MODIS NDVI TIME SERIES FOR IDENTIFICATION OF DEGRADED LAND HOTSPOTS

摘要

Land degradation is recognized as a serious threat to environment. Restoring degraded lands and soils is one of the Sustainable Development Goals (SDGs) of the United Nations Development Programme (UNDP). Given the importance of the problem, many attempts have been made word wide to map the distribution, type and severity of degradation. In India, most of the country level frameworks for land degradation assessments are based on expert opinion and visual interpretation of satellite data and have provided estimates ranging from 47 m ha to 187 m ha. None of the studies in India have considered the crop phenology and productivity as an indicator of land degradation. Further, these approaches are subjective inconsistent and time and cost consuming. These delays in assessments lead to a delayed solutions. A quick, simple, robust, quantitative, cost effective and consistent method has been developed to identify and map degraded lands at regional scale. It utilizes MODIS (or Moderate Resolution Imaging Spectroradiometer) NDVI (normalized Difference Vegetation Index) time series data (16 days composite for 20 years) as a proxy indicator of land productivity. Time series NDVI data have been used successfully to identify land degradation using trend analysis and local NPP (Net primary Productivity) scaling methods. However, the trend analysis of time series NDVI cannot be expected to identify historically degraded areas. The other method classifies the degraded lands as land capability units. The present study identifies the degraded lands (both historic and ongoing) based on the assumption that the degraded lands exhibit a consistently low productivity over time, indicated by constantly low NDVI. In contrast, the healthy soils will show a cycle of increase and decrease of NDVI over time with crop phenology. This pattern can be well identified by applying principal component analysis (PCA) on the time series NDVI data to identify constantly low productive hotspot areas. Finally, the method relies on field observations along with other data available in public domain to validate the overall assessment. The methodology was tested in different agro-ecological regions of India including, alluvial plains (Indo-gangetic plains), coastal plains, deserts, rain forests, basaltic terrains and was found effective. The simplicity and quantitative nature of method, use of freely available input data make it suitable for rapid assessment of land degradation on a national scale in a time and labour effective manner.