Knowledge of vegetation distribution patterns is very important. Their relationships with topography and climate were explored through a geographically weighted regression （GWR） framework in a subtropical mountainou...Knowledge of vegetation distribution patterns is very important. Their relationships with topography and climate were explored through a geographically weighted regression （GWR） framework in a subtropical mountainous and hilly region, Minjiang River Basin of Fujian in China. The HJ-1 satellite image acquired on December 9, 2010 was utilized and NDVI index was calculated representing the range of vegetation greenness. Proper analysis units were achieved through segregation based on small sub-basins and altitudinal bands. Results indicated that the GWR model was more powerful than ordinary linear least square （OLS） regression in interpreting vegetation-environmental relationship, indicated by higher adjusted R2 and lower Akaike information criterion values. On one side, the OLS analysis revealed dominant positive influence from parameters of elevation and slope on vegetation distribution. On the other side, GWR analysis indicated that spatially, the parameters of topography had a very complex relationship with the vegetation distribution, as results of the various combinations of environmental factors, vegetation composition and also anthropogenic impact. The influences of elevation and slope generally decreased, from strongly positive to nearly zero, with increasing altitude and slope. Specially, most rapid changes of coefficients between NDVI and elevation or slope were observed in relatively flat and low-lying areas. This paper confirmed that the non-stationary analysis through the framework of GWR could lead to a better understanding of vegetation distribution in subtropical mountainous and hilly region. It was hoped that the proposed scale selection method combined with GWR framework would provide some guidelines on dealing with both spatial （horizontal） and altitudinal （vertical） non-stationarity in the dataset, and it could easily be applied in characterizing vegetation distribution patterns in other mountainous and hilly river basins and related research.展开更多
基金National Natural Science Foundation of China, No.41071207 No.41001254+2 种基金 Scientific Research Foundation for Returned Scholars, No.[20121940, Project from Ministry of Education of China and Science Foundation of Fujian Province, No.201210005 No.2012J01167 Research Foundations from Fuzhou University, No.2009-XQ-19
文摘Knowledge of vegetation distribution patterns is very important. Their relationships with topography and climate were explored through a geographically weighted regression （GWR） framework in a subtropical mountainous and hilly region, Minjiang River Basin of Fujian in China. The HJ-1 satellite image acquired on December 9, 2010 was utilized and NDVI index was calculated representing the range of vegetation greenness. Proper analysis units were achieved through segregation based on small sub-basins and altitudinal bands. Results indicated that the GWR model was more powerful than ordinary linear least square （OLS） regression in interpreting vegetation-environmental relationship, indicated by higher adjusted R2 and lower Akaike information criterion values. On one side, the OLS analysis revealed dominant positive influence from parameters of elevation and slope on vegetation distribution. On the other side, GWR analysis indicated that spatially, the parameters of topography had a very complex relationship with the vegetation distribution, as results of the various combinations of environmental factors, vegetation composition and also anthropogenic impact. The influences of elevation and slope generally decreased, from strongly positive to nearly zero, with increasing altitude and slope. Specially, most rapid changes of coefficients between NDVI and elevation or slope were observed in relatively flat and low-lying areas. This paper confirmed that the non-stationary analysis through the framework of GWR could lead to a better understanding of vegetation distribution in subtropical mountainous and hilly region. It was hoped that the proposed scale selection method combined with GWR framework would provide some guidelines on dealing with both spatial （horizontal） and altitudinal （vertical） non-stationarity in the dataset, and it could easily be applied in characterizing vegetation distribution patterns in other mountainous and hilly river basins and related research.
