This paper introduces the use of Remote Sensing (RS) technology and GIS, combining with multidisciplinary methods, at the Zhangye and Jiuquan Oases in northwest China. Results show that: The integration of RS and GIS, Landscape Ecology, Settlement Geography, Community Ecology and social science is an efficient way to analyze landscape pattern and human impacts; The combination of Human Impact Index (HIM) and Human Pressure Index (HPI) can illustrate the influencing intensity of human beings explicitly; Nearest Distribution Pattern Index (NDPI) and spatial neighboring proportion can describe landscape pattern superior to partial traditional indices. NDPI is a potential index to replace Contagion. The neighboring proportion can reveal the neighboring relation more clearly than the Interspersion & Juxtaposition Index; The conclusions obtained from the Landsat TM data are well in accordance with the field survey and multidisciplinary analysis: Farmland and settlement are the key components in the both oases; At the patch types level, the distributing pattern is quite complex in the both oases. At the landscape level, the both oases are distributed randomly; Because of higher population pressure, more developed agriculture and communication, and higher density of manual corridor, the spatial pattern is more homogeneous and the human impacts on Zhangye Oasis are stronger than Jiuquan Oasis.
Remote sensing(RS) technology, which has made great progress in many applied fields, can provide an efficient means for active
identification of grassland degradation. In this paper, we used the Landsat TM images in 1980’s and 2000 to monitor the dynamics of
grassland degradation in Shandan County where was famous with its army horse feeding and the important self-restraint regions of soil
and water in the upper Heihe basin in Northwest China. By integrating RS and Geographic Information System(GIS), we calculated the
amounts and the changes of grassland types during the past 15 years. Using satellite monitoring and retrieval means to get the productivity
of grassland, analyzed the carrying capacity of livestock, and classified the grassland into 4 degradation levels and created the grassland degradation maps. Then, we use the landscape ecology principles to design the recovering planning of the corresponding degraded regions. At last, we promoted the corresponding strategies to restore the different degraded grasslands in Shandan County. This approach to dynamic monitoring of grassland degradation and the retrieval of grassland productivity is a fully digital operation, so it makes best use of computer and GIS to manage, display, query and map the grassland degradation data.
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