文献类型：期刊文献

中文题名：基于Landsat 8卫星时序影像的森林病虫灾害时空监测

英文题名：Temporal and spatial monitoring of forest pest and disease disasters based on Landsat 8 satellite time-series images

作者：张浩芫[1,2] 李世明[1,2] 齐志勇[1,2] 刘晴[1,2] 庞勇[1,2] 李增元[1,2]

第一作者：张浩芫

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2025

卷号：29

期号：9

起止页码：2748-2764

中文期刊名：遥感学报

外文期刊名：NATIONAL REMOTE SENSING BULLETIN

收录：;EI(收录号：20254019276767);北大核心:【北大核心2023】；

基金：国家重点研发计划(编号:2022YFD1400400)。

语种：中文

中文关键词：森林病虫灾害;时间序列数据;光谱分析;LandTrendr算法;随机森林算法

外文关键词：forest pest disaster;time series data;spectral analysis;LandTrendr algorithm;random forest algorithm

分类号：TP7;P2

摘要：由于气候变化和人类活动等多种因素的共同作用,森林受病虫灾害干扰的频率和规模不断增加,严重影响了森林生态系统的结构和服务。准确识别区域性森林病虫灾害干扰,分析其爆发的时空特征,对于森林生态系统的保护具有重要意义。本研究基于Landsat 8卫星年度时序数据,以辽宁省朝阳市为研究区域,全面分析了森林冠层时序光谱特征对火灾、砍伐和森林病虫灾害的可分离性,并调整LandTrendr算法的控制参数提升森林弱扰动信息提取的“敏感性”,精准提取了森林扰动发生的时空和光谱信息,结合随机森林算法提取2013—2023年的森林病虫灾害扰动时空信息,分析了朝阳市森林病虫灾害的时空特征。结果表明:(1)Landsat 8卫星影像的森林冠层光谱时序特征能够有效区分健康森林、火灾、砍伐和病虫灾害,作为区域性森林病虫灾害识别依据。(2)参数调整后的LandTrendr算法可以精准提取森林扰动的光谱变化信息并用于森林病虫灾害识别;森林扰动识别和病虫灾害监测总体精度(OA)分别为89.3%和86.6%,Kappa系数分别为0.785和0.812。(3)朝阳市森林扰动以病虫灾害为主,森林病虫灾害主要发生在西部的建平县和凌源市,发生面积占全市病虫灾害发生面积的67.97%;朝阳市森林病虫灾害在时间维度上存在“间歇性”爆发现象。综上,本研究可为森林经营管理提供数据支持,为不同森林扰动的分类以及森林病虫灾害时空监测提供方法借鉴。
With the combined effects of climate change and human activities,the frequency and scale of forest disturbances caused by pests and diseases have been continuously increasing,significantly affecting the structure and services of forest ecosystems.Accurately identifying regional forest pest and diseases disturbances and analyzing the spatiotemporal characteristics of their outbreaks are of great significance for the protection of forest ecosystems.In this study,based on Landsat 8 satellite annual time-series data,with Chaoyang City in Liaoning Province as the study area,we comprehensively analyzed the separability of forest canopy temporal spectral characteristics for fire,logging,and pest and diseases.Adjusting the control parameters of the LandTrendr algorithm improved the“sensitivity”of extracting weak forest disturbance information.Ultimately,the random forest algorithm was used to extract spatiotemporal information of forest pest and diseases from 2013 to 2023.Results are as follows:(1)The temporal spectral characteristics of Landsat 8 satellite images precisely distinguish forest pest and diseases from fire and logging in Chaoyang City,providing a basis for identifying regional forest pest and diseases.(2)Time-series satellite images can provide spatiotemporal information of forest disturbances and be used for forest pest and diseases identification.The overall accuracy for forest disturbance identification and pest and diseasesmonitoring in this study was 89.3%and 86.6%,respectively,with Kappa coefficients of 0.785 and 0.812.(3)Forest disturbances in Chaoyang City are mainly due to pest and diseases,primarily occurring in Jianping County and Lingyuan City in the west,accounting for 67.97%of the total pest disturbance area in the city.The forest pest and diseasesin Chaoyang City exhibit an“intermittent”outbreak phenomenon in the temporal dimension.The study results can provide data support for forest management and offer methodological references for the classification of forest disturbances and the spatiotemporal monitoring of forest pest and diseases.