Forest Biomass Estimation Research Articles

Improving plot-level above ground biomass estimation in tropical Indian forests

Accurate plot-level estimates of above ground biomass (AGB) are crucial for reducing uncertainty in spatial AGB quantification, a key requirement for Reducing Emissions from Deforestation and Forest Degradation framework (REDD+) and achieving Intended Nationally Determined Contributions (INDC) goals. This study explores the impact of plot size, selection and use of height-diameter (H–D) model, and allometric models on plot-level AGB estimation in tropical Indian forests. We analyze a dataset of 8179 tree measurements from 51 one-hectare plots across four tropical sites to investigate these factors and improve estimation accuracy. Our findings highlight the importance of locally derived H–D models for superior accuracy. We propose two H–D models for improved plot-level AGB estimation accuracy in tropical Indian forests: a general model with a residual standard error (RSE) of 4.18 m and a forest type-specific model with a RSE of 3.65 m. These models outperform global models, which significantly underestimate tree height by up to 51.3% and overestimate by 43.4% across our sites. The developed H–D models were evaluated using spatial cross-validation to ensure broader applicability across Indian tropical forests. Further, we evaluated the impact of the choice of the allometric model on plot-level AGB estimation using the prevalent models (local destructive models by the Forest Survey of India and global pantropical models). Our results indicate minimal variability between the local destructive models and the pantropical model when height measurements are derived from this study's developed H–D models. We have also investigated the effect of plot size on plot-level AGB estimation using synthetic plots generated from 1-ha plot data. Notably, the relative error diminishes from approximately 22% to about 5% as plot size increases from 0.04 ha (20x20m) to 0.49 ha (70x70m) across all sites, and we recommend field plots of size ≥0.5 ha for AGB mapping over Tropical Indian Forests. By making this data publicly available, this study paves the way for more accurate spatial AGB mapping in tropical Indian forests, ultimately contributing to better forest management.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Comparison of QRNN and QRF Models in Forest Biomass Estimation Based on the Screening of VIs Using an Equidistant Quantile Method

The investigation of a potential correlation between the filtered-out vegetation index and forest aboveground biomass (AGB) using the conventional variables screening method is crucial for enhancing the estimation accuracy. In this study, we examined the Pinus densata forests in Shangri-La and utilized 31 variables to establish quantile regression models for the AGB across 19 quantiles. The key variables associated with biomass were based on their significant correlation with the AGB in different quantiles, and the QRNN and QRF models were constructed accordingly. Furthermore, the optimal quartile models yielding the minimum mean error were combined as the best QRF (QRFb) and QRNN (QRNNb). The results were as follows: (1) certain bands exhibited significant relationships with the AGB in specific quantiles, highlighting the importance of band selection. (2) The vegetation index involving the band of blue and SWIR was more suitable for estimating the Pinus densata. (3) Both the QRNN and QRF models demonstrated their optimal performance in the 0.5 quantiles, with respective R2 values of 0.68 and 0.7. Moreover, the QRNNb achieved a high R2 value of 0.93, while the QRFb attained an R2 value of 0.86, effectively reducing the underestimation and overestimation. Overall, this research provides valuable insights into the variable screening methods that enhance estimation accuracy and mitigate underestimation and overestimation issues.

COMPARISON OF THE USE OF SENTINEL-1 SAR AND ALOS-2 PALSAR-2 IN MANGROVE ABOVEGROUND BIOMASS ESTIMATION IN SAN JUAN, BATANGAS, PHILIPPINES

