Can Stereoscopic Density Replace Planar Density for Forest Aboveground Biomass Estimation? A Case Study Using Airborne LiDAR and Landsat Data in Daxing’anling, China DOI Creative Commons
Xuan Mu, Dan Zhao, Zhaoju Zheng

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1163 - 1163

Published: March 25, 2025

Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. Accurate regional-scale AGB estimation crucial advancing research on global climate change, ecosystem cycles, ecological conservation. Traditional methods, whether based LiDAR or optical remote sensing, estimate using planar density (t/ha) multiplied by pixel area, which fails to account vertical structure variability. This study proposes novel “stereoscopic (stereo) × volume” approach, upgrading stereo (t/ha/m) integrating canopy height information, thereby improving accuracy exploring the feasibility of this new method. In Daxing’anling region, plot-scale models were developed stepwise linear regression (SLR) both “planar area” “stereo methods. Results indicated that model arithmetic mean (HAM) achieved comparable (R2 = 0.83, RMSE 2.77 t) with 2.52 t). At regional scale, high-precision estimates derived from airborne combined vegetation indices Landsat Thematic Mapper (TM), topographic factors DEM develop models, SLR random (RF) algorithms. The results 10-fold cross-validation demonstrated superiority method over method, RF outperforming SLR. optimal RF-based HAM 0.65, rRMSE 26.05%) significantly improved compared 0.59, 30.41%). Independent validation 75 field plots higher R2 0.45 model’s 0.35. These findings suggest approach mitigates underestimation caused variability in no significant differences observed across types. conclusion, use superior sensing. offers scalable solution stock assessment.

Language: Английский

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests DOI Creative Commons
Louise Terryn, Kim Calders, Félicien Meunier

et al.

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(8)

Published: Aug. 1, 2024

Abstract Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) tree height (H). However, these often combine H obtained through various measurement methods, each distinct error patterns, affecting the resulting H:D allometries. In recent decades, laser scanning (TLS) has emerged as a widely accepted method accurate, non‐destructive structural measurements. This study used TLS data to evaluate prediction accuracy inventory‐based allometries develop more accurate pantropical We considered 19 tropical rainforest plots across four continents. Eleven had RIEGL VZ‐400(i) TLS‐based D data, allowing assessment local Additionally, from 1951 trees all were create new rainforests. Our findings reveal that in most plots, underestimated compared For 30‐metre‐tall trees, underestimations varied −1.6 m (−5.3%) −7.5 (−25.4%). Malaysian plot reaching up 77 height, underestimation was much −31.7 (−41.3%). propose allometry, incorporating maximum climatological water deficit site effects, mean uncertainty 19.1% bias −4.8%. While is roughly 2.3% greater than Chave2014 model, this model demonstrates consistent uncertainties size delivers less biased estimates (with reduction 8.23%). summary, recognizing errors methods vital, they can propagate into inform. underscores potential rainforests, refining

Language: Английский

Citations

4
Terrestrial Laser Scanning (TLS) for tree structure studies: a review of methods for wood-leaf classifications from 3D point clouds DOI Creative Commons

Stefano Arrizza,

Susanna Marras,

Roberto Ferrara

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 101364 - 101364

Published: Sept. 1, 2024

Language: Английский

Citations

4
A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests DOI Creative Commons
Ye Ma, Jungho Im, Zhen Zhen

et al.

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: Jan. 17, 2025

Secondary forests, a typical forest type in the sub-frigid zone of Northeast China, have significant potential for carbon sequestration. Accurate estimation Aboveground Biomass (AGB) secondary forests and assessment multiscale uncertainties are crucial promoting Reduced Emissions from Deforestation Degradation. This study developed novel framework to upscale AGB tree landscape level assessed based on multi-platform laser scanning data Unmanned Aerial Vehicle (UAV) hyperspectral images. The included two stages: (1) quantifying multiple (uncertainties individual crown delineation, parameters estimation, species classification) tree-based using Monte Carlo simulations; (2) upscaling plot estimated Nonlinear Simultaneous Equation (NSE) with error-in-variables model residuals, parameters, independent variables. findings revealed high accuracy (R2: 0.75, Root Mean Square Error (RMSE): 6.65 Mg/ha, relative RMSE (rRMSE): 5.40%), total 16.85 Mg/ha 16.29%, respectively, highest uncertainty (9.73 Mg/ha) observed classification. NSE achieved an R2 0.69, 9.91 rRMSE 10.43% level; caused by variables, residuals were 5.52 14.56 25.25 accounting 3.46%, 24.09%, 72.45% uncertainty. develops large-scale mixed approach quantification estimates provides foundation precise forestry, sustainable management, neutrality.

Language: Английский

Citations

0
TLSLeaf: Unsupervised Instance Segmentation of Broadleaf Leaf Count and Area from TLS Point Clouds DOI
Guangpeng Fan,

Ruoyoulan Wang,

Cheng‐Ye Wang

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2025, Volume and Issue: 63, P. 1 - 15

Published: Jan. 1, 2025

Language: Английский

Citations

0
Can Stereoscopic Density Replace Planar Density for Forest Aboveground Biomass Estimation? A Case Study Using Airborne LiDAR and Landsat Data in Daxing’anling, China DOI Creative Commons
Xuan Mu, Dan Zhao, Zhaoju Zheng

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1163 - 1163

Published: March 25, 2025

Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. Accurate regional-scale AGB estimation crucial advancing research on global climate change, ecosystem cycles, ecological conservation. Traditional methods, whether based LiDAR or optical remote sensing, estimate using planar density (t/ha) multiplied by pixel area, which fails to account vertical structure variability. This study proposes novel “stereoscopic (stereo) × volume” approach, upgrading stereo (t/ha/m) integrating canopy height information, thereby improving accuracy exploring the feasibility of this new method. In Daxing’anling region, plot-scale models were developed stepwise linear regression (SLR) both “planar area” “stereo methods. Results indicated that model arithmetic mean (HAM) achieved comparable (R2 = 0.83, RMSE 2.77 t) with 2.52 t). At regional scale, high-precision estimates derived from airborne combined vegetation indices Landsat Thematic Mapper (TM), topographic factors DEM develop models, SLR random (RF) algorithms. The results 10-fold cross-validation demonstrated superiority method over method, RF outperforming SLR. optimal RF-based HAM 0.65, rRMSE 26.05%) significantly improved compared 0.59, 30.41%). Independent validation 75 field plots higher R2 0.45 model’s 0.35. These findings suggest approach mitigates underestimation caused variability in no significant differences observed across types. conclusion, use superior sensing. offers scalable solution stock assessment.

Language: Английский

Citations

0