1. Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images
- Author
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Pengju Liu, Jingning Yao, Tao Yu, Qiang Zhang, and Yi Ren
- Subjects
validation ,010504 meteorology & atmospheric sciences ,multi-scale ,Science ,forest degradation ,0211 other engineering and technologies ,Primary production ,02 engineering and technology ,Enhanced vegetation index ,01 natural sciences ,Normalized Difference Vegetation Index ,indicators ,MODIS ,Remote sensing (archaeology) ,Photosynthetically active radiation ,Thematic Mapper ,General Earth and Planetary Sciences ,Environmental science ,Moderate-resolution imaging spectroradiometer ,Leaf area index ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - 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.
- Published
- 2021
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