森林年龄遥感估算和应用研究进展  

Advancements in remote sensing based forest age estimation and its applications

作　　者：马勤 张旭 袁敬毅 龚梓彤 商荣 程凯 陈茂龙 谭启昀 居为民[8] MA Qin;ZHANG Xu;YUAN Jingyi;GONG Zitong;SHANG Rong;CHENG Kai;CHEN Maolong;TAN Qiyun;JU Weimin(School of Geography,Nanjing Normal University,Nanjing 210023,China;Key Laboratory of Virtual Geographic Environment(Nanjing Normal University),Ministry of Education,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China;Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education,School of Geographical Sciences,Fujian Normal University,Fuzhou 350117,China;Institute of Remote Sensing and Geographic Information System,Peking University,Beijing 100871,China;GeoInformatic Unit,Geography Section,School of Humanities,Universiti Sains Malaysia,11800 USM Pulau Pinang,Malaysia;Beijing Yuhang Intelligence Technology Co.,Ltd,Beijing 100193,China;International Institute for Earth System Sciences,Nanjing University,Nanjing 210023,China)

机构地区：[1]南京师范大学地理科学学院,南京210023 [2]南京师范大学虚拟地理环境教育部重点实验室,南京210023 [3]江苏省地理信息资源开发与利用协同创新中心,南京210023 [4]福建师范大学地理科学学院湿润亚热带生态—地理过程教育部重点实验室,福州350117 [5]北京大学遥感与地理信息系统研究所,北京100871 [6]马来西亚理科大学人文学院地理系地理信息组,马来西亚槟城11800 [7]北京远度互联科技有限公司,北京100193 [8]南京大学国际地球系统科学研究所,南京210023

出　　处：《遥感学报》2025年第1期70-82,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42201366);国家重点研发计划项目(编号:2023YFB3907401);江苏省特聘教授项目;南京师范大学启动基金(编号:184080H202B349)。

摘　　要：林龄是决定森林碳汇能力及其变化趋势的关键因子。定量刻画林龄的时空差异是预估森林生态系统碳源汇变化趋势的重要环节。传统林龄调查仅限于森林样地,随着遥感技术的发展,其估算范围扩展到区域及全球尺度。与林龄相关的研究在林学、生态学、地学等领域也日益受到广泛关注。本文综合论述了自2000年以来,林龄的估算方法及其应用前景的研究进展。基于遥感的林龄估算方法主要分为:光谱纹理信号反演、时间序列变化检测、树高生物量生长方程模拟3大类。光谱纹理信号反演方法简单,但饱和效应明显且精度有限;时序变化检测精度较高,但只适用于有连续遥感观测的中幼林。基于树高生物量生长方程的方法拓宽了林龄估算的限度,但估算精度对生长方程及输入参数非常敏感。因此,综合多源数据、结合多模型方法已成为林龄估算的主流策略,并成功用于中国、加拿大等国家的高分辨率林龄制图。大范围的林龄数据在森林碳循环模拟、生物多样性评估、林业经营与管理等方面有着广阔的应用前景。针对林龄遥感估算,当前亟需完善并更新森林样地数据集,充分挖掘多源、多时空遥感信息,并着力提升估算模型的可迁移性和普适性;以进一步提高林龄估算的精度和效率,从而为林龄相关的研究提供更加全面、可靠的数据和技术支持。Forest age is a critical parameter determining forest carbon sequestration capacity and its temporal trends.Quantifying spatiotemporal variations in forest age is essential for predicting forest ecosystem carbon dynamics.While traditional forest age assessments were limited to forest plots,the development of remote sensing technology has expanded the estimation from plots to regional and global scales.Research related to forest age has gaining increasing attention across fields of forestry,ecology,and geography,etc.This article aims to review the process in forest age estimation by summarizing the main methods and their applications from related literatures and datasets published since the year of 2000.Remote sensing-based approaches fall into three main categories:1.regression from image spectral and texture features,2.time series change detection,and 3.tree height or biomass growth equation modeling.1.The spectral image regression method is straightforward but often has limited regression accuracy due to the saturation effect in the spectral image information-forest age relationship.2.The time series change detection method can achieve high accuracy but only applicable to forests with continuous remote sensing observations.3.The tree height or biomass growth equations based strategy can broaden the limits of forest age estimation,but its estimation accuracy is sensitive to the selections of model equation and input parameters.Consequently,integrating multisource datasets and combining multiple modeling approaches have become the predominant strategy for forest age estimation.This strategy has been successfully implemented in highresolution forest age mapping at national scales across China and Canada.The advancement of remote sensing technology has substantially improved the efficiency and accuracy of forest age estimation,extending its applicability from individual plots to regional and global scales.Large-scale forest age data have great potential for applications in forest carbon cycle modeling,biodiversity

关 键 词：遥感 森林年龄 树高 生物量 碳循环 变化检测 生长方程 森林管理 生物多样性 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]