Satellite remote sensing for mapping vegetation in New Zealand freshwater environments: A review

Research Article

Satellite remote sensing for mapping vegetationin New Zealand freshwater environments:A reviewnzg_1168 33..43

Salman Ashraf,1,2 Lars Brabyn,1 Brendan J. Hicks2 and Kevin Collier2,3

1Department of Geography, Tourism and Environmental Planning, Universityof Waikato, Private Bag 3105, Hamilton, New Zealand, 2Centre for Biodiversityand Ecology Research, Department of Biological Sciences, University ofWaikato, Private Bag 3105, Hamilton, New Zealand, 3Environment WaikatoBox 4010, Hamilton, New Zealand

Abstract: Freshwater environments in New Zealand provide a range of ecosystemservices and contain important biodiversity. Managing these environments effectivelyrequires a comprehensive inventory of the resource and cost-effective tools forregular monitoring. The complex and extensive margins of natural water bodies makethem difficult to sample comprehensively. Problems thus occur with extrapolatingpoint-specific sampling to accurately represent the diversity of vegetation in largefreshwater bodies. Mapping freshwater vegetation using satellite remote sensing canovercome problems associated with access, scale and distribution, but it requireshigh-resolution images that have appropriate spectral characteristics. This paper pro-vides an overview of the optical satellite data characteristics required for mappingriparian, submerged and emergent vegetation associated with freshwater environ-ments in New Zealand.

Key words: emergent aquatic vegetation (EAV), riparian vegetation, satellite remotesensing, submerged aquatic vegetation (SAV).

New Zealand has 425 000 km of rivers andstreams, and almost 4000 lakes that are largerthan 1 ha (MFE 2007). Inland freshwaterssustain important plant and animal life, as wellas having cultural, recreational, environmentaland aesthetic values to local communities(Robb & Bright 2004). These highly valuedenvironments require careful and sustainablemanagement supported by knowledge of thestate and trends in ecological condition.However, their quality is threatened by a rangeof anthropogenic factors including drainageworks, flow regulation, nutrient and sediment

inputs, loss of riparian cover and also invasiveweeds and pests (see Harding et al. 2004).Freshwater vegetation can respond to environ-mental changes in lakes and rivers, and its com-position and extent are commonly used as amonitoring indicator of ecological health(Clayton & Edwards 2006; Collier et al. 2007).Regular monitoring of the extent and compo-sition of freshwater plant communities (ripar-ian, emergent and submerged), at appropriatescales, is difficult because of the large numberof isolated pockets distributed along riverbanksand lake margins. Furthermore, access to

Note about the authors: Salman Ashraf is PhD candidate in the Departments of Biological Sciences andGeography, Tourism and Environmental Planning at the University of Waikato. Lars Brabyn is Senior Lecturerof Geography and Brendan Hicks is an Associate Professor of Fish Ecology at the University of Waikato. KevinCollier holds the joint position of Senior Lecturer of Freshwater Ecology at the University of Waikato andFreshwater Ecologist at Environment Waikato.

E-mail: [email protected]

New Zealand Geographer (2010) 66, 33–43

© 2010 The AuthorsJournal compilation © 2010 The New Zealand Geographical Society

doi: 10.1111/j.1745-7939.2010.01168.x

freshwater environments often requires the useof boats or overland visits to remote areas.On-site assessments are usually subjective andlabour intensive, and are based on a discretenumber of points along transects for eachhabitat unit that does not capture the variabil-ity present at the scales required for manage-ment (Gilvear et al. 2007).

Alternatively, remotely sensed informationacquired from sensors on airborne or space-based platforms can be very time and cost-effective for water-related habitat assessmentat a regional scale (Ausseil et al. 2007; Johansenet al. 2007). Recent technological advances insatellite remote sensing (SRS) have resulted ina variety of new sensors being available thatcapture images of different environmental phe-nomena and features with improved dataquality. SRS is a rapidly changing discipline asnew satellites are being launched every year.There have been many reviews that demon-strate its effectiveness for mapping differentaquatic environments (Muller 1997; Ozesmi &Bauer 2002; Goetz 2006; Silva et al. 2008). Thefocus of this paper is to assess the currentdevelopments in the optical satellite dataquality in relation to the specific requirementsfor mapping freshwater vegetation in NewZealand.

