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UAV Photogrammetry and Terrestrial Laser Scanning for Precision Forestry

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Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI), unmanned aerial vehicles (UAVs), and LiDAR technology is revolutionizing precision forestry. Data from aerial and terrestrial platforms are becoming indispensable. Unlike traditional, labor-intensive forestry surveys, these technologies enable efficient, large-scale forest monitoring. By leveraging UAVs and AI, it is now feasible to significantly reduce manpower, enhance efficiency, and achieve sustainable, fine-grained management of extensive forest areas.

This Special Issue aims to collect studies utilizing UAV photogrammetry and terrestrial laser scanning in precision forestry. Topics of interest cover a broad spectrum: from optical images to 3D point clouds, from understory to canopy, from tree-level to stand-level, and from structural analysis to functional inference. We also welcome submissions on multisource data fusion and artificial intelligence for extracting forest parameters.

Multi-modal andmulti-platform forest data fusion;
Forest LiDAR point clouds registration;
Tree species identification;
Individual tree segmentation;
Diameter at Breast Height(DBH);
Quantitative Structure Model(QSM);
Canopy height;
Forest volume;
Forest biomass;
Forest carbon storage and sink.

Dr. Ningning Zhu
Prof. Dr. Zhenyang Hui
Dr. Wenxia Dai
Dr. Linyuan Li
Guest Editors

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Research

34 pages, 40338 KB

Open AccessArticle

A Multi-Source Remote Sensing-Based AGB Synergistic Inversion Approach Integrating Terrain-Corrected Canopy Height and Forest-Type Heterogeneity

by Li Zhang, Zhenyang Hui, Duan Huang, Hua Liu and Xiaowei Xie

Remote Sens. 2026, 18(14), 2304; https://doi.org/10.3390/rs18142304 - 9 Jul 2026

Viewed by 396

Abstract

ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation. These challenges include sparse sampling, signal saturation, terrain effects, and limited model generalization. To solve these challenges, this study proposes a new synergistic multi-source remote sensing framework for regional-scale AGB estimation by integrating terrain-corrected ICESat-2 canopy height and forest-type heterogeneity. The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types. In this paper, multi-source datasets were integrated, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission (SRTM), WorldClim, and a terrain-corrected canopy height model (CHM). Subsequently, candidate features were derived such as spectral, textural, topographic, and climatic variables. In terms of the terrain-corrected CHM, canopy structural parameters were extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data after terrain correction based on a high-resolution DEM. Footprint-level AGB samples were first generated using ICESat-2-derived canopy structural parameters through four regression approaches, including Multiple linear regression, Stepwise multiple regression, Ridge regression, and Lasso regression. These generated AGB samples were then used as response variables for subsequent regional-scale modeling. To build accurate AGB estimation model, key features were first identified using correlation analysis. To account for forest structural heterogeneity, three models including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed for regional AGB mapping. To evaluate the performance of the proposed AGB estimation model by integrating terrain-corrected canopy height and forest-type heterogeneity, this study conducted AGB estimation at the Harvard Forest (HARV) site in the United States. The experimental results show that forest-type-specific modeling improves model adaptability and robustness. Among the models (RF, XGBoost and SVM), RF achieved the best performance, with an average coefficient of determination of 0.694. The optimized model was applied to produce a 30 m resolution AGB map. The validation was conducted using airborne LiDAR-derived AGB referenced results. The validation shows that an overall coefficient of determination (R²) of 0.606 and a root mean square error (RMSE) of 16.53 Mg ha⁻¹. These results demonstrate that the proposed new synergistic AGB estimation framework, which integrates terrain-corrected ICESat-2 canopy height with forest-type-specific modeling, provides an accurate and reliable solution for regional-scale forest biomass mapping and carbon stock assessment. Full article

(This article belongs to the Special Issue UAV Photogrammetry and Terrestrial Laser Scanning for Precision Forestry)

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