Journal
FRONTIERS OF EARTH SCIENCE
Volume 6, Issue 4, Pages 445-452Publisher
SPRINGER
DOI: 10.1007/s11707-012-0338-7
Keywords
land use change; forest land; spatial autocorrelation; ESDA; GIS; Beijing-Tianjin-Hebei area
Categories
Funding
- National Natural Science Foundation of China [41061049, 41161087]
- Planning Project of National Science and Technology [2006BAB15B02]
- Technology Foundation of Jiangxi Education Department [GJJ12269]
- Ministry of Environment Protection of China [STSN-09-00]
- Young Scientist Plan Fund of Jiangxi Province
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Forest land is the essential and important natural resource that provides strong support for human survival and development. Research on forest land changes at the county level about its characteristics, rules, and spatial patterns is, therefore, important for regional resource protection and the sustainable development of the social economy. In this study we selected the GIS and Geoda software package to explore the spatial disparities of forest land changes at the Beijing-Tianjin-Hebei area county level, based on the global and local spatial autocorrelation analyses of exploratory spatial data. The results show that: 1) during 1985-2000, the global spatial autocorrelation of forest land change is significant in the study area. The global Moran's I value is 0.3122 for the entire time period and indicates significant positive spatial correlation (p < 0.05). Moran's I value of forest land change decreases from 0.3084 at the time stage I to 0.3024 at the time stage II; 2) the spatial clustering characteristics of forest land changes appear on the whole in Beijing-Tianjin-Hebei area. Moran's I value decreases from the time stage I to time stage II, which means that trend of spatial clustering of forest land change is weakened in the Beijing-Tianjin-Hebei area; 3) the grid map of the local Moran's I for each county reflects local spatial homogeneity of forest land change, which means that spatial clustering about regions of high value and low value is especially significant. The regions with High-High correlation are mainly located in the north hilly area. However, the regions with Low-Low correlation were distributed in the middle of the study area. Therefore, protection strategies and concrete measures should be put in place for each regional cluster in the study area.
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