文献类型：期刊文献

中文题名：多时间分辨率遥感森林变化监测方法综述

英文题名：Review of Multi-Time Resolution Remote Sensing Forest Change Detection Methods

作者：杨宇迪[1] 郭颖[1] 田昕[1] 刘清旺[1] 柴国奇[1] 黄建文[1] 罗鑫[1] 陈树新[1] 王海熠[1]

第一作者：杨宇迪

机构：[1]中国林业科学研究院资源信息研究所,北京100091

年份：2025

卷号：40

期号：4

起止页码：1026-1035

中文期刊名：遥感技术与应用

外文期刊名：Remote Sensing Technology and Application

收录：;北大核心:【北大核心2023】；

基金：“十四五国家重点研发计划"植被覆盖类型和森林定量参数多源遥感监测技术”(2023YFD220170300)。

语种：中文

中文关键词：森林变化监测;遥感;时间序列;深度学习;机器学习

外文关键词：Forest change detection;Remote sensing;Time series;Deep learning;Machine learning

分类号：TP751;TP79

摘要：森林生态系统在调节气候、水土保持、碳平衡等方面具有重要作用,但近年来该系统受到气候变化和人类活动的扰动在日益加剧,急需精准及时的森林变化监测。遥感技术凭借其多时间分辨率数据优势和自动化处理能力,已成为森林变化监测的关键手段。本研究聚焦多时间分辨率遥感变化监测方法,系统梳理并比较了双时相和时间序列遥感两类技术,其中双时相变化监测涵盖了人工目视解译、传统机器学习和深度学习技术;时间序列则包括时序趋势分析、动态变化监测和多算法集成等方面研究。通过归纳深度学习与多模态时序数据在实际运用中的相关问题,提出了相关解决方案,为提升森林变化监测的精度提供借鉴。
Forest ecosystems play a crucial role in regulating climate,maintaining water and soil,and balancing carbon.However,in recent years,this system has been increasingly disturbed by climate change and human activities,making precise and timely forest change monitoring urgently needed.Remote sensing technology,with its advantage of multi-temporal resolution data and automated processing capabilities,has become a key means for forest change detection.This paper focuses on multi-temporal resolution remote sensing change detection methods,systematically reviews and compares two types of technologies:bi-temporal and time series remote sensing.Bi-temporal change detection includes manual visual interpretation,traditional machine learning,and deep learning techniques;time series include research on temporal trend analysis,dynamic change monitoring,and multi-algorithm integration.By summarizing the related problems of deep learning and multi-modal time series data in practical applications,relevant solutions are proposed,providing references for improving the accuracy of forest change detection.