Abstract
Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran’s major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.
Highlights
Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts
Large databases have been developed that relate to a plethora of vegetation and climate p roperties[25,26], including the normalized difference vegetation index (NDVI)[26,27,28], land cover changes[29], leaf area index (LAI)[30], land surface temperature (LST)[30], multidisciplinary indices comprising LAI, albedo and evapotranspiration (ET)[30,31], water use efficiency (WUE), net primary production (NPP)[32], enhanced vegetation index (EVI)[33], and rainfall and vegetation d atasets[34]
Recent studies have reported the high efficiency of vegetation indices such as NDVI, NPP, LAI and EVI for evaluating the spatial and temporal changes across different scales[35,36]
Summary
Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Recent studies have reported the high efficiency of vegetation indices such as NDVI, NPP, LAI and EVI for evaluating the spatial and temporal changes across different scales[35,36] These variables are commonly correlated with other climate parameters such as rainfall, temperature or evapotranspiration, which are useful for assessing and forecasting the potential for land degradation into the future[24,34,35,36,37,38]. With satellite observations providing long time-series data of relevant parameters at a relatively high spatial resolution ( overcoming the risk of misinterpreting natural inter-annual variation), a new opportunity arises to explore data at multi-decadal, regional scales, as well as exploring the data with increased granularity to explore temporal and spatial patterning within the data
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