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：Zhang, Haoyan[1] Li, Shiming[1] Qi, Zhiyong[1] Liu, Qing[1] Pang, Yong[1] Li, Zengyuan[1]

第一作者：Zhang, Haoyan

机构：[1] 1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China, 2. Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

年份：2025

卷号：29

期号：9

起止页码：2748-2764

外文期刊名：National Remote Sensing Bulletin

收录：EI(收录号：20254019276767)

语种：中文

外文关键词：Abiotic - Classification (of information) - Climate change - Decision trees - Deforestation - Ecosystems - Information management - Landsat - Logging (forestry) - Random forests - Time series - Time series analysis

摘要：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. ? 2025 Science Press. All rights reserved.