| Technology Application |
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Remote sensing dynamic monitoring and driving force analysis of land use/cover changes in Qingchuan County |
| LIU Meng, YANG Wunian, SHAO Huaiyong, SUN Xiaofei |
| Key Laboratory of Geo-spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China |
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Abstract Qingchuan County of Guangyuan City in Sichuan Province is a national ecological pilot county and implementation country of returning farmland to forest. The understanding of the current situation and spatial-temporal dynamic change of land use/cover is of great significance in providing the scientific basis for relevant government departments. In this paper, with the application of RS and GIS technology, on the basis of TM images in 2000, 2005 and 2010, and through image processing and information extracting, the authors acquired land use/cover maps in different years and established the database. On such a basis, the land use/cover dynamic change process in Qingchuan in the past 10 years was analyzed, and the driving force that caused the change was identified. At last, land use/cover area ratio of the study area in 2015 and 2020 were predicted. According to the results obtained, between 2000 and 2005, the area of arable land, water and unused land decreased, while the area of woodland, grassland and construction land increased; between 2015 and 2020, the area of arable land and grassland decreased, while the area of woodland, water, construction land and unused land increased. During the ten years, the area of arable land continually decreased with the scale reduced, the area of woodland continually increased with the scale reduced, the area of construction land increased continually with the scale increased, the area of water and unused land decreased and then increased, and the area of grassland increased and then decreased. An analyses reveals that policy, economic development, population growth, and natural disasters seem to be the principal impact factors of land use changes in the study area. It is inferred that, under the conditions that relevant policy is unchanged and no natural disasters occur, the proportion of woodland area will increase from 58.57% in 2010 to 59.01% in 2015, and increase to 59.44% in 2020. The proportion of arable area will decrease to 29.13% and the proportion of construction land will continually increase to 0.22% by 2020.
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| Keywords
empirical model decomposition (EMD)
weighted filter empirical model decomposition (WFEMD)
image fusion
intrinsic model functions (IMF)
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Issue Date: 01 July 2014
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2014,
Vol. 26
