Dryland observation at local and regional scale: comparison of Landsat TM and NOAA AVHRR time series

The aim of this study was to evaluate the potentials and limits of remote sensing time series regarding change analysis of drylands. We focussed on the assessment and monitoring of land degradation using different scales of remote sensing data. Special interest was paid on how the spatial resolutions of different sensors influence the derivation of vegetation related variables, such as trends in time and the shift of phenological cycles. Hence, a comparison was performed using high and medium resolution sensors and their suitability for monitoring land degradation will be evaluated. Long time series of Landsat TM and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a test area in the Mediterranean. At local scale additional information was delivered by a multi-seasonal land use/cover change detection (LUCC) analysis. The test site which is located in Central Macedonia (Greece) is mainly characterized by long-term, gradual processes mainly driven by grazing and the extension of irrigated arable land.

[1]  C. Justice,et al.  Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations , 1988 .

[2]  Robert H. Shumway,et al.  Time series analysis and its applications : with R examples , 2017 .

[3]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[4]  Taskin Kavzoglu,et al.  Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data , 2004 .

[5]  Compton J. Tucker,et al.  VARIATIONS IN THE SIZE OF THE SAHARA DESERT FROM 1980 TO 1997 , 1999 .

[6]  José F. Moreno,et al.  An optimum interpolation method applied to the resampling of NOAA AVHRR data , 1994, IEEE Trans. Geosci. Remote. Sens..

[7]  William J. Emery,et al.  Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data , 1995 .

[8]  Roland Geerken,et al.  Assessment of rangeland degradation and development of a strategy for rehabilitation , 2004 .

[9]  C. Tucker,et al.  Recent trends in vegetation dynamics in the African Sahel and their relationship to climate , 2005 .

[10]  O. Viedma,et al.  Modeling rates of ecosystem recovery after fires by using landsat TM data , 1997 .

[11]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[12]  Lars Eklundh,et al.  Vegetation index trends for the African Sahel 1982–1999 , 2003 .

[13]  J. Hill,et al.  Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands , 2003 .

[14]  O. Viedma,et al.  Interactions between land use/land cover change, forest fires and landscape structure in Sierra de Gredos (Central Spain) , 2006, Environmental Conservation.

[15]  J. Hill,et al.  Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece , 2008 .

[16]  Robin P. White,et al.  DRYLANDS, PEOPLE, AND E COSYSTEM GOODS AND SERVICES: A Web-Based Geospatial Analysis (PDF Version) , 2003 .

[17]  S. Prince,et al.  Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series , 2004 .

[18]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[19]  H. Geist,et al.  The Causes and Progression of Desertification , 2005 .

[20]  C. Justice,et al.  Spatial degradation of satellite data , 1989 .

[21]  J. Braun-Blanquet,et al.  Les groupements végétaux de la France méditerranéenne , 1951 .

[22]  Lee De Cola,et al.  Multiresolution convariation among landsat and AVHRR vegetation indices , 1997 .