Search Results (897)

Aboveground biomass (AGB) serves as a crucial indicator for assessing vegetation carbon sequestration capacity. While AGB levels vary significantly across different vegetation types and regions, the spatial distribution of AGB for specific wetland communities remains poorly characterized. To address this, we integrated field-collected data with Sentinel-2 spectral bands and remote sensing indices, employing random forest (RF) regression and Backpropagation Neural Network (BPNN) for AGB modeling. Through comparative evaluation of their inversion performance, the optimal model was selected to estimate vegetation AGB in the Nanji Wetland. By incorporating wetland classification data, we further generated spatial distribution maps of AGB for four dominant vegetation types during the dry season. The main findings are as follows. Important variables for the RF model included spectral bands B12, B11, B3, B2, B9, B1, B8, B6, and B4 and the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Kernel Normalized Difference Vegetation Index (KNDVI), and Simple Ratio Index (SR). RF demonstrated significantly higher predictive accuracy (R² = 0.945, RMSE = 109.205 g·m⁻²) compared to the BPNN (R² = 0.821, RMSE = 176.025 g·m⁻²). The total estimated AGB reached 4.03 × 10⁹ g; Carex spp. dominated AGB accumulation (1.49 × 10⁹ g), followed by P. australis spp. (6.69 × 10⁸ g), M. lutarioriparius spp. (4.60 × 10⁸ g), and Polygonum spp. (3.61 × 10⁸ g). The AGB exhibited a clear spatial gradient, decreasing from higher-elevation lakeshore areas towards the central lake. The results provide detailed spatial quantification of AGB stocks across dominant vegetation types, revealing distinct spatial characteristics and interspecies variations in AGB. This study offers a valuable baseline and methodological framework for monitoring wetland carbon dynamics. Full article

Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m², N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices. Full article

Accurate estimation of plantation aboveground biomass (AGB) is critical for quantifying carbon cycles and informing sustainable forest resource management, but enhancing estimation accuracy remains a key challenge. Although tree height and stand age are recognized as critical predictors for enhancing AGB models in addition to spectral vegetation indices, their individual and combined contributions in regional plantation forests remain insufficiently quantified, especially concerning the potential for leveraging the distinct characteristics of fast-growing plantations to facilitate AGB estimation. This study developed multi-source remote sensing-based Eucalyptus AGB estimation models for Nanning, Guangxi, integrating stand age and tree height to assess their impacts. Stand age was mapped from Landsat time-series imagery, and tree height was derived from UAV-LiDAR data. Plot-level reference AGB was obtained using fused UAV and terrestrial LiDAR point clouds. A random forest model, incorporating these variables with Sentinel-2 spectral information and topography, then achieved regional AGB estimation. The findings demonstrate that (1) tree height serves as the most influential predictor for AGB estimation at the regional scale, yielding a robust model performance (R² = 0.84). (2) Tree height captures the majority of the explanatory power associated with stand age. Once tree height was included as a predictor, the subsequent addition of stand age offered no significant improvement in model accuracy (R² = 0.85). (3) Given the challenges in obtaining precise tree height data and the robust correlation between stand age and tree height in fast-growing plantations, the integration of stand age substantially improved the accuracy of AGB estimations (from the spectral model of R² = 0.54 to R² = 0.74), with performance approaching that of tree height-based models (ΔR² = 0.10). Consequently, in fast-growing plantations, which are often characterized by high stand homogeneity, a hybrid model incorporating stand age can offer a reliable and cost-effective solution for AGB estimation. Full article

