88

Radar remote sensing .............................................................................................. 91

Sentinel-1........................................................................................................................ 94

Sentinel-2........................................................................................................................ 96


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Table of figures Figure 1.1: Chronology and relationship of projects funded under the MEOW umbrella (red boxes) or complementary projects by other initiatives (yellow boxes) ............................................................... 9 Figure 2.1: Change in SAR backscatter response from mature heather to burned ground at A .......... 15 Figure 2.2: An example of an NDVI temporal curve (Simonetti et al., 2014) ........................................ 16 Figure 3.1: Location of counties, within which the search for suitable case study sites was focused .. 21 Figure 3.2: Location and habitat designations of case study sites ...................................................... 24 Figure 3.3: Condition history of case study sites ................................................................................. 25 Figure 3.4: Subset of RapidEye image (left) and capture dates (right) ................................................ 27 Figure 3.5: Pan-sharpened VHR image supplied by RPA with noticeable compression artefacts visible. ................................................................................................................................................. 28 Figure 3.6: Subset of S-1A time-series imagery (left) and the dates of capture (right) ......................... 30 Figure 3.7: A pie chart illustrating the proportion of grassland conditions across England, and the proportion of candidate grasslands in favourable condition ................................................................ 31 Figure 3.8: The proportion of individual candidate grasslands and their respective condition status. Note that all candidate grasslands have only approximately 25% in favourable condition. ..... 31 Figure 3.9: Distribution of individual scrub cover survey records, for each candidate grassland within the case study sites. Note that the majority of survey stops were observed to have less than 5% scrub cover (the threshold value for a fail). ................................................................................... 32 Figure 4.1: AP image, segmentation, classification and spatial extent (%) of EO condition indicators for SSSI Aller Hill, Unit 4 ...................................................................................................... 34 Figure 4.2: AP image, segmentation, classification and spatial extent (%) of EO condition indicators for SSSI Aller Hill, Unit 5 ...................................................................................................... 34 Figure 4.3: Example of a spectral curve of a single pixel, at a single point in time .............................. 35 Figure 4.4: Productivity (NDVI) histogram for lowland calcareous grassland CG2, with AP and NDVI imagery for each SSSI analysed ................................................................................................ 37 Figure 4.5: Productivity (NDVI) histogram for fen meadow & rush pasture M22-23 with AP and NDVI imagery for each SSSI analysed ................................................................................................ 39 Figure 4.6: Productivity (NDVI) histogram for lowland calcareous grassland CG9, with AP and NDVI imagery for each SSSI analysed ................................................................................................ 41 Figure 4.7: Productivity (NDVI) histogram for purple moor grass & rush pasture M24-25, with AP and NDVI imagery for each SSSI analysed ......................................................................................... 42 Figure 4.8: Productivity (NDVI) histogram for upland calcareous grassland CG9-14, with AP and NDVI imagery for each SSSI analysed ................................................................................................ 44 Figure 4.9: Categorised NDVI analysis surrounding SSSIs, as a proxy for nitrate-rich areas ............... 45 Figure 4.10: SSSI Ruttersleigh Unit 3, Fen meadow & Rush pasture ................................................... 46 Figure 4.11: Changes in backscatter for single pixel over three years of a SAR time-series dataset (Breyer et al., 2015) ............................................................................................................................. 47 Figure 4.12: Time series SAR analysis of SSSI South Exmoor ............................................................. 48 Figure 5.1: ‘Data cube’ detailing the considerations for an EO approach ............................................ 51 Figure 5.2: Tier 1 to 5 of the Crick Framework for mapping habitat extent; after looking up the Tier associated with any habitat of interest, information on the data requirements for mapping using remote sensing methods are readily available. ................................................................................... 51 Figure 27: Example evaluation of condition change scenarios across different grassland habitats and suggested EO techniques for monitoring these............................................................................ 52 Figure 6.1: Processes present around and within a site ...................................................................... 65 Figure 6.2: Examples of spatial resolutions from different EO sensors, from low resolution (left) to very high resolution (right) ................................................................................................................... 66 Figure 6.3: Comparison of Segmentation from RapidEye (left) and pansharpened WorldView-2 (right) ................................................................................................................................................... 66 Figure 6.4: EO platform coverage vs level of detail captured .............................................................. 67 Figure 6.5: conceptual flow of EO condition analysis .......................................................................... 68 Figure 6.6: Roadmap highlighting the need for further research and testing ....................................... 70 Figure 8.1: The spectral reflectance curve for vegetation .................................................................... 88


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Figure 8.2: The profile of a leaf and how light is reflected and absorbed ............................................ 89 Figure 8.3: A RapidEye image displayed in CIR (NIR, R & G) ............................................................. 90 Figure 8.4: Interaction of vegetation canopy with different structural elements of a pine tree (adapted from Walker, 2010) ............................................................................................................... 91 Figure 8.5: Impact of target surface roughness on signal intensity ...................................................... 92 Figure 8.6: RGB satellite and multi-temporal SAR image of the North York Moors .............................. 93 Figure 8.7: Examples of statistical approaches to reduce radar speckle; a) raw image, b) spatial adaptive filter, c) multi-temporal filter .................................................................................................. 93 Figure 8.8: Spatial differences between the different acquisition modes ............................................. 95 Figure 8.9: Coverage of Sentinel-1 in IW mode over a 12 day period (European Space Agency, 2013) ................................................................................................................................................... 95 Figure 8.10: Sentinel-2 spectral bands and spatial resolution. ESA 2014 ............................................ 96


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List of tables

Table 2.1 Habitat condition concept terms used through this report and their definitions: .................. 12 Table 3.1: Cross-comparison to identify habitats with potential to make use of a range of EO derived measures of habitat condition (bounded in red and blue) (Gerard et al., 2015) ..................... 19 Table 3.2: CEH grassland habitat condition measures, and the potential for an EO approach ........... 19 Table 3.3: Summary of the steps for case study site selection ............................................................ 20 Table 3.4: Candidate site evaluation ................................................................................................... 23 Table 3.5: Case study sites with available historic field survey data .................................................... 25 Table 3.6: Historic and current survey indicators, and survey results. Note that the historic survey only provided Pass or Fail, compared to the current survey percentage values. ................................ 26 Table 3.7: Radar sensors considered .................................................................................................. 29 Table 3.8: Price comparison between similar single scenes of RADARSAT-2 and Sentinel-1 ............. 29 Table 5.1: Comparison of the terms used in the original Crick framework for habitat extent monitoring and suggested changes to adapt the Crick framework for habitat condition monitoring ... 49 Table 2.2: Benefits of EO-based approaches ...................................................................................... 86 Table 8.1: SAR wavelength bands ....................................................................................................... 91 Table 8.2: Radar channels................................................................................................................... 92 Table 8.3: Sentinel-1 image modes ..................................................................................................... 94


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1 Introduction

This study is the fourth in a series of projects, Making Earth Observation Work for UK Biodiversity (MEOW), commissioned by Defra and the JNCC, that are developing practical and cost-effective ways for earth observation (EO) to be incorporated into the management of natural capital in the UK.

Previous projects (Figure 1.1) assessed habitat extent mapping of areas of high nature conservation value and explored the potential and efficiencies of a range of uses of EO in informing policy and assisting practical land management decisions across the UK. A key aspect of this research was to build on previous experience, add value to ongoing complementary work and move techniques of EO closer towards a practical application for habitat condition and change assessment, accepted and useable throughout the habitat and biodiversity surveillance community.

Figure 1.1: Chronology and relationship of projects funded under the MEOW umbrella (red boxes) or

complementary projects by other initiatives (yellow boxes)

Phase 1 of MEOW (Medcalf et al., 2011) considered the effectiveness of a range of EO techniques for the mapping and surveillance of terrestrial and freshwater semi-natural habitats. The desk-study proposed an innovative evaluative framework, known as the Crick Framework, to group habitats based on the ability of EO and ancillary data to accurately map them.

Phase 2 of MEOW (Medcalf et al., 2013) built on these findings and implemented a pilot project, based on Norfolk, to test the Crick Framework approach for applied use of EO for operational habitat surveillance and monitoring of Annex 1 and Priority Habitats. The EO approach works by creating a rule-base that combines various data. The Phase 2 pilot also tested the use of EO techniques to measure four habitat conditions in small case study areas: vegetation productivity, single species stands of negative and positive indicators, wetness/dryness and freshwater metrics. The detailed content of the Crick Framework was peer-reviewed and further expanded in the light of new knowledge and experience.

Phase 3 of MEOW (Medcalf et al., 2015) developed a business case and costings to demonstrate the potential increased efficiencies and uses which would be derived from roll out of the Crick approach to habitat mapping at a country or UK level. It also involved further technical product

http://jncc.defra.gov.uk/page-6281


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development and testing of the transferability of the approach to the uplands and coordination and information exchange with others in the EO and conservation sector. Phase 3 further introduced the concept of a ‘Living Map’ which is constantly updated through fieldwork and occasional automated interpretation of imagery and would provide local stakeholders with an up to date habitat map, along with a record of the changes that have occurred over time. It could act as a powerful tool for identifying and targeting land and habitat management, along with monitoring change in habitat extent.

This project concerns Phase 4 of MEOW, which takes forward the MEOW concept, alongside previous projects and other initiatives to examine the usefulness of EO for the assessment of habitat condition and change in condition, using a series of case studies that focus on grassland habitats.

1.1 Policy context

The focus of the project is on applications that support the England Biodiversity Strategy Biodiversity 2020. Achieving favourable habitat condition is a key objective of Biodiversity 2020, the new Countryside Stewardship Scheme and the EU Habitats Directive. In the past, EO data has focused primarily on assessing habitat extent. Grassland habitats are the focus of this project, being a major element of the targeting of the EU Habitats Directive, the new Countryside Stewardship Scheme and being a very extensive habitat within the UK.

Currently habitat condition is assessed through field survey. Application of EO techniques potentially offers opportunities to reduce costs associated with field survey and to improve the consistency, coverage and currency of data, as well as the effective targeting of field surveys. EO methods also offer a wider range of applications in Mapping and Assessment of Ecosystems and their Services (MAES).

Condition monitoring of habitats to support policy delivery and reporting measures (indicators) shows some deterioration (long and short term), for example in the proportion of SSSI’s in favourable condition and the percentage of habitats of European importance in favourable or improving condition. There is also evidence of pressure on biodiversity from invasive species in freshwater environments & terrestrial environments.

A range of policy action and interventions are in place to tackle this situation. This means that on the ground, we might expect to see:

improvements in the condition of some grassland habitats (following successful implementation of policy intervention that involves changing management);

continued deterioration of the condition of grassland habitats (areas not subject to policy intervention but subject to negative drivers of change);

no change in condition (for grasslands being maintained in good condition, stable sites in unfavourable condition, or sites where management changes have not yet manifested in habitat change on the ground).

When considering assessment of practical applications, ideally an evidence-based system of condition monitoring using EO needs to enable policy makers and practitioners to provide the type of evidence that can be used to:

monitor and act as a warning system;

align with current fieldwork approaches and the nature of interventions;

provide a picture of the resource from site to national scale.


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1.2 Aims and objectives

The project sought to provide a practical test of the findings from the preceding CEH project (Gerard et al., 2015) and develop them further. It focused on testing and developing new methods in assessment of grassland habitat condition and changes in these within England. The overall project aims were to:

Identify EO based condition assessment methods for grassland that can support fieldwork.

Use case studies to develop new methods in assessment of grassland habitat condition and related ecosystem services and changes.

Establish benefits and practicality (what worked well, what didn’t, and why).

The detailed aims and objectives of the project specification are summarised below.

Proof of concept of condition monitoring:

Identify potential sources of data from existing inventories or sample-based surveys.

Select pilot areas to use for the practical test.

Investigate imagery availability and use this to shortlist pilot areas.

Acquire other data for use as training and validation.

Test the methods at regional and local scales to establish whether parameters calculated from that data can identify significant ecological changes in grassland condition.

Assessment of practical applications:

Establish what works well, what doesn’t and why;

Use headline questions to guide analysis, looking to address those set out in the previous project, as a starting point;

Assess the usefulness and practicality of EO techniques in targeted field surveys and estimate the associated costs (particularly in primary data/ field survey requirements) or savings, and other benefits, such as improved accuracy, frequency of update, and uses at local level;

Assess how EO could contribute to more accurate national-scale assessments of change in habitat condition delivered by Countryside Stewardship (CS)


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1.3 Headline questions

Based on the project aims and objectives given above, discussion with the Steering Group at the outset of this project and previous work, the following headline questions were created to be addressed by the project in order to facilitate the evaluation of case study analyses. These are addressed in section 4.

1. Which EO indicators / measures have the potential for condition monitoring? 2. Are there useful ways/approaches in which we can consider the roles of EO in contributing

to landscape and site assessment? 3. What EO outputs can support condition monitoring for grasslands and at what scale? 4. What field or other reference data might be needed? 5. What scope is there to create a working interface between field data and EO? 6. Is there scope for more automated methods of interpretation of EO data? 7. What can be shown over a regional area for rapid risk type assessment? 8. Is it likely to be possible to set-up threshold values for change? 9. How would we assess the cost effectiveness of producing EO measures? 10. How can we determine whether any useful sensitivity or additional information is obtained

by adding more EO data layers? 11. What does the project tell us about the likely resource and infrastructure needs going

forward in developing a condition mapping service?

2 Earth Observation and condition monitoring

2.1 How is habitat condition monitored?

The purpose of assessing and monitoring habitat condition is to establish whether the habitat are in a satisfactory condition compared to agreed thresholds for a range of condition indicators and whether condition has changed in a measurable way; this is generally assessed by field monitoring.

It is important to note that in many habitat condition monitoring programmes, the detailed measurements are of the feature(s) of interest within a bigger site (in the case of this project the specific grassland habitat). The condition of the site in which the habitat is located (e.g., a field or designated area) and the surrounding context are not necessarily monitored in any detail, although notes are often taken about site management and pressures (such as overgrazing) that are present.

For example, the purpose of Site Condition Monitoring of Sites of Special Scientific Interest (SSSI) is to determine the condition of the designated natural feature within a site. This is to establish whether the natural feature is likely to maintain itself in the medium to longer term under the current management regime and wider environmental or other influences.

Previous MEOW project reports have repeatedly referred to EO data and techniques as a “toolbox” to be employed in various combinations with and complementary to field surveys to answer questions around habitat extent and condition. To gain the maximum benefit for both applications the appropriate data and technical solutions have to be applied while considering factors such as scale, phenology and biogeography. This section aims to give a brief overview of the key EO concepts, techniques and data relevant to habitat condition assessment as well as current programmes supporting the feasibility of any future working system.

To ease the communication throughout this review and study, a number of key terms have been defined and are explained in Table 2.1 below.

Table 2.1 Habitat condition concept terms used through this report and their definitions:


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Habitat Condition Concepts

Definition/Examples

1 Field condition measures CSM (or other fieldwork) derived measures.

2 EO condition measures EO derived measures (Indicators and Indices that will aid the assessment of condition).

3 EO Indicators Extent related, spatially explicit EO condition measures (e.g., scrub, bare ground, extent of feature of interest).

4 EO Indices

Spectral Indices, e.g., NDVI, NDWI, Band ratios etc. which give information on productivity or wetness. Structural indices, e.g., derived from SAR backscatter profiles or standard deviation of spectral measures.

5 Site Key Factors E.g., biogeography, habitat component features, designation reasons, soil/geology.

6 Site Context Position in the landscape, isolation/connectedness.

2.2 Key EO concepts

EO, a specific subset of remote sensing, is the use of imagery from satellite and airborne systems for mapping and monitoring the Earth. It provides an accurate and repeatable methodology for ecological mapping and monitoring, particularly where vegetation is difficult to survey or study areas are remote.

