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Title l 题目

Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height

Citation l 引文格式

Petar DONEV,Hong WANG,Shuhong QIN,Pengyu MENG,Jinbo LU. Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3): 13-24.DOI: 10.11947/ j.JGGS.2021.0302.

Abstract l 摘要

Reliable and prompt information on forest above-ground biomass (AGB) and tree diameter at breast height (DBH)are crucial for sustainable forest management. Remote sensing technology, especially the Light Detection and Ranging (LiDAR) technology, has been proven to estimate important tree variables effectively. This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics. In the suggested DBH prediction, we developed a nonlinear estimation equation using the total tree height. As for the AGB prediction approach, we used regression methods such as multiple linear regression (MLR), random forest (RF) and support vector machine for regression (SVR). We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees, located in the Yellow River Delta (YRD), China. For our developed approaches, we used Unmanned Aerial Vehicle (UAV) and Backpack LiDAR point cloud datasets obtained in June 2017, and three field data measurements gathered in June 2017 and 2019 and October 2019, all from the same study area. The results demonstrate that: ① The LiDAR data individual tree segmentation (ITS) from which we extracted individual tree information like tree location and tree height, was carried out with an overall accuracy F=0.91; ② We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error (RMSE)=3.61cm, later validated by the 2017 dataset; ③ Forest AGB at stand level was estimated with the MLR, RF and also SVR regression methods, and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR. Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’ accuracy. Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS. The UAV LiDAR ability to provide high accuracy tree height abstraction, the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB. This study shows that being cost-free is not the only advantage of free available software. In the performance of ITS and the LiDAR, metrics extraction proved to be as good as the commercially available software.

Key Words l 关键词

forest AGB; DBH estimation; UAV LiDAR; Backpack LiDAR

Authors l 作者

Petar DONEV,Hong WANG,Shuhong QIN,Pengyu MENG,Jinbo LU. 

Petar DONEV

Master student at Hohai University, College of Hydrology and Water resources, Major Cartography and GIS. Working on research topics related to environmental science and remote sensing technology.

Hong WANG

王红，女，1968.07生，毕业于南京大学，博士。现任地理信息科学系主任，兼任中国自然资源学会湿地专委会理事、中国地理信息产业协会地理信息文化工作委员会副主任委员、以及中国地理学会、国际数字地球学会和中国城市土地利用制图学会等学会委员。

主要从事区域生态环境变化遥感监测、评估和模拟以及城市土地利用制图方面的教学和科研工作。主持国家自然科学基金面上项目3项、水专项子课题1项。在森林健康研究方面主要包括森林健康多源遥感数据的分类、生物量估算、动态变化监测及驱动因子分析等。

Shuhong QIN

覃书鸿，男，1994.10生，河海大学测绘科学与技术专业博士研究生。研究方向为林业定量遥感。

Full Paper | 全文如下

本文选自JGGS 2021, Volume 4, Issue 3, P13-24。本期审图号（Map Approval Number）：GS（2021）5520。点击阅读原文即可下载。

请登录《测绘学报（英文版）》（JGGS）官网http://jggs.sinomaps.com 查看更多精选论文

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