The purpose of this paper is to illustrate a modelling framework to simulate large-scale land use changes, and its effect
on the structural and functional diversity of the ecosystem and social-economy based on the remotely sensed digital
images. The improved DLS model is developed with three scenarios in China from 2001 to 2020. The projection results
show that obvious land use changes will take place in the forestry area, grassland, cultivated land and unused land.
Dramatic changes will appear in Cultivated area in Northeast China, Huang-Huai-Hai plain and Southwest China. The
changes of forestry area are characterized by regional diversification. Grassland decreases mainly along the Great Wall
of Inner Mongolia and on Tibets Plateau. The newly expanded urban land, comparably smaller, distributes mainly
around the old towns or residential centers. There is no obvious change in water area. The unused area shrinks with the
expansion of forest and grass area in Western China. Based on this study, the capability of improved DLS modelling
framework in projecting the LUCC scenarios was tested successfully, and a conclusion was made that DLS model is an
useful model in scenario construction.
Eco-water demand is a key subject in ecology and hydroscience research. Under the macro background of implementing the Western Development Strategy, pursuing the ecological project of grain for green and actualizing regional sustainable development of our country, the distribution of water and soil resources, especially the deficiency of water resources has been restraining the development and utilization of resources in Western China, which should be settled urgently. In this context, it is significant to assess the amount and intensity of ecosystem eco-water demand. The authors taking Keriya River Watershed, the typical arid watershed in Western China, as an example, to assess the comprehensive eco-water demand and discuss the demand-supply equilibrium of eco-water demand by analyzing the structure characteristics of different ecosystem. The result has much significance for local government to formulate the development and utilization plan of water and soil resources, to guarantee the benign cycle and to realize the sustainable utilization of water resources, and further more to rebuild the ecological environment.
Landscape is a dynamic phenomenon that almost continuously changes. General speaking, landscape change is a dynamic process affected by geophysical conditions as well as human activities. However, numerous activities by a large number of individuals are not concerted and contribute to the autonomous evolution of the landscape in a similar way as natural processes do. There is a well-established need to detect landscape change so that appropriate policies for the regional sustainable development can be developed. Landscape change detection is considered to be effectively repeated surveillance and needs especially strict protocols to identify the change categories and intensity. Methods for monitoring and analyzing landscape change - for example, remote sensing and GIS - are increasingly used in attempts to understand the consequences of such change. This paper developed a hierarchical approach that combines remote sensing technology, GIS, and sophisticated analytical techniques to quantify land cover change at several spatial scales. Through human-machine interactive interpretation, the interpretation precision was 92.00% in 1986 and 89.73% in 2000. Based on the interpretation results of TM images and take Yulin Prefecture as the case study area, the area of main landscape types was summarized respectively in 1986 and 2000. The landscape pattern changes in Yulin could be divided into ten types.
With a subtropical climate, Guangxi has a typical karst landscape. Land degradation has become a serious environmental issue due to its high vulnerability caused by the joint effect of natural settings in geology, topography, rainfall, and vegetative cover, as well as human activities such as deforestation. Its eco-environment has deteriorated over recent years while cultivated land is disappearing quickly. This, in turn, has exacerbated the poverty level in rural areas. In this study we monitored the spatial distribution of land degradation and its temporal evolution using Landsat TM/ETM images of the late 1980s, mid-1990s and late 2000 (for simplicity, we identified them as 1985, 1995 and 2000). We also explored the causes of its initiation and expansion. Through constructing regression models using all the relevant variables and considering the lagged effects as well as fixed effects, we quantified the exact role of different factors in causing land degradation in the study area with new findings. Based on these results we further analyzed the hazard of land degradation and proposed a few practical rehabilitation measures, including forestation, infrastructure projects, and ecological projects. The findings in this study are invaluable in preserving, restoring, and reconstructing the degraded environment in Guangxi and other karst areas in Southwest China while alleviating poverty in rural areas.
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