Monitoring and Assessing of Biodiversity in China Based on Multispectral
Remote Sensing Data
YANG Wen-fu1, 2, 3, LIU Jun4*, WANG Wen-wen2, 3, LIU Xiao-song2, 3, HAO Xiao-yang2, 3
1. School of Land Science and Technology, China University of Geosciences, Beijing, Beijing 100083, China
2. Key Laboratory of Monitoring and Protection of Natural Resources in Mining Cities, Ministry of Natural Resources, Jinzhong 030600, China
3. Shanxi Provincial Key Lab of Resources, Shanxi Coal Geology Geophysical Surveying Exploration Institute, Jinzhong 030600, China
4. College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Abstract：Biodiversity is the basis of human survival. Affected by the environment and climate change, the decrease in global biodiversity is becoming increasingly serious. Therefore, the study of regional biodiversity has great significance in protecting endangered species’ habitat, planning and utilising the regional resources reasonably. Three dynamic habitat indices (DHI) of cumulative, minimum and difference were constructed by datasets of NDVI, EVI, FPAR, LAI and GPP of multispectral remote sensing vegetation products from 2002 to 2018. The multiple regression analysis was used to study ① the applicability of DHIs constructed by different multispectral remote sensing indices (NDVI, EVI, FPAR, LAI and GPP); ②the complementarity of species diversity expressed by cumulative, minimum and difference DHI models; ③the impact of climate change on biodiversity in China; ④the ability of cumulative, minimum and difference DHIs to express species richness. The research shows that ① there is a strong correlation between corresponding DHIs based on the same MODIS multispectral vegetation indices (correlation coefficient from 0.77 to 0.98), so they can substitute for each other; There is a certain correlation among these three DHIs, while they cannot replace each other. ②Compared with DHIs constructed by NDVI, EVI, FPAR and LAI product data, GPP-DHIs have the strongest ability to monitor biodiversity in China and have a good correlation with species richness (correlation coefficient from 0.32 to 0.84). ③Continuous climate change will affect the total productivity of vegetation significantly in the large region, and extreme climate has little impact on the large region; Evapotranspiration has a more significant impact on the total productivity of vegetation in large-scale regions than temperature, and precipitation. ④Environmental change has the greatest impact on amphibian species richness, followed by birds and mammals. ⑤The cumulative DHI and minimum DHI in China gradually increase from the northwest inland to the southeast coastal area. The minimum DHI in the northwest, north China, high altitude area, high latitudes, and northwest desert areas are very small, which indicate the ecological environment in the southeast coastal area is more suitable for biological survival, and the harsh environment affects biodiversity seriously. The difference in DHI shows a higher spatial pattern in Northeast and North China, and a lower spatial pattern in Central and South China, which indicates that the living environment of species in Northeast and North China changed greatly, and the living environment of species in Central and South China was relatively stable.
Key words：Biodiversity; Dynamic habitat indices; Species richness; Climate change
杨文府，刘 珺，汪雯雯，刘小松，郝晓阳. 基于多光谱遥感数据的生物多样性监测与评估[J]. 光谱学与光谱分析, 2023, 43(04): 1282-1290.
YANG Wen-fu, LIU Jun, WANG Wen-wen, LIU Xiao-song, HAO Xiao-yang. Monitoring and Assessing of Biodiversity in China Based on Multispectral
Remote Sensing Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1282-1290.
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