The estimation of rubber plantation aboveground biomass (AGB) is crucial for carbon sequestration assessment and management optimization. Unmanned Aerial Vehicles (UAVs) fitted with multispectral sensors present an economical approach for local-scale AGB monitoring. However, the prevailing studies primarily concentrate on spectral characteristics and algorithmic enhancements, failing to incorporate key ecological parameters such as stand age. Moreover, the current approaches remain constrained to local-scale assessments due to the absence of reliable upscaling methodologies from UAV to satellite platforms, limiting their applicability for regional monitoring. Thus, this study aims to establish an improved estimation model for rubber plantation AGB based on UAV multispectral imagery and stand age, develop an upscaling algorithm to bridge the gap between UAV and satellite scales, and ultimately achieve accurate regional-scale monitoring of rubber forest AGB. Combining optimized multispectral features, Landsat-derived stand age, and machine learning techniques yields the most accurate UAV-scale AGB estimates in this study, with performance metrics of R² = 0.90, an RMSE = 13.24 t/ha, and an MAE = 11.09 t/ha. Notably, the novel ‘UAV-satellite’ upscaling approach proposed in this study enables regional-scale AGB estimation using Sentinel-2 imagery, with remarkable consistency (correlation coefficient of 0.93). The developed framework synergistically combines Landsat-derived stand age data with spectral features, effectively improving rubber plantation AGB estimation accuracy through machine learning and enabling UAVs to replace manual measurements. This cross-scale upscaling framework demonstrates applicability beyond rubber plantation AGB monitoring, while providing novel insights for estimating critical parameters, including regional-scale stock volume and leaf area index, across diverse tree species. Full article

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China. Full article

Molecular research for genetic variants underlying body weight (BW) provides crucial information for this important selected trait when developing productive poultry breeds, lines and crosses. We searched for molecular markers—single nucleotide polymorphisms (SNPs)—and candidate genes associated with this trait in 240 F₂ resource population Japanese quails (Coturnix japonica). This population was produced by crossing two breeds with contrasting growth phenotypes, i.e., Japanese (with lower growth) and Texas White (with higher growth). The birds were genotyped using the genotyping-by-sequencing method followed by a genome-wide association study (GWAS). Using 74,387 SNPs, GWAS resulted in 142 significant SNPs and 42 candidate genes associated with BW at the age of 1, 14, 28, 35, 42, 49 and 56 days. Hereby, 25 SNPs simultaneously associated with BW at more than one age were established that colocalized with nine prioritized candidate genes (PCGs), including ITM2B, SLC35F3, ADAM33, UNC79, LEPR, RPP14, MVK, ASTN2, and ZBTB16. Twelve PCGs were identified in the regions of two or more significant SNPs, including MARCHF6, EGFR, ADGRL3, ADAM33, NPC2, LTBP2, ZC2HC1C, SATB2, ASTN2, ZBTB16, ADAR, and LGR6. These SNPs and PCGs can serve as molecular genetic markers for the genomic selection of quails with desirable BW phenotypes to enhance growth rates and meat productivity. Full article

This review considers fundamental issues related to the genomics of birds (Aves), including the special organization and evolution of their chromosomes. In particular, we address the capabilities of molecular genetic/genomic approaches to clarify aspects of their evolutionary history, including how they have adapted to multiple habitats. We contemplate general genomic organization, including the small size and typical number of micro/macrochromosomes. We discuss recent genome sequencing efforts and how this relates to cytogenomic studies. We consider the emergence of this unique organization ~245 million years ago, examining examples where the “norm” is not followed. We address the functional role of synteny disruptions, centromere repositioning, repetitive elements, evolutionary breakpoints, synteny blocks and the role of the unique ZW sex chromosome system. By analyzing the cytogenetic maps and chromosomal rearrangements of eight species, the possibility of successfully applying modern genomic methods/technologies to identify general and specific features of genomic organization and an in-depth understanding of the fundamental patterns of the evolution of avian genomes are demonstrated. An interpretation of the observed genomic “variadicity” and specific chromosomal rearrangements is subsequently proposed. We also present a mathematical assessment of cross-species bacterial artificial chromosome (BAC) hybridization during genomic mapping in the white-throated sparrow, a species considered a key model of avian behavior. Building on model species (e.g., chicken), avian cytogenomics now encompasses hundreds of genomes across nearly all families, revealing remarkable genomic conservation with many dynamic aspects. Combining classical cytogenetics, high-throughput sequencing and emerging technologies provides increasingly detailed insights into the structure, function and evolutionary organization of these remarkable genomes. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Forest aboveground biomass (AGB) products serve as essential references for research on carbon cycle and climate change. However, significant uncertainties exist regarding forest AGB products and their evaluation methods. This study aims to evaluate AGB products in the context of discrepancies in plot size and product scales, while also investigate the applicability of large-scale AGB products at a regional level. The National Aeronautics and Space Administration (NASA)’s Global Ecosystem Dynamics Investigation (GEDI) and the European Space Agency (ESA)’s Climate Change Initiative (CCI) biomass data were evaluated using sample plots from the National Forest Inventory (NFI). The study was conducted in Jilin Province, located in Northeast China, which is predominantly covered by natural forests. Spatial representativeness evaluation indicators for sample plots were established, followed by a comprehensive representativeness assessment and the selection of sample plots based on the criteria importance through the intercriteria correlation (CRITIC) method. Additionally, the study conducted an overall evaluation of the products, as well as evaluations across different biomass ranges and various forest types. The results indicate that the accuracy metrics demonstrated improved performance when using representative plots compared to all plots, with the R² increasing by 15.38%. Both products demonstrated optimal accuracy and stability in the 50–150 Mg/ha range. GEDI and CCI biomass data indicated an overall underestimation, with biases of −25.68 Mg/ha and −83.95 Mg/ha, respectively. Specifically, a slight overestimation occurred in the <50 Mg/ha range, while a gradually increasing underestimation was observed in the ≥50 Mg/ha range. This study highlights the advantages of spatial representativeness analysis in mitigating evaluation uncertainties arising from scale mismatches and enhancing the reliability of product evaluation. The accuracy trends of AGB products offer significant insights that could facilitate improvements and enhance their application. Full article

