文献类型：期刊文献

中文题名：基于GF-6 WFV的森林冻害遥感监测

英文题名：Remote sensing monitoring of spring frost on forest based on GF-6 WFV

作者：高影[1,2] 武红敢[1,2] 李世明[1,2] 曾庆伟[3] 马宇龙[4] 米国兵[5]

第一作者：高影

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实验室,北京100091;[3]北京中云伟图科技有限公司,北京100096;[4]易智瑞信息技术有限公司,北京100028;[5]关帝山国有林管理局二道川林场,山西文水032199

年份：2025

卷号：47

期号：9

起止页码：122-128

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：;北大核心:【北大核心2023】；

基金：高分辨率对地观测系统重大专项(21-Y30B02-9001-19/22-6)。

语种：中文

中文关键词：GF-6 WFV数据;时序NDVI;倒春寒;深度学习;遥感监测

外文关键词：GF-6 WFV data;multi-temporal NDVI;late spring coldness;deep learning;remote sensing monitoring

分类号：S761

摘要：【目的】近年来我国森林冻害频发,给森林生长和生态系统造成很大影响。本文旨在探讨GF-6 WFV数据在森林冻害监测中的应用,为森林精准监管提供科学依据。【方法】以山西省关帝山国有林管理局二道川林场为研究区,基于2019年森林植被生长季的GF-6 WFV时序数据,分析NDVI时空变化对倒春寒引起的森林冻害发展过程的响应。通过ENVI软件的Net5深度学习模型,筛选最优模型,对冻害进行评估。【结果】海岸蓝波段对森林植被黄化信息较为敏感,有助于提高亚健康森林植被的遥感监测精度。其构成的深度学习模型训练的用户精度为81.0%,冻害遥感分类空间分布验证的准确性为90.7%。基于Landsat-8 OLI和样地调查数据的验证结果表明,GF-6 WFV的NDVI时序数据能够有效监测研究区的冻害发生、发展情况。研究期间重度受损、中度受损、轻度受损以及健康林分的面积占比分别为17.4%、36.3%、30.9%和15.4%。冻害主要分布在1600 m以下的低海拔区域。【结论】研究揭示了GF-6 WFV时序数据在冻害所致树冠黄化或生长胁迫预警监测中的应用潜力,为森林健康监测和森林精准经营提供技术手段。
[Objective]In recent years,forest frost damage has occurred frequently in China,causing significant impacts on forest growth and ecosystems.This study aims to explore the application of GF-6 WFV data in forest frost damage monitoring and provide a scientific basis for precise forest regulation.[Method]Taking the Guandishan National Forestry Bureau in Shanxi Province of northern China as the research area,this study analyzed the spatiotemporal changes of NDVI in response to the development process of forest frost damage caused by spring cold snaps based on the GF-6 WFV time-series data during the 2019 forest vegetation growing season.Through the Net5 deep learning model of the ENVI software,the optimal model was selected to evaluate frost damage.[Result]The coastal blue band is highly sensitive to the yellowing information of forest vegetation,which helps improve the remote sensing monitoring accuracy of sub-healthy forest vegetation.The user accuracy of deep learning model training composed of this band was 81.0%,and the accuracy of spatial distribution verification of frost damage remote sensing classification was about 90.7%.The time-series NDVI data of GF-6 WFV can effectively monitor the frost damage in the study area during spring and had been verified by landsat-8 OLI and field survey data.During the study period,the area proportions of severely damaged,moderately damaged,slightly damaged,and healthy forest stands were 17.4%,36.3%,30.9%,and 15.4%,respectively.Frost damage was mainly distributed in lowaltitude areas below 1600 m.[Conclusion]The study reveals the potential application of GF-6 WFV timeseries data in early warning monitoring of tree crown yellowing or growth stress caused by frost damage and provides scientific technical means for forest damage monitoring and precise forest management.