地理研究 ›› 2019, Vol. 38 ›› Issue (4): 831-843.doi: 10.11821/dlyj020180299

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基于卫星遥感数据的森林病虫害监测与评价

郭仲伟1,2(), 吴朝阳2,3(), 汪箫悦1,2   

  1. 1. 中国科学院遥感与数字地球研究所,遥感科学国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院地理科学与资源研究所,陆地表层格局与模拟院重点实验室,北京 100101
  • 收稿日期:2018-04-01 修回日期:2018-06-01 出版日期:2019-04-20 发布日期:2019-04-30
  • 作者简介:

    作者简介:郭仲伟(1987-),男,河北石家庄人,硕士,研究方向为全球变化遥感。E-mail:guozw@radi.ac.cn

  • 基金资助:
    国家自然科学基金项目(41371013,41522109)

Forest insect-disease monitoring and estimation based on satellite remote sensing data

Zhongwei GUO1,2(), Chaoyang WU2,3(), Xiaoyue WANG1,2   

  1. 1. The State Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049; China
    3. The Key Laboratory of Land Surface pattern and simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2018-04-01 Revised:2018-06-01 Online:2019-04-20 Published:2019-04-30

摘要:

森林病虫害由于在森林资源中造成的重大破坏而被人们称为“不冒烟的火灾”,其对生态系统的研究具有重要意义。现有基于遥感数据的病虫害研究多集中在森林病虫害的监测、爆发原因以及发病区域内生产力的变化情况,而对于森林病虫害发生后森林中植被指数与叶面积指数之间的相关性的变化情况还相对较少,处于需要持续性深入探讨的阶段。以加拿大不列颠哥伦比亚地区2002—2012年森林病虫害数据为基础,分析不同严重程度的病虫害对叶面积指数(LAI)与归一化植被指数(NDVI)和增强型植被指数(EVI)的影响。结果表明:① 受病虫害感染的像元在轻度(Light)、中度(Moderate)和重度(Severe)三个严重级别中,NDVI与LAI之间的相关性由弱变强,又由强变弱;② EVI与LAI之间的相关性,在轻度(Light)、中度(Moderate)和重度(Severe)三个严重级别的像元中则依次变强。这一研究将为今后利用遥感数据识别病虫害、评价生态系统影响提供基础。

关键词: 森林病虫害, 归一化植被指数NDVI, 增强型植被指数EVI, 叶面积指数LAI, 相关性

Abstract:

Forest insect has been known as the "Fire without Smoke" due to their major damages to forest resources. Therefore it is of great significance for research on ecosystem safety. Current remote sensing based monitoring of forest insect outbreak mainly focuses on its distributions, reasons and consequent influences on productivity. Using insect outbreak data acquired at British Colombia in Canada over 2002-2012, we here provide an analysis of the impacts of insect intensity on relationship between leaf area index (LAI) and the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). We showed that for pixels converting from light to moderate and from moderate to severe, correlations between LAI and NDVI became higher first, then lower evidently and correlations between LAI and EVI became higher consistently. Furthermore, for severely-affected regions, the correlation became stronger. Our results provide a good reference for the future assessment of insect damage on ecosystem function using remote sensing observations.

Key words: forest pests and diseases, Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), correlation