A summary of key EO concepts is included within the Crick Framework User Manual which can be downloaded from the project pages on the JNCC website, available at www.jncc.defra.gov.uk/page-5563.

The most common types of EO used for habitat mapping include aerial photography and satellite-based optical sensors. Over the past 50 years there have been progressive improvements in the spatial, temporal and spectral resolution of these sensors, making them a valuable resource across a range of mapping scales for a variety of mapping requirements. Imagery at different working scales and timings can provide information from species-level right through to the wider area perspective, as well as tracking cause, effect and change which are not directly possible to ascertain with field methods due to practical or resource constraints.


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2.3 Optical remote sensing

Box 1: Optical remote sensing key facts summarises the remote sensing characteristics of optical imagery.

Box 1: Optical remote sensing key facts

Optical imaging can capture data at a range of suitable wavelengths, fundamentally related to the reflectance of light from a target.

It is affected by cloud cover, which can be a significant issue in the northern and western parts of the UK.

Large area coverage enables surveying at a range of geographical scales, at different resolutions and through time.

The use of wavelengths outside the visible spectrum (e.g., NIR and SWIR) provides data to develop new, robust and repeatable measures with information that is not visible to the human eye.

Generally speaking, higher spatial resolution systems are relatively more expensive and cover less area than lower spatial resolution systems.

Contextual, ancillary information (e.g., elevation) may be required to identify certain habitats.

2.4 Radar remote sensing

Box 2: Radar remote sensing key facts summarises the remote sensing characteristics of radar.

Box 2: Radar remote sensing key facts

Radar remote sensing is an active method of collecting data. Whilst passive systems (e.g., Landsat optical data) typically measure reflected light, active radar systems transmit microwave energy from the sensor and record the energy returned from objects interacting with the beam.

Radar can capture data at night and through most weather conditions, but is more complicated to process and interpret than optical imagery.

Radar measurements are fundamentally related to the structure of a target. Simply put, greater surface roughness leads to increased backscatter.

The wavelength and polarisation of a radar sensor dictates the type of features that will interact with the beam, and therefore the suitability of the system for application in various habitats.

Time-series based analyses of radar data provide the greatest information to the user.

2.5 Key EO techniques

There are a wide variety of remote sensing techniques that utilise EO data. This section focusses on those considered most applicable to habitat condition assessment, including measuring the productivity of a habitat, examining radar backscatter through time and the creation of spectral libraries to develop our knowledge and improve interpretation of EO characteristics relating to habitat condition over time.

Measuring productivity

Box 3 describes the Normalised Difference Vegetation Index (NDVI), which is a commonly used technique used for quantifying vegetation productivity from EO data. Measures of vegetation productivity derived in this way can be used to quantify how productive the vegetation is, allowing un-vegetated surfaces to be easily separated from vegetated ones, and differences in stands of vegetation within habitat patches to be separated. A time-series of data showing when species


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are in full growth and when they are senescent allows different types of vegetation to be separated from one another based on phenotypic seasonal and annual variation.

Box 3: Normalised Difference Vegetation Index (NDVI)

What it is

The Normalised Difference Vegetation Index (NDVI) is a well-established spectral index, it summarises the relative red and NIR reflectance values.

𝑁𝐷𝑉𝐼 = (𝑁𝐼𝑅 − 𝑅𝑒𝑑)/(𝑁𝐼𝑅 + 𝑅𝑒𝑑)

Why it is considered useful for habitat condition

The NDVI is related to vegetation productivity and has been in use for many years to measure and monitor plant growth (vigour), vegetation cover, and biomass production from multi-spectral optical satellite data. Chlorophyll in plants absorbs red light from sunlight, whereas the mesophyll leaf structure creates considerable reflectance in the NIR band (Tucker, 1979). As a result, vigorously growing, healthy vegetation has low red-light reflectance and high NIR reflectance, and therefore a high NDVI value. In terms of habitat condition there are times of year when we expect either a high or low NDVI in particular habitats of a specific condition status and tracking these expected responses over time would allow us to detect deviations from the expected trajectories indicating a change in condition.

Radar backscatter profiles

In a time-series of radar images, single and / or groups of pixel values (backscatter values) do oscillate naturally between images, but change dramatically when there is a major physical change in the vegetation structure that exposes the soil and / or makes the surface rougher (e.g., ploughing). This knowledge can be used to asses and monitor the profile of parcels of land. Those with management that does not lead to major changes in the vegetation (e.g., grazed permanent grassland) will demonstrate minor fluctuations in backscatter but where land has been cultivated for reseeding (e.g., a temporary grassland) or for a catch crop, a dramatic change in the backscatter is observed. Figure 2.1 provides an illustrative example where ‘A’ indicates the time that a major change in backscatter occurred (in this case heather burning). This characteristic has been previously shown to apply to permanent and temporary grasslands in the Norfolk Broads (Brown et al., 1999), heather burning in the North York Moors (Breyer et al., 2015) and meadow cutting in the Pennine Dales (Brown et al., 1999).

Figure 2.1: Change in SAR backscatter response from mature heather to burned ground at A


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Spectral libraries

If the location and extent of semi-natural habitat parcels are known, e.g., through a Living Map approach or spatial mapping in the field, it would be possible to use this information to create a “library” recording typical spectral and/or backscatter characteristics of specific habitats of varying, known condition. This approach could be used to set a baseline against which to compare the same responses from parcels of unknown condition as well as creating the opportunity to monitor and interpret trends over time, the first steps towards a monitoring system.

In practice, known spatially well-defined parcels of any habitat would be used to extract optical and radar characteristics from available imagery, both seasonal and multi-annual, which can be matched against known phenological cycles for any habitat type and the typical modelled signatures derived for each over time.

Increasing the knowledge of the typical ranges of spectral/backscatter signatures in this way, enhances the ability to correctly match these to various habitat condition scenarios. If the collected signatures of a habitat type deviate from modelled spectra/backscatter profiles for habitats of the same type and condition, this could trigger further investigation as an early warning system. More targeted EO analysis, complemented by appropriate fieldwork could then be applied to identify the possible cause of any change in condition.

With large sample populations, typical signatures across a range of biogeographical areas (e.g., from north to south) could be determined for many habitat types, including grasslands. Rarer habitats may provide fewer signature samples, but enough may be learnt about them to identify features relating to their condition through their unique spectral characteristics and profiles.

All spatial and spectral resolutions can be used to extract the spectral information, however, it would be preferable to extract these signatures from the same sensor over time, to ensure that the information gathered remains consistent as different sensors use slightly different wavelengths and it is important to reduce sources of uncertainty at this stage. Sentinel-2 imagery (see Appendix A) would provide a free data source for this purpose and captures large areas of land in a single pass, compared to higher spatial resolution sensors which are limited in the spatial extent of their acquisitions.

Figure 2.2: An example of an NDVI temporal curve (Simonetti et al., 2014)


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2.6 The Copernicus programme

Copernicus is the new name for the Global Monitoring for Environment and Security programme, previously known as GMES. This initiative is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). ESA is developing a new constellation of satellites, called Sentinels, specifically for the operational needs of the Copernicus programme.

There are currently two operational Sentinel missions, Sentinel-1 and Sentinel-2, with another four satellite systems planned, each with differing sensor payloads. The first two missions are particularly suited for land monitoring, Sentinel-1 being an all-weather, day-and-night radar imaging mission and Sentinel-2 a high resolution, multispectral imaging system. Each Sentinel mission will include at least two operational and identical systems in orbit simultaneously; allowing for very high temporal captures over the same location. A detailed description of Sentinel-1 and Sentinel-2 are included in Appendix E and Appendix F respectively.

In line with its data and information policy, access to Copernicus data, including the Sentinel missions, is free at the point of use for all member states, including the UK. ESA have also developed open source processing software, specifically for the Sentinel missions which can be downloaded and used for free.

The combination of free access, high spatial resolution, high temporal frequency, SAR and multi-spectral data, together with free to use processing software, provides powerful, robust and standardised tools for land classification and condition monitoring. This creates an environment in which techniques that previously were technically able to describe both habitat extent and condition, can now also be applied in an operational environment instead of just one-off studies.

2.7 EO Data Integration Pilot (EODIP)

The project described in this report is part of the EO Data Integration Pilot (EODIP), which is the first flagship project for the Defra EO Centre of Excellence (EOCoE). The EOCoE, working with SSGP, has developed a roadmap to identify and realise the unique potential of EO (EO) data for Defra and the UK economy over the next five years. The overall aim of the roadmap is to ensure satellite data are contributing to their full potential in policy development and operations across Defra by 2020. EODIP I is looking across a number of application areas and building the means for common requirements (data, methods, processing) to be delivered once and shared, achieving an additional level of efficiency and economies of scale. These projects are cross-cutting and will inform EODIP programmes and other related activities in the future.

One example is the EODIP intermediate layers project. This project is to help EODIP refine how intermediate products can be produced effectively, the quality needed in their production, the level of processing required and the level of validation needed to support use of the intermediate products over large areas and through time. It will test the value of intermediate product data set creation and delivery by focusing on three product types: Non Photosynthetic Vegetation (NPV) calculated using linear spectral un-mixing, Normalised Difference Wetness Index (NDWI) and Normalised Difference Vegetation Index (NDVI). The project will look at how a production system would operate and will produce some initial intermediate datasets.

There are a wide range of intermediate products that can be derived from the optical EO data sources. These can be valuable in their own right in informing land management activities, or may be an important contributing data set to analyses that need to use multiple geospatial data sources to inform land management and policy applications. The basic research on how to create intermediate products is done, and in many cases calculation is a standard feature in image processing software. For Synthetic Aperture Radar (SAR) EO data, the intermediate products that can be generated, the algorithms needed, and the applications are less developed. The findings of this project are of direct relevance and will help to demonstrate how a service and system for habitat condition monitoring can be progressed.


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3 Selection of measures, test sites and data acquisition

3.1 Building on previous research

This project follows on from a recent desk study (see Figure 1.1) by CEH (Gerard et al., 2015) that set out the evidence of the suitability of EO data and indices for the purpose of monitoring habitat condition and made recommendations for testing. The study proposed a three stage monitoring system:

Search for evidence of hotspots of change (less spatially detailed) in condition of pre-defined measures (indices) - using EO;

Where change is detected – more spatially detailed and quantitative evaluation of the type of change – using EO;

Sample and validate the measured change – using follow up fieldwork.

The CEH study identified habitats with the potential for application of EO-derived measures that appeared most cost-effective for assessing their ecological condition. This assessment was summarised in a cross-comparison (Table 3.1) of habitats and measures, grouping the semi-natural habitats into broad categories and correlating them with the generic measures identified that could be used to asses change in condition. Based on this, Gerard et al., (2015) suggested that initial pilot studies be carried out in grassland habitats because these cover a significant proportion of land cover in the UK and can change rapidly.


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Table 3.1: Cross-comparison to identify habitats with potential to make use of a range of EO derived measures of habitat condition (bounded in red and blue) (Gerard et al., 2015)

The condition measures selected for this study derive from the recommendations made in the research described above. Gerard et al., (2015) identified habitat condition measures that tie in closely with measures used in fieldwork in designated areas (e.g., Common Standards Monitoring). These included measures where ‘EO approaches already exist and which should be relatively easily adopted’ and a further set of measures ‘where there is clear potential to apply EO approaches’ despite a low readiness of the techniques themselves. When considering grasslands, this resulted in the following condition measures being selected for assessment by this study, listed in

Table 3.2:

Table 3.2: CEH grassland habitat condition measures, and the potential for an EO approach

Condition measure Conclusion of Gerard et al., 2015

Productivity (e.g., related to vegetation height, evidence of grazing/browsing, nutrient enrichment)

Clear potential to apply EO approaches and should be tested

Extent of woody cover Medium readiness from Vegetation Indices, low readiness from SAR

Extent of bare ground High readiness from Vegetation Indices, low readiness from SAR

Extent of dead material Clear potential to apply EO approaches and should be tested


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3.2 Test sites and data acquisition

Table 3.3 provides a summary of the steps taken to determine suitable case study areas and field data availability, as well as EO data and ancillary data to be used in this study.

Table 3.3: Summary of the steps for case study site selection

Step Rationale

1

Define broad geographic test areas of interest in England for establishing case study sites

Identify broad areas based largely on professional knowledge of presence and variation of grassland types and likelihood of suitable field survey data.

2 Search for suitable field data in the broad areas of interest

Approach conservation agencies and local partners to find field survey data containing generic condition measures (defined as surveys using CSM or Integrated Site Assessment field method techniques).

3 Assess suitability of field survey data

Use criteria to select suitable field data:

What is the range of grassland types covered?

Is the data consistent?

Does the data cover a suitable period of time?

Is the data spatially explicit?

Which types of conditions have been monitored in the field and how have they been recorded?

4

Select case study areas and sites and complementary field data

These will be at the farm / parcel level, identifying specific field / parcels with the best data, and time series of data, for analysis.

5 Searches for EO and ancillary data

Searches for a wide range of EO and ancillary data are carried out in selected case study sites (availability of WorldView-2/3, RapidEye and SPOT-5 imagery).

Step 1: Define broad geographic test areas of interest

The search for case study areas and sites was focused around five counties which had been identified as locations where potentially useful field survey data on grassland condition is available and which were agreed at the 1st Steering Group meeting: Yorkshire, Oxfordshire, Devon, Somerset, and Dorset (Figure 3.1).

Subsequently the northern area, Yorkshire, was selected due to the wide range of different grassland types potentially found in that biogeographic region. The southern areas were narrowed to Somerset and Devon for their downland grassland types and the wet grasslands that are subject to flooding.


21

Figure 3.1: Location of counties, within which the search for suitable case study sites was focused

Step 2: Search for suitable field data in the broad geographic areas of interest

The project sought to focus on repeatability (i.e., to test selected condition measures at several different sites) and to establish a time series wherever possible. It would not be possible to look at every site for every measure and the aim was to have 30 - 50 individual case study sites to test different measures on in order to allow for a variety of scenarios and a certain degree of redundancy.

Criteria were set to help direct the search for field data and enable the most suitable field data to be identified; the criteria were:

What is the range of grassland types covered?

Is the data consistent?

Does the data cover a suitable period of time?

Is the data spatially explicit?


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Which condition measures have been monitored and how have they been recorded?

Ideal data would be: o surveys that have collected the most promising measures of condition reported by

CEH; o surveys for which concurrent EO data is available; o surveys that have a before and after EO dataset and monitoring record (i.e., a

compatible time series); o those with area measures recorded (e.g., as percentages or extent); o those with repeat surveys with enough time between the surveys to allow for

identification of some sites where significant change has occurred between surveys’

In addition, data should be no older than the early 2000s, to ensure that suitable concurrent EO data can be sourced and should contain detailed information on habitat location and condition (sufficient to be used in a GIS for visual and statistical analysis in site selection, by comparing the spatial extent of the survey data to imagery, and extracting the size of the land parcels concerned).

Step 3: Assess suitability of field survey data for comparison with EO measures

Field surveys considered included SSSI habitat condition monitoring data, Agri-Environment monitoring data, surveys of the Environmental Change Network (ECN) sites as well as local grassland survey data held by biological record centres.

SSSI habitat condition monitoring data collected using the Common Standards Monitoring (CSM) technique was eventually chosen for consideration in the study on the basis of accessibility, usability and practicality with regards to the following factors:

the measures collected during fieldwork are those of interest to the project, including % cover measures;

it is consistently captured, for large numbers of sites of different grassland types and is geographically widespread across the country with a large number of sites in the larger test areas under consideration;

historic data exists that has been captured using the same consistent method.

However, it should be noted that while the CSM data often records percentage covers of the habitat of interest within each SSSI unit, the location and extent of the habitats are not routinely mapped and the data does not therefore fulfil the criteria of spatial explicitness defined in Section 3.2.2.

