Evolution pattern of land use and driving factors in India based on Shannon information entropy
India is one of the fastest-growing emerging economies in the world and one of the world's top two most populous countries.The fast-growing economy has also driven changes in land use patterns,so that India now faces severe land degradation problems.In this paper we introduce Shannon's information entropy,to construct land use Shannon's information entropy measurement index system from four aspects(statistics,structure,topology and theme),to excavate land use pattern and spatiotemporal evolution characteristics in India,and to analyze relevant driving factors.Land use diversity in India decreases first and then rises;structural and topological complexity has always been at a low level.Thematic complexity is constantly decreasing,so that in the past 20 years,India maintains its agricultural characteristics of mainly arable land.Thematic complexity has been decreasing,India has maintained the characteristics of a largely agricultural country dominated by arable land in the past 20 years,the degree of land use change is not high.Spatial autocorrelation of information entropy values of each land use patch is significant,with uneven rate of urban expansion,and environmental dependence on agricultural development.The slope,elevation,and population density all have driven land use changes in India.This work enriches the study of spatial complexity and provides lessons for land planning and management in developing countries.
land useinformation entropytri-clusteringdriving factors
万方数据
维普
