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林业科学 ›› 2017, Vol. 53 ›› Issue (3): 163-174.doi: 10.11707/j.1001-7488.20170318

• 研究简报 • 上一篇    

呼中林区火烧迹地遥感提取及林火烈度的空间分析

李明泽, 康祥瑞, 范文义   

  1. 东北林业大学林学院 哈尔滨 150040
  • 收稿日期:2016-05-12 修回日期:2016-09-02 出版日期:2017-03-25 发布日期:2017-04-25
  • 通讯作者: 范文义
  • 基金资助:
    国家自然科学基金项目(31470640)。

Burned Area Extraction in Huzhong Forests Based on Remote Sensing and the Spatial Analysis of the Burned Severity

Li Mingze, Kang Xiangrui, Fan Wenyi   

  1. College of Forestry, Northeast Forestry University Harbin 150040
  • Received:2016-05-12 Revised:2016-09-02 Online:2017-03-25 Published:2017-04-25

摘要: [目的] 利用Landsat TM影像,采用遥感指数构建决策树分类模型,提出一种识别火烧迹地面积与林火烈度分析的新方法,并结合坡度、坡向、海拔等地形因子对过火区域火烈度的空间分布进行科学系统的分析研究,为大兴安岭地区森林防火和林火管理提供一定的理论依据和数据支持。[方法] 以大兴安岭地区呼中林区为研究区,以2010年9月火后TM影像以及2007年9月火前TM影像为基础数据,以DEM影像、林相图为辅助数据,利用NDVI、NDSWIR、MNDWI和dNBR等遥感指数构建决策树分类模型,对呼中林区2010年10场火烧迹地进行识别,根据dNBR阈值法将过火区域火烈度分为4级,并利用Arcgis软件将火烈度图分别与坡度、坡向、海拔图叠加分析。[结果] 利用决策树分类模型所提取火烧迹地面积的分类总体精度和Kappa系数分别为97.97%和0.943 2,与平行六面体法和ISODATA法的分类的精度相比分别提高了7.56%和17.32%,Kappa系数也相应提高。决策树模型提取火烧迹地的制图精度和用户精度分别为97.51%和97.54%,而平行六面体分类法分别为90.43%和96.52%,ISODATA法分别为94.35%和95.68%。利用dNBR阈值法将已提取的过火区林火烈度分为:未过火、轻度火烧、中度火烧、重度火烧4个级别,其中中度火烧和重度火烧分别占总过火面积的46.6%和33.2%。叠加分析后,海拔在1 000~1 500 m的地区过火面积共4 177 hm2,占总过火面积的64.4%。Ⅲ级坡(6°~15°)过火面积最大,占总过火面积的45.9%。南坡过火面积最大,为1 391 hm2,约占总过火面积的21.4%。[结论] 本文所使用的决策树分类模型能够准确地识别过火区域,在精度上相较平行六面体法与ISODATA法有显著提高,且过火面积也更接近目视解译判读所得到的过火面积,精度均达到82%以上。dNBR阈值法可将过火区域火烈度分为4个等级,结果表明过火区域中度火烧和重度火烧占总过火面积的比重较大,林火烈度与海拔、坡度、坡向之间存在一定相关关系。

关键词: 火烧迹地, 决策树分类, 林火烈度, 过火面积, dNBR

Abstract: [Objective] This paper puts forward a new method for identifying burned areas and fire intensity by using Landsat TM images and RS indices to construct the decision tree classification model. In combination with topographic factors such as slope, aspect and elevation the spatial distribution of fire severity was scientifically and systematically analyzed in this study to provide theoretical basis and data support for forest fire prevention and management in Daxing'anling Mountains.[Method] In this paper, Huzhong region of the Daxing'anling Mountains was targeted. TM images of post-fires in September 2010 and September 2007 were taken as the basic data, and DEM images and forest type maps were used as the auxiliary data. The NDVI, NDSWIR, MNDWI, dNBR and other RS indices were employed to build a decision tree classification model which then was used to identify ten burned areas of Huzhong in 2010. Fire severity was divided into four classes according to the threshold value of dNBR, and the Arcgis software was used to do an overlaying analysis on the fire severity map with slope, aspect, elevation.[Result] The overall accuracy and Kappa coefficient of the decision tree classification were 97.97% and 0.943 2. Compared with the Parallelepiped method and ISODATA method, the total classification accuracy was increased by 7.56% and 17.32%, respectively. The Kappa coefficient was also increased. In the decision tree method, the producer's accuracy and user's accuracy were 97.51% and 97.54%, the Parallelepiped method were 90.43% and 96.52%, and the ISODATA method were 94.35% and 95.68%. Fire severity was divided into four classes according to the threshold of dNBR:unburned, low, moderate and high. Moderate severity burned area accounted for 46.6% of the total, and high severity burned area was 33.2%. After overlaying analysis, 64.4% (4 177 hm2) of burned area located at the elevations from 1 000 m to 1 500 m, and 45.9% of burned area located at level Ⅲ slope (6°-15°). The burned area at the southern slope occupied 21.4% (1 391 hm2) of the total.[Conclusion] The decision tree classification model presented in this paper could identify burned areas accurately and the total classification accuracy was higher than the parallelepiped method and ISODATA method, and the burned area is closer to the method of visual interpretation. Moderate and high severity burned areas occupied most of the total burned areas, and there were some relations between the burn severity and slope, aspect, elevation.

Key words: burned areas, decision tree classification, fire severity, fire size, dNBR

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