Abstract. This study compares the potential of Sentinel-1 and ALOS-2 PALSAR-2 in estimating mangrove aboveground biomass (AGB) in San Juan, Batangas, Philippines. Mangrove forests are essential coastal ecosystems that are facing growing threats. One way of conserving them is by creating policies that can protect them. To do this effectively, information like AGB can be used as a guide. However, conventional AGB estimations are labor-intensive and ecologically disruptive. Conversely, remote sensing technologies, such as synthetic aperture radar (SAR), offer a more efficient alternative. Sentinel-1, operating in C-band, and ALOS-2 PALSAR-2, operating in L-band, are two prominent SAR platforms with global coverage, offering data for land cover classification, forest monitoring, and forest biomass estimation. This research used the backscatter values of Sentinel-1 and ALOS-2 PALSAR-2 as predictor variables in estimating mangrove AGB by correlating them to the observed AGB from a mangrove survey. The models developed using these platforms yielded limited accuracy, with low coefficient of determination (R2) (Sentinel-1 = 0.13; ALOS-2 PALSAR-2 = 0.12) and RMSE (Sentinel-1 = 8.72 Mg ha-1; ALOS-2 PALSAR-2 = 8.78 Mg ha-1). Potential sources of errors were identified, including small sample size and data noise. On the results, Sentinel-1 demonstrates a slightly better performance in terms of the R2 and RMSE in the modeling while ALOS-2 PALSAR-2 performed better in the validation, however, both still yielded suboptimal AGB estimates compared to other studies. Refining the models by incorporating additional parameters, exploring machine learning, and considering other data sources are recommended to enhance AGB estimation.

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon.

Connecting spaceborne lidar with NFI networks: A method for improved estimation of forest structure and biomass

Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense measurements of vertical canopy structure. For full waveform instruments such as the Global Ecosystem Dynamics Investigation (GEDI), measurements take the form of reflected energy as a function of height within an observed footprint. Many forest attributes cannot be directly computed from the waveforms, and thus statistical models relating the measurements to target attributes must be trained using field plot data. Because the discrete footprint samples produced by spaceborne lidar instruments have little chance of collocation with pre-existing field plots, model calibration remains a primary challenge. We leveraged the density and spatial correlation of GEDI observations to make predictions of the cumulative waveform over 326,787 NFI plots distributed across the contiguous United States. The predictions yield probability distributions, giving not only a predicted waveform but quantification of prediction uncertainty. The product of this work is a data set comprised of the NFI plots paired with the predictive waveform distributions. The predicted waveforms and their uncertainties can be used to train models with “error-in-variables” techniques that account for and filter the uncertainty in the predicted waveforms. This data set and the corresponding techniques allow statistically rigorous model training without requirements of collocation, yielding downstream forest attribute predictions that are consistent with NFI measurements and estimates. We demonstrate the data set by training a model for forest above ground biomass density (AGBD) and then use the model to produce a spatially complete 1 km map of AGBD for the contiguous United States. We further compare our AGBD predictions to US NFI estimates at a 64,000 hectare scale, showing the increase in precision to be proportional to the collection of almost 500,000 additional NFI plots across forested regions of CONUS.

A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning

The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05°, 0.025°, and 0.01°. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1° to 0.05°, 0.025°, and 0.01° resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing’anling Mountains and Xiaoxing’anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing’anling Mountains with complex topography, while both performed comparably in the Xiaoxing’anling Mountains with milder topography, highlighting SBSD’s advantages in high heterogeneity areas.

Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

Evaluating the Research Status of the Remote Sensing-Mediated Monitoring of Forest Biomass: A Bibliometric Analysis of WOS

Forests serve as the largest carbon reservoir in terrestrial ecosystems, playing a crucial role in mitigating global warming and achieving the goal of “carbon neutrality”. Forest biomass is intrinsically related to carbon sinks and sources in forest ecosystems, and thus, the accurate monitoring of forest biomass is of great significance in ensuring ecological security and maintaining the global carbon balance. Significantly, remote sensing is not only able to estimate forest biomass at a large spatial scale but does so quickly, accurately, and without loss. Moreover, it can obtain forest biomass in areas inaccessible to human beings, which have become the main data source for forest biomass estimation at present. For this reason, this study analyzes the current research status, research hotspots, and future research trends in the field of remote sensing monitoring of forest biomass based on 1678 forest biomass remote sensing monitoring results from 1985 to 2023 obtained from the Web of Science Core Collection database. The results showed that the following: (1) The number of publications showed an exponential upward trend from 1985 to 2023, with an average annual growth rate of 2.64%. The top ten journals contributed to 53.76% of the total number of publications and 52.89% of the total number of citations in the field. (2) In particular, Remote Sensing of Environment has maintained a leading position in the field for an extended period, boasting the highest impact factor. Additionally, the author Saatchi S. stands out with the highest total number of citations for articles. (3) Keyword clustering analysis revealed that the main research topics in the remote sensing monitoring of forest biomass can be categorized into the following: optical remote sensing, LiDAR remote sensing, SAR remote sensing, and carbon stock. The explosion of keywords in the last six years indicates that an increasing number of researchers are focusing on carbon, airborne LiDAR data, biomass mapping, and constructing optimal biomass models.

