Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

Sun, Xuejun; Li, Guicai; Wang, Meng; Fan, Zemeng

doi:10.3390/rs11060722

Cited by 25 publications

(12 citation statements)

References 60 publications

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“…The number of samples and the selection of environmental variables are two key factors that affect the accuracy of species distribution models. Generally, an increase in sample size leads to improved model accuracy, as demonstrated by various studies [21,[70][71][72][73]. In our study, the third and fourth AGB class models had relatively small sample sizes and significantly lower AUCs than the first and second AGB class models.…”

Section: Accuracy and Applicability Of Agb Estimation Modelssupporting

confidence: 58%

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Xue

Wang

2023

Forests

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Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm−2; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies.

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Section: Accuracy and Applicability Of Agb Estimation Modelssupporting

confidence: 58%

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Xue

Wang

2023

Forests

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“…For forest biomass models derived from remote sensing data, the RF non-parametric model represents one of the best estimation models in terms of accuracy, with larger R 2 and smaller RMSE as compared to other parametric and non-parametric methods (Sun et al 2019). The linear regression achieved less accuracy of the estimate, probably due to the nonlinear relationship between remote sensing predictors and the observed forest AGB.…”

Section: Discussionmentioning

confidence: 95%

Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy

Maesano¹,

Santopuoli²,

Moresi³

et al. 2022

iForest

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Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R 2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha -1 and from 34.2 to 22.1 Mg ha -1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model.

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“…Most studies related to AGB modeling have used the correlation between LiDARderived metrics and in situ measurements [52], applying various regression methods, such as linear regression [53], random forest (RF) algorithm [11], artificial neural networks [54], support vector machines [33], nonparametric regression [55] and Gaussian process regression [56]. Specifically, [54] tested the effectiveness of different statistical approaches for AGB stock and change (∆AGB) estimation, using a plot-based approach, in a selectively logged tropical forest, reporting that the ordinary least squares model provided the most accurate estimations compared with the machine learning algorithms (i.e., RF, k-nearest neighbor, SVM and ANN).…”

Section: Introductionmentioning

confidence: 99%

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

Georgopoulos,

Gitas,

Korhonen

et al. 2023

Remote Sensing

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The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted.

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Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

Cited by 25 publications

References 60 publications

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

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