Identifying factors affecting regional fractional vegetation cover based on a combination of factor regression and interaction—A case study on the Three-River Headwaters Region
ZHAO Dan1,2, WANG Zu-wei1, ZHANG Guo-zhuang2,3, XU Yong-min4, SUN li-jian2
1. School of Geography and Environment Science, Tianjin Normal University, Tianjin 300387, China; 2. Chinese Academy of Surveying and Mapping, Beijing 100830, China; 3. School of Earth Science and Resources, Chang'an University, Xi'an 710054, China; 4. National Quality Inspection and Testing Center for Surveying and Mapping Products, Beijing 100830, China
Abstract:The spatial heterogeneity of FVC in the Three-River Headwaters region was analyzed using the normalized vegetation index (NDVI) obtained from SPOT VEGETATION, and the impacts of natural and anthropogenic factors on FVC were evaluated with a combination of factor regression and interaction. The results show that:(1)There is a clear spatial heterogeneity in FVC across the Three-River Headwaters region; (2)The natural environmental factors have more impact on FVC spatial distribution than anthropogenic factors;(3)The natural environmental factors have greater impact on FVC spatial distribution than do anthropogenic factors, of which annual precipitation can explain 0.777 of the total variance despite its strongest interaction with other factors; (4) Factors interaction found that the driving explanation system shows a two-factor enhancement, indicating that there is no independent factor from the perspective of the system, and the interaction between annual precipitation and other factors is the strongest; (5)The precipitation gradient affects the degree of explanation of the spatial heterogeneity of FVC in the Three-River Headwaters Region. With an increase in precipitation, the explanatory power of the factors tends to stabilize, and the FVC tends to be more influenced by elevation and temperature in the eastern part of the Three-River Headwaters area where precipitation is higher; (6) The data results also confirm that factor-independent global optimal screening is only an optimal function that simulates the characteristics of the dependent variable, and its explanatory effect is not fully equivalent to the driven explanation of the dependent variable.
赵丹, 王祖伟, 张国壮, 徐永敏, 孙立坚. 因子回归和交互联合探索区域植被覆盖度的影响因素——以三江源地区为例[J]. 中国环境科学, 2022, 42(8): 3903-3912.
ZHAO Dan, WANG Zu-wei, ZHANG Guo-zhuang, XU Yong-min, SUN li-jian. Identifying factors affecting regional fractional vegetation cover based on a combination of factor regression and interaction—A case study on the Three-River Headwaters Region. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(8): 3903-3912.
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