Extraction of forest disturbance information from multi-source and long-term series of remote sensing data.

Q3 Environmental Science
Zhuo-Han Hou, Ying Yu, Xi-Guang Yang
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引用次数: 0

Abstract

We developed a method of comprehensive forest disturbance identification system based on the distur-bance characteristics of forest ecosystem and the integrated multi-source remote sensing data to evaluate the overall forest disturbance intensity of Heilongjiang Province from 2001 to 2023. We further conducted disturbance extraction and types identification. The results showed that forest disturbance intensity peaked in 2003, primarily due to large-scale forest fires. The spatial consistency between disturbance detection using the LandTrendr method and the Global Forest Change dataset exceeded 90%. Forest disturbances could be categorized into three types, including fire disturbance, pest disturbance, and logging disturbance. The overall classification accuracy for disturbance types was 87.8% (Kappa coefficient=0.81). Different spectral indices had different responses to disturbance types. Specifically, the normalized burn ratio was the most sensitive to fire disturbance. The normalized difference vegetation index was more responsive to overall vegetation change. The normalized difference moisture index made a more significant contribution to the identification of pest disease, while the modified greenness difference index could assist in detecting logging activities. In conclusion, the integrative analysis of multi-spectral indices and the fusion of temporal features could effectively improve the accuracy of identifying forest disturbance types, which would provide a scientific basis for forest ecosystem management in cold temperate zone of Northeast Asia.

基于多源长期序列遥感数据的森林扰动信息提取。
基于森林生态系统扰动特征和多源遥感数据,建立了黑龙江省2001 - 2023年森林扰动综合识别系统方法。进一步进行了扰动提取和类型识别。结果表明:森林干扰强度在2003年达到峰值,主要原因是大规模森林火灾;LandTrendr扰动检测方法与全球森林变化数据集的空间一致性超过90%。森林干扰可分为三种类型,即火灾干扰、害虫干扰和伐木干扰。干扰类型的总体分类准确率为87.8% (Kappa系数=0.81)。不同的光谱指数对扰动类型的响应不同。其中,归一化燃烧比对火灾干扰最为敏感。归一化植被指数对整体植被变化的响应更强。归一化水分差指数对病虫害的识别贡献更显著,而改进的绿度差指数有助于检测采伐活动。综上所述,多光谱指数综合分析和时间特征融合可有效提高森林干扰类型识别的准确性,为东北亚寒温带森林生态系统管理提供科学依据。
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来源期刊
应用生态学报
应用生态学报 Environmental Science-Ecology
CiteScore
2.50
自引率
0.00%
发文量
11393
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