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林业资源管理 ›› 2009, Vol. 0 ›› Issue (2): 106-110.

• 科学技术 • 上一篇    下一篇

基于卫星遥感数据空中抽样的大尺度森林资源动态监测

李春干1,2, 陈琦2, 谭必增2   

  1. 1.北京林业大学 省部共建森林培育与保护教育部重点实验室,北京 100083;
    2.广西林业勘测设计院,南宁 530011
  • 收稿日期:2009-02-12 修回日期:2009-04-13 出版日期:2009-04-28 发布日期:2020-12-16
  • 作者简介:李春干(1962-),男,广西横县人,研究员,主要从事森林资源监测与管理研究。
  • 基金资助:
    国家自然科学基金(30872023);广西壮族自治区林业局科学基金(200246)

Large-scale Forest Resources Monitoring by Means of Spatial Sampling of Hi-resolution Remote Sensing Images

LI Chungan1,2, CHEN Qi2, TAN Bizeng2   

  1. 1. Key laboratory for Silviculture and Conservation of Ministryof Education, Beijing Forestry University, Beijing 100083, China;
    2. Guangxi Forest Inventory & Planning Institute, Nanning 530011, China
  • Received:2009-02-12 Revised:2009-04-13 Online:2009-04-28 Published:2020-12-16

摘要: 为监测大尺度宏观性森林资源动态变化,以SPOT5和ALOS高空间分辨率卫星遥感数据为源数据,辅以相关调查资料,在GIS支持下,采用空中抽样的方法,在遥感图像上布设样地,通过目视判读方法确定样地的土地类型、优势树种(组)等,据此计算森林面积和构造土地类型转移矩阵。根据历史调查资料分析得到森林类型单位面积蓄积量,并以此来计算各类林木蓄积量,从而实现研究区域森林面积、蓄积的动态监测。应用结果表明,在具备高空间分辨率遥感数据以及历史调查资料较丰富的情况下,该方法具有工作量小、精度高,且易于进行动态分析的特点,不但适用于大尺度宏观性森林资源监测,也适用于中尺度(如县级行政区域)的森林资源动态宏观监测。

关键词: 森林资源监测, 遥感, 空中抽样

Abstract: In order to monitor forest resources dynamic changes at the regionlevel, such as a province, spatial sampling was used in GIS environment for hi-resolution remote sensing images of SPOT5 and ALOS, while historical surveying data and other data were used as ancillary data.The attributes of sampling spotsincluded land use/cover types and species groups and others were determined byvisual interpretation, thus areas of forest types and their conversion matrix wereattainable. Furthermore, timber volumes were estimated with the areas of forest types and volume per-hectare derived from the analysis of historical surveyingdata.The application indicated that if hi-resolution images and historical surveying data were available, the present method has advantage in dynamic analysis, and is efficient and has high accuracy. It is also suitable to monitor forestresources at local level, such as a county.

Key words: forest resources monitoring, remote sensing, spatial sampling

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