Accuracy Of Biomass Estimates Research Articles

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

Carbon Stock Estimation of Poplar Plantations Based on Additive Biomass Models

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared (Ra2), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha−1. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

High-resolution temporal NDVI data reveal contrasting intratidal, spring-neap and seasonal biomass dynamics in euglenoid- and diatom-dominated biofilms

Intertidal microphytobenthos (MPB) are a major contributor to primary production in estuarine ecosystems. While their biomass is highly variable at multiple spatial and temporal scales, the underlying drivers are as yet little understood. Both in situ sampling and remote-sensing techniques often lack the temporal resolution or coverage to simultaneously capture short-term (intratidal to daily) and longer-term (weekly to annual) biomass changes. Our field setup with in-situ NDVI sensors allowed us to study MPB surface biomass variability at high temporal resolution (10 mins) for up to two years in a freshwater euglenoid dominated mudflat, and a brackish and a marine diatom dominated mudflat. MPB biomass showed marked periodicities at multiple temporal scales: seasonal, spring-neap and intratidal. The diatom-dominated MPB community showed a seasonal biomass peak in winter, while the euglenoid-dominated community showed biomass peaks during spring and summer, probably caused by underlying divergent responses to mainly irradiance, temperature and wind-induced resuspension, and macrobenthos grazing. Spring-neap periodicity likely resulted from differential migratory responses of the MPB communities to variation in timing and duration of daylight exposure. In the freshwater community, upward migration only occurred when exposure duration was sufficiently long (≥4 h). In the diatom-dominated community, morning daylight exposure resulted in highest NDVI values. This study highlights the differences in MPB biomass dynamics between MPB communities within estuarine ecosystems, and underscores the great potential of high-resolution temporal NDVI monitoring for more accurate estimates of MPB biomass and primary production.

Cryptogam biomass estimation using taxonomic and life form models for accurate assessment

Accurate estimation of cryptogam biomass, encompassing bryophytes and lichens, is crucial for understanding their ecological significance. This estimation is conducted based on the strong correlations between mass and volume of cryptogams. However, mass–volume correlations vary among cryptogams because of their morphological differences. This problem can be solved using models that consider life forms that classify cryptogams based on morphological similarities. In this study, we investigated whether life form models improve cryptogam biomass estimation accuracy. The cryptogam mass–volume correlation of each life form was estimated using Bayesian linear models. The coefficients and intercepts of linear models differed between life forms, which was attributed to the morphological characteristics of each life form. Therefore, life form models can improve the accuracy of estimation models by incorporating morphological differences. However, taxonomic models that consider only the taxonomic difference (bryophytes vs lichens) demonstrated better overall estimation than the life form models, probably because of the ability of taxonomic models to capture systematic differences between bryophytes and lichens. Furthermore, these models may mitigate estimation errors related to morphological variations that cannot be adequately represented by life form types. Based on these results, we propose the appropriate use of estimation models.

Biomass equations and annual growth of various Eucalyptus clones in commercial plantations across Thailand

Plantation forestry is pivotal in meeting global timber and fiber demands while promoting sustainable practices. Eucalyptus plantations, renowned for their rapid growth and adaptability, have significantly expanded in tropical regions such as Thailand. Improved Eucalyptus clones have enhanced productivity, emphasizing the role of genetic improvement programs. Accurate biomass estimation in these plantations is crucial for sustainable management and bioenergy production. This study employs a nonlinear mixed model approach to evaluate tree variables combined with rotation, clones, and region on aboveground biomass (AGB) estimation. The results showed that the "rotation, clones, region" model emerged as the most precise, achieving the highest R² and the lowest SEE, ASE, and MPSE values. However, over-parameterization is a concern. The more straightforward "clone" model performed well, achieving a high R² and relatively low prediction error, with no systematic bias and comparable ASE, MPE, and MPSE values, making it a strong choice when fewer predictor variables are preferred. Our results revealed that clones H4 and K7 for the northeastern region and K58 for the eastern region show the highest annual productivity, with growth rates up to 20 t ha−1 year−1. The rapid AGB increment in clones K58, K62, and K7 during the first rotation suggests improved performance in subsequent rotations. As in eastern Thailand, selecting less arid sites can enhance these clones' AGB productivity. Additionally, intensive silvicultural practices could further boost their productive efficiency.

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning.

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass.

Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.

Burrow Opening Measurements of Intertidal Macroinvertebrates from Optical Drone Images

Intertidal macroinvertebrates, such as crabs and mud shrimps, are invertebrates inhabiting the intertidal zone that are sufficiently large to be identified with the naked eye. Most intertidal macroinvertebrates typically construct burrows of various shapes in sediment to protect themselves from environmental extremes, with surface openings that reflect features of their species and body size. Especially circular burrow openings correlate with an organism’s body size; thus, measuring these openings can provide estimates of the organism’s size, weight, growth rate, and biomass. Traditional studies of these organisms have relied on field surveys, which are constrained by time, cost, and logistical limitations. This study introduces an innovative method for measuring the burrow opening diameters of intertidal macroinvertebrates using high-resolution optical images from a portable drone system. By leveraging the reflectance disparity between the sediment and burrow openings, this method facilitates the extraction and sizing of burrow openings. Our methodology was applied to three crab species known for their circular burrow opening: the red-clawed fiddler, the milky fiddler, and the ghost crab. Validation was confirmed through field data from the Mageumri and Sinduri tidal flats, South Korea. The method achieved a correlation coefficient (R2) of 0.94 and a root mean squared error (RMSE) of 1.68 mm across a diameter range of 6.21–33.59 mm. These findings suggest the potential of drone remote sensing systems as a non-invasive and efficacious approach for quantifying burrow sizes over extensive intertidal areas, thereby facilitating more accurate biomass estimations and surmounting the limitations of conventional field surveys. Future research could extend this method to additional species and further refine its precision.