The most recent CSM survey data for the wider study areas and beyond, dating from the period 2010 to 2015, were compiled and supplied by Natural England (NE) in spreadsheet format. These datasets included information on the percentage cover of habitat extent, bare ground, litter and scrub, as well as the overall condition of each management unit (a SSSI may comprise one or more management units). In some cases the values recorded at individual points of transects had been recorded, in other cases only the average.

On receipt of the data, the spreadsheets were checked for their quality and completeness. The data received were of very variable quality across sites, reflecting inconsistency in recording survey findings in the field. There were many instances of missing and incomplete information, inconsistent transect recording styles (e.g., the same variable may be recorded as a percentage cover, or a binary Y/N) and slight variations within duplicate rows (e.g., all values in two rows are identical across all 52 columns of data, except one).

The information held within these surveys is valuable, and could be a powerful tool beyond their primary use for statutory reporting of SSSI condition. However, accessibility of the data was low, as it had to be created and compiled from information held across the country, as opposed to a central source or database.


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3.2.3.1 Choice of grassland types

A wide range of grassland types were present within the available field survey data.

Six grassland types were chosen for this study (four calcareous and two marshy (see Figure 3.4), based on expected differences in the presence of EO indicators (i.e., bare ground, scrub and litter) through time, their cohesive botanical characteristics and their similar management.

3.2.3.2 Preparation of the data

The field data were prepared for use within a GIS by using many-to-one spatial joins, linking the spreadsheet survey data which included information on the unique SSSI unit ID, to a SSSI unit shapefile that had been clipped to within the geographic test areas of interest. To maximise the capabilities of EO analysis, spatial thresholds of SSSI unit sizes were defined, as follows:

Any SSSI unit less than one hectare would not be considered for the study, in order to ensure a sufficient number of pixels in each management unit to produce robust and repeatable analysis (e.g., a unit of one hectare would contain only 100 Sentinel-2 pixels or 25 Sentinel-1 pixels for analysis);

Any SSSI unit greater than 400 hectares was excluded, due to the likely level of heterogeneity of the vegetation present;

Any SSSI unit with more than one habitat type was excluded, to reduce error and ensure the spectral values would relate to condition only as far as possible, rather than condition and habitat type.

Step 4: Select final case study sites and complementary field data

All remaining SSSI units (i.e., of the selected grassland types, within the broad geographic areas of interest, that adhere to the EO spatial thresholds) were visually assessed in VHR imagery or aerial photography to consider their individual contextual characteristics, including the topography, the surrounding habitats and the unit heterogeneity.

Following this process, there were 57 candidate study sites identified, for the six grassland types, within the three counties. The subset of Natural England SSSI data shows the semi-natural habitats to be well represented in terms of overall numbers in England (Table 3.4).

Table 3.4: Candidate site evaluation

Grassland Habitat

No. of management units

Northern counties Southern counties

Upland calcareous grassland 21 0

Lowland calcareous grassland

(3 types)

3 12

Fen meadow and rush pasture 2 10

Purple Moor Grass and Rush Pasture 2 7


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Figure 3.2 illustrates the spatial location of the selected case study sites for analysis, with their respective habitat type.

Figure 3.2: Location and habitat designations of case study sites


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3.2.4.1 Historic CSM

Within the selected case study sites, a historic survey search was undertaken to identify sites with multiple surveys in the period between the years 2000 and 2015.

These were initially requested for all 57 sites, but accessibility of the historic survey data was very low. It could not be determined if they were held in digital formats, scanned copies, original survey forms, or for some areas even if they still existed.

Through a visual analysis of the case study sites, 30 of the potentially most interesting sites were selected as a shortlist. Of this shortlist, 3 case study sites had available and useable (based on the criteria defined above) historic survey information as scanned copies (Table 3.5), the condition history of which is displayed in Figure 3.3.

Table 3.5: Case study sites with available historic field survey data

SSSI and unit number Grassland habitat

Pen-y-Ghent Gill, Unit 4 Upland calcareous grassland, CG9-14

Giggleswick Scar & Kinsey Cave, Unit 3 Lowland calcareous grassland CG9

Lune Forest, Unit 5 Upland calcareous grassland, CG9-14

Figure 3.3: Condition history of case study sites

It was not possible to accurately compare any archive EO imagery against the historic field surveys due to the binary (pass or fail) nature of recording the condition indicator information. Using bare ground as an example, a tick classed as an indication of passing the 10% cover thresholds could mean there was no bare ground evident, or up to 9.99% present. Similarly, and for the same reason, it was not possible to compare the historic survey results to the current survey beyond an approximate agreement measure of pass or failure against the defined thresholds. This discrepancy in recording field survey information highlights the requirement of standardising field survey data to include the observed units, as it is not possible to identify if any condition indicators have been improving or declining, or how close to the fail threshold an indicator might be (Table 3.6).

An EO approach would allow for systematic percentage monitoring of condition indicators to continue between field surveys and, if the field work conforms to recording the unit values, would validate and inform each other and assist land owners in their land management decisions.


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Table 3.6: Historic and current survey indicators, and survey results. Note that the historic survey only provided Pass or Fail, compared to the current survey percentage values.

SSSI & site unit ID Penultimate indicator Penultimate survey

Contemporary indicator

Contemporary survey

Giggleswick Scar & Kinsey Cave, Site unit 3

Cover of bare ground <10% (Pass / Fail)

Pass Cover of bare ground (%)

1

Cover of litter <10% (Pass / Fail)

Pass Cover of litter (%)

1

Cover of trees and scrub <10% (Pass / Fail)

Pass Cover of trees and scrub (%)

0

Extent of feature (Ha)

Not recorded


2.5

Lune Forest, Site unit 5



Not recorded



Not recorded



Not recorded


Not recorded


Not recorded

Pen-y-Ghent Gill, Site unit 4



1



0



1


not recorded


0

Step 5: Searches for EO data and ancillary data

Final searches of satellite imagery commenced once the case study sites had been selected. Both optical and radar imagery was acquired.

The image search process using the EOLi-SA (EO Link - Stand Alone) website and an explanation of the imagery available from the Rural Payments Agency (RPA) is given at the end of this section. The following images were selected for use:

11 RapidEye images sourced from the EOLi-SA website

1 WorldView-2 (pansharpened to 0.5m resolution) provided by RPA

Sentinel-1A Interferometric Wide Swath (IWS) dual-polarised (VV/VH) SAR data was acquired for the southern region of interest (15 separate dates from 4th February 2015 to 2nd October 2015 (Figure 3.4).


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3.2.5.1 EOLi-SA

Satellite imagery may be acquired through the Copernicus Space Component Data Access Portfolio (ESA, 2015), which documents the datasets that can be made available to Copernicus Users. These include both optical and radar systems at a range of resolutions, such as the WorldView platforms, SPOT-5, RapidEye, ERS and Envisat. These are accessed through ESA’s EOLi-SA (EO Link - Stand Alone) portal where EO metadata can be browsed and downloaded with a registered account. This is available at www.earth.esa.int/web/guest/eoli, where a java application can be downloaded on all major platforms.

Access was granted to the ESA EOLi-SA data catalogue for this project which allowed for the viewing and downloading of imagery metadata and datasets. The image search was not restricted to any particular satellite system, but was limited to the permissions granted through the registered user.

The metadata information provided gave no information on the quality or prior processing of the images, only the location, and so all search results had to be downloaded in order to assess their suitability for potential use in the study.

In total, ten RapidEye images from across the growing season, from 2011 to 2013, were downloaded and processed. A subset example of a RapidEye is displayed in Figure 3.4, together with the capture dates for each case study area.

Northern area (Yorkshire)

Southern area (Somerset & Devon)

2011 May 2011 Sep

Oct

2012 May (x2)

2012 May Oct

2013 July 2013 July (x2)

Figure 3.4: Subset of RapidEye image (left) and capture dates (right)

The value of these downloaded datasets was estimated to be approximately 12,255 EUR, plus VAT, for a single user licence, which demonstrates the potential of EOLi-SA as a data source.

It was found that only RapidEye imagery could be used within an analytical framework, as other satellite data available (such as the SPOT-5), were resampled to a low spatial resolution, missing bands useful for vegetation analysis or pan-sharpened (which, whilst increasing the visual resolution of the imagery, can change the spectral values and distort any analysis).

Pen-y-ghent

Ingleborough

Malham-Arncliffe

Boreham Cave

Birkwith Caves & Fell

Brants Gill Catchment

Birks Fell Caves

Pen-y-ghent Gill

Upper Wharfedale

Swarth Moor

Oxenber & Wharfe Woods

Scoska Wood

Strans Gill

Greenfield Meadow

Hawkswick Wood

Austwick & Lawkland Mosses

River Wharfe

Ling GillYockenthwaite MeadowsAshes Pasture & Meadows

Giggleswick Scar & Kinsey Cave

Foredale

Salt Lake Quarry

Langcliffe Scars & Jubilee, Albert & Victoria Caves

Deepdale Meadows, Langstrothdale


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3.2.5.2 Imagery held by the Rural Payments Agency (RPA)

A potential image source for very high resolution (VHR) data comes from the Rural Payments Agency (RPA), which acquires satellite data throughout England in predefined Control with Remote Sensing Zones (CwRS).

Permission was granted by the Rural Payments Agency (RPA) to use their 2015 HR and VHR satellite imagery acquisitions. Sixteen Remote Sensing Zones were programmed across England, three of which covered the regions of interest, but only one was successfully captured. The VHR imagery provided was derived from Worldview-2, and the HR from SPOT-6 satellite systems. Both of these captures were delivered as 4-band, pan-sharpened, 8-bit, 5km tiles (as provided to the RPA by subcontractors). The imagery would have been pre-processed following Joint Research Centre (JRC) guidelines (Kapnias et al., 2008), explicitly designed to optimise the images for visual interpretation during the CwRS process.

A visual analysis of the imagery concluded that it upheld the standards expected for visual interpretation purposes, however, due to the pan-sharpening algorithm, low bit-depth and compression of the data, spectral analysis would not have been possible (Figure 3.5). However, the image brightness, contrast and spatial characteristics of the pan-sharpened VHR imagery could be used within segmentation algorithms, to delineate spatial objects for use within zonal statistics from other data sources. This is demonstrated in Section 4.

Figure 3.5: Pan-sharpened VHR image supplied by RPA with noticeable compression artefacts visible.


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3.2.5.3 Radar imagery

Data from a variety of radar sensors (Table 3.7) are available for commercial applications and research projects, with a range of technical specifications, such as the different radar bands and polarisations. Airborne radar sensors are also available but have not been considered by this study due to cost, scale of imagery and because they would not be operationally feasible for a future national roll-out.

Table 3.7: Radar sensors considered

Sensor Wavelength & frequency (GHz)

Polarisation Orbit repeat cycle (days)

NovaSAR-S* S-band, 3.2 VV, VH, HH, HV 14

RADARSAT-2 C-band, 5.405 VV, VH, HH, HV, VV/VH and/or HH/HV

24

Sentinel-1 C-band, 5.405 VV/VH or HH/HV 12 (6)

TerraSAR-X X-band, 9.65 VV,HH, VV/VH and HH/HV

11

* launch planned 2016

Sentinel-1 data from the Copernicus programme is available with imagery dating back to early 2015, and can be searched for and downloaded free of charge with a registered account at ESA’s Sentinels Scientific Data Hub. A combination of a limited image budget and the large image swath of Sentinel-1 was the reason for the selection of this satellite system. An example of comparisons between Sentinel-1 and the similar RADARSAT-2 is provided in Table 3.8.

Table 3.8: Price comparison between similar single scenes of RADARSAT-2 and Sentinel-1

SAR system Mode Polarisations Swath (km)

Nominal resolution (m)

Price per scene

Sentinel-1 IW dual pol 250 20 Free

RADARSAT-2 Standard dual pol 100 25 >2000 GBP

Full technical details of Sentinel-1 are included in Appendix E.

Sentinel-1A Interferometric Wide Swath (IW) dual-polarised (VV/VH) SAR data was acquired through ESA’s Sentinels Scientific Data Hub for the southern region of interest. SAR data is best used through a time series analysis, therefore all available data captured with the same satellite geometry (i.e., where the sensor capturing the data was at the same location in orbit) is required.

Raw SAR data cannot be used as a standalone product; the data it holds needs to be processed to enable it to be an interpretable dataset and, in common with other imagery, requires correction for topographical effects. Tasks in image processing include aligning all the pixels in an image stack together (assuming multiple datasets of different times), calibrating the data values into decibels, correcting the imagery for terrain and reducing the influence of speckle.

As a proxy for a multi-year time series analysis, a near-complete single year Sentinel-1 time-series dataset was downloaded and processed, comprised of 15 separate dates from 4th February 2015 to 2nd October 2015 (i.e., from the start of Sentinel-1s operational captures and the start of data collection). Figure 3.6 shows a subset of the processed Sentinel-1A imagery in the southern region of interest, together with the dates of capture.


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Sentinel-1A capture dates

04Feb2015 11May2015

16Feb2015 23May2015

28Feb2015 04Jun2015

12Mar2015 22Jul2015

24Mar2015 15Aug2015

05Apr2015 20Sep2015

17Apr2015 02Oct2015

29Apr2015

Figure 3.6: Subset of S-1A time-series imagery (left) and the dates of capture (right)

3.3 Preliminary analysis of CSM data

After the selection of the case study sites, a preliminary analysis of the field survey data was undertaken to understand the overall condition of the different grasslands, and how the condition measures under consideration in this project vary across all the sites.

This analysis was first performed for all the six grassland types of interest, across all of England, assessing:

The proportion of units with multiple habitat feature;

Min / max / sum / mean and standard deviation of area;

Their general condition status (

Figure 3.7);

And how many unfavourable condition grasslands failed on one, two or more EO indicators.


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Figure 3.7: A pie chart illustrating the proportion of grassland conditions across England, and the proportion of candidate grasslands in favourable condition

The same analysis was performed on those grasslands sites that were considered of an acceptable size for EO analysis (i.e., greater than one hectare, less than 400 hectares). An example of how the condition status of the six grasslands varies across England amongst this subset is given in Figure 3.8.

Figure 3.8: The proportion of individual candidate grasslands and their respective condition status. Note that all candidate grasslands have only approximately 25% in favourable condition.

Each grassland type was then assessed individually, to identify the common causes of failure within the SSSI units, and the distribution of condition measures recorded. Figure 3.9 illustrates the distribution of the candidate grasslands that recorded scrub cover, against the target threshold of 5%.


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Figure 3.9: Distribution of individual scrub cover survey records, for each candidate grassland within the case study sites. Note that the majority of survey stops were observed to have less than 5% scrub cover (the threshold value for a fail).

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Fre

quency,

perc

ent

%

Scrub cover, percent %

Scrub cover of candidate grasslands >1ha, case study sites

Target

FenMeadow

LowlCalcCG2

LowlCalcCG3,4,5

LowlCalcCG9

PurpleMoor

UplandCalc


33

4 EO case studies

4.1 Boundaries and extent of features

The boundaries of semi-natural grassland habitat features would ideally have been delineated prior to this project through a spatial mapping technique (such as described through previous MEOW initiatives) with the features being mapped as distinct polygons of representative habitats either by previous field surveys or from imagery as data becomes available. If this were the case, it is possible that previously identified and mapped areas of grassland, or rather habitat features, could be monitored over time to detect a range of changes which might have taken place e.g., increase or decrease in spatial extent. Any boundary change could be a result of either an actual change within or around the grassland habitat feature or an artifact of the monitoring methodology:

previously poorly spatially described boundaries

confusion in the classification

Any boundary discrepancies for grassland habitats over time would warrant further investigation of the site. The geometry of change can be calculated through spatial statistics to identify the extent of ground features that have potentially increased or decreased, indicating a change in condition.

