Development of agro-envi indicators based on land use/land ... · Development of agro-envi indicators based on land use/land cover changes for assessment of rural landscape changes

Development of agro-envi indicators based on land use/land cover changes for assessment of rural

landscape changes

Lukáš Brodský, Tomáš SoukupGISAT

[email protected]

Geographical information in support of the CAP, Toulouse 27 – 29 Nov. 2006

Goals and motivations

• development of a methodology • testing usage of agro-environmental indicators based on land use/land cover

changes• assessment of temporal development of environment and rural landscape in

context of agricultural policy

Motivation:Agro-environmental policy and landscape – to have a feedback

Associated regulations• the Act on Nature and Landscape Conservation• GAP (not to convert grassland into arable land, wide-space crops on slope …)• GAEC (not to disturb the landscape features)• AP in NVZs (not to disturb stream-side vegetation, to avoid a cultivation of selected

crops on slopes...)• … etc.


Hypotesis

• the grass-covered area has increased during the whole time period (especially 1990-1995 and 2000-2005)

• since 2000 aimed at LFA• since 2000 aimed at slopes (since 2000 slope above 7o, since 2004 above 12o)• 2001-2003 aimed at watercourses• bigger growth in the extensive agricultural areas than in the intensive during

1990-2005 (but 2000-2005 ?)• … etc.

Information sources for the analysis• use of spatial data rather than only statistical figures• IACS-LPIS (since 2004 / 2003?) - long term trends• LPIS does not include whole agricultural land (~10-15 % missing)

• limited class information


Agro-Envi Indicators

The use of AEI– understanding of relationship between agriculture x environment– identification of risks– providing information about trends and changes in the system– help for policy-makers by their decision-making– simplification of communication between participants who take

part in the decision-making process

• Remarks– great share of land on farms is leased (willingness to make

changes on agricultural land) – cca 88%– obligations for the future – hardly possibility to plough the

grassland


Main steps

– Land cover / land use state classification via remote sensing techniques (time sequence: 1990 – 1995 – 2000 – 2003 – 2005)

– Change DB creation– Change typology– LC/LU statistics

- stocks & change accounts- structural indexes

– AE indicators selection– Analysis in agro-environmental policy context


Methodology

• Land cover / land use state classification – Multi-temporal HR data (e.g. LANDSAT) for each year

– Auxiliary data (LPIS) MMU: 1 ha


Methodology

• Nomenclature– Artificial surfaces– Forest areas– Water– Agricultural areas

• arable land– 2.1.1.1 Spring Cereals– 2.1.1.2 Winter Cerelas– 2.1.1.3 Forage Crops (Clover and Lucerne only)– 2.1.1.4 Summer Crops (Maize, Sugar beet, Sunflower, etc.)– 2.1.1.5 Oilseed Rape (only)

• permanent crops• pasturess /grassland fields


Classification

Classification results 1990 - 2005


1990 1995

2000 2005

1000 Urban2111 Spring Cereal2112 Winter Cereal2113 Fodder Crops2114 Summer Crops2115 Oilseed Rape2200 Permanent crops2310 Pastures/Grassland3000 Forest5100 Water

Classification

• Abandoned land mapping– VHR data– visual interpretation 2003– automatic classification:

grassland– share of agri.: 1,98 %


Classification

Automated extraction of SLU (Small LandscapeUnits / Landscape features) from satellite images – on VHR data (IKONOS)– Main classes include (hedgerow, solitary trees, groups of trees) = focus

lies on vegetation units– Object oriented image processing– Auxiliary data (LPIS, forest mask)


Classification

• Extraction of SLUOBIAIn eCognition


Analysis

• Land Cover Flows = classification of identified changes– Urban sprawl– Agricultural rotation and intensification– Conversions (grassland vs. arable land)– Forest creation and management– Change of lc due to natural and multiple causes

• lc/lu data - link with socio-economic statistical data or policy data

– administrative regions– natural areas (e.g. LFA, HNV areas, designated areas, elevation zones)

• insight into processes in zoning context relevant to particular AE policy targets (e.g. water protection, soil erosion, improvement of landscape ecological stability etc.)


Change Analysis Framework


stock & change accounts structural indexes

change matrix

DB of change

lc flows classification

DB of land cover

AE indicators

Change Database Creation


• short time changes

• long time changes

• time sequences (sequence typology -> e.g. for land abandonment)

Land Cover Flows Definition (theory)


• Land Cover Flows = classification of identified changes

• change typology based on change matrix• different typologies can be applied - simple /

mature • [e.g. EUROSTAT LEAC project (2002) – 44x44

changes into ~50 LCF]

Aim• reduce complexity, but still keep detailed insight• group changes of the same type / cause / level• by this to help in change assessment

Land Cover Flows Definition (application)


• 14 LC hierarchical flows defined relevant to DAIFOR classes

Some results

Structural indexes: neighbourhood statistics (area, length) - to access important interactions from the biodiversity– boundary between grassland and other lc classes (length)– boundary between grassland and forest (length)– the total area of these grasslands– length of water streams and lc classes in neighbourhood


Some results

LU changes:* permanent pastures (extensive agri. area)

Changes: 1990 – 2000 % increase

2000 – 2005 % decrease (temporal?) Rate of grassing higher in 450 – 600 m altitudeAbove 600 m 93.5 % of grasslandSlope: up to 7deg. = 46.2 % grassed

7 – 12 deg. = 61.9 % above 12 deg. = 69.8 %

LFA: 62 % of the agri. land is grasslandGrassland along water courses: 3 % increase


Some results

Spatial distribution of identified processes can be easily presented

Changes:• to grassland 14 % • to arable land 10 %

Problems encountered:Lucerne-grass mix classified as grassland= higher rate of changes


Some results

SLU distributionand basic statisticsINT: 112 ha; EXT: 203 ha

SLU according to agriculturalland in neigbor


category % of SLU area % of agr. land

SLU: grass-arable 17%

13%

70%

SLU: arable land 44,6 %

SLU: grassland 55,4 %

Some results

SLU breakdown according LPIS

Inside / outside of LPIS ?


Inside LPIS (%) More than 10m inside(%)

Intensivearea

18,58 0.55

Extensivearea

23,50 1.81

Acknowledgements


• The project was financialy supported by the Government of Flandersunder the Co-Operation Programme between Flanders and theCandidate Member States in Central and Eastern Europe.

• The data used in the study were partly obtained from CwRS campaign [1]. The authors are grateful to JRC-Ispra and Czech Paying Agency for the support in the research programe.

REFERENCES:• [1]: European Commission - Technical recommendations 1 (§ 5.6), REMOTE-

SENSING CONTROL OF AREA-BASED SUBSIDIES, JRC – Ispra 2006, MARS Ref. JRC IPSC/G03/P/PAR/par D(2006)(5608)Available on-line:http://agrifish.jrc.it/marspac/CwRS/default.htm

Acknowledgements


Thank you for your attention!

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Development of agro-envi indicators based on land use/land ... · Development of agro-envi indicators based on land use/land cover changes for assessment of rural landscape changes