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Advancements in remote sensing based forest age estimation and its applications

  • role： First author第一作者
  • Affiliation：

    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

  • Email：maqin@nnu.edu.cn
  • Introduction：马勤，研究方向为植被遥感、森林生态系统监测、气候变化。E-mail： maqin@nnu.edu.cn
MA Qin123,  
  • Affiliation：

    School of Geography, Nanjing Normal University, Nanjing 210023, China

ZHANG Xu1,  
  • Affiliation：

    School of Geography, Nanjing Normal University, Nanjing 210023, China

YUAN Jingyi1,  
  • Affiliation：

    School of Geography, Nanjing Normal University, Nanjing 210023, China

GONG Zitong1,  
  • Affiliation：

    Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China

SHANG Rong4,  
  • Affiliation：

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

CHENG Kai5,  
  • Affiliation：

    GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 USM Pulau Pinang, Malaysia

CHEN Maolong6,  
  • Affiliation：

    Beijing Yuhang Intelligence Technology Co., Ltd, Beijing 100193, China

TAN Qiyun7,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

JU Weimin8

resumen

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 high-resolution 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 assessment, and forest management. Future research should focus on improving and updating forest measurement datasets, fully leveraging multisource and multispatial-temporal remote sensing information, and enhancing the transferability and generality of estimation models.

palabra clave

remote sensing;forest age;tree height;above ground biomass;carbon cycle;change detection;tree growth equation;forest management;biodiversity

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