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively. Full article

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (${\mathrm{R}}^2$) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 × ${\mathrm{10}}^6$ Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts. Full article

Most crops accumulate above-ground biomass (AGB) through photosynthesis, inspiring the development of the Photosynthetic Accumulation Model (PAM) and Simplified PAM (SPAM). Both models estimate AGB based on time-series optical vegetation indices (VIs) and canopy height. To further enhance the model performance and evaluate its applicability across different crop types, an improved PAM model (IPAM) is proposed with three strategies. They are as follows: (i) using numerical integration to reduce reliance on dense observations, ($ii$) introduction of Fibonacci sequence-based structural correction to improve model accuracy, and ($iii$) non-photosynthetic area masking to reduce overestimation. Results from both soybean and cotton demonstrate the strong performance of the PAM-series models. Among them, the proposed IPAM model achieved higher accuracy, with mean $R^2$ and RMSE values of 0.89 and 207 g/m² for soybean and 0.84 and 251 g/m² for cotton, respectively. Among the vegetation indices tested, the recently proposed Near-Infrared Reflectance of vegetation (NIRv) and Kernel-based normalized difference vegetation index (Kndvi) yielded the most accurate results. Both Monte Carlo simulations and theoretical error propagation analyses indicate a maximum deviation percentage of approximately 20% for both crops, which is considered acceptable given the expected inter-annual variation in model transferability. In addition, this paper discusses alternatives to height measurements and evaluates the feasibility of incorporating synthetic aperture radar (SAR) VIs, providing practical insights into the model’s adaptability across diverse data conditions. Full article

Agroforestry systems (AFSs) enhance long-term carbon sequestration through tree biomass accumulation. As the European Union’s Carbon Farming Certification (CRCF) Regulation now recognizes AFSs in carbon farming (CF) schemes, accurate tree biomass estimation becomes essential for certification. This review examines field-destructive and remote sensing methods for estimating tree aboveground biomass (AGB) in AFSs, with a specific focus on their advantages, limitations, timing, and associated costs. Destructive methods, although accurate and necessary for developing and validating allometric equations, are time-consuming, costly, and labour-intensive. Conversely, satellite- and drone-based remote sensing offer scalable and non-invasive alternatives, increasingly supported by advances in machine learning and high-resolution imagery. Using data from the INNO4CFIs project, which conducted parallel destructive and remote measurements in an AFS in Tuscany (Italy), this study provides a novel quantitative comparison of the resources each method requires. The findings highlight that while destructive measurements remain indispensable for model calibration and new species assessment, their feasibility is limited by practical constraints. Meanwhile, remote sensing approaches, despite some accuracy challenges in heterogeneous AFSs, offer a promising path forward for cost-effective, repeatable biomass monitoring but in turn require reliable field data. The integration of both approaches might represent a valid strategy to optimize precision and resource efficiency in carbon farming applications. Full article