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources.

Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China

ABSTRACT Accurate estimation of forest aboveground biomass is essential for the assessment of regional carbon cycle and the climate change in the terrestrial ecosystem. Currently, ensemble learning algorithms and cross-validation methods have been widely applied to estimate regional forest Above Ground Biomass (AGB). However, the effects of ensemble learning algorithms, validation methods, and their interactions on forest AGB estimation were rarely investigated. Based on Landsat 8 Operational Land Imager (OLI) imagery, Airborne Laser Scanning (ALS) data and China’s National Forest Continuous Inventory data, this study explored the effects of five ensemble learning algorithms, including Simple Averaging (SA), Weighted Averaging (WA), Stacked Generalization (SG), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and two validation approaches (i.e. 10-fold and leave-one-out cross-validation) on the AGB estimation of the Natural Secondary Forests (NSFs) in northeast China. The results revealed that the ensemble learning algorithms that combine heterogenous-based models (i.e. SA, WA, SG) generally produced higher accuracy than the base models (i.e. Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Convolutional Neural Network (CNN)). Among all ensemble learning algorithms, the SG algorithm has the highest accuracy whereas the XGBoost algorithm has the lowest accuracy. Although prediction models considerably impact the accuracy of AGB estimation, the validation approach also plays a non-negligible role in AGB estimation. The leave-one-out cross-validation produced much higher accuracy than the 10-fold cross-validation using the same prediction model and tends to generate over-optimistic AGB estimates compared to 10-fold cross-validation, especially for the averaging and stacking ensemble learning algorithms (i.e. SA, WA, SG). This study highlights the potential challenges of applying a leave-one-out cross-validation approach and provides a scientific foundation for the feasibility of different ensemble learning algorithms and cross-validation approaches for accurate AGB estimation.

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

Assessment of Forest Biomass and Carbon Storage in Habitat 9340 Quercus ilex L. to Support Management Decisions for Climate Change Mitigation

The assessment of forest biomass has been a focus of research, aiming to understand matter–energy relationships in forest ecosystems and address forestry practice issues. In recent decades there has been increased societal interest in rational forest resource exploitation, necessitating accurate biomass estimation. In Greece there has been limited efforts in estimating forest biomass, and the current study focuses on habitat type 9340, “Quercus ilex and Quercus rotundifolia forests,” located in the protected areas “GR1420004–Karla–Mavrovouni–Kefalovryso Velestinou–Neochori” and “GR1430001–Oros Pilio and Paraktia Thalassia Zoni (Mount Pilion and Coastal Sea Zone)” in the Natura 2000 network. The habitat falls within the thermo-Mediterranean zone and the study aims to estimate the biomass and carbon storage to contribute to sustainable EU forest strategies. Due to resource limitations, a generalized allometric equation was proposed as an alternative to traditional biomass estimation methods. The above-ground biomass per hectare was estimated, ranging from 16.10 to 205.27 Mg ha−1 (mean 61.91 Mg ha−1). Furthermore, two approaches were used to estimate the total biomass in the habitat: regional averages based on spatial distribution and spatial interpolation using a geographic information system. The total estimated biomass for habitat 9340 is 183,505 Mg, with the carbon storage in standing dry biomass amounting to 83,725.25 Mg. Despite the absence of sampled biomass specimens, this study combines robust statistical techniques with published empirical values to provide a solid framework for estimating assimilated CO2. Sequestered CO2 in the study area is estimated at 306,992.58 Mg. Therefore, the significant role of Quercus ilex L. in carbon storage in Mediterranean forest ecosystems is highlighted by sequestering a substantial amount of CO2.

Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery

A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling.