Freshwater environments

The margins of freshwater habitats are a con-tinuum of variable-size zones, extending fromterrestrial regions to deep water. They arenarrow and highly diverse in terms of physicalcharacteristics. Moving from land towardswater, these zones are called riparian (river-bank), littoral (near shore) and limnetic (openwater). The riparian zone of streams and riversis often defined by hydrological interactionssuch as the extent of the flood plain, but it canalso encompass influences from vegetation pro-viding shade and inputs of large wood. It maybe narrow in numerous incised headwaterstreams that flow through constrained valleys,whereas in mid-size streams, the riparian zoneis typically larger, being represented by a dis-tinct band of vegetation whose width is deter-mined by long-term (>50 years) channeldynamics and the annual discharge regime.Riparian zones of large lowland rivers are char-

acterized by well-developed, but physicallycomplex flood plains with long periods of sea-sonal flooding, lateral channel migration,oxbow lakes in old river channels, a diversevegetative community and moist soils (Naiman& Décamps 1997).

Most large, vascular aquatic plants (macro-phytes) occur in the littoral zone, where shallowwater allows light to penetrate to the bed of thewater body. Macrophytes constitute a diverseassemblage of taxonomic groups and are oftenseparated into five categories (emergent, freefloating, bottom rooted but with floating leaves,submerged and rafted or sprawling) based ontheir habit of growth (Coffey & Clayton 1988).Free-floating macrophytes can occur anywhereon the system’s surface where water currentsallow (Fig. 1). In the context of this paper,‘freshwater vegetation’ refers to vegetationgrowing in the riparian zone or within thelittoral zone of lakes and rivers.

Significance of imagecharacteristics for freshwater

remote sensing

Silva et al. (2008) suggested that freshwatervegetation types are more difficult to detectfrom RS when compared to terrestrial vegeta-tion, and thus require thorough understandingof the physical interaction between electromag-netic energy, the vegetation and its environ-ment. Previous studies (Peñuelas et al. 1993;Malthus & George 1997; Valta-Hulkkonenet al. 2003) have identified the following param-eters that influence the ability to accuratelymap any freshwater vegetation, in particular,submerged aquatic vegetation:

• Biophysical characteristics of the targethabitat, which include biomass, canopydensity and its physical form

• Physical, chemical and environmental condi-tions of the surrounding matter (i.e. waterand atmosphere) such as water clarity (whichdepends on the concentration of chlorophylla and suspended sediments), height of thewater column above vegetation and atmo-spheric condition (e.g. high concentration ofthe suspended aerosols such as water or dustcaused by humidity or other factors)

S. Ashraf et al.34


• Meteorological conditions at the time ofimage capture such as cloud, haze and solarazimuth

• Sensor characteristics (e.g. spatial, spectraland radiometric resolutions; on-board datastorage abilities; and agility to manoeuvre forside scan) and overpass schedules.

It is important to understand how the funda-mental image parameters impact on the feasi-bility of freshwater vegetation mapping. Thereare numerous sensors currently on-board earthobservation (EO) satellites, and they vary interms of spatial, spectral, radiometric andtemporal resolutions. These parameters areexplained in the following sections, and areused to discuss the feasibility of using thedifferent satellite images available. Table 1summarises the optical sensors suitable formapping freshwater environments.

Spatial resolution is often discussed, andcommonly refers to the ground sample distance(GSD) of an image (i.e. how much of theearth’s surface a single pixel covers).The largerthe pixel, the poorer is its spatial resolution.Technological advances have enabled aerial-based images to acquire up to 2 cm GSD

(Booth et al. 2007); however, the highest spatialresolution of any commercially availableorbital sensor is 41 cm from the GeoEye-1 sat-ellite. Remotely sensed data with varied spatialresolutions are often referred to as low, moder-ate, high or very high, depending upon thenature of the study. The following divisions ofspatial resolutions are used in this paper: (i)very high spatial resolution (VHSR) data:below 1 m; (ii) high spatial resolution (HSR)data: 1–10 m; and (iii) moderate spatial resolu-tion (MSR) data: 10–100 m.

