资源科学 ›› 2021, Vol. 43 ›› Issue (12): 2465-2474.doi: 10.18402/resci.2021.12.09

• “澜沧江—湄公河流域农业资源与环境”专栏 • 上一篇    下一篇

滇中地区典型火烧迹地恢复率动态变化及其影响因子

吴超1(), 徐伟恒1,3,4(), 肖池伟2, 王秋华5, 袁华6, 董建娥1, 黄邵东1, 熊源1   

  1. 1.西南林业大学大数据与智能工程学院,昆明 650233
    2.中国科学院地理科学与资源研究所,北京 100101
    3.西南林业大学大数据与智能工程研究院,昆明 650233
    4.西南林业大学生态大数据国家林业与草原局重点实验室,昆明 650233
    5.西南林业大学土木工程学院,昆明 650233
    6.西南林业大学林学院,昆明 650233
  • 收稿日期:2021-05-11 修回日期:2021-07-19 出版日期:2021-12-25 发布日期:2022-02-16
  • 通讯作者: 徐伟恒,男,云南宣威人,博士,教授,博士生导师,研究方向为资源遥感、林业装备与信息化。E-mail: weihengx@gmail.com
  • 作者简介:吴超,男,新疆奎屯人,硕士研究生,研究方向为资源遥感监测。E-mail: 924765071@qq.com
  • 基金资助:
    国家自然科学基金项目(32060320);云南省基础研究计划面上项目(202101AT070039);云南省“万人计划”青年拔尖人才专项(YNWR-QNBJ-2020047);澜沧江—湄公河合作专项

Dynamic change of recovery ratios and influencing factors of typical post-fire burn areas in central Yunnan Province

WU Chao1(), XU Weiheng1,3,4(), XIAO Chiwei2, WANG Qiuhua5, YUAN Hua6, DONG Jian’e1, HUANG Shaodong1, XIONG Yuan1   

  1. 1. College of Big Data and Intelligent Engineer, Southwest Forestry University, Kunming 650233, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650233, China
    4. Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming 650233, China
    5. College of Civil Engineering, Southwest Forestry University, Kunming 650233, China
    6. College of Forestry, Southwest Forestry University, Kunming 650233, China
  • Received:2021-05-11 Revised:2021-07-19 Online:2021-12-25 Published:2022-02-16

摘要:

定量评估林火燃烧程度、坡度、植被类型等对火烧后植被动态恢复的影响,对及时掌握火烧迹地的变化及更新演替规律和提升森林资源可持续经营管理水平具有重要价值。本文以2006年云南省安宁市3·29和2012年云南省易门县3·18森林火烧迹地为研究对象,利用Landsat-5/TM、Landsat-7/ETM+和Landsat-8/OLI时间序列影像,基于Google Earth Engine云计算平台实现影像收集与指数计算。首先,利用归一化燃烧率和差分归一化燃烧率提取火烧迹地,并进行燃烧程度分级。其次,利用燃烧恢复率,探究火烧迹地的恢复率动态变化规律,并利用因子探测法分析燃烧程度、坡度和植被类型对燃烧恢复率的独立解释力。结果表明:①对安宁3·29和易门3·18火烧迹地,从燃烧恢复率指标看,燃烧程度为低和中低的火烧迹地,在火灾发生6~7年后火烧迹地恢复率达100%以上,而中高和高燃烧程度的火烧迹地恢复率达100%以上则需要7~9年。②在燃烧恢复率的独立解释因子中,灾后植被类型的独立解释力最高,也即灾后植被类型对火烧迹地的恢复率影响最大,燃烧程度次之,坡度最低。本文结果将为火烧迹地恢复状况评估提供参考,也有助于了解滇中乃至中国西南地区复杂地形下森林火灾发生后的植被动态恢复过程。

关键词: 火烧迹地, Landsat影像, 恢复评估, 燃烧程度, 燃烧恢复率, 地理探测器, 滇中地区

Abstract:

Quantifying the effects of burning degree, slope, and vegetation types on the dynamic post-fire vegetation recovery deserves further research for the timely assessment of the rejuvenation and succession of forest-fire burned areas and for promoting the sustainable management of forests. In this study, we selected the Landsat-5/TM, Landsat-7/ETM+, and Landsat-8/OLI time series imagery of two forest-fire burned areas in Yunnan Province (the forest fire in Anning City on 29 March 2006 and the forest fire in Yimen County on 18 March 2012) to assess the burned area recovery. All the images used in this study were collected and the indicators were calculated using the Google Earth Engine (GEE) platform. The normalized burn ratio (NBR) and the differential normalized burn ratio (dNBR) were calculated for extracting the burned areas and classifying the burning degrees, the burned areas were classified into four degrees including low, moderate-low, moderate-high, and high severities according to the dNBR, and we mapped the burned areas and identified the burning degrees for these forest fires. Based on the burn recovery ratio (BRR), we explored the influence of burning degree, slope, and vegetation types (arbor and shrub) on the change of recovery rate of the forest-fire burned areas, and calculated the values of q for burning degree, slope, and vegetation types respectively by using the Geodetector software. The results show that: (1) For the 29 March 2006 Anning and 18 March 2012 Yimen forest-fire burned areas,based on the BRR, the recovery rate of the burned areas with low and moderate-low burning degrees reached 100% and above after 6-7 years, while the recovery rate of the burned areas with moderate-high and high burning degrees reached 100% after 7-9 years. (2) Among the independent explanatory factors of BRR, the strongest is vegetation type. The second strongest influencing factor is burning degree. In another word, post-fire vegetation type has the greatest influence on the recovery rate of burned areas. The lowest impact independent explanatory factor is slope. The results of this study provide a reference for the evaluation of the recovery status of other burned areas, and also help to understand the dynamic vegetation recovery process after the forest fires in the complex terrain of central Yunnan Province.

Key words: burned areas, Landsat imagery, recovery assessment, burning degree, burn recovery ratio, Geodetector, central Yunnan Province