Multi-scale approach to estimating aboveground biomass in the Brazilian Amazon using Landsat and LiDAR data

Santos, Erone Ghizoni; Shimabukuro, Yosio Edemir; Moura, Yhasmin Mendes de; Gonçalves, F. G.; Anderson, Jorge; Gasparini, Kaio Alan; Arai, Egídio; Duarte, Valdete; Ometto, Jean Pierre

doi:10.1080/2150704x.2019.1619955

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Because the sample size (number of plots) in this study was modest, the prediction accuracy of the six models was evaluated using the leave-one-out cross-validation (LOOCV) method [42], which is commonly used for AGB model evaluation. It also improves the generalization ability of the model and mitigates overfitting [43]. Model accuracy evaluation metrics were R 2 , RMSE (Mg/ha), and rRMSE between reference and predicted plot AGB.…”

Section: Model Groupmentioning

confidence: 99%

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Ma,

Zhang,

et al. 2023

Remote Sensing

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Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R2 = 0.80, RMSE = 9.58 Mg/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R2 = 0.65, RMSE = 18.55 Mg/ha, rRMSE = 0.17) but also the best machine learning model (RF: R2 = 0.50, RMSE = 19.42 Mg/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs.

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Section: Model Groupmentioning

confidence: 99%

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Ma,

Zhang,

et al. 2023

Remote Sensing

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“…Despite the low cost and the great potential of 3D products generated by DAP in areas with vegetation, few studies have been carried out to estimate AGB and other variables of interest in tropical forests in Brazil and other regions of Latin America [47][48][49]. Currently, most studies of AGB estimation using 3D remote sensing data are carried out in the Amazon rainforest using LiDAR [50][51][52] and interferometric synthetic aperture radar (InSAR) [53][54][55], as these technologies tend to perform well even in continuous areas of dense forest cover, where the generation of DTMs may be challenging for DAP. The Atlantic Forest biome is also highly important for Brazil, mainly because of its relationship with the country's history and its high biodiversity [56].…”

Section: Introductionmentioning

confidence: 99%

Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds

et al. 2020

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Knowing the aboveground biomass (AGB) stock of tropical forests is one of the main requirements to guide programs for reducing emissions from deforestation and forest degradation (REDD+). Traditional 3D products generated with digital aerial photogrammetry (DAP) have shown great potential in estimating AGB, tree density, diameter at breast height, height, and basal area in forest ecosystems. However, these traditional products explore only a small part of the structural information contained in the 3D data, thus not leveraging the full potential of the data for inventory purposes. In this study, we tested the performance of 3D products derived from DAP and a technique based on Fourier transforms of vertical profiles of vegetation to estimate AGB, tree density, diameter at breast height, height, and basal area in a secondary fragment of Atlantic Forest located in northeast Brazil. Field measurements were taken in 30 permanent plots (0.25 ha each) to estimate AGB. At the time of the inventory, we also performed a digital aerial mapping of the entire forest fragment with an unmanned aerial vehicle (UAV). Based on the 3D point clouds and the digital terrain model (DTM) obtained by DAP, vertical vegetation profiles were produced for each plot. Using traditional structure metrics and metrics derived from Fourier transforms of profiles, regression models were fit to estimate AGB, tree density, diameter at breast height, height, and basal area. The 3D DAP point clouds represented the forest canopy with a high level of detail, regardless of the vegetation density. The metrics based on the Fourier transform of profiles were selected as predictors in all models produced. The best model for AGB explained 93% (R2 = 0.93) of the biomass variation at the plot level, with an RMS error of 9.3 Mg ha−1 (22.5%). Similar results were obtained in the models fit for the tree density, diameter at breast height, height, and basal area, with R2 values above 0.90 and RMS errors of less than 18%. The use of Fourier transforms of profiles with 3D products obtained by DAP demonstrated a high potential for estimating AGB and other forest variables of interest in secondary tropical forests, highlighting the value of UAV as a low-cost tool to assist the implementation of REDD+ projects in developing countries like Brazil.

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Land Use Land Cover (LULC) Changes and Biomass Estimation in the Amazon Biome Using Multisensor (Optical, LiDAR and SAR) Data

Shimabukuro,

Arai,

Ghizoni Santos

et al. 2024

Reference Module in Earth Systems and Environmental Sciences

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Multi-scale approach to estimating aboveground biomass in the Brazilian Amazon using Landsat and LiDAR data

Cited by 7 publications

References 37 publications

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds

Land Use Land Cover (LULC) Changes and Biomass Estimation in the Amazon Biome Using Multisensor (Optical, LiDAR and SAR) Data

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