Dryland observation at local and regional scale - Comparison of Landsat TM/ETM plus and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice. (C) 2010 Elsevier Inc. All rights reserved.