Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate
Identifieur interne : 000B45 ( Main/Merge ); précédent : 000B44; suivant : 000B46Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate
Auteurs : Ruth Sonnenschein [Allemagne] ; Tobias Kuemmerle [Allemagne] ; Thomas Udelhoven [Allemagne] ; Marion Stellmes [Allemagne] ; Patrick Hostert [Allemagne]Source :
- Remote sensing of environment [ 0034-4257 ] ; 2011.
Descripteurs français
- Pascal (Inist)
- LANDSAT, Télédétection spatiale, Satellite Landsat, Analyse tendance, Milieu aride, Choix, Végétation, Ressource naturelle, Fonctionnement écosystème, Utilisation terrain, Occupation sol, Changement climatique, Pression, Ecosystème, Résultat, Dynamique végétation, Echelle spatiale, Capture, Haute résolution, Série temporelle, Observation par satellite, Imagerie, Mélange, Analyse quantitative, Couvert végétal, <<>>, Crète, Ile Crète, Monde, Région Méditerranéenne.
- Wicri :
English descriptors
- KwdEn :
- Capture, Choice, Crete, Ecosystem functioning, Landsat, Landsat satellite, Mediterranean region, Mixture, Plant cover, Result, Satellite observation, Space remote sensing, Spatial scale, Time series, Vegetation dynamics, arid environment, climate change, ecosystems, global, high resolution, imagery, land cover, land use, natural resources, pressure, quantitative analysis, trend-surface analysis, vegetation.
Abstract
Drylands cover about 47% of the globe's surface and provide important ecosystem services, but land use and climate change exert considerable pressure on these ecosystems. Both of these drivers frequently result in gradual vegetation change and landscape-scale trend analysis based on yearly vegetation estimates can capture such changes. Such trend analyses based on high-resolution time series of satellite imagery have so far not widely been used and existing studies in drylands relied on different vegetation measures. Spectral mixture analysis (SMA) has been chosen due to its superiority to simpler vegetation estimates in quantifying vegetation cover in single-date studies, however SMA can be challenging to implement for large areas. Here, we quantify the trade-off involved when using simple vegetation estimates instead of SMA fractions for subsequent trend analyses. We calculated NDVI, SAVI and Tasseled Cap Greenness, as well as SMA green vegetation fractions for a time series of Landsat images from 1984-2005 for a study region in Crete. Linear trend analysis showed that trend coefficients and the spatial patterns of trends were similar across all vegetation estimates and the entire study region, especially for areas where vegetation changed gradually. On average, trends based on simple measures differed less than 5% from SMA-based trends with decreasing similarity in trend results from Tasseled Cap Greenness to SAVI and NDVI. Vegetation estimates differed markedly in their response to disturbance events such as fires. Trend analyses based on qualitative measures can easily be applied across very large areas and using multi-sensor time series based on high-resolution data. While the subtle differences between vegetation estimates may still be important for some applications, the robustness of trend analyses regarding the choice of vegetation estimate bears considerable promise to reconstruct fine-scale vegetation dynamics and land use histories and to assess climate change impacts on the world's drylands.
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type="city">Berlin</settlement><region type="land" nuts="2">Berlin</region></placeName></affiliation></author><author><name sortKey="Kuemmerle, Tobias" sort="Kuemmerle, Tobias" uniqKey="Kuemmerle T" first="Tobias" last="Kuemmerle">Tobias Kuemmerle</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Humboldt-Universität zu Berlin, Geomatics Lab, Unter den Linden 6</s1><s2>10099 Berlin</s2><s3>DEU</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>Allemagne</country><wicri:noRegion>10099 Berlin</wicri:noRegion><placeName><settlement type="city">Berlin</settlement><region type="land" nuts="2">Berlin</region></placeName></affiliation><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>Department of Earth System Analysis, Potsdam Institute for Climate Impact Research, PO Box 601203</s1><s2>14412 Potsdam</s2><s3>DEU</s3><sZ>2 aut.</sZ></inist:fA14><country>Allemagne</country><placeName><region type="land" nuts="2">Brandebourg</region><settlement type="city">Potsdam</settlement></placeName></affiliation></author><author><name sortKey="Udelhoven, Thomas" sort="Udelhoven, Thomas" uniqKey="Udelhoven T" first="Thomas" last="Udelhoven">Thomas Udelhoven</name><affiliation wicri:level="4"><inist:fA14 i1="03"><s1>University of Trier, Remote Sensing Department, Behringstr. 15</s1><s2>54286 Trier</s2><s3>DEU</s3><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Allemagne</country><wicri:noRegion>54286 Trier</wicri:noRegion><wicri:noRegion>Behringstr. 15</wicri:noRegion><wicri:noRegion>54286 Trier</wicri:noRegion><orgName type="university">Université de Trèves</orgName><placeName><settlement type="city">Trèves (Allemagne)</settlement><region type="land" nuts="1">Rhénanie-Palatinat</region></placeName></affiliation></author><author><name sortKey="Stellmes, Marion" sort="Stellmes, Marion" uniqKey="Stellmes M" first="Marion" last="Stellmes">Marion Stellmes</name><affiliation wicri:level="4"><inist:fA14 i1="03"><s1>University of Trier, 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nuts="2">Berlin</region></placeName></affiliation></author></analytic><series><title level="j" type="main">Remote sensing of environment</title><title level="j" type="abbreviated">Remote sens. environ.