Case study

Figure 4.1 and Figure 4.2 shows two examples of purple moor grass and rush pasture from SSSI Aller Hill. Each unit has been segmented from a 2m spatial resolution WorldView-2 image using eCognition software, and classified into broad habitat land covers, including any spatial EO condition indicators. Using the spatial extent of each SSSI unit, it is possible to calculate the percentage cover of each identified spatial EO condition indicator, as detailed in Table 3.1.


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SSSI Aller Hill, Unit 4

Total SSSI area 2.9 Ha Grassland 32% Scrub cover 35% Leaf litter cover 33%

Figure 4.1: AP image, segmentation, classification and spatial extent (%) of EO condition indicators for SSSI Aller Hill, Unit 4

SSSI Aller Hill, Unit 5

Total SSSI area 2.8 Ha Grassland 36% Scrub cover 34% Leaf litter cover 30%

Figure 4.2: AP image, segmentation, classification and spatial extent (%) of EO condition indicators for SSSI Aller Hill, Unit 5

Recording the change in extent in features such as scrub and areas with a lot of leaf litter can help ascertain whether the grassland is in a stable seral stage or is in fact changing from one habitat type to another. SSSIs have been chosen here for the case studies as they generally represent excellent examples of their habitat types. Alteration of this type to another would therefore be seen as a negative change in condition, with scrubbing over perhaps the result of too little management and an increase in competitive grasses producing a large amount of leaf

Scrub cover

Grassland

Leaf litter cover

Other

Scrub cover

Grassland

Leaf litter cover

Other


35

litter indicating increases in the nutrient regime of the site. Using the combination of ecological theory and remote sensing knowledge it is possible to calculate how different remote sensing algorithms could be used to indicate ecological change.

4.2 Productivity characteristics of features (NDVI)

The methods outlined throughout this project require the development and collection of spectral signature databases, or libraries, for each habitat, within each biogeographical region and for each season, to be employed as part of a workable system of condition change assessment. The collection of these spectral profiles, over time and space, would allow for the creation of a comprehensive spectral library of different habitats, in different conditions, throughout the country, providing a greater understanding of the dynamics of vegetation spectral behavior, and thus creating a baseline model of the “expected” or “preferred” spectral response. This would be a crucial element to any EO-based assessment or monitoring program. An example of a spectral profile of a single pixel, at a single point in time, at a variety of wavelengths is shown in Figure 4.3.

Figure 4.3: Example of a spectral curve of a single pixel, at a single point in time

In order to asses any change, the baseline model of the habitat’s condition is required (spectrally through both time and space). The ability to utilise a single index (i.e., NDVI) is limited, without this prior spectral model knowledge. With the collection of a habitat-specific spectral library, a grassland’s unique properties can be analysed and assessed against the spectral model of that grassland (i.e., the expected spectral/index profile). Any statistically significant deviation from the model (for that habitat, for that EO index, for that season, in that biogeographic region) would identify the feature as one that requires further investigation.

Histograms for individual image bands/indices can be plotted, including information on the mean and standard deviation of values, and would allow for the visual and statistical characterisation and spectral separability of the different grassland conditions. This can be performed and analysed against a predefined model distribution of a favourable condition habitat (for that habitat, for that EO index, for that season, in that biogeographic region), or compared to collected histograms of previous years taking into account all factors influencing spectral responses. Any statistical deviation from either method would therefore indicate that a change may have occurred to that grassland type. For example, an area of grassland with a catastrophic increase in bare ground cover (i.e., from a landslide) could display a significant increase in the standard deviation of the NIR band, which would flag up this particular parcel in an automated monitoring system.

4.1 Basis of evaluation

The evaluation of results is based on generalised analysis of the project findings together with professional judgement about the practical application and development of an EO service for habitat condition assessment. The team’s EO experts and habitat practitioners attended a two day workshop to review all of the project findings, identify and consider the relationship with


36

related R&D activities, strategic initiatives and policy/scheme developments. The workshop was used to scope what a practical service might look like and to consider next steps.

Case study

The following figures visually demonstrate the variability found within SSSI grassland units, using histogram collections of NDVI values collected during the spring months of 2012. The choice of which SSSI units to analyse was restricted by the available data, and the units were selected based on the greatest number of different habitat features that were covered by a single season of imagery (i.e., 26th May 2012).

Five habitat types were analysed: Fen Meadow and Rush Pasture, M22-23; Purple Moor Grass & Rush Pasture, M24-25; Upland calcareous grassland, CG9-14; Lowland calcareous grassland, CG2 and Lowland calcareous grassland, CG9. The NDVI values were extracted from within each SSSI unit habitat feature, at a pixel level, in order to produce a histogram of NDVI value distribution across the site. The frequency values were then scaled into percentages, to allow for comparable analyses across all SSSIs of the same habitat type.

It is important to note that the following graphs do not represent a single habitat feature, but an entire SSSI area that may include areas of scrub, woodland, limestone pavement and similar features that are not predominantly the main habitat feature type. The output graphs offer a visual analysis of the differences within a habitat feature type, across SSSI units, but crucially, at the same point in time. If, as outlined above, individual features were mapped spatially, the spectral/productivity characteristics of each feature could be measured.

Below are the output histograms for the five habitat types, accompanied by an aerial and NDVI image of the SSSI unit analyses, to aid the interpretation.

The following figures illustrate the NDVI value distribution variations between sites containing the same habitat type, highlighting the relative differences in productivity. Direct comparison of single absolute values between sites is not a relevant measurement of condition due to inherent ecological and environmental variation impacting on averaged NDVI values

Lowland Calcareous Grassland, CG2

Both of the SSSIs analysed for lowland calcareous grassland, CG2, respond with near identical NDVI histograms. This would suggest that both of the areas are comprised of similar extents of habitat features and productivity (i.e., scrub and woody cover, and grassland). The AP confirms that both SSSIs demonstrate similar distribution covers of woody material and grassland, with the NDVI images suggesting that the lowest areas of productivity (green) relate to leaf litter, and the highest productivity (red) with the calcareous grassland itself.


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SSSI Batcombe Down

SSSI Mapperton and Poorton Vales

Figure 4.4: Productivity (NDVI) histogram for lowland calcareous grassland CG2, with AP and NDVI imagery for each SSSI analysed

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

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Lowland Calcareous Grassland, CG2

Batcombe Down Mapperton and Poorton Vales


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Fen Meadow and Rush Pasture M22 & M23

Three of the four SSSIs analysed for Fen Meadow display very similar NDVI responses being generally very productive and mostly following a left-skewed distribution, indicating some areas of low productivity present. Blackmore Vale Commons & Moors, however, illustrates a bimodal (double-peaked) distribution, suggesting almost equal percentages of high and low productivity. This is confirmed by both the AP and NDVI imagery, which show the grassland areas of Blackmore Vale Commons & Moors are significantly more unproductive than their Fen Meadow counterparts.

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SSSI Blackmore Vale Commons and Moors

SSSI Drakenorth

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Fen Meadow and Rush Pasture, M22 & M23

Blackmore Vale Commons and Moors Drakenorth

Lambert's Castle Ruttersleigh


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SSSI Lambert's Castle

SSSI Ruttersleigh

Figure 4.5: Productivity (NDVI) histogram for fen meadow & rush pasture M22-23 with AP and NDVI imagery for each SSSI analysed

Lowland calcareous grassland, CG9

The lowland calcareous grassland SSSIs show differences in the NDVI histograms in both intensity (percentage of frequency values) and peak productivity. Giggleswick Scar & Kinsey Cave SSSI demonstrates a significant peak at the extreme end of productivity, but with a slight left-skewed distribution. This could be predominantly comprised of highly-productive grassland, but with elements of less productive material such as leaf-litter. The aerial imagery confirms that these less productive elements are limestone pavement. Bastow Wood SSSI’s histogram suggests a relatively highly productive area, which might be woodland or less productive grassland, which is confirmed in the AP and NDVI imagery. Whernside SSSI peaks at the equivalent productivity of woodland, but with an extreme left-skewed distribution, suggesting a higher proportion of less productive material, such as leaf litter or limestone pavement. The AP and NDVI confirm the distribution of limestone pavement, but that the productive habitat is the lowland calcareous grassland, not woodland, illustrating the requirement for using all available information for interpretation.


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SSSI Bastow Wood

SSSI Giggleswick Scar & Kinsey Cave

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Lowland calcareous grassland, CG9

Bastow Wood Giggleswick Scar And Kinsey Cave Whernside


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SSSI Whernside

Figure 4.6: Productivity (NDVI) histogram for lowland calcareous grassland CG9, with AP and NDVI imagery for each SSSI analysed

Purple Moor Grass & Rush Pasture, M24/25

The purple moor & rush pasture SSSIs produce very different NDVI histogram results, with each graph describing the area and EO condition indicators with some success.

Birkwith Caves & Fell SSSI is the least productive of the four areas assessed with a right-skewed distribution suggesting the low productive material is the most dominant feature of the area. The AP and NDVI confirm that the dominating single, unproductive habitat is purple moor grass & rush pasture, with some elements of more productive grassland.

The NDVI histograms for Deadman and Ringdown SSSIs have a similarly productive peak and are both right-skewed distributed (suggesting a significant proportion of less productive vegetation and/or material), though Ringdown SSSI has a proportionally higher dominance of productive vegetation. Both of these SSSIs contain a high proportion of woody cover, with Deadman including a significant portion of grassland, which would explain the steady increase in productivity, relative to Ringdown.

The plateau distribution of Ingleborough SSSI suggests several habitats with normal distributions combined, ranging from mid to high productivity but with no dominant habitat feature. The AP and NDVI confirm a near equal split between purple moor grass, rush pasture and relatively highly productive grassland.

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Purple Moor Grass & Rush Pasture, M24/25

Birkwith Caves And Fell Deadman Ingleborough Ringdown


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SSSI Birkwith Caves & Fell

SSSI Deadman

SSSI Ringdown

SSSI Ingleborough

Figure 4.7: Productivity (NDVI) histogram for purple moor grass & rush pasture M24-25, with AP and NDVI imagery for each SSSI analysed


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Upland calcareous grassland, CG9-14

The upland calcareous grassland SSSIs show relatively similar right-skewed distributions and productivity peaks, suggesting similar ground cover of predominantly grassland with some cover of bare ground or leaf litter. The NDVI histogram clearly shows a dramatic difference between Upper Wharfdale SSSI and the other four examples, by illustrating a much higher percentage cover of highly productive vegetation, identified in the AP and NDVI as potentially improved grassland.

SSSI Appleby Fells

SSSI Ingleborough

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Upland calcareous grassland, CG9-14

Appleby Fells Ingleborough

Malham-Arncliffe Oughtershaw And Beckermonds

Pen Y Ghent Gill Upper Wharfedale


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SSSI Malham-Arncliffe

SSSI Pen Y Ghent Gill

SSSI Oughtershore and Beckermonds

SSSI Upper Wharfedale

Figure 4.8: Productivity (NDVI) histogram for upland calcareous grassland CG9-14, with AP and NDVI imagery for each SSSI analysed


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Productivity charcteristics of features: Conclusion

The above graphs (Figure 4.4; Figure 4.5; Figure 4.6; Figure 4.7 and Figure 4.8), which visually draw attention to the differences found between habitat features within SSSI’s, demonstrate the robust and informative nature of NDVI distribution as a measure for monitoring EO condition indicators.

If, after applying a broad-scale analysis of EO indices across a biogeographic region, any grassland land parcels have been identified as potentially unfavorable (i.e., not adhering to the favourable spectral responses expected for that habitat, in that biogeographical region, for that season, using that EO index), then a smaller scale (i.e., field and/or site scale) analysis would be required to investigate the reason for the spectral deviation, related to the EO indicators of scrub cover, bare ground cover, litter cover and the physical change in the feature of interest.

This method can be used within previously identified habitat boundaries, across SSSIs or through time as regular health checks, where any significant deviation from the expected or favourable condition would warrant further investigation. The collection of these distributions could also feed into any Living Map initiative, or the condition spectral library to aid monitoring over time of both habitat extent and condition.

4.1.1.1 NDVI as a proxy for risk

By expanding the EO indicator analysis outside the SSSI or feature of interest, it may be possible to identify the reason for a decline in condition, or target features at risk, particularly from nitrogen leaching from nearby agricultural practices. NDVI analysis has been used within the precision agriculture industry to identify those areas of a crop that are nitrogen-defienct or nitrogen-overfertilised (Tremblay, N., Wang, Z., Ma, B., Belec, C. and Vigneault, P., 2009) A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application (Presision Agricultyre, 2009, 10, 145-161).

A simplified NDVI analysis is demonstrated in Figure 4.9, on a 1km buffer of SSSI features. The SSSI and buffer zones are segmented using available EO imagery, with the buffer areas graded into areas of low, medium and high NDVI values, from growing season imagery, as a proxy for nitrogen-rich surrounding grasslands.

Figure 4.9: Categorised NDVI analysis surrounding SSSIs, as a proxy for nitrate-rich areas

This could form the basis of an initial assessment, so that if a SSSI or an area of interest borders an area of high NDVI, a warning for further analysis is triggered. For example, if the surrounding agricultural land class is known (i.e., through anomonised RPA field data) and cross-referenced

High

Medium

Low


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with the typical quanities of chemical input required for that agricultural class, an estimate on the volume of fertiliser being applied, on a field scale, can be calculated.

4.2 SAR radar

SAR radar is a powerful tool in EO remote sensing, particularly because of its ability to capture through cloud cover, but also because the output imagery is based on physical measurements of the environment. Coarse measures of biomass can be determined (Gerard et al., 2015), which indicates the volume of vegetation, as well as its structure and sward composition. Figure 4.10 shows a temporal Sentinel-1 C-band VH polarisation image over SSSI Ruttersleigh, comprised of 3 separate capture dates, in February, June and October. The time series composite shows the differences in vegetation structure throughout the year, with redder hues indicating more structure prevalent in February than any other month, greener hues for more structure in June, and bluer hues for October. The whiter and/or blacker areas indicate very little change throughout the year (e.g., the woodland and hedgerows).

Figure 4.10: SSSI Ruttersleigh Unit 3, Fen meadow & Rush pasture

A time-series spectral analysis of backscatter can be very sensitive to changes in vegetation structure, particularly when using a cross-polarisation system (Gerard et al., 2015). The potential for using Sentinel-1 to analyse the condition of heathland has been explored as part of the Space for Smarter Government Programme (SSGP) (Breyer et al., 2015). The key findings indicated that time-series analysis is essential in order to identify those areas that have experienced a change in backscatter (either gradually, or as a rapid response), as there is a natural variability in the signal that would hinder any direct image-to-image comparison. Figure 4.11 illustrates the backscatter signal of a single pixel of Calluna over three years. Despite the natural variability, the signal remains stable until point A, where a burn event occurs and the Calluna is replaced by moist bare ground. If the plot was to continue, and the Calluna was allowed to fully regenerate, the high backscatter recorded at A would gradually reduce until it resembles a pre-burn signal.


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Figure 4.11: Changes in backscatter for single pixel over three years of a SAR time-series dataset (Breyer et

al., 2015)

If a library of backscatter responses was collected, through a year or more (i.e., from Sentinel-1) for a particular habitat, a model of expected backscatter responses could be calculated. Continuous monitoring of backscatter values against a model library, would allow any significant changes to the structure of the vegetation (i.e., through an increase in dry, bare ground or scrub encroachment) to be identified, and flagged for further analyses.

Case study

The time-series aspect of analysis is highlighted in Figure 4.12, which illustrates the average Sentinel-1 backscatter intensities collected within the SSSI South Exmoor, Unit 18 (purple moor grass & rush pasture) over a period of 10 months. Here, the SSSI unit has been segmented using RapidEye imagery, with some polygons manually identified into broad habitat categories (as a proxy for digitised field-collected habitat data). Zonal statistics extracted the mean VH sigma naught values from each habitat, for every Sentinel-1 capture date.

