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REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (2) : 126-132     DOI: 10.6046/gtzyyg.2015.02.20
Technology Application |
A tentative discussion on the geographical condition monitoring method based on long time series Landsat data
LYU Guijun1,2, LI Yingcheng1,3, BAI Jie1, ZHAO Yali1
1. China TopRS Technology Co.Ltd, Beijing 100039, China;
2. Mine Spatial Information Technology Key Laboratory of the State Bureau of Surveying and Mapping, Henan Polytechnic University, Jiaozuo 454003, China;
3. Chinese Academy of Surveying & Mapping, Beijing 100039, China
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Abstract  To fully grasp the nature and human geography situation information and solve the problem of ecological environment, economic and social development of China, it is of great significance to monitor the resource and environment indexes. The authors studied the spatial and temporal distribution of resources and environment in Haidian District of Beijing from 1986 to 2010 with Landsat TM images. Meanwhile, the driving forces were analyzed. The results show that, from 1986 to 2010, farmland decreased by 45.7%, build-up land increased by 38.95%, forest and grass land increasd by 23.44%, and water and other land kept constant. Urban area extended westward and northward, especially in Xibeiwang area. The mass center of Haidian also moved to the northwest. The urban compactness decreased and the fractal index increased gradually, indicating that the city saturation degree was reduced, and the city boundary became complicated gradually. The comprehensive land use dynamic degree first decreased and then increased. Population growth, economic development, infrastructure construction and the formulation of policies and regulations all contributed to the changes of resources and environment.
Keywords LiDAR      mathematical morphology      point clouds classification      contour clusters      regional multi-return points density     
:  TP79  
Issue Date: 02 March 2015
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LI Lelin,JIANG Wanshou,GUO Chengfang. A tentative discussion on the geographical condition monitoring method based on long time series Landsat data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 126-132.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.02.20     OR     https://www.gtzyyg.com/EN/Y2015/V27/I2/126
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