</title><idno type="ISSN">0034-4257</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Remote sensing of environment</title><title level="j" type="abbreviated">Remote sens. environ.</title><idno type="ISSN">0034-4257</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Capture</term><term>Choice</term><term>Crete</term><term>Ecosystem functioning</term><term>Landsat</term><term>Landsat satellite</term><term>Mediterranean region</term><term>Mixture</term><term>Plant cover</term><term>Result</term><term>Satellite observation</term><term>Space remote sensing</term><term>Spatial scale</term><term>Time series</term><term>Vegetation dynamics</term><term>arid environment</term><term>climate change</term><term>ecosystems</term><term>global</term><term>high resolution</term><term>imagery</term><term>land cover</term><term>land use</term><term>natural resources</term><term>pressure</term><term>quantitative analysis</term><term>trend-surface analysis</term><term>vegetation</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>LANDSAT</term><term>Télédétection spatiale</term><term>Satellite Landsat</term><term>Analyse tendance</term><term>Milieu aride</term><term>Choix</term><term>Végétation</term><term>Ressource naturelle</term><term>Fonctionnement écosystème</term><term>Utilisation terrain</term><term>Occupation sol</term><term>Changement climatique</term><term>Pression</term><term>Ecosystème</term><term>Résultat</term><term>Dynamique végétation</term><term>Echelle spatiale</term><term>Capture</term><term>Haute résolution</term><term>Série temporelle</term><term>Observation par satellite</term><term>Imagerie</term><term>Mélange</term><term>Analyse quantitative</term><term>Couvert végétal</term><term><<>></term><term>Crète</term><term>Ile Crète</term><term>Monde</term><term>Région Méditerranéenne</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Ressource naturelle</term><term>Changement climatique</term><term>Analyse quantitative</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Drylands cover about 47% of the globe's surface and provide important ecosystem services, but land use and climate change exert considerable pressure on these ecosystems. Both of these drivers frequently result in gradual vegetation change and landscape-scale trend analysis based on yearly vegetation estimates can capture such changes. Such trend analyses based on high-resolution time series of satellite imagery have so far not widely been used and existing studies in drylands relied on different vegetation measures. Spectral mixture analysis (SMA) has been chosen due to its superiority to simpler vegetation estimates in quantifying vegetation cover in single-date studies, however SMA can be challenging to implement for large areas. Here, we quantify the trade-off involved when using simple vegetation estimates instead of SMA fractions for subsequent trend analyses. We calculated NDVI, SAVI and Tasseled Cap Greenness, as well as SMA green vegetation fractions for a time series of Landsat images from 1984-2005 for a study region in Crete. Linear trend analysis showed that trend coefficients and the spatial patterns of trends were similar across all vegetation estimates and the entire study region, especially for areas where vegetation changed gradually. On average, trends based on simple measures differed less than 5% from SMA-based trends with decreasing similarity in trend results from Tasseled Cap Greenness to SAVI and NDVI. Vegetation estimates differed markedly in their response to disturbance events such as fires. Trend analyses based on qualitative measures can easily be applied across very large areas and using multi-sensor time series based on high-resolution data. While the subtle differences between vegetation estimates may still be important for some applications, the robustness of trend analyses regarding the choice of vegetation estimate bears considerable promise to reconstruct fine-scale vegetation dynamics and land use histories and to assess climate change impacts on the world's drylands.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Berlin</li><li>Brandebourg</li><li>Rhénanie-Palatinat</li></region><settlement><li>Berlin</li><li>Potsdam</li><li>Trèves (Allemagne)</li></settlement><orgName><li>Université de Trèves</li></orgName></list><tree><country name="Allemagne"><region name="Berlin"><name sortKey="Sonnenschein, Ruth" sort="Sonnenschein, Ruth" uniqKey="Sonnenschein R" first="Ruth" last="Sonnenschein">Ruth Sonnenschein</name></region><name sortKey="Hostert, Patrick" sort="Hostert, Patrick" uniqKey="Hostert P" first="Patrick" last="Hostert">Patrick Hostert</name><name sortKey="Kuemmerle, Tobias" sort="Kuemmerle, Tobias" uniqKey="Kuemmerle T" first="Tobias" last="Kuemmerle">Tobias Kuemmerle</name><name sortKey="Kuemmerle, Tobias" sort="Kuemmerle, Tobias" uniqKey="Kuemmerle T" first="Tobias" last="Kuemmerle">Tobias Kuemmerle</name><name sortKey="Stellmes, Marion" sort="Stellmes, Marion" uniqKey="Stellmes M" first="Marion" last="Stellmes">Marion Stellmes</name><name sortKey="Udelhoven, Thomas" sort="Udelhoven, Thomas" uniqKey="Udelhoven T" first="Thomas" last="Udelhoven">Thomas Udelhoven</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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