Search Results (691)

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

Moso bamboo, widely distributed in subtropical regions of China, plays an important role in forest management and carbon cycle research. However, accurate estimation of tree density and aboveground biomass (AGB) remains challenging due to the unique characteristics of Moso bamboo forests in their growth and stand structure. This research aims to develop a new procedure for bamboo tree density and AGB estimation based on UAV-LiDAR and sample plots from multiple sites through comparative analysis of the incorporation of two groups of variables—regular point cloud metrics (e.g., height, point density) and layered texture metrics—and three modeling methods—multiple linear regression (MLR), mixed-effects modeling (MEM), and hierarchical Bayesian modeling (HBM). The results showed that incorporating layered texture metrics with regular variables substantially improved the estimation accuracy of both tree density and AGB. Among these models, HBM achieved the highest predictive performance, yielding coefficient of determination (R²) values of 0.54 for tree density and 0.59 for AGB, with corresponding relative root mean square errors (rRMSE) of 21.46% and 17.97%. This study presents a novel and effective method for estimating Moso bamboo tree density and AGB using multi-site UAV-LiDAR and sample plots, offering a scientific basis for precise management and carbon stock assessment. Full article

Precise estimation of cotton aboveground biomass (AGB) plays a crucial role in effectively analyzing growth variations and development of cotton, as well as guiding agricultural management practices. Multispectral (MS) sensors mounted on UAVs offer a practical and accurate approach for estimating the AGB of cotton. Many previous studies have mainly emphasized the combination of spectral and texture features, as well as canopy height (CH). However, current research overlooks the potential of integrating spectral, textural features, and CH to estimate AGB. In addition, the accumulation of AGB often exhibits synergistic effects rather than a simple additive relationship. Conventional algorithms, including Bayesian Ridge Regression (BRR) and Random Forest Regression (RFR), often fail to accurately capture the nonlinear and intricate correlations between biomass and its relevant variables. Therefore, this research develops a method to estimate cotton AGB by integrating multiple feature information with a deep learning model. Spectral and texture features were derived from MS images. Cotton CH extracted from UAV point cloud data. Variables of multiple features were selected using Spearman’s Correlation (SC) coefficients and the variance inflation factor (VIF). Convolutional neural network (CNN) was chosen to build a model for estimating cotton AGB and contrasted with traditional machine learning models (RFR and BRR). The results indicated that (1) combining spectral, textural features, and CH yielded the highest precision in cotton AGB estimation; (2) compared to traditional ML models (RFR and BRR), the accuracy of applying CNN for estimating cotton AGB is better. CNN has more advanced power to learn complex nonlinear relationships among cotton AGB and multiple features; (3) the most effective strategy in this study involves combining spectral, texture features, and CH, selecting variables using the SC and VIF methods, and employing CNN for estimating AGB of cotton. The R² of this model is 0.80, with an RMSE of 0.17 kg·m⁻² and an MAE of 0.11 kg·m⁻². This study develops a framework for evaluating cotton AGB by multiple features fusion with a deep learning model. It provides technical support for monitoring crop growth and improving field management. Full article

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSI_VI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSI_VI) and AGB was enhanced by 13.60% overall, with the CGSI_CIre exhibiting the highest correlation with cotton AGB (R² = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSI_VI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field. Full article

Managed disturbances and their consequences for plant community structure, productivity, and foliar nutrients in the habitat of the endangered Eld’s deer remain inadequately characterized. We assessed the effects of prescribed fire (PF), mechanical mowing (MM), and their combination (PF_MM) on plant communities in the Datian National Nature Reserve of Hainan, China. Our findings demonstrated that the PF_MM treatment produced the greatest number of species (38 species, representing increases of 26.6% and 72.7% compared to PF and MM, respectively) and diversity indexes, indicating enhanced structural stability relevant to ecological conservation. In contrast, MM yielded the highest aboveground biomass (AGB) and the highest foliar nitrogen (N, 14.28 g kg⁻¹), phosphorus (P, 2.08 g kg⁻¹), and potassium (K, 3.61 g kg⁻¹) concentrations, but concurrently promoted shrub dominance, potentially risking long-term nutrient depletion and functional group imbalance. Legume (Fabaceae) richness was negatively associated with foliar P and K, which is consistent with the nutrient dilution effect often observed in more diverse plant communities. Structural equation modeling indicated that treatment effects on AGB were mediated by the importance value of Fabaceae, whereas treatment effects on foliar N and P were expressed both directly and indirectly via the richness of Fabaceae and other families. Consequently, no single management approach can simultaneously enhance all desired metrics or indices. New management strategies or technologies should be explored to balance biodiversity conservation with improved pasture quality, thereby further supporting the recovery of Eld’s deer habitat while maintaining ecosystem health. Full article