A comparative study on the efficacy of dual-pol and full-pol ASAR data in radiative transfer modeling for forest above-ground biomass estimation

ABSTRACT In this study, a comprehensive evaluation of the potential of dual-polarization and full-polarization L- and S-band airborne SAR (LS-ASAR) data (ISRO’s dual-frequency airborne SAR mounted on NASA’s Gulfstream III aircraft) in forest above-ground biomass (AGB) estimation has been carried out. The study area comprises the temperate forests of the Lenoir landing site in Southwest Alabama, United States of America. A trunk scattering model based on Vector Radiative Transfer (VRT) theory is used in this study to estimate the AGB. Full-polarization ASAR data inversion retrieves three critical biophysical parameters: tree height, diameter at breast height (DBH), and tree count, whereas with dual-polarization ASAR data, the model is limited to retrieving only two parameters (tree height and DBH). These parameters are then employed in a suitable allometric equation to estimate the AGB over the study area. Validation of the retrieval results is performed through comparison with ground-truth measurements taken from the study site. The findings demonstrate that the inversion of L-band full-polarized data yields the highest correlation (R2 = 0.87, RMSE = 20.37 t/ha), revealing high accuracy in AGB estimation. The L-band dual-polarized data shows a high correlation (R2 = 0.83, RMSE = 30.66 t/ha), indicating reasonable accuracy. However, the S-band data, both full and dual-polarized, reveals weaker correlations with higher RMSE values (R2 = 0.59, RMSE = 55.07 t/ha, and R2 = 0.49, RMSE = 58.31 t/ha, respectively), suggesting a lower degree of reliability. This study emphasizes the pre-eminence of L-band fully polarized data inversion for reliable and accurate AGB estimation while also showing the utility of relatively ubiquitous dual-pol data (L-band) to achieve reasonable accuracy for the same.

Propagating Uncertainty in Predicting Individuals and Means Illustrated with Foliar Chemistry and Forest Biomass

Quantifying uncertainty is important to establishing the significance of comparisons, to making predictions with known confidence, and to identifying priorities for investment. However, uncertainty can be difficult to quantify correctly. While sampling error is commonly reported based on replicate measurements, the uncertainty in regression models used to estimate forest biomass from tree dimensions is commonly ignored and has sometimes been reported incorrectly, due either to lack of clarity in recommended procedures or to incentives to underestimate uncertainties. Even more rarely are the uncertainty in predicting individuals and the uncertainty in the mean both recognized for their contributions to overall uncertainty. In this paper, we demonstrate the effect of propagating these two sources of uncertainty using a simple example of calcium concentration of sugar maple foliage, which does not require regression, then the mass of foliage and calcium content of foliage, and finally an entire forest with multiple species and tissue types. The uncertainty due to predicting individuals is greater than the uncertainty in the mean for studies with few trees—up to 30 trees for foliar calcium concentration and 50 trees for foliar mass and calcium content in the data set we analyzed from the Hubbard Brook Experimental Forest. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks can safely ignore the uncertainty in individuals, which becomes negligible with large enough numbers of trees. Ignoring the uncertainty in the mean will result in exaggerated confidence in estimates of forest biomass and carbon and nutrient contents.

Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR

Quantifying forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures by aiding local forest management, studying processes driving af-, re-, and deforestation, and improving the accuracy of carbon accounting. Owing to the 3-dimensional nature of forest structure, remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. Harnessing the full dimensionality of the data, we present deep learning systems predicting wood volume and above ground biomass (AGB) directly from the full LiDAR point cloud and compare results to state-of-the-art approaches operating on basic statistics of the point clouds. For this purpose, we devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in the Danish national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression give the best results. The deep neural networks produce significantly more accurate wood volume, AGB, and carbon stock estimates compared to state-of-the-art approaches. In contrast to other methods, the proposed deep learning approach does not require a digital terrain model and is robust to artifacts along the boundaries of the evaluated areas, which we demonstrate for the case where trees protrude into the area from the outside. We expect this finding to have a strong impact on LiDAR-based analyses of biomass dynamics.

Developing crown width model for mixed forests using soil, climate and stand factors

Abstract The tree crown is a useful measure of tree vigour and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focussed on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected. Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 were located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity and their interactions. We observed that the cross‐species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index and species mixture. The interaction between quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modelling than the aridity index, and structural diversity had a greater effect on crown width than species diversity. Synthesis. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.

Collaborative Estimation of Aboveground Forest Biomass Using P-Band and X-Band Interferometric Synthetic Aperture Radar Based on Feature Optimization

Collaborative Estimation of Aboveground Forest Biomass Using P-Band and X-Band Interferometric Synthetic Aperture Radar Based on Feature Optimization