Arid Zone Research ›› 2019, Vol. 36 ›› Issue (2): 451-458.doi: 10.13866/j.azr.2019.02.22

• Plant Physiology • Previous Articles     Next Articles

3S-Based Extraction of Spatial Distribution of Picea schrenkiana var. tianschanica in History in the Tianshan Mountains

XING Fei1, LI Hu2, LI Jian-gui3, ZHANG Nai-ming4, LIU Yu-feng2, CHEN Dong-hua2   

  1. 1. College of Grassland and Environment Science, Xinjiang Agricultural University, Urumqi 830052, Xinjiang,China;
    2. College of Geographical Information and Tourism, Chuzhou University, Chuzhou 239000, Anhui,China;
    3. Institute of Forestry, Xinjiang Agricultural University, Urumqi 830052, Xinjiang,China;
    4. College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, Xinjiang,China
  • Received:2018-06-06 Revised:2018-08-15 Published:2025-10-18

Abstract: The spatial distribution information of Picea schrenkiana var. tianschanica in the Tianshan Mountains in historical period was extracted based on the vegetation index, topographic factor, principal component analysis, the decision tree classification method and the habitat characteristics of P. schrenkiana in the study area using the remote sensing methods combined with the historical remote sensing image data. So as to provide support for the benefit evaluation of the natural forest resources protection project under the situation of missing historical data. Results showed that: ① the historical spatial distribution information of P. schrenkiana in the Tianshan Mountains could be extracted from the remote sensing images, the forest stand age of P. schrenkiana was set as a fixed factor, and the present remote sensing images with high spatial resolution and forest management investigation data were used as the background information. The accuracy of information extraction of P. schrenkiana in the study area could be as high as 93.3%, and the remote sensing images can be used to extract the spatial distribution information of P. schrenkiana in the Tianshan Mountains;② In the vegetation index factors, the response of P. schrenkiana in the Tianshan Mountains to NDVI was the most sensitive, and the best NDVI range for extracting the information of P. schrenkiana in the Tianshan Mountains was [0.35, 0.8]; ③ Topographic factor and principal component analysis method could greatly compress the redundant information of image, which improved the accuracy of information extraction of P. schrenkiana forest and improved the running speed. On the whole, the spatial distribution information of P. schrenkiana forest in the historical period can be well extracted by using the historical remote sensing images and combining with the habitat characteristics of P. schrenkiana forest in Tianshan Mountains, so as to provide data support for the formulation of forest resource management measures and the response to climate change in the context of data missing.

Key words: historical remote sensing image, decision tree classification, Picea schrenkiana var. tianschanica, spatial distribution, Fukang Forest Farm