The results clearly show an increase in sigma naught has been measured over the areas of Calluna-rich habitat, when compared to the other grassland mosaic habitats, as would be expected given a higher presence of wet, woody structural vegetation. If a parcel of land or protected area was previously identified as (or thought to be) calcareous grassland, but the sigma naught signal was characteristically higher than expected, it could be an indicator for the increasing cover of scrub in that area.

However, the phenological pattern of the Calluna signal through the 10 months closely mirrors that of the Molinia (observing the dotted trend lines). On closer inspection of the AP within SSSI Exmoor, the Calluna-rich swards do contain proportions of Molinia and Ulex. The backscatter graph could also illustrate the seasonal growth pattern of the abundant species analysed, as well as the common or frequent species. It is important to understand that there is a natural variability found within SAR intensity data (the sudden increase then decrease in intensity during March is a reasonable example). In both the Calluna-rich and Molinia-rich habitat polygons, a steady but rapid increase in backscatter occurs during the spring months which could represent the emergent vegetation following winter. The backscatter then decreases slowly as the sward reaches and maintains a ubiquitous and homogeneous appearance. If the phenological pattern of the habitat to be analysed is understood in terms of SAR backscatter, a statistical deviation from what is expected could be a result of other species (or scrub, leaf litter or bare ground) emerging within the sward.


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Figure 4.12: Time series SAR analysis of SSSI South Exmoor


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5 Using the Crick approach to consider EO needs for condition monitoring

5.1 The Crick Framework

The Crick Framework (Medcalf et al., 2011) was developed as a conceptual tool to assess the role of EO in identifying approximately 120 types of semi-natural land habitat in the UK. It addresses the capacity of EO to monitor habitats and defines the EO requirements for habitat mapping. It has been used as a starting point to help conceptualise and consider the EO requirements for measures that can support habitat condition monitoring.

As part of the Crick approach for habitat monitoring, an analysis was carried out to determine the EO characteristics and mapping needs for each habitat. A similar approach could be adopted for habitat condition monitoring with some modifications to the detailed framework tables. The suggested modifications are shown in Table 5.1.

Table 5.1: Comparison of the terms used in the original Crick framework for habitat extent monitoring and suggested changes to adapt the Crick framework for habitat condition monitoring

Crick Framework for habitat extent monitoring (Medcalf et al., 2011)

Suggested Crick Framework for habitat condition monitoring

Term Description Term Description

Habitat

Detailing the type of habitat and where definitions are obtained from

Condition measure

Detailing and describing the condition measured and from where definitions are obtained.

Biogeography

Describes the geographical range and known location of the habitat, as well as the scale at which it is commonly found and variations to be expected

Relevance / associations

Describes the habitats to which this condition applies, which regional controls can be used and which other condition measures to which this one is linked.

Habitat character

Details all relevant information about the habitat, e.g., dominant species, the substrate it occurs on, temporal changes over different time scales, etc.

Condition measure definition

Details all relevant information about the condition measure; e.g., the unit of measurements, the spatial extent at which it is measured, threshold types, etc.

EO datasets required

Details of the minimum requirements for the EO data, spatial, spectral and temporal, to meet the habitat character defined above

EO datasets required

Details of the minimum requirements for the EO data, spatial, spectral and temporal, to meet the condition measure defined above

Ancillary datasets required

Details of the minimum requirements for the ancillary data to meet the habitat character defined above

Ancillary datasets required

Details of the minimum requirements for the ancillary data to meet the condition measure defined above


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Method

A description of the methods that have been applied successfully or could potentially be applied.

Method

A description of the likely methods that could potentially be applied to derive the condition measure.

Accuracy

Details how accurately and with which level of confidence the habitat can be mapped

Accuracy

Details how accurately the condition measure can be mapped and the confidence in the results’ in relation to habitat condition

How far can EO go?

A summary of the role the EO would play.

How far can EO go?

A summary of the role the EO would play.

Level of definitions

Information on how clearly defined the habitats and habitat interactions are

Level of definitions

Information on how clearly defined the condition measure is, how specific it is

Habitat Tier Which Tier level the habitat is in

Condition Tier Which Tier level the condition measure is in

Status

Details common problems and how operational mapping of this habitat is

Status of the condition measure with EO

Details common problems and how operational mapping of this condition measure is

Features pertaining to good condition

Which features indicate good habitat condition

N/A N/A

Mechanisms for monitoring

Requirements for a fully operational monitoring system

Mechanisms for monitoring

Requirements for a fully operational monitoring system

5.2 Additional complexity of an EO approach to habitat condition monitoring

When conceptualising how EO can support habitat condition monitoring it becomes clear that data availability requires consideration at three levels (Figure 5.1).

EO measures look at the different types of remotely sensed imagery that is available for the region of interest.

Different grassland habitats consider the type of data available for different grassland types that can be used for rule base training and validation of EO results.

Site context looks at a wide range of considerations that impact upon ancillary data needs and other information regarding the features of interest that lie outside the remotely sensed imagery.

It becomes clear that the process is more complex when dealing with three dimensions, in contrast to habitat extent mapping.


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Figure 5.1: ‘Data cube’ detailing the considerations for an EO approach

5.3 Summarising EO needs

An output of the Crick Framework is a ‘Tier table’ illustrated below (Figure 5.2) that categorises and summarises EO data needs for the range of habitats considered.

Figure 5.2: Tier 1 to 5 of the Crick Framework for mapping habitat extent; after looking up the Tier

associated with any habitat of interest, information on the data requirements for mapping using remote sensing methods are readily available.

The Crick Framework groups habitats into one of five Tiers depending on their data requirements for classification using a desk-based EO analysis. For instance, small scale or narrow habitats such as field margins can only be mapped with spatially detailed image data, and are therefore placed in tier 2b or 3b. Similarly, certain habitats are only associated with particular geological substrate conditions so are likely to be in the tier 2c or 3c.


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Figure 5.3: Example evaluation of condition change scenarios across different grassland habitats and suggested EO techniques for assessing these (summarised from “Grassland_Condition_Table_DRAFT_250116.xls”)


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Review of the Crick tables for condition monitoring

The adapted and populated Crick tables for condition monitoring of six grassland types, as outlined in Table 5.1, are appended as an attachment to this report (“Grassland_Condition_Table_DRAFT_250116.xls”). A summary of an example evaluation of likely condition change scenarios for various grassland habitats and the EO techniques suitable to assessing these is given in Figure 5.4.

The main picture emerging from this comparative approach is that there are some obvious commonalities across the habitats, which is promising for the development of EO-based indicators that can be used in a variety of change situations.

Whilst compiling the tables further notes were made and six aspects of key learning were identified:

Key Learning 1: Most of the changes being identified under the CSM are looking to detect whether grassland is

transitioning to another habitat type or to a less desirable grassland type.

The field condition measures are really looking at where there is evidence that the type or overall nature of the grasslands, are changing. For example, is the heavily grazed species-rich CG2 sward transitioning to a ranker CG4/5 sward because of an absence of grazing? In the field this change would be spotted by indicator species and the change in the fundamental litter/ species relationships. These types of subtle within sward changes cannot be picked up from 10m resolution satellite imagery. However, EO indicators and indices could provide a good baseline from which to assess change in overall structure and productivity, as well as well-drawn boundaries for the extent of each grassland polygon.

Key Learning 2: If the site is designated as an example of a particular type of grassland then the type of grassland

and its ecology will be key to how change in condition should be assessed and monitored and

what would signal a change in the habitat to a less desirable state/ another form of grassland.

Individual types of grassland have their own distinct makeup and pattern of bare ground, litter, scrub and productivity. In some grassland types, a small amount of bare ground is an important part of a healthy, functioning grassland. Some have high litter content and some no or little litter content.

Key Learning 3: Some suggestions have been made about the degree of change which would indicate an undesirable shift in status of the grassland, but further research is necessary to document this for the key grassland types.

The likely changes in each grassland type to indicate gradual decline towards an undesirable grassland type rather than those accompanying a catastrophic event/sudden management-induced change, will need to be documented for each community.

Key Learning 4: Different EO techniques will be needed to identify change from different types of grassland. In most cases changes in productivity at a particular season or overall would be the best feature to pick up this gradual change. In others, the change in heterogeneity of the site would indicate the change in condition. For example productivity is already high in marshy grasslands and will be largely unrevealing. Boundary shifts on the edge of these habitats will be the best way of monitoring these sites for change.


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Key Learning 5: If the extent of each habitat is well documented Radar will be worth exploring as a way of monitoring swards where the change in heterogeneity is the key factor, rather than a change in productivity, (for example limestone pavements and marshy grasslands).

Key Learning 6: Where grassland types fall under Crick Framework type 4 (e.g., species-rich Rush Pasture – which has the same phenotypic appearance as species-poor rush pasture but a low frequency of small herbs within the sward), EO will not be a suitable method for the assessment of condition. However, it would still be possible to monitor for gross changes within known species-rich rush pastures, or those that are likely to be species rich, from EO data and this might be a valuable tool where fieldwork is not feasible across large or remote areas.


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6 Assessment of practical applications:

6.1 Limitations encountered by the project

The ‘proof of concept’ aims and objectives set out to use field-derived data on habitat condition to test and demonstrate the efficacy of proposed EO measures. Historic field data was collected and analysed for its appropriateness for producing evidence that EO can provide useable indicators of ‘change in condition’. This data was gathered from SSSI habitat condition monitoring data, Agri-Environment monitoring data and surveys of the Environmental Change Network (ECN) sites. However, when analysed there were significant limitations for the use of this data as a comparator for EO. This being the case it has only been possible to deliver comparisons in a qualitative (rather than quantitative) way and for a very limited number of sites (see Section 4).

The main limiting factor was that EO techniques are spatially explicit, that is the spectral properties of a particular polygon or area of the earth surface are examined to give information on habitat type and condition. The field survey methods were designed to collect detailed information on the species present but were not spatially explicit and many records of different habitat types are collected in the same polygon / unit drawn on the map. With up to six different habitats present in a polygon and no indication of their exact location it was not possible to disaggregate the data into specific locations for each habitat type to compare against the EO imagery. There was indeed no indication if the area was comprised of discrete blocks of smaller habitats, an intricate mosaic or if there were ecotones and boundaries. Having spatially explicit data is fundamental to EO validation (MacLean and Congalton, 2012). Because of this, changes in features over time could not be linked to any specific area and there was no possibility of looking at historic change and EO.

The field data also had a number of inconsistencies such as survey data which should have indicated poor condition (based on the scoring criteria), being marked as ‘good condition’; these records were therefore considered suspect and dropped from the analysis. This cut down the number of useable surveys even further and resulted in a change in scope of the project.

The project has therefore concentrated more on the delivery of the ‘assessment of practical applications’, scoping how a workable system could be developed, taking into account:

The current status of field condition assessment approaches;

The wider context of the use of EO within Defra, including the development of the concept of a Living Map;

The increasing availability of EO, in particular from the Copernicus programme.

It makes recommendations for next steps in the development of EO tools, systems and approaches to support habitat condition assessment and monitoring.


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6.2 Addressing the headline questions

HQ1. Which EO indicators / measures have the potential for condition assessment and monitoring?

As part of the Crick approach for habitat monitoring, an analysis was carried out to determine for each grassland habitat, what the EO characteristics and mapping needs are for each of the condition measures assessed (vegetation productivity, extent of dead material, extent of bare ground, percentage of woody cover). Based on this analysis, supported by qualitative evidence from the case studies, we conclude that all four measures have the potential for contributing to condition assessment and monitoring but the usefulness of each condition measure is determined by the grassland type and its ecology. We have described products (see HQ2) and outlined a ‘roadmap’ towards delivery which will be compatible with the concept of a ‘Living map’, other EODIP initiatives and plans for the EO Centre of Excellence.

In terms of providing quantitative evidence through practical testing, the findings from the project do not provide any further substantive evidence. It was not possible to test the reliability of the EO indicators with the field data available. Field data was not suitable to support an analysis of change due to limitations in the way the historic field data had been recorded. Recent field survey had limited value in supporting the study primarily because the area of the habitat of interest in SSSIs is rarely mapped with an accurate boundary, meaning:

EO would derive measurements for the whole SSSI unit;

Field survey is based on measurement for different unknown areas within the unit.

There is scope to overcome this issue through creating accurate maps of the extent of the relevant habitats either through a combination of the activities of MEOW, specifically through the Living Map together with CSM fieldwork (e.g., mapping using tablets).

NDVI has potential for condition assessment and monitoring as an indicator of productivity. The study sought to establish whether coarse and medium scale EO indices of NDVI are sensitive enough to detect change in condition. Analysis of the outputs of the Crick framework suggests that they could potentially be very useful for some grassland types where change in condition is associated with an increase in productivity in either the summer or early spring.

The study also considered which EO techniques could be used to distinguish the main condition measures within a given grassland type (bare ground, presence of scrub (woody cover), dead material (vegetation litter) and productivity). We concluded that spectral reflectance values, the use of EO indices (photosynthetic vegetation and non-photosynthetic vegetation) and NDVI could all play an important part in a future monitoring scheme. There is also the possibility that RADAR may give useful information on the structure and heterogeneity within an individual grassland parcel. The measures that are appropriate are very much dependent on the grassland type (See Crick Condition Table). The road map outlines a plan for how work on how the ecological features tie in with EO analysis in order to increase the readiness of a future operational system.

Intermediate layers, particularly if they are established as a time series, should have a significant role to play in the development of the future monitoring system. At a regional scale such layers may also provide significant condition information in terms of the landscapes themselves.


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HQ2. Are there useful ways/approaches in which we can consider the roles of EO indices in contributing to landscape and site assessment?

Yes, analysis using the Crick approach enabled us to identify how to specify the EO requirements for producing outputs of EO indices and to identify suitable EO techniques for specific condition change scenarios. We believe that to produce products of immediate value to users, real-world objects (e.g., field boundaries, habitat boundaries) are needed to make sense of mapped products that use EO indices (see section 3).

Using the Crick approach will also tell us whether there are sites/objects which have characteristics e.g., size / granularity, that do not lend themselves to an EO approach and from a practical perspective this could be taken into account in the outputs produced (e.g., exclude them / do not provide outputs for objects below a minimum size).

HQ3. What EO outputs can support condition assessment and monitoring for grasslands and at what scale?

EO outputs

We envisage a production system that generates outputs rapidly, providing outputs that are relatively simple to interpret, are applicable to policy, scalable and transferable (across the country and adaptable to producing other measures).

The case studies in section 4 demonstrate that EO can pick up potential risks at the site scale. However, from a production perspective outputs (maps, derived interpretations such as traffic light risk maps) are likely to be generated for larger areas (catchment, regional, landscape) as this is more cost-effective. However, these maps would contain information which is relevant to the individual site assessment, particularly by flagging the likely ecological transition from one habitat type to another which would signal a decline in condition and would take account of pressures on the sites (i.e., providing data to assess the site in context or identify a significant risk change or potential change in a condition measure at a specific site).

Being able to specify the EO requirements for the production of each EO indicator means that we can look at the range of imagery, ancillary data and objects that already exist, or which are planned (e.g., a NDVI layers, national hedge layer, Copernicus layers, Living map) and assess which are appropriate to use. Knowing how specific habitats are likely to be represented as objects can be derived from the Living map (where they are, how far they have been mapped, whether they have been field validated) which provides an important data source in this respect. Understanding requirements also means we can make recommendations for cost-effective steps to align with existing and planned activities to produce useful data (e.g., EODIP activities, field campaign to map ‘feature’ extent in SSSIs during planned fieldwork).

