Remote Sensing Land Usage Classification and Landscape Pattern Analysis Based on Random Forest
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    Abstract:

    In 2009, Fujian Pingtan Comprehensive Experimental Zone was established as a window for cooperation between Fujian and Taiwan and the country's opening to the outside world. Its land use change is mainly affected by social and economic factors and natural geographical environment, and is also closely related to future land use planning. Landsat remote sensing image data of 1990, 2000, 2010, and 2017 is used to quantitatively analyze the impact of land use change on landscape pattern in the past 27 years. The results show that:(1) high accuracy of remote sensing land use classification can be obtained by using random forest method when selecting suitable training samples (the overall accuracy of the 4 remote sensing image classifications is above 87%, and the Kappa coefficient is above 0.84). (2) From 1990 to 2017, the water area decreased sharply by 31.04 km2, and the lost water area is mainly converted into construction land and forest land; the construction land is increased by 40.98 km2, and the annual average growth is 1.52 km2. In the past ten years, it has shown a rapid growth trend with an average annual growth of 3.87 km2. (3) At the plaque type level, the construction land is increasing year by year. The largest plaques accounted for the proportion of landscape area (LPI), degree of polymerization (AI), and edge density (ED), and the LPI was most affected by the increase of construction land. At the landscape type level, diversity (SHDI) and landscape shape (LSI) are declining.

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周正龙,沙晋明,范跃新,帅晨,高尚.基于随机森林的遥感土地利用分类及景观格局分析.计算机系统应用,2020,29(2):40-48

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History
  • Received:June 09,2019
  • Revised:July 05,2019
  • Adopted:
  • Online: January 16,2020
  • Published: February 15,2020
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