Abstract
Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.
Highlights
Forest degradation could be defined as a loss of carbon stocks from forestland without land cover change caused by either natural or anthropogenic activities with relating to climate change [1,2]
Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) showed an increasing trend in most areas of the Three-North Forest Shelterbelt, which demonstrated that great achievements have been achieved since the implementation of the program, and the forests were in good condition in most areas
The same situation occurred in eastern and southeastern areas in Liaoning and Jilin, the NDVI, EVI, RVI and LAI were increasing while FPAR and NPP were decreasing
Summary
Forest degradation could be defined as a loss of carbon stocks from forestland without land cover change caused by either natural or anthropogenic activities with relating to climate change [1,2]. Deforestation and forest degradation have become an important issue concerning climate change, and have been the second leading cause of anthropogenic greenhouse emissions [3,4]. The uncertainty is mainly due to the difficulties in monitoring degradation over large areas as the forest kinds, climatic characteristics, forestry planning and management vary. Developing efficient methods for forest degradation is of great significance in revealing the process of forest quality deceased, stand structure destructed and permanent loss of forest and giving a better understanding of regional or global carbon emissions and its relationship with climate change.
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