JOURNAL OF NATURAL RESOURCES ›› 2017, Vol. 32 ›› Issue (4): 568-581.doi: 10.11849/zrzyxb.20160476

• Resource Ecology • Previous Articles     Next Articles

Forest Landscape Pattern Dynamic Change and Scenarios Simulation at Community Level

WANG Lu1, SHAO Jing-an1, 2, GUO Yue1, 2, XU Xin-liang3   

  1. 1. College of Geographical Science, Chongqing Normal University, Chongqing 400047, China;
    2. Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, Chongqing 400047, China;
    3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2016-05-06 Revised:2016-08-23 Online:2017-04-20 Published:2017-04-20
  • Supported by:

    Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, No.2015BAD07B04; Major Program of National Natural Science Foundation of China, No.41161140352; Humanity and Social Science Youth foundation of Ministry of Education of China, No.10YJCZH122; Important National Project of High Resolution Earth Observation System, No.00-Y30B15-9001-14/16

Abstract:

This paper took Shixing village in Sanxing town, Shizhu County as the sample area. Based on the forest landscape pattern in 2004, the Logistic stepwise regression was adopted to select factors playing essential roles in the forest landscape pattern in different periods, which were terrain, altitude, drainage course, transportation and distribution of the settlement. CLUE-S was applied to simulate the distribution of the forest landscape pattern in Shixing village in 2014, and the result was validated by comparing with the actual pattern. Then the forest landscape pattern ten years later was simulated based on three scenarios: the historical development trend, the boom of returning home of the second generation of farmers, and the intervention of industrial and commercial fund. Besides, this paper analyzed the landscape dynamics from 2004 to 2024 by using the landscape pattern index. There are four aspects of the result. Firstly, based on the distribution pattern of the forest landscape in 2014, the precision of forest landsacpe pattern in 2014 was 85%, and the average Kappa coefficient was over 0.816, which illustrated the applicability of CLUE-S. Secondly, in the three scenarios, the forest landscape always occupied the main position in the landscape matrix during the 20 years from 2004, and the result illustrates that the total area of the forest increased compared with that in 2004. In addition, the degraded forest lands decreased in all three scenarios. In the second scenario, the reductions of the degraded forest land accompanied with the increase of the artificial forest and the agricultural land. In the third scenario, the reductions of the degraded forest land accompanied with the increase of the artificial forest and the decrease of agricultural land. Thirdly, the spatial distributions of the forest lands are regular. The degraded primary forests are mainly seen in the hills and deep hillock areas, while the secondary forests, degraded forest land and artificial forests are in mosaic structure, being scattered in some matrix landscape. Fourthly, the degree of forest landscape fragmentation is different in three simulated scenarios. Generally, the recoveries of forest landscape are better in the second and the third scenarios. The result of this study will provide the reference and support to the administration, planning, and policy making of the forest landscape in following years.

Key words: CLUE-S model, community level, forest landscape pattern, Logistic stepwise regression method

CLC Number: 

  • S718.5