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of multi-parameter models, which makes its practical application and the potential impact on predictive precision challenging due to the inclusion of non-essential or redundant variables. Therefore, this study systematically evaluated the performance of different parameter combinations derived from Sentinel-2 imagery, using variable optimization approaches with four machine learning algorithms (Random Forest Regression, RF; XGBoost Regression, XGBR; K Nearest Neighbor Regression, KNNR; and Support Vector Regression, SVR) for the estimation of the AGB of rubber plantations. The results indicate that RF achieved the best estimation accuracy (R2 = 0.86, RMSE = 15.77 Mg/ha) for predicting rubber plantation AGB when combined with Boruta-selected variables, outperforming other combinations (variable combinations obtained based on importance ranking, univariate combinations, and multivariate combinations). Our research findings suggest that the consideration of parameter-optimized remote sensing variables is advantageous for improving the estimation accuracy of forest biophysical parameters, when utilizing a large number of parameters for estimation.

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

Estimation of water consumption volume and water efficiency in irrigated and rainfed agriculture based on the WaPOR database in Iran

ABSTRACT To evaluate the WaPOR model across the entire country of Iran, initially, 16 provinces located in four different climatic regions were selected for calculating and comparing the evapotranspiration using both the FAO-56 method and the WaPOR approach. The comparison of 10-day evapotranspiration values obtained from the FAO56 method and WaPOR demonstrates that the WaPOR model exhibits the highest correlation with FAO-56 values in semi-arid regions, with an R2 = 0.95 and an RMSE = 0.43. The analysis of evapotranspiration variations indicates that the evapotranspiration in the Caspian Sea and Zagros foothill regions has experienced more significant changes from 2015 to 2022. The annual analysis of net blue water productivity demonstrates that the net productivity in rainfed lands strongly depends on the precipitation received. Also, considering the importance of investigating the accuracy of biomass estimation, the correlation between the accuracy of biomass estimation and actual evapotranspiration was examined in four Iran climatic regions. Using the WaPOR model provides acceptable results for water consumption management and assessment in different regions and climates of Iran, particularly in agricultural lands. The WaPOR model can serve as a guide for determining reliable values of evapotranspiration and planning related to water resources in the agricultural sector in Iran.

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.

Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R2 = 0.80, root mean squared error = 15.8 MgC ha−1). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha−1, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year−1 during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget.

Decadal changes in biomass and distribution of key fisheries species on Newfoundland's Grand Banks.

Canadian fisheries management has embraced the precautionary approach and the incorporation of ecosystem information into decision-making processes. Accurate estimation of fish stock biomass is crucial for ensuring sustainable exploitation of marine resources. Spatio-temporal models can provide improved indices of biomass as they capture spatial and temporal correlations in data and can account for environmental factors influencing biomass distributions. In this study, we developed a spatio-temporal generalized additive model (st-GAM) to investigate the relationships between bottom temperature, depth, and the biomass of three key fished species on The Grand Banks: snow crab (Chionoecetes opilio), yellowtail flounder (Limanda ferruginea), and Atlantic cod (Gadus morhua). Our findings revealed changes in the centre of gravity of Atlantic cod that could be related to a northern shift of the species within the Grand Banks or to a faster recovery of the 2J3KL stock. Atlantic cod also displayed hyperaggregation behaviour with the species showing a continuous distribution over the Grand Banks when biomass is high. These findings suggest a joint stock assessment between the 2J3KL and 3NO stocks would be advisable. However, barriers may need to be addressed to achieve collaboration between the two distinct regulatory bodies (i.e., DFO and NAFO) in charge of managing the stocks. Snow crab and yellowtail flounder centres of gravity have remained relatively constant over time. We also estimated novel indices of biomass, informed by environmental factors. Our study represents a step towards ecosystem-based fisheries management for the highly dynamic Grand Banks.

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.

Site-Species Allometry Equation for Theobroma cacao L. Biomass Estimation in Agroforestry Systems of Cameroon

The accuracy of biomass estimates through mathematical expressions remains essential for the sustainability of the REDD+ process. The objective of this research was to develop allometric models by site species to evaluate the biomass of Theobroma cacao in agroforestry systems in the Central Region of Cameroon. Biomass data were obtained by the destructive method on a sample of 50 trees (5 cm ≤ D ≤ 27 cm). Allometric models were developed using aboveground (AGB), belowground (BGB), and total biomass (TB) as dependent variables and tree dendrometric parameters as independent variables. Nine linear models were adjusted based on the Akaike information criterion (AIC), residual standard error (RSE), coefficient of determination (R2), and various statistical tests including the normality test, heterogeneity, and autocorrelation for the analysis of residuals. The different results show that only the diameter appears to be a good predictor of biomass with an R2 greater than 0.94, 0.85, and 0.95, respectively, for aboveground biomass (M1: ln B = −1.613 + 1.83 × ln (D)), belowground biomass (M1: ln B = −2.611 + 1.65 × ln (D)), and total biomass (M1: ln B = −1.297 + 1.79 × ln (D)). Incorporating crown diameter and height into the models slightly improved the quality of adjusted. Comparison of the models in this study with pantropical equations previously used to estimate Theobroma cacao biomass shows that the models in this study provide a better estimate. The allometric equations developed in this work to estimate the AGB, BGB, and TB of Theobroma cacao can be used under the same environmental conditions to accurately predict the biomass accumulated in agroforestry systems by this species and thus allow the implementation of activities aimed at reducing emissions from deforestation and degradation (REDD+) for the benefit of local communities through the carbon market.