PROGRESS IN GEOGRAPHY ›› 2016, Vol. 35 ›› Issue (10): 1269-1278.doi: 10.18306/dlkxjz.2016.10.010

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Assessment of ecological environment quality in the Changbai Mountain Nature Reserve based on remote sensing technology

Shiyuan WANG(), Xuexia ZHANG*(), Tong ZHU, Wei YANG, Jingyao ZHAO   

  1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
  • Online:2016-10-28 Published:2016-10-26
  • Contact: Xuexia ZHANG;
  • Supported by:
    National Science and Technology Support Program of China, No.2015BAD07B03;National Basic Research Program of China (973 Program), No.2012CB955403;National Natural Science Foundation of China, No.41571154


The well-being of the human race is closely related to the ecological environment. In the past few decades, indicators retrieved from remote sensing data have been increasingly more widely applied to ecological environment evaluation for their advantages of spatial visualization, and remote sensing technology offers important reference to regional ecological environment management, improvement, and development planning by quickly assessing regional ecological environment quality. Remote sensing ecological index (RSEI), constructed by four indicators including green degree, humidity degree, heat degree, and dry degree retrieved from remote sensing images, can reflect ecological environment status. RSEI is based on real-time remote sensing images and accordingly more capable of quick evaluation of temporal and spatial changes of ecological environment quality. In this article, Landsat5 TM images from 1995 and 2007 and Landsat8 OLI images from 2015 were used as data source to retrieve values of the four indicators to construct the RSEI by the principal component analysis method, and Changbai Mountain Nature Reserve ecological environment quality from 1995 to 2015 was evaluated using the RSEI. The result shows: (1) The green degree and humidity degree have an positive effect on promoting the ecological environment quality of the region while the heat degree and dry degree have a restraining effect on the regional ecological environment quality, and the humidity degree is more significant than the other three indicators. (2) In this region, the proportion of excellent and good RSEI classes accounted for 49.5%, 66.5%, and 76.2% of the total area in 1995, 2007, and 2015. Meanwhile, the degenerated, unchanged, and improved RSEI classes were 3.9%, 55.6%, and 40.5% of the total area respectively, indicating that the overall ecological environment quality has gradually improved. To some extent this is attributed to the Natural Forest Resources Protection Project and a series of ecological conservation measures taken in the Changbai Mountain Nature Reserve area. Although the overall ecological environment quality of the study area has gradually improved, the ecological environment quality around Tianchi has declined. This area has a relatively fragile ecological environment and suffers from high intensity of tourism activities. The decline in ecological environment quality may be attributed to the increasingly intense tourism activities. (3) The stepwise regression analysis results show that each of the selected indicators is key to indicating the ecological environment quality, and the model prediction result reveals that the control of barren and dry surfaces is a critical step for improving the ecological environment quality.

Key words: ecological environment quality, remote sensing ecological index, principal component analysis, Changbai Mountain Nature Reserve