Spatial resolution is the most significantfactor that influences the accuracy of fresh-water vegetation classifications because of theirlimited width and their heterogeneous nature(Ozesmi & Bauer 2002; Goetz 2006; Boothet al. 2007). Traditionally, mapping vegetationover large areas was performed using eitheraerial photography or MSR satellite data.MSR satellite data (such as the early SPOTand Landsat images) have yielded degradedclassifications compared to the HSR data(Congalton et al. 2002; Johansen & Phinn2006). Many studies have demonstrated theneed for HSR to VHSR data for mapping dif-ferent freshwater vegetation types (Lonard

Figure 1 Sketch representation of freshwater environments.

Remote sensing of NZ freshwater environs 35


Tab

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sat-

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9024

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02

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S. Ashraf et al.36


et al. 2000; Weber & Dunno 2001; Nagler et al.2005; Becker et al. 2007; Yang 2007).

Spatial extent is the area covered by animage. Often, the spatial extent increases withdecreasing spatial resolution. Aerial imagingwith VHSR has restricted geographical cover-age, while most environmental monitoringsatellites are designed to scan the earth withswathe generally ranging above 50 km. At theMSR, Landsat satellites are considered thewidest swathe (185 km) for the last 35 years forvegetation mapping, but a careful design hasenabled Disaster Monitoring Constellation(DMC) satellites to capture data with muchwider swathe (600 km) at similar or betterspatial resolution than the Landsat TM sensor.Recently launched HSR to MSR sensors,such as HRG on SPOT-5 and LISS-III onResourceSat-1, have considerably improvedtheir spatial resolution as compared to theirearlier sensor models without compromisingtheir spatial extent. The latest MSR satelliteshave also focused on larger spatial extent suchas ALOS (70 km), Terra (60 km) and RapidEye(77 km) to ensure that the coverage of largeregions remains cost-effective. In contrast, HSRto VHSR satellites usually scan the earth withswathe ranging between 10 and 15 km. Theirlimited spatial extent requires many images formapping a large region, which adds to acquisi-tion costs, seasonal inconsistency and process-ing complexity.

Spectral resolution is a measure of the sensi-tivity of a sensor to record information acrossthe electromagnetic spectrum.It is often definedas the number of spectral bands per image andtheir bandwidths. Remote sensors have threemain types of spectral resolution: (i) very broadspectral bands (i.e. panchromatic); (ii) multiplediscrete and broad spectral bands (i.e. multi-spectral); or (iii) contiguous and very narrowspectral bands (i.e. hyperspectral). More bandsmean that more data are collected, requiringmore resources and time to process them. Thespectral characteristics of freshwater vegetationresemble those of terrestrial vegetation;however, the interaction of light with water, andthe requirement to distinguish between sub-merged, emergent or floating plants adds com-plexity. The spectral resolution thus criticallyinfluences what can be extracted from satelliteimages. The lower spectral resolution of SPOT

HRV data as compared to Landsat TM hasshown restricted accuracy for different aquatichabitats and vegetation types (Arbuckle et al.1998; Gao 1999; Harvey & Hill 2001).

For sensing freshwater macrophytes, thegreen region of the spectrum is the most suit-able followed by red and red-edge regionswithin the visible spectrum in tandem with nearinfrared (NIR) (Silva et al. 2008). The red-edgeband lies in the region of an abrupt change(between 680 and 750 nm) of the reflectancespectra of vegetation that is caused by the com-bined effects of strong chlorophyll absorptionand leaf internal scattering in healthy vegeta-tion. Information recorded in this region pro-vides the basis for vegetation identificationprocedures, and is valuable for the assessmentof vegetative chlorophyll status, leaf area index,early stress detection and detection of differentsubmerged aquatic species (Horler et al. 1983;Artigas & Yang 2006). Almost all multispectralEO satellites lack a band in the red-edge regionwith the exception of a recently launched con-stellation of five identical satellites (RapidEye1–5) that carry red-edge enabled sensors toacquire 6.5 m HSR data. A successful launch ofWorldView-2 satellite on 8 October 2009 willsoon provide 1.84 m spatial resolution data ineight multispectral bands including a red-edgeband.