Accurate estimation of individual tree Above-Ground Biomass (AGB) is essential for assessing forest carbon sequestration. This study integrates multi-source Unmanned Aerial Vehicle (UAV) remote sensing (LiDAR and RGB) with machine learning to estimate the AGB of Larix principis-rupprechtii in a natural secondary forest. We applied an instance segmentation approach to identify individual trees and extract structural and spectral features, which were subsequently optimized before model training. Our results demonstrate that models utilizing combined multi-source features significantly outperformed those relying on a single data source. The Extreme Gradient Boosting (XGBoost) algorithm achieved the best performance, with an R² of 0.770 using the combined feature set. SHapley Additive exPlanations (SHAP) interpretation revealed that structural attributes—particularly tree height and crown volume—were the most influential predictors, underscoring their greater importance over spectral information. This study presents an effective and interpretable framework for accurate tree-level AGB estimation, supporting scalable monitoring of regional forest carbon dynamics. Full article

Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U²-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U²-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U²-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was W_A = 0.0025D^1.943H^0.690 (R² = 0.912), the model for belowground biomass (BGB) was W_B = 0.012D^1.231H^0.525 (R² = 0.693), the model for CA-D was D = 4.31427C^0.513 (R² = 0.751), CA-H model was H = 226.51939C^0.268 (R² = 0.500). (2) The optimal AGB model for CA single-parameter was W_A = 1.80901C^1.181 (R² = 0.845), and the model for BGB was W_B = 1.25043C^0.772 (R² = 0.741). (3) The R² of Olea europaea L. biomass, as estimated by CA derived from the U²-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U²-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity. Full article

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly labeled datasets. To address these issues, we present a data-efficient deep learning framework using smartphone-captured top-down RGB images for AGB estimation (Fresh Weight, FW, and Dry Weight, DW). Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning (SSL) method. Subsequently, this pre-trained model is fine-tuned on a small, custom-labeled rapeseed dataset (N = 833) using a Multi-Task Learning (MTL) framework to simultaneously regress both FW and DW. This MTL approach acts as a powerful regularizer, forcing the model to learn robust features related to the 3D plant structure and density. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance for both Fresh Weight (Coefficient of Determination, R² = 0.842) and Dry Weight (R² = 0.829). The model significantly outperformed a range of baselines, including models trained from scratch and those pre-trained on the generic ImageNet dataset. Ablation studies confirmed the critical and synergistic contributions of both domain-specific SSL (vs. ImageNet) and the MTL framework (vs. single-task training). This study demonstrates that an SSL+MTL framework can effectively learn to infer complex 3D plant attributes from 2D images, providing a robust and scalable tool for non-destructive phenotyping to accelerate the rapeseed breeding cycle. Full article

High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide extensive coverage while lacking the spatial resolution required for precise city-scale analysis. To address the dilemma of achieving both high spatial resolution and broad coverage, this study integrated 149 feature variables derived from multi-source datasets and implemented quality-control procedures to select high-quality samples from two globally representative AGB products (GEDI AGB and CCI AGB). This strategy substantially improved the performance of the random forest model and generated 10 m resolution urban trees’ AGB maps for 51 C40 cities across Eurasia continent. The results indicate that: (1) after applying quality control to the target variables, the mean R² of ten-fold cross validation improved from 0.37 to 0.75, and the MAE decreased substantially from 47.02 Mg/ha to 17.48 Mg/ha; (2) by enhancing the spatial resolution of AGB maps to 10 m, the resulting products exhibit superior spatial detail, better capture local variations, and maintain greater spatial continuity compared with the CCI AGB and GEDI AGB datasets; (3) the mean AGB density across the Eurasian continent was 39.44 Mg/ha, with total urban tree s’ AGB reaching 83.83 × 10⁶ t. Comparison with previous single-city C40 studies shows that our estimated AGB density and total AGB closely align with previously reported values. The above data implies that cities carry an undeniable amount of carbon storage, both in terms of carbon density and total amount. This study provides a robust foundation for accurately assessing the potential of urban carbon sinks and optimizing the path to achieving carbon neutrality. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

This study quantifies forest aboveground biomass (AGB) using integrated remote sensing features from high-resolution GaoFen-7 (GF-7) satellite imagery. We combined texture features, vegetation indices, and RGB spectral bands to improve estimation accuracy. Three machine learning algorithms—Random Forest (RF), Gradient Boosting Tree (GBT), and XGBoost—were compared with a stacking ensemble model using five-fold cross-validation on forest plots in Beijing’s Daxing District. Feature importance was evaluated through SHAP to identify key predictive variables. Results show that texture features exhibit scale-dependent predictive power, while visible-band vegetation indices strongly correlate with AGB. The Stacking ensemble achieved optimal performance (R² = 0.62, RMSE = 57.34 Mg/ha, MAE = 39.99 Mg/ha), outperforming XGBoost (R² = 0.59), RF (R² = 0.58), and GBT (R² = 0.57). Compared to the best individual model, Stacking improved R² by 5.1% and effectively mitigated over- and underestimation biases. These findings demonstrate the effectiveness of ensemble learning for forest AGB estimation and suggest potential for regional-scale carbon monitoring applications. Full article