Scale of EO outputs

From a technical perspective the specification of data requirements for EO output is at two scales. A regular collection of time series data should be sourced and standard indices determined using high resolution data such as Sentinel 2 data. Change or unusual threshold signals could then be flagged as risks. Where risks are flagged, then very high resolution imagery such as WorldView-2 imagery could be used to show change at individual sites.

Two examples can illustrate the relationship between features and EO indices:

Scrub: growth rates vary, but in general, change is relatively slow. We may not need frequent updates. The features (e.g., scrub) for some habitats can indicate damaging change in the habitats at a relatively fine scale. There is existing mapping activity at a variety of scales but which is not all complete (Living map: woody layer; RPA national


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hedge layer, CAP eligible area mapping, Copernicus woody layer). There is 3 yearly AP acquisition programme. VHR data is available. This would suggest a practical way forward that involves using a combination of this data to produce a woody layer map at a relatively fine resolution (at a meter or so). A nested approach could use a woody Copernicus layer as a start-point, utilising any digital outputs that already exist (QA) and mapping gaps in coverage. Whilst some data is at a national level, outputs are likely to be generated at county/landscape scale (e.g., via production of the Living map) as gaps are dealt with due to the limited spatial coverage of the input data. .

Productivity: a general indication of this for each individual habitat block is probably sufficient to feed into risk-based mapping at medium resolution. Time series data is likely to be needed to understand which habitats will have productivity differences in spring which may indicate adverse change and where summer may be more revealing (e.g., CG2 grasslands) or where a seasonal ratio may be significant spring/summer. Sentinel 2 (10m resolution) is likely to be suitable and has good area coverage and is acquired regularly, meaning some coverage is likely everywhere. A national output with updates on demand is envisaged.


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Example: landscape scale assessment to help target uptake in AE schemes:

Scenario

Natural England request suitable information on productivity to help assess whether proposed ‘uptake’ in Countryside Stewardship will be sufficient for:

reducing nutrient loading across a catchment (landscape scale assessment); and,

specifically, reducing nutrient loading around designated habitats in that catchment.

The EO Centre of Excellence responds by identifying, from its existing data holdings, suitable imagery and objects to address the request. This would be field objects and designated site boundaries. If Natural England have their own digital boundaries (e.g., of proposed uptake) it is likely that these too can be used to produce outputs.

The means of producing the NDVI at a pixel level is already understood. Considerations concerning appropriate specifications for producing useful EO outputs for NDVI are known (Crick analysis and spectral profiles). The output comprises mapped layers with relevant objects such as (similar to ecosystem services map layers):

designated site boundaries and the fields/ land surrounding them with colour ramp of NDVI;

green layer map (perhaps derived from Copernicus) with colour ramp of NDVI showing proposed uptake areas;

time-series profiles of derived NDVI for selected sites with unusually high productivity.


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Using a similar approach the following types of question could be addressed:

How many lowland calcareous grasslands are at risk of scrub encroachment from the margins of the sites?

Where are there rush pastures where grazing has been relaxed to such an extent that rushes are becoming dominant?

HQ4. What field or other reference data might be needed?

Field data has value both in providing training / validation data. If recorded consistently and brought together, field data can also be used to make generalised assessments of whether there are particular factors affecting or driving habitat change across a large number of sites (e.g., via national monitoring programmes such as Countryside Survey or Common Standards Monitoring and via agri-environment scheme assessments and monitoring). National datasets with consistent recording of measures that are compiled electronically and with inbuilt systems to ensure the quality of the input data, would be best. Specific numeric measures are most informative and compatible with EO outputs (e.g., % cover rather than a ‘pass’ or ‘fail’ outcome).

Spectral libraries of phenological change showing how the EO indices for each habitat type manifest change through the seasons, will further understanding of how habitat condition manifests itself in imagery both biogeographically and seasonally. A spectral library would also enable concurrent known changes on the ground to be examined to help set relevant thresholds of change in spectral characteristics that are relevant to the habitat condition status. They will also be informative in terms of the frequency / degree / rapidity of habitat condition change.

They can be derived for areas where the spatial extent of habitat features has been mapped, for example from the Living Map. If future field survey of designated sites, included a programme of work to map habitat feature extent this could be used, and incorporated into the Living map.

Photographs (dated, GPS referenced) are also informative for increasing knowledge of EO characteristics.

HQ5. What scope is there to create a working interface between field data and EO?

There is far more survey information available for designated sites than for non-designated sites and so this is likely to be where field data can be most readily sourced. Agri-environment surveys also collect suitable information in the wider countryside.

Analysis of the CSM data provided by Natural England indicates that there is clear potential for an interface between EO and field data, where there is commonality in the nature of the habitat condition feature (i.e., for measures identified by CEH as having applicability/ potential). This will depend upon fieldwork approaches adapting the way that survey data is recorded and storing it centrally; it is recognised that this will take time.

Spatially explicit, digital mapping of habitat extent (using consistent practices) during routine visits to designated sites would benefit CSM assessment and through links to the Living Map, could provide objects for the production of EO indices.

How can the two inform each other?

1. EO informing fieldwork: Risk products, can inform sampling strategy for frequency of survey of sites (see conceptual example below), where broad scale mapped outputs identify areas of particularly high productivity coupled with information about the location of habitats of interest. These could be interpreted by NE and follow-up fieldwork identified for NE site managers.


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2. Fieldwork informing EO: Analysis of fieldwork outputs provides information on the prevalence of issues that are affecting particular habitats (e.g., number and location of designated habitats that are above threshold levels, or approaching thresholds) for condition measures, that EO can assist with. Fieldwork data can be analysed periodically to ensure the EO work is cost-effective by targeting the location, frequency and scope of EO analyses appropriately. It can also act as training and validation data.


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HQ6. Is there scope for more automated methods of interpretation of EO data?

There is likely to be scope to develop simple, easy to understand interpretative material (e.g., traffic light maps) for some measures. For example, in relation to threshold values, or as colour ramps for productivity.

It may also be possible to produce maps showing objects /areas within them that have a QA process. This will require a high level of standardisation of image consistency (and other data inputs) which should be possible if there is standardised processing through EODIP.

Spectral profiles can be automatically generated and will be an important input in the development of automated material as they will help set the parameters that drive the mapping.

This approach is most likely to be successful for products derived from Sentinel imagery due to its wide area coverage (i.e., regional scale).

To develop automated approaches requires further work to scope the specific products users would find useful, establish if further research is required (e.g., radar), establish workflows to develop the products, consider needs in terms of accessibility to data, methods and output and the knowledge / expertise required to produce and use the outputs.


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HQ7. What can be shown over a regional area for rapid risk type assessment?

Maps of risk at a specific time, identifying hotspots and their distance to sites, maps of change in condition between threshold based categories (regional/ catchment scale), could be produced.

A field based framework provided by the Living Map would allow representations of arable / pasture proportions.

Intermediate products, such as the new Copernicus layers (grass cover and non-woody), are being produced and will be updated every three years at 20 metre resolution and are likely to provide useful input as well as having value in their own right. The proposed RPA permanent grassland monitoring tool would also produce regional level information of value to risk type assessment.

HQ8. Is it likely to be possible to set-up threshold values for change?

Yes, but these are not likely to involve precise thresholds (e.g., 4.5%). Thresholds values can be used to create bands of ‘low’ medium’ and ‘high’ risk, specific to particular habitats and with relevance to CSM thresholds. The actual EO thresholds will differ depending on habitat type, biogeographical zone, and seasonality.

To determine the threshold EO values with confidence will require a large time-series of imagery (optical or radar) dataset for analysis with spectral / backscatter libraries that are matched to reference field data.

HQ9. How would you assess the cost effectiveness of producing EO measures?

Cost effectiveness could be established through cost-benefit assessment techniques, looking at the cost of generating specific products and relating these to the likely policy uses and value of benefits arising for users.

This would include:

Identifying where ‘added value’ arises, including for other products / activities (e.g., Living Map) if the outputs are of known value to the users of that product. The ability to monitor more regularly and tailor the survey / EO data capture will bring added value and there will be new information of value to policy (e.g., on productivity) and the ability to take a more synoptic view.

Efficiencies / cost savings arising for fieldwork (reduced frequency, improved procedures), more streamlined reporting processes.

Economies of scale / re-use of data: reusing other available EO data, making it more cost effective.


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HQ10. How can we determine whether any useful sensitivity or additional information is obtained by adding more EO data layers?

Some techniques, particularly those using radar, rely on a time-series of imagery to identify the status of habitat condition as demonstrated in the SSGP project on heather mapping. There is a point that is reached where the level of confidence required and benefits gained from the habitat condition information is well balanced with the cost (data /processing/production effort). There are statistical techniques that can be used to:

establish the imagery that is making the greatest contribution in any classification process (Principal Components Analysis);

allow the optimal amount of imagery (e.g., in a time-series of imagery) to be decided (stepwise).

However, understanding the EO mapping needs for a particular condition measure (correct time of year, suitable monitoring interval, inter-annual data requirements, frequency, type of imagery) is key and the start-point would be the development of a suitable matrix of these factors as a means of selecting relevant inputs.

HQ11. What does the project tell us about likely resource and infrastructure needs going forward in developing a condition mapping service?

A condition mapping system cannot be developed in isolation, as it is clear that it needs spatially accurate data on habitat extent and this will become available as the Living Map provides the means to generate this information:

producing the objects;

identifying specific habitats;

bringing in the results of fieldwork that identifies habitats (locally generated via fieldwork – e.g., for MEOW Tier 4 habitats) or potentially from future CSM mapping activities of designated sites.

The service would require access to Sentinel data (both 1 and 2) as a primary data source and to a range of other image and ancillary data that is under Defra license and accessed through a central system. The service would benefit from access to high volumes of pre-processed, standardised, image /data products to generate the spectral libraries as well as batch processing of EO indices e.g., NDVI, NDWI. Tools are available for this purpose (Sentinel Toolbox and downstream data access).

Cross sector collaboration is needed as other initiatives are generating intermediate products that could be cost-effective, and there is a need to interface with:

The major field-based habitat monitoring initiatives assessing habitat condition and generating information that enables an interface between EO and fieldwork;

Other EODIP projects and activities (common needs, new techniques, collaboration and co-ordination of effort);

A demand-led service with varying demands on staff time and expertise which will require flexibility.

The service will need a mechanism to specify, commission and act on the findings of further research / service development needs. There will be a ‘set-up / investment’ requirement (e.g., for creating spectral profiles and using these to develop automated mapping processes and outputs). This will also require specialist EO and ecological knowledge.


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User requirements (primarily Defra family) for mapped outputs need to be better understood and products need to be tested to ensure fitness for purpose. The service also needs an overview of Departmental needs to identify other opportunities, employ long-term strategic thinking and identify operational funding.

6.3 Considerations of scale

There are three perspectives regarding scale that can influence the analysis of habitat condition: a technical perspective such as the sensor resolution, a habitat perspective looking at the size of the vegetation sward, and the perspective of time through change in habitat condition as well as the natural seasonal cycle of that habitat.

It is also important to recognise that processes influencing the condition of a particular patch of habitat can be occurring at three scales (Figure 6.1):

within the habitat itself, affecting the component features of the habitat;

within the site in which the habitat is located (e.g., field, management unit);

the condition of the area surrounding the site.

Figure 6.1: Processes present around and within a site

Scale of sensor resolution

The spatial resolution of a sensor relates to the smallest distance between two objects that can be distinguished in an image; this normally corresponds to the area of a pixel in optical sensors such as RapidEye or WorldView-2. The higher the image resolution, the easier it is to distinguish between different objects and map their physical extent on the ground. Figure 6.2 demonstrates the ease with which it is possible to identify ground features with a higher resolution system. While the lower resolution systems (left) can broadly identify different fields but not their physical boundary, the very high resolution systems (right) are able to identify the field boundary and the individual stands of vegetation.


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Figure 6.2: Examples of spatial resolutions from different EO sensors, from low resolution (left) to very high resolution (right)

Through an object-based analysis, the effects of spatial resolution can be demonstrated by comparing a segmentation of the same area, derived from RapidEye (5m resolution) and WorldView-2 (pan-sharpened to 0.5m resolution), shown in Figure 6.3.

Figure 6.3: Comparison of Segmentation from RapidEye (left) and pansharpened WorldView-2 (right)

In this demonstration, the very high resolution of the WorldView-2 sensor allows for the correct spatial delineation of the features within the SSSI, when compared to the RapidEye segmentation.

However, the degree of image coverage by higher resolution systems and the actual area of land captured in an image, are generally not as high as for their lower resolution counterparts (Figure 6.4). As an example, the swath (width) of an image from WorldView-3 is (at 2m resolution) about13.1km compared to a Sentinel-2 swath (at 10m resolution) of 290km.


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Figure 6.4: EO platform coverage vs level of detail captured

Scale of habitat and EO indicator

Related to the sensor resolution, it is important to understand the scale of the habitat being assessed, and the EO indicator being analysed, such as scrub encroachment. If the sensor for analysis is Sentinel-2 (at 10m resolution) and it is unlikely that the encroaching scrub will reach an area of 100m2, then the likelihood of that feature being identified is reduced.

However, even if the spatial extent of the feature to be analysed (such as scrub) is of an area smaller than a pixel, the spectral characteristics of that pixel would still be influenced by the feature in question, and therefore could display unfavourable spectral profiles when compared to the previously collected spectral libraries of favourable habitats.

Scale of EO indicators through time

The ability to distinguish and identify a change in condition over time is inherent on the sensor resolution, the size of the habitat and EO indicator being analysed, and the time-scale over which change is likely to occur. Condition change events that occur quickly (such as landslides) could be analysed either during a time-series analysis, or during the next EO survey. However, changes in condition that can take years or decades to form (such as the seeding of dykes in limestone pavements) would not be identified by comparing consecutive yearly surveys.


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6.4 EO condition concept prerequisites

The EO condition concepts outlined here rely upon two prerequisites: the collection of a spectral library database of grassland spectra, and spatially-defined habitat features. Spectral libraries should be collected separately for each grassland habitat type, within each biogeographical region and for each season, in order to understand the habitat EO spectral behavior across the country and at any time of the year. With this underpinning knowledge, it would be possible to analyse and test the previously defined habitat extent, using EO derived indices, against the representation of a grassland in favourable condition (for that EO index, for that season, in that biogeographic region).

If any of the EO index tests suggest a less than favourable spectral response, that site would then undergo further analysis using the EO indicators to investigate the possible ecological reasons for the spectral deviation. Figure 6.5 represents the conceptual flow of events for analysis.

Figure 6.5: conceptual flow of EO condition analysis

Understanding how a grassland habitat behaves in relation to its spectral characteristics through time would be an invaluable tool for condition monitoring. Such analyses are robust and transferable, and can be operationally achieved across an entire country (at high resolutions of ~10m) or at individual sites (at very high resolutions of ~2m).

With a spectral library database as described, the timing of these analyses should be dependent upon the grassland type and the knowledge of how it is expected to behave in a particular season. For example, analysing the structure of a marshy grassland would preferably occur in the middle of summer (e.g., August), as this is the time of year that most growth would occur.

6.5 Generating products

We propose that data layers of EO condition indices / indicators are generated in an EO hub and then through a demand-led system compiled into mapped products most likely at landscape scale (but also relevant to site assessment). In the longer term national scale products could be generated.

6.6 Conceptual System

We have produced a conceptual demand-led (Section 6.3, HQ 3), data driven system to show how requests would be processed. This is a simplified version of the process to facilitate discussion and help Defra and partners establish whether and how the concept fits with wider EO plans and considerations. This is illustrated in the examples provided in Section 6.3.


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6.7 Next steps

We have produced a roadmap that sets out proposed next steps in the development of a system for producing EO indices, highlighting the various activities that are likely to be involved in providing inputs. This roadmap also highlights the need for further research and testing to underpin the understanding of how EO can be used most effectively to produce indices /indicators of habitat condition (Figure 6.6 overleaf).

MEOW Living Map

An EO system providing information on habitat condition cannot be developed in isolation as it is clear that the start point is detailed spatially accurate data on habitat extent and this is not yet readily available for most habitats. A ‘Living Map’ (Section 1) could act as the driver to generate this information:

producing the necessary habitat polygons;

identifying specific habitats;

bringing in the results of fieldwork that also identifies habitats (e.g., locally generated for Crick Framework Tier 4 habitats) or potentially from future CSM mapping activities of designated sites.

Condition assessment work will benefit from having a well-established operational Living Map in place for the UK. This is the key next step.


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Figure 6.6: Roadmap highlighting the need for further research and testing


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7 Glossary of terms and acronyms

Active remote sensing system

A system that transmits its own energy source for illumination, which is detected and measured by the same sensor.

Amplitude A measure of the strength of a signal, with units of voltage.

AP Aerial photograpy

API Aerial photographic interpretation

Backscatter Proportion of outgoing radar signal that the target object redirects directly back to the sensor.

Band In passive systems, denotes a portion of the electromagnetic system such as red or shortwave infrared. In active systems, it represents the broadcasting frequency limits of the sensor, such as C-band (4000-8000 MHz).

Calibration The process whereby the digital numbers of an image are converted to a physical measurement of the objects within.

Case study areas The broad geographical areas within which case study sites will be identified.

Case study sites Sites with suitable data availability to assess the value of EO techniques for assessment of habitat condition and change.

Colour composite image

A single image prepared by displaying three individual black and white bands (optical) or polarisations (radar), each through a different colour filter channel (red, green and blue).

CIR Colour-infrared: A three band image using the near infrared (NIR) band, as well as the red and green parts of the visible spectrum.

CSM Common Standards assessmenting: Site monitoring and assessment of designated nature conservation sites developed by JNCC and the country agencies.

dB Decibel: A logarithmic unit used to measure the return signal strength in radar systems.

DEM Digital Elevation Model: A raster dataset containing height values.

DSM Digital Surface Model: A raster dataset containing height vales that include surface features (e.g., vegetation).

DTM Digital Terrain Model: A raster dataset containing height vales that does not include surface features.

EO EO: Regional-to-global scale measurements from spaceborne or airborne platforms.

Electromagnetic spectrum

Continuous sequence of electromagnetic energy arranged according to wavelength and/or frequency.

Envisat An ESA operated satellite which included the Advanced Synthetic Aperture Radar (ASAR) instrument, a C-band SAR sensor with a choice of polarisations and modes.

ERS European Remote Sensing satellites (ERS-1 and ERS-2) operated by ESA, and which included a C-band SAR instrument.


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ESA European Space Agency, based in France.

False-colour image

Similar to a colour composite image but one that specifically uses elements of the non-visible parts of the electromagnetic spectrum. The most common false-colour image in optical systems is CIR. For radar systems, it is possible to map both different wavelengths and/or different polarisations into RGB.

GIS Geographic Information System

IHM Integrated Height Model: A DEM created using multiple datasets.

Landsat 30m satellite imagery available from the USGS (United States Geological Survey).

Lidar An active remote sensing system that measures distance through the recording of short pulses of light.

NDVI Normalized Difference Vegetation Index: Ratio between the red and NIR bands of a multispectral image. Useful for identifying the productivity of vegetation.

NDWI Normalised Difference Water Index: Ratio between NIR and SWIR bands of multispectral imagery. Useful for identifying plant water stress.

NIR Near-infrared: ~780 nm to ~1000 nm region of the electromagnetic spectrum.

OBIA Object based image analysis: process of classification where objects are considered rather than individual pixels.

OSMM Ordnance Survey MasterMap dataset.

Polarisation The direction of orientation to a radar sensor, in which the transmitted and received electromagnetic radiation vibrates. Can be single (i.e., only VV or HH) or dual (i.e., VV/VH or HH/HV).

SAC Special Area of Conservation

SAR Synthetic Aperture Radar: type of active satellite sensor.

Swath The distance of the terrain covered by the width of a scanning system. Can be interchangeably referred to as ground-swath or image-swath.

SWIR Shortwave-infrared: ~1000 nm to ~1700 nm region of the electromagnetic spectrum.

True colour photography (RGB)

Photography with detail in the red, green, and blue spectral bands, creating imagery approximating to the perception of the human eye.

GSD Ground Sample Distance: The size of an image pixel projected to the ground surface, usually measured in metres which is often referred to as image resolution.

Ground truth The validation phase of remote sensing analysis where the actual places shown in the images are visited, and the classification output is compared to the real world environment.

Image analysis The process of studying an image in order to extract meaningful information.

Interferometry This assesses the degree of change in the relative position of objects at the same location with high degrees of accuracy. A pair of images is captured from offset positions and is used to generate a ‘coherence


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image’ that can be interpreted to determine surface elevation. Interferometry can be single pass (two SAR antennas capture data at the same time) or repeat pass (there is a delay of hours to days between repeat observations). In the case of repeat pass interferometry (also known as differential interferometry) the coherence images generated can also inform an understanding of change to the Earth’s surface structure, e.g., from deforestation. It should be noted that any surface movement, e.g., wind on vegetation, will also affect coherence.

Monitoring Being aware of how the condition of a system changes over time.

Platform A system that carries a sensor – usually airborne or spaceborne.

Radar Radio Detection and Ranging: An active form of remote sensing using the microwave part of the electromagnetic spectrum.

Remote sensing The action of collecting information about the surface of the Earth, from measurements made at some distance above the Earth and through the electro-magnetic spectrum, processing these data and analysing them.

Rulebase training Data used to aid in the creation of a rule-base, a method of automated image classification.

Sensor The device that records a remote sensing image, much like a camera.

Sigma Nought

(σ0)

A dimensionless measure of the strength of the return radar signal, normalized to the area observed.

Sentinel-1 C-band SAR European radar imagery satellite constellation (Sentinel-1A launched in 2014, Sentinel-1B is scheduled for launch in 2016), operated by the ESA.

Sentinel-2 A multi-spectral European satellite constellation launched and operated by the ESA.

SAR Synthetic aperture radar: An active microwave remote sensing technology.

VHR Very high resolution: EO imagery with a very small GSD, typically less than 2 metres.

VNIR Visible/near-infrared: A four band dataset containing the visible (RGB) and near-infrared (NIR) spectrum.


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8 References

Aitkenhead, N. et al., (2002) British Regional Geology: The Pennines and adjacent areas. British Geological Survey (4th edition).

Breyer, J., Pike, S., Cameron, I. and Parker, J. (2015) Space for Smarter Government Programme 3: Developing an operational service, using SAR data, for routine monitoring of land management in the uplands. A report produced for the UK Space Agency, delivered in collaboration with the Satellite Applications Catapult

Brown, R., Slater, J., Fletcher, P., Riding, A., Seaman, E. and Zmuda, A. (1999) Developing the use of satellite radar for environmental monitoring in the lowlands. RSAC, Medstead.

Catchpole (2009) Habitat network analysis, Natural England. www.naturalengland.org.uk Accessed 2009/2010

Costigan, P. et al., (1999) Developing the use of satellite radar for environmental monitoring in the lowlands. Final report. London: Ministry of Agriculture, Fisheries and Food.

European Space Agency (2012) Sentinel-1: ESA’s radar observatory mission for GMES operational services. [Online]. Available from: https://sentinel.esa.int/documents/247904/349449/S1_SP-1322_1.pdf [Accessed 20 January 2016].

European Space Agency (2013) Sentinel-1 User Handbook, Draft. [Online]. Available from: https://earth.esa.int/documents/247904/685163/Sentinel-1_User_Handbook [Accessed 20 January 2016].

European Space Agency (2014) Radar polarisation. [Online]. Available from: https://earth.esa.int/handbooks/asar/CNTR5-5.html#eph.asar.gloss.geo:POLARISATION [Accessed 03 January 2016].

Gerard, F.F., Acreman, M.C., Mountford, J.O., Norton, L., Pywell, R.F., Rowland, C., Stratford, C. and Tebbs, E. (2015) Earth observation to produce indices of habitat condition and change. CEH final report to JNCC. JNCC Ref. C14-0171-0901

Haung, Y. and Genderen, J. (1996) Evaluation of speckle filtering techniques for ERS-1&2 imagery. [Online]. In: International Archives of Photogrammetry and Remote Sensing, XXXI, Part B2, Vienna, July 9-19 1996. Available from: http://www.isprs.org/proceedings/XXXI/congress/part2/164_XXXI-part2.pdf

Hutchinson, J., Jacquin, A., Hutchinson, S. and Verbesselt. (2015) Monitoring vegetation change and dynamics on U.S. Army training lands using satellite image time series analysis. Journal of Environmental Management, 150, 355-366.

Jacquin, A., Hutchinson, J., Goulard, M. (2015) A statistical approach for predicting grassland degradation in disturbance-driven landscapes. Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images July 22-24, 2015 – Annecy, France.

Kapnias, D., Milenov, P., and Kay, S. (2008) Guidelines for best practice and quality checking of ortho imagery. Joint Research Centre, (3.0). Available from: http://publications.jrc.ec.europa.eu/repository/bitstream/JRC48904/10133.pdf

Lucas, R., Medcalf, K., Brown, A., Bunting, P., Breyer, J., Clewley, D., Keyworth, S. & Blackmore, P. (2011) Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data. ISPRS Journal of Photogrammetry and Remote Sensing 66: 81–102


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MacLean, G. and Congalton, R. (2012) Map accuracy assessment issues when using an object-oriented approach, ASPRS 2012 Annual Conference Sacramento, California

Medcalf K. A., Parker J.A., Turton, N., and Finch C. (2011) Making Earth Observation Work for UK Biodiversity Conservation – Phase 1. JNCC Report No. 495 Phase 1, JNCC Peterborough

Medcalf K. A., Parker J.A., Turton, N., and Bell, G. (2013) Making Earth Observation Work for UK Biodiversity Conservation – Phase 2. Report to the JNCC and Defra.

Medcalf, K., Parker, J., Breyer, J., and Turton, N. (2015) MEOW Phase 3: Cost effective methods to measure extent and condition of habitats. A report produced by Environment Systems Ltd., for Defra and the JNCC.

Schuster, C., Schmidt, T., Conrad, C., Kleinschmit, B. and Forster, M. (2015) Grassland habitat mapping by intra-annual time series analysis – Comparison of RapidEye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation, 34, 25-34.

Simonetti, E., Simonetti, D. and Preatoni, D. (2014) Phenology-based land cover classification using Landsat-8 time series. Joint Research Centre. https://ec.europa.eu/jrc/sites/default/files/lb-na-26841-en-n_.pdf. Accessed 05/01/2016

Sims, D and Gamon, J. (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and development stages. Remote Sensing of Environment, 81, 337-354.

Tremblay, N., Wang, Z., Ma, B.-L., Bélec, C., and Vigneault, P. (2009). "A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application.", Precision Agriculture, 10(2), pp. 145-161. doi : 10.1007/s11119-008-9080-2

Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring

vegetation. Remote Sensing of the Environment, 8, 127-150.

Walker, W. (2010) Introduction to Radar Remote Sensing for Vegetation Mapping and Monitoring. [Presentation]. In: Workshop on Methods for Biomass Estimation and Forest-Cover Mapping in the Tropics, East Kalimantan, Indonesia, 8 - 12 November 2010. Available from: http://www.whrc.org/education/rwanda/pdf/Walker_SAR_Veg_Mapping.pdf [Accessed 02 January 2016].


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EO to produce indices of habitat condition and change (Gerard et al, 2015)

The following is an extract from the desk study research, conducted by CEH, from the Executive Summary and Bibliography sections.

Summary

The research reported here sought to test if the principles used in the National Forest Inventory (NFI) to monitor the habitat resource (forest) through earth observation (EO) might be extended to a wider suite of habitat types. The NERC Centre for Ecology and Hydrology (CEH) was asked to focus the selection of habitat types and review the understanding of how EO data could aid the identification of changed land use and management. CEH was also asked to draw up a plan for a practical test of how the project findings could be carried out. The approach comprised five steps, which are described within the present report.

Step 1 (Report Section 2.1): Appraisal of relevant extant and ongoing projects that have reviewed EO based techniques for condition monitoring of specific habitats.

Step 2 (Report Section 2.2): Comprehensive assessment of condition measures as they are applied to all UK habitats, both at a Broad and Priority scale (see Appendix 4 and Summary Table 3). CEH identified those habitats for which existing data are most detailed, and where knowledge of the mechanisms of habitat change and their causes are best understood, and thus where EO-derived measures appeared most relevant.

Step 3 (Report Sections 3.1, 3.2 and 3.3): Examination of detection of change by EO at a broad scale across the UK through both passive and active approaches, followed by a systematic appraisal of the condition measures identified in Step 2.

Step 4 (Report Sections 3.4 and 3.5): The project then described how a system might operate in practice, using a theoretical example for monitoring grassland condition, based upon the readiness of each method and whether parameters required calibration/ validation.

Step 5 (Report Section 4): A detailed outline is included for a practical test to be conducted on the application of the project findings. Four pilot studies are considered, each reviewed in terms of relevant EO variables and condition measures, together with their readiness for application.

Key issues and conclusions

Mapping habitats and changes from one habitat to another is a different activity from determining and monitoring the changes in condition of a habitat. Consequently the earth observation approaches available to carry out the latter are likely to be different from the approaches developed for the former. A variety of EO approaches are elaborated and summarised in Section 3.3 including Tables 3.3.1 and 3.3.2 and in greater detail in a separate Excel Workbook).

Those condition measures for which there is a clear potential to apply EO approaches and where the use of EO should be tested are:

productivity (amount of live plant material);

productivity (sward height);

extent of dead material;

presence of linear features; and

presence of problem species.

Those condition measures for which EO approaches already exist and which should be relatively easy to adopt and implement are:


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extent of bare ground;

extent of burning;

percentage woody cover; and

extent of water.

Those condition measures for which EO is unlikely to deliver reliable estimates are:

vegetation composition (positive indicator species); and

vegetation composition (graminoid/forb ratio).

There is a strong case for developing and testing a 3 stage condition monitoring system:

Stage 1: consists of a less spatially detailed and thus quick but effective search for evidence of change in condition.

Stage 2: delivers, where a change has been detected, a more spatially detailed and quantitative evaluation of the type of change.

Stage 3: samples and validates the measured changes.

Delivery of stage 1 and 2 require different approaches using different types of EO data, whilst Stage 3 involves field work. The variety of EO approaches available is limited by the type of EO data available at the moment and in the future. Very high (<1m to 2m) to high (5m to 10m) spatial resolution imagery (optical or radar) is important to deliver Stage 2, while the ability to build time series of imagery (annually, monthly or daily) is important to deliver Stage 1.

Many EO approaches discussed in this report involve a vegetation index that is based on one or two visible bands and/or one Near-infrared band. This focus derives from the limited amount of spectral bands that are currently available at very high (< 1m to 2m) to high (5m to 10m) spatial resolution.

A theoretical example is worked out for monitoring grassland habitat condition where Stage 1 and Stage 2 of the monitoring system are elaborated using the vegetation index NDVI. As part of Stage 1, the example demonstrates the difference between using a map-to-image and image-to-image approach for change detection. The example also highlights outstanding questions which have to be resolved before such a system can be made operational:

1) Is the vegetation index, when used at medium (25m to 30m) to coarse (250m to 1km) spatial resolution, sensitive enough to detect change in condition?

2) Can the vegetation index be used to distinguish clearly the main condition measures (extent of bare ground, woody cover, dead material, and productivity (amount of life plant material) within a given grassland type?

For several of the condition measures (extent of water, extent of woody cover, extent of bare ground, productivity (vegetation height), linear features) there is a clear potential for using radar data. This potential needs to be tested.

Validation to establish the accuracy or the consistency of the estimated condition measures is important. Accuracy (bias and precision) is established by comparing the EO derived condition measures with independently collected (field work) observations. Consistency (precision) is established by evaluating repeat estimates that are acquired independently for the same sample or site using the same method or an ensemble of methods.

To help further evaluate the elaborated EO approaches in a systematic manner, a set of attributes has been identified and defined that enables JNCC and CEH to consider the readiness (availability of EO data across time and across the UK; time required for implementation; amount of staff training required) and feasibility (staff resources required; data volume involved; cost of data; and pre-processing level of data at which it is available) of the approaches. A framework to help to assign readiness and feasibility scores to EO approaches was not fully developed in this report, but could usefully be developed further.


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Generic benefits of EO based approaches

The general benefits associated with any EO approach are relevant to developing tools to support habitat condition assessment and monitoring; these are described in Table 8.1.

Table 8.1: Benefits of EO-based approaches

Repeatability & consistency

Satellite sensors take consistent repeated measures of the same area of the Earth. Standardised products and methods can be developed. If EO data is correctly processed, a time series of EO derived measures can be produced that have very good spatial and temporal consistency. When the time series indicates a change in the measured output, it can be confidently assigned as a real change for further investigation, particularly if it is persistent over a number of consecutive time steps.

Spatial explicitness

Boundaries of differing habitats and / or conditions can be well defined, depending on the spatial resolution of the system relative to the scale of the phenomenon. For example, a site survey may not record a clearing within a patch of woodland, whilst this would be picked up from EO imagery.

Synoptic view

EO imagery captures large areas of interest uniformly at the same time, with lower spatial resolution systems tending to cover more area than very high spatial resolution systems. For example, Sentinel-2, which has an image swath of 290km, can cover over half the UK (east to west) in a single pass. By comparison, field survey campaigns may take anything from days, to months, and even years to capture data for the targeted population of sites.

Ease of re-analysis

The same EO data can be re-used to create a range of outputs that help deliver efficiencies in accordance with the principle of “gather once, use many times”. As well as mapping and monitoring habitats, EO data becomes most cost-effective when the inputs (imagery, data, rule-base) or outputs (maps, measures) are reworked, further developed or reused for a range of other purposes as envisaged in the EODIP.

Accessibility to

data

Through the Copernicus programme (see section 2.6), all Member States (including the UK) have free access to the Sentinel mission products and the ESA processing Toolbox. Other satellite imagery can

be freely searched for, with quick-looks and metadata describing the quality of the imagery. These can be easily downloaded or ordered through the satellite operators, or 3rd party companies.

Frequency

To select the most appropriate acquisition dates for a specific area, high revisit frequencies are required. With the advent of a fully operational Sentinel-1 and Sentinel-2 system (described in Appendix E and Appendix A), both using two orbiting satellites, the revisit frequencies of


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each would be less than a week. This would mean a higher chance of collecting cloud-free imagery at the time required for analysis and a high volume of radar data.

Extend your measure

Wavelengths outside the visible spectrum (e.g., NIR and SWIR) provide information to develop new robust and repeatable measures that cannot be easily observed or measured on the ground. For example, NDVI can be used as a tool to record relative changes in productivity on sites (Box 3).

Landscape context

Site condition may be strongly influenced by the management of the surrounding land, for example, by the impact of water and nutrient inputs. EO techniques offer the additional ability to adopt a multi-scale approach, made possible by the synoptic nature of the imagery, to map and produce a range of concurrent information relevant to a particular site. It is therefore possible to view processes affecting the site at the scales at which they occur and this can be used to help target appropriate site management. This includes external pressures, internal pressures, site dynamics and processes and opportunities for habitat expansion.

Cost

Commissioning a field survey can be relatively costly, and as such is usually performed at extended intervals. Obtaining optical satellite imagery usually costs less than £1 per km2 for medium resolution systems, with some sensors free at the point of use (e.g., the Sentinel missions). Pre-processing and analysis will incur a cost for every image but these can be lowered with increased automation and would likely decrease over time. This can result in the possibility of using multiple EO surveys to supplement the fieldwork, or target where best to perform the fieldwork, at relatively low cost with the aim of reducing the overall cost of the monitoring.


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Optical remote sensing

Optical remote sensing techniques have been used for many years with manual interpretation of RBG (red, green, blue or true colour) aerial photography. Satellite imagery advances these techniques by recording information at different wavelengths to those visible to the naked eye. These include the near infrared (NIR) bands and the shortwave infrared (SWIR) bands. These bands are particularly useful for land cover mapping as they have a close relationship to vegetation.

Figure 8.1 shows the reflectance curve for vegetation. The horizontal axis shows the electromagnetic spectrum with the blue, green and red visible part of the spectrum, and then the longer wavelengths into the NIR and SWIR.

Figure 8.1: The spectral reflectance curve for vegetation

Green light is reflected from the top surface of the leaf, red and blue light are absorbed by the leaf and are used in photosynthesis, NIR light passes through the top surface of the leaf but is generally reflected from the lower surface, shown in Figure 8.2. The NIR signal is particularly useful for recording vegetation types as its strength is related to the leaf structure; therefore the more open and productive the leaf, the higher the NIR signal. Species such as agricultural grasses have a much stronger signal in the NIR band than scrub species for example, even though the RGB signal could be very similar.

Within the SWIR bands the signal is influenced by the water content of the vegetation and the soil and therefore can be useful for separating wet habitats such as mangrove from those with similar species but on a drier soil type such as limestone scrub.


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Figure 8.2: The profile of a leaf and how light is reflected and absorbed

EO images are gathered from above and therefore are best at identifying plant communities which have a distinctive appearance from above, either as an even cover of one or a range of canopy forming species. It struggles to find communities that are distinguished by the presence of small sized plants which occur in low frequency hidden by the over-storey canopy. The classification provided by earth observation will therefore not completely equate to that which a botanist may record on the ground. However, as remote sensing can quantify other biophysical features of the vegetation such as productivity, it enables mapping of ecological functions which have a range of effects in terms of the ecosystem services that an area provides.

In order to show the maximum differences in the vegetation, the imagery can be coloured up viewing the NIR band as red, red as green and the green band as blue. Since plants reflect greatest in the NIR portion of the electromagnetic spectrum, areas of productive vegetation would appear with red hues. Human eyes are most sensitive to green, and thus is linked with the most variable reflectance with regards to the vegetation curve, the red portion of the visible spectrum. Finally, the green band is displayed as blue. An image coloured to this combination would show very productive vegetation as red, and non-photosynthetic materials as hues of cyan (Figure 8.3: A RapidEye image displayed in CIR (NIR, R & G)


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Figure 8.3: A RapidEye image displayed in CIR (NIR, R & G)

Satellites and their sensors vary in terms of spatial, spectral and temporal resolutions. The image extent is the area covered by a single image and can range from a few kilometres to hundreds of kilometres. Higher spatial resolution typically means a smaller image extent. However, wider area coverage can be achieved by combining several scenes into a mosaic, taking any timing differences into account. Spectral wavebands refer to the number of image bands or discrete spectral samples that are recorded for each image pixel. Satellite imagery affords the opportunity to map surface features over a variety of geographical and temporal footprints using different parts of the electromagnetic spectrum.

Typically the presence of more spectral wavebands increases the ability of the imagery to discriminate between land cover types as there is more data and therefore more discriminating power in the image. Temporal resolution is related to the repeat frequency with which a system can acquire images of the same location. Although this may be fixed for an acquisition system, environmental factors such as cloud cover have an overriding impact on the availability of usable images.


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Radar remote sensing

SAR systems can operate at a range of different wavelengths, categorised into different bands (see Table 8.2 below). The wavelength of a system principally determines the size of objects that interact with the radar signal.

Table 8.2: SAR wavelength bands

Radar waves interact most strongly with objects of similar or larger size related to the wavelengths. This introduces a size-to-wavelength dependency that can then be utilised when looking at natural land cover. Taking the example of a forest, different radar wavelengths will interact with different structural elements; longer wavelengths (such as L-band e.g., the ALOS PALSAR satellite) will mainly see trunks and branches, whilst shorter wavelengths (such as C-band e.g., Sentinel-1) will mainly see the leaves and twigs, in addition to the trunks and branches (see Figure 8.4).

Figure 8.4: Interaction of vegetation canopy with different structural elements of a pine tree (adapted from

Walker, 2010)

The size-to-wavelength dependency also influences the intensity of the backscattered response. The intensity of the return signal is related to the roughness of the target surface, relative to the length of the wavelength emitted; where a greater relative roughness equates to higher backscatter intensity. Rougher areas therefore appear brighter in a SAR image. However, a surface which is observed as being rough relative to the C-band, may be smooth when observed by longer wavelength systems, such as L-band (see Figure 8.5).


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Figure 8.5: Impact of target surface roughness on signal intensity

It is also important to take into account the orientation, or polarisation of the transmitted and received radar signal. Radar systems can typically transmit vertically (V) and/or horizontally (H) polarised waves. Most modern SAR systems are capable of measuring the strength of the radar wave in both the same polarisation as transmitted (termed co-polarised), and in the opposite polarisation as transmitted (cross-polarised). These different configurations, or channels, and how they are named, are outlined in Table 8.3 below.

Table 8.3: Radar channels

Transmitted Received Channel name

Horizontal Horizontal HH

Vertical Vertical VV

Horizontal Vertical HV

Vertical Horizontal VH

Understanding the relative strength of co- and cross- polarised responses reveals much about the nature of the interaction between radar wave and target. For example, for a flat surface or sharp corner, such as a building, the orientation of the wave will not tend to change. Therefore we would expect a strong co-polarised signal, and weak cross-polarised signal. By contrast, in a vegetation canopy the more chaotic structure of the target will change the orientation of the wave, increasing the strength of the cross-polarised signal. Co-polarisations (VV and HH) therefore relate to surface scattering such as from buildings or the surface of a forest canopy, while cross-polarisations (VH and HV) relate to scattering from a volume, such as the branches within a forest canopy. It should also be noted that the co-polarised response will typically be considerably stronger than the cross polarised response for any given target. Figure 8.6 below shows a multi-polarised SAR image and a RGB satellite image for comparison. In the SAR image (on the right hand side) you can clearly see variation in different land covers and vegetation types based upon both the overall intensity of the SAR image, and how the ratio between the different channels produces different colours in the image. For example, bare earth appears blue/purple and dense vegetation appears green/yellow.


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Figure 8.6: RGB satellite and multi-temporal SAR image of the North York Moors

Images captured through radar systems also suffer from speckle, a noise-like phenomena that generates the characteristic “salt and pepper” appearance of SAR imagery. This effect is a result of the interference of the coherent electromagnetic waves scattered from the target objects (Huang and Genderen, 1996). While chaotic and unpredictable, it is not random; given the same configuration of sensor and target, the same speckle pattern would be generated. Therefore, careful efforts must be made to reduce the influence of speckle without destroying the statistical validity of the underlying data. Three broad approaches can be taken here:

Multi-looking: Whereby the SAR image is re-processed to average backscatter over multiple independent looks. This has the effect of degrading spatial resolution in return for reducing speckle.

Spatial adaptive filtering: An adaptive filter (e.g., Lee or Gamma-Map) is passed over the image, with pixel values averaged for areas with similar characteristics. This preserves pixel resolution at the cost of “smearing” features.

Multi-temporal filtering: A stack of co-registered SAR images is analysed over time using a multi-temporal filter (e.g., De-Grandi filter) to produce a speckle-filtered image at each time step. This approach preserves both pixel resolution and the appearance of features, at the cost of requiring a stack of 10+ images in the same configuration and geometry.

Examples of each of these approaches are provided in Figure 8.7 below.

Figure 8.7: Examples of statistical approaches to reduce radar speckle; a) raw image, b) spatial adaptive

filter, c) multi-temporal filter


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Sentinel-1

Sentinel-1 is an ESA space mission of the Copernicus Programme. It will consist of a constellation of two satellites and will provide continuity of radar data from the previous ERS and Envisat missions. Sentinel-1A was launched in 2014, with Sentinel 1B scheduled for launch in 2016. The Sentinel-1 missions will also allow for differential interferometric SAR (DinSAR or DiffSAR), which is capable of detecting small changes in surface movement on the ground.

Wavelength: Sentinel-1 operates in C-band (7.5 – 3.75cm wavelength), selected to provide continuity with the ERS and ENVISAT missions where this wavelength proved ideal for operational ocean and sea-ice monitoring. In a habitat-mapping context, C-band mostly interacts with smaller elements within the canopy meaning that its ability to differentiate between different high-biomass vegetation covers is limited. However, numerous studies have demonstrated the use of C-band SAR for vegetation mapping and monitoring.

Polarisation: Sentinel-1 allows for either single-polarised (VV or HH) or dual-polarised (VV/VH or HH/HV) capture. For vegetation studies, either dual polarised configuration will proved preferable to single-polarised data as variations in vegetation structure are most clear observed in the cross-polarised channel (VH or HV).

Acquisition modes: There are three image acquisition modes available from the Sentinel-1 missions suitable for land cover mapping. As Table 8.4: Sentinel-1 image modes illustrates, these modes have differing swath widths and resolutions. In each case, increasing swatch width results in a lower resolution product. While strip map and interferometric wide swath are both noted as having the same 10m2 pixel spacing, the reduced swatch width of strip map data allows it to provide a product with greatly improved radiometric characteristics and reduced speckle. Despite its improved image quality, strip map is not a routinely planned capture mode, and thus it has not been evaluated in this project.

Table 8.4: Sentinel-1 image modes

Terrestrial acquisition mode Swath width (km) Pixel spacing

Strip Map (SM) 80 10 x 10m

Interferometric Wide Swath (IWS) 250 10 x 10m

Extra-Wide Swath (EW) 400 25 x 25m

Strip Map (SM) will be available upon request, such as disaster and emergency management, and is therefore a non-standard product. The Interferometric Wide Swath (IWS) is the default mode over terrestrial areas. The Extra-Wide Swath mode will be predominantly programmed over European, Arctic and Southern Ocean areas (European Space Agency, 2012).

The final resolution of a fully processed image captured in IWS mode, could be in the region of 10m to 20m in British National Grid. This means that the recommended maximum width of heather moorland burning, according to the Heather and Grass Burning Code 2007, would only be a few pixels wide.

Coverage: Figure 8.8 illustrates differences in the geographic coverage provided by the different acquisition modes.


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Figure 8.8: Spatial differences between the different acquisition modes

At full operation, a single Sentinel-1 satellite is be able to map the world once every 12 days (Figure 8.9), and six days in constellation (i.e.,, once Sentinel-1B is launched and operational).

Figure 8.9: Coverage of Sentinel-1 in IW mode over a 12 day period (European Space Agency, 2013)


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Sentinel-2

Sentinel-2 is an ESA space mission of the Copernicus programme. It will consist of a constellation of two satellites and will provide continuity of multispectral data from the previous SPOT and current Landsat missions. Sentinel-2A was launched in 2015, with Sentinel-2B scheduled for launch in 2016.

Wavelength: Sentinel-2 captures 13 bands in the visible, near infrared and shortwave infrared part of the spectrum, across three spatial resolutions, selected to provide continuity with the SPOT and Landsat missions, where these wavelengths proved ideal for operational land cover monitoring. The band number, location in the spectrum and resolution are displayed in Figure 8.10.

Figure 8.10: Sentinel-2 spectral bands and spatial resolution. ESA 2014

Coverage and revisit: The Sentinel-2 mission will focus its coverage on all continental land (excluding Antarctica), and any islands outside the EU greater than 100km2. When fully operational, with two orbiting satellites, it is expected that the revisit frequency will be every five days.

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