In traditional multispectral data, differencesamong different floral species are not signifi-cantly recognisable because of relativelybroad spectral bandwidths. Becker et al.(2007) suggested that band centres of com-monly available high-resolution satellitesystems are not optimal for differentiatingwetland vegetation. Alternatively, narrow andcontiguous hyperspectral data provide infor-mation about the composition and physicalproperties of different materials observed inthe freshwater system. Available hyperspectralsatellite sensors (e.g. Hyperion and CHRIS)are heavily compromised on either spatialresolution or spatial extent, and are thereforeinappropriate for detailed large-scalemapping.

Aerial spectrometry is not only costly, butrequires more resources and time to store andanalyse collected data if captured over a largeregion at maximum spatial and spectral resolu-tion. Becker et al. (2005) used two combina-



tions of spectral and spatial settings (i.e. 1 mresolution imagery with 18 non-contiguousbands, and 4 m resolution imagery with 46 con-tiguous bands). Their research verified that aminimum of seven strategically located bandcentres (425.4, 514.9, 560.1, 685.5, 731.5, 812.3and 916.7 nm) in the visible to NIR wavelengthregion are necessary to maintain classificationaccuracy above the 85% threshold.These sevenbands generated a mildly degraded classifica-tion result compared to that obtained from full-spectral-resolution hyperspectral imagery.

Radiometric resolution refers to how pre-cisely a sensor can measure intensity within aparticular wavelength band. The radiometricresolution of an imaging system describes itsability to discriminate slight differences in elec-tromagnetic radiation. The finer the radiomet-ric resolution of a sensor, the more sensitive it isto detecting small differences in reflected oremitted energy. Traditionally, RS data are cap-tured in 256 intensity levels (i.e. 8 bits), but newsatellites offer greater resolution, usually up to12 bits or 4096 intensity levels per band. Thesedata cannot be completely visualised on com-puter screens; however, their higher radiomet-ric resolution contributes mathematically toidentifying subtle variations in the reflectanceof different features, and thus improves classi-fication results.

Small rivers provide difficult conditions formapping underwater vegetation, because oftrees on the riverbanks overhanging and over-shadowing the water surface to a large extent(Schulz et al. 2003), and low radiometric reso-lution data make it difficult to differentiatebetween SAV and shaded water surfacebecause of bank vegetation. Large rivers likethe Waikato River provide sufficient habitatsfor SAV that are unobscured by bank vegeta-tion or shadow. High radiometric resolutiondata provide a means to differentiate betweenSAV and shaded water surfaces; alternatively,data should be acquired at dates close tosummer solstice to minimize shadows (Sawayaet al. 2003).

Temporal resolution refers to the frequencyof image acquisition for a given area, or therevisit period of a satellite to pass over thesame area. EO sensors use circular, near-polar,sun-synchronous orbits where each orbit isphased out with the preceding one because of

the earth’s rotation on its axis. Over a periodof several days, these sensors perform a cycle oforbits of the earth that returns them to theirinitial position and enable each area of theglobe to be viewed. High temporal resolutionbecomes critical when an area is often cloudy,as several passes may be required before acloud-free image is obtained.

The revisit period of an orbital sensordepends upon a variety of factors such as spatialextent (or swathe) of the sensor, its ability tocapture off-nadir data, onboard data storagecapability and the latitude of the target area.Asmost EO satellites operate in near-polar sun-synchronous orbits, there is higher revisit fre-quency for the regions at higher latitudes.Satellite sensors with larger spatial extent alsohave higher temporal resolution. Broad swathesensors such as MODIS and AVHRR have 1–3days global repeat cycles. Medium swathe satel-lites like Landsat and SPOT revisit areas every2–4 weeks, and narrow swathe satellites of10–20 km revisit every 3–6 months.To overcometheir poor revisit capability,medium and narrowswathe satellites can swivel their sensors tocapture off-nadir (side viewing) images. High-agility swivels enable the IKONOS-2 satellite toscan 4700 km2 of contiguous areas. The highagility coupled with large on-board data storagecapacity on newly launched and future satellites(i.e. 1 Tb for GeoEye-1 and 2199 Gb forWorldView-2) enable them to scan 15 000 km2

(i.e. 300 ¥ 50 km) and 7200 km2 (i.e. 110 ¥65.6 km) contiguous area (about the sizes ofHawke’s Bay and Auckland regions), respec-tively, in a single pass.

