Biomