院报 ›› 2016, Vol. 33 ›› Issue (6): 150-154.DOI: 10.11988/ckyyb.20150205

• 信息技术应用 • 上一篇    

基于Landsat遥感影像解译的神农架林区 近30年土地覆盖动态变化

姜哲1,2,刘芳2,张微1,栾晓峰1,李迪强2,李佳2   

  1. 1.北京林业大学 自然保护区学院,北京 100083;
    2.中国林业科学研究院 森林生态环境与保护研究所,北京 100091
  • 收稿日期:2015-03-23 出版日期:2016-05-25 发布日期:2016-06-12
  • 作者简介:姜 哲(1990-),男,河南长垣人,硕士研究生,主要从事自然保护区学的研究,(电话)18001397125(电子信箱)812288862@qq.com。    通讯作者:李迪强(1966-),男,湖南湘潭人,研究员,博士,主要从事生物保护学研究,(电话)010-62888594(电子信箱)lidq@caf.ac.cn。
  • 基金资助:
    “十二五”农村领域国家科技计划课题资助项目(2013BAD03B03-03)

Changes of Land Cover in Shennongjia Forest Region in the Past 3 Decades Based on Interpretation of Landsat Images

JIANG Zhe1,2, LIU Fang2, ZHANG Wei1, LUAN Xiao-feng1, LI Di-qiang2, LI Jia2   

  1. 1.School of Nature Conservation, Beijing Forestry University, Beijing 100083, China;
    2.Research Institute ofForest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
  • Received:2015-03-23 Online:2016-05-25 Published:2016-06-12

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摘要： 利用神农架林区1987年、2000年、2013年Landsat卫星遥感影像,结合地面调查信息,运用ERDAS软件分别解译出这3个时期地表覆盖类型,同时对比3个时期地表覆盖类型的变化,统计分析出3个时期神农架林区森林变化面积和来源。结果显示1987—2000年森林面积增长缓慢,净增长了14.70 km2,变化主要来自灌丛和草地的转化;2000—2013年期间森林面积增长迅速,净增长了207.49 km2,变化主要来自灌丛、草地和农田的转化。运用遥感技术连续、宏观、动态地监测神农架林区地表覆盖变化,不仅丰富了神农架林区的本底资料,同时也为生态环境监测与生物多样性保护提供了重要的数据。

关键词: 遥感影像, 监督分类, 森林变化, 保护成效, 神农架林区

Abstract: According to Landsat images and ground investigation data, we interpreted the land cover types in 1987, 2000, and 2013 in Shennongjia Forest Region using Erdas Software, compared changes of land cover types in these periods, and analyzed the area and sources of changes in forest cover. The results indicated that forest area increased slowly by 14.70 km2 during 1987-2000, in which shrub and grassland contributed the most to the forest increasing area; however, during 2000-2013 forest area increased rapidly by 207.49 km2, in which shrub, grassland and farmland contributed the most. The application of remote sensing technology in monitoring the changes in land cover continuously and dynamically at macro level can enrich the background data and provide essential data for environment monitoring and biodiversity conservation.

Key words: remote sensing image, supervised classification, change of forest, achievement of conservation, Shennongjia forest region

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