The use of relatively inexpensive microsatel-lites weighing less than 100 kg has provided analternative to meet the needs for highertemporal resolution by means of launching anaffordable constellation of EO satellites. Aconstellation of six DMC satellites launchedover a time frame of 7 years has a combinedability to capture the entire globe on a dailybasis. The launch of five equally spaced Rapid-Eye satellites over a single sun-synchronousorbit on 29 August 2008 has ensured a globaldaily revisit to acquire HSR (6.5 m) data.Future launches of WorldView-2 and GeoEye-2will supplement their existing networks andwill substantially improve the rapid imagingcapability for VHSR data.

S. Ashraf et al.38


Acquired digital SRS data require furtherprocessing using specialised software toconvert it into a thematic map. There arenumerous computerised classification algo-rithms that have been used to map differentaquatic habitats (Ozesmi & Bauer 2002).However, it is generally difficult to say that oneclassification technique is always better thananother because performance also depends onthe SRS data (Schowengerdt 2007).

Freshwater remote sensing ofNew Zealand

Remote sensing is not new to New Zealand;the first aerial surveys were flown overChristchurch in 1926, and the first SRS data forNew Zealand became available in 1973 fromLandsat-1 (Belliss 1984; Stephens 1991). Thesoil maps by the New Zealand Soil Bureau(1954, 1968) and Water and Soil Division (1979)were the earliest sources of information whichidentified the extent of wetland soils, and modi-fied and improved the mapped boundaries atscales 1:50 000 or smaller by using manualinterpretation of remotely sensed imagery(Ward & Lambie 1999). Other studies thatshow the effectiveness of SRS data for vegeta-tion mapping at a local level in marine environ-ments include Israel & Fyfe (1996), Gao (1999),Gao et al. (2004), and a low-cost aerial RS tech-nique to map macro-algae in estuarine environ-ments by Alexander (2008) has shown theutility of this approach for the study of fresh-water systems. Within the riparian zone,Arbuckle et al. (1999) used SPOT XS data tomap riparian zone of the Taieri River. Theyreported a partial success because of the mod-erate spatial and low spectral nature of data.Although we are not aware of any remote-sensing studies that have addressed emergentor submerged freshwater environments in NewZealand, the techniques used to process datahave been established in other aqueous envi-ronments (Wolter et al. 2005; Everitt et al. 2008;Yuan & Zhang 2008).

The Land Cover Database (LCDB) repre-sents the first nationally comprehensive vegeta-tion monitoring undertaken in New Zealand(Walker et al. 2006). The LCDB1 used 1996/97SPOT-2 XS images, while the LCDB2 used2001/02 Landsat-7 ETM+ images. However,

they provide only a coarse assessment of indig-enous habitats and ecosystems, because of thebroad, qualitative nature of LCDB coverclasses; the reliance on subjective manual dis-tinction of spectral signatures; and resolutionissues associated with the 1 ha minimummapping unit (MMU) used (Thompson et al.2003). Jensen et al. (1986) have reported anMMU of less than 0.5 ha for the classificationand mapping of freshwater habitats whichmeans that small freshwater environmentshave not been accurately mapped in case ofLCDB.

While SRS offers certain advantages overconventional aerial photography (such aswider area coverage and increased spectralresolution), cloud cover has been a significantproblem in New Zealand (Belliss 1984). Tocapture cloud-free optical satellite imagesrequires high temporal resolution, but suchsatellites are often associated with low spatialand limited spectral resolutions (e.g. geosta-tionary, weather observation satellite sensors).Aerial photography is often seen as a goodalternative in New Zealand because of thegreater flexibility and availability to utilise thesensor when the sky is cloud free. However,aerial photography also has problems withweather, which has delayed the completecapture of aerial photography of the WaikatoRegion for 3 years under Waikato RegionAerial Photography Syndicate initiative. SRSthus possesses certain advantages over aerialimaging for mapping freshwater environmentsover a large region.

Two national-level mapping initiatives areenvisaged to ensure cloud-free data acquisitionfrom moderate- to high-resolution satellites.These are the Land Use and Carbon AnalysisSystem (LUCAS) project and the ‘All of Gov-ernment’ KiwImage initiative. The Ministry forthe Environment acquired 10-m resolutionimages of SPOT-5 satellite to analyse thecarbon stocks of New Zealand’s forest andsoils, which is required for the country’s report-ing requirements under the Kyoto Protocol tothe United Nations Framework Convention onClimate Change. Under the title of KiwImage,the New Zealand Defence Force is purchasingQuickBird-2 satellite data for the whole of NewZealand and its offshore islands. These imageswill be available to the participating central or



local government agencies. Although per-scenearea coverage of QuickBird-2 is far less thanSPOT-5, its availability to regional councils andCrown Research Institutes at a national scaleprovides a better prospect for detailed mappingof aquatic habitat because of its improvedspatial resolution.

The KiwImage project may take 5 years ormore to capture the whole of New Zealand,necessitating a mosaic to cover a region. Sea-sonal variability may compromise monitoringof macrophytes, which requires consistentreflectance for given vegetation classes.However, LUCAS SPOT-5 imagery acquiredover two summers (2006/2007 and 2007/2008)can provide a baseline for freshwaterenvironments.

Conclusions

The frequent launches of many experimentaland low-cost microsatellites have made it diffi-cult to determine exactly the number of satel-lites in orbit at any time. In 2008 alone, 11 newsatellites carrying optical sensors were success-fully added to an already long list of EO satel-lites. SRS has become viable as its spatialresolution and cost are competitive with aerialimages. Table 1 attempts to provide a summaryof significant medium- to high-resolution mul-tispectral optical sensors suitable for theregional/national level mapping of freshwatervegetation.While there are many other EO sat-ellites, many of these cannot be accessed easilyas their operating agencies are either onlyinterested in providing data for their owncountry or region, or they lack a user-friendlydata search/retrieval mechanism for interna-tional clients. Most commercially orientatedsatellite operators either offer online mecha-nisms of archived data search and its acquisi-tion, or facilitate launching of new dataacquisition requests directly into their systems.

The cost of SRS data is an important consid-eration for any large regional mapping project.In New Zealand, Landsat, SPOT-5 andQuickBird-2 are cost-effective for mappinglarge areas. Landsat data have been effectivelyprovided free of charge by the U.S. govern-ment, while SPOT-5 and QuickBird-2 are beingpurchased effectively by New Zealand govern-

ment organisations, and will be available free inthe case of SPOT-5, or at a bulk purchase costfor QuickBird-2.

A combination of high spatial, spectral,radiometric and temporal resolution isrequired, as well as large spatial extents. Withimage capture, there is a trade-off betweenthese parameters, and therefore no availableimage is ideal. QuickBird-2 multispectral datahave advantages because of their HSR com-pared to Landsat TM/ETM+ and SPOT XS.The small spatial extent of QuickBird-2, andthe fact that collecting regional coverage takestime, mean that QuickBird-2 imagery under theKiwImage project will not be seasonally consis-tent. The recent addition of RapidEye1–5 satel-lites and a planned launch of the WorldView-2satellite will widen options to choose suitabledata to map freshwater habitats at a regional/national scale in New Zealand. They havehigher spatial, and better spectral and radio-metric data with more frequent revisit capabili-ties compared to SPOT-5, ALOS and TERRA,and IKONOS-2, QuickBird-2 and GeoEye-1,respectively. Considering the current availabil-ity of archived images and data characteristics,a good choice of sensor for mapping vegetationin New Zealand freshwater environments isQuickBird-2. This does not mean that othersensors will not provide better information inthe future, but additional negotiations arerequired to ensure data capture.

This is a fruitful time for research in remotesensing and the development of applicationssuch as freshwater vegetation mapping. Theavailability of satellite images has beenimproved dramatically not only because ofadvances in technology and the number ofsatellites that have been launched, but alsobecause New Zealand government organisa-tions have invested millions of dollars in pur-chasing images of the country. It is imperativefor geographers and natural resource managersto be aware of these developments, and also tounderstand the basic limitations and advan-tages of different satellite images.

Acknowledgements

Funding from FRST OBI contractUOWX0505, and Environment Waikato isacknowledged.

S. Ashraf et al.40


Endnotes

1 The temporal resolution is a theoretical represen-tation of a sensor; it does not imply that sensorscapture data in every single overpass.

2 Data cost is calculated for 100 km2 data from thefull scene; it is mostly for radiometric corrected,archived, multispectral and panchromaticbundled, and single-user licensed data.

3 Landsat-5 is expected to continue until 2010.4 The scan line corrector (SLC) malfunctioned on

31 May 2003 that resulted in the onward acquisi-tion of downgraded data.

5 SPOT-2 does not capture data in SWIR spectralrange.

6 IRS-1D carries PAN and LISS-III sensors only.7 IRS-P6 carries improved LISS-III, LISS-IV and

AWiFS sensors; LISS-IV sensor captures datawith 24 km swathe in multispectral mode, or70 km swathe for any single band (i.e. monochro-matic mode).

8 A malfunction in the SWIR detector coolersystem has resulted in progressive deteriorationand ultimately degraded data quality since May2008.

9 CHRIS sensor captures data in 19 spectral bandsat 25 m spatial resolution up to 62 bands at 50 mresolution.

10 Beijing-1 satellite carries an additional high reso-lution panchromatic sensor.

11 Recently launched UK-DMC-2 and Spain-Deimos-1 capture data at 22 m resolution.

12 Data cost is shown for the non-commercialuse through Geoscience Australia; LandcareResearch New Zealand also provides data forcommercial use.

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Appendix I

List of acronyms and abbreviations

ALI Advanced Land ImagerALOS Advanced Land Observing

SatelliteASTER advanced spaceborne thermal

emission and reflectionradiometer

AVHRR advanced very high-resolutionradiometer

AVNIR advanced visible and near infraredradiometer

AWiFS advanced wide-field sensorCHRIS compact high-resolution-imaging

spectrometerDMC disaster monitoring constellationEAV emergent aquatic vegetationEO earth observationESA European Space AgencyETM+ Enhanced Thematic Mapper PlusEU European UnionGISTDA Geo-Informatics and Space

Technology DevelopmentAgency

GSD ground sample distanceHRC high-resolution cameraHRG high-resolution geographicHRV high-resolution visible

HRVIR high-resolution visible andinfrared

HSR high spatial resolutionIRS Indian remote sensing satelliteISRO Indian Space Research

OrganisationJAXA Japan Aerospace Exploration

AgencyJSS Jena Spaceborne ScannerKARI Korea Aerospace Research

InstituteLISS linear imaging self-scannerMMU minimum mapping unitMODIS moderate-resolution-imaging

spectroradiometerMSC multispectral cameraMSR moderate spatial resolutionNASA National Aeronautics and Space

AdministrationNIR near infraredNSPO National Space Program OfficePAN panchromaticPROBA project for onboard autonomyPRISM panchromatic remote-sensing

instrument for stereo mappingRALCam Rutherford Appleton laboratory

cameraRSI remote sensing instrumentSAV submerged aquatic vegetationSLC scan line correctorSLIM Surrey linear imagerSPOT Satellite Pour l’Observation de la

TerreSRS satellite remote sensingSWIR short-wave infraredTHEOS Thailand Earth Observation

SatelliteTIR thermal infraredTM thematic mapperTopSat tactical optical satelliteUSGS United States Geological SurveyVHSR very high spatial resolutionWIST warehouse inventory search tool



Documents

Satellite remote sensing for mapping vegetation in New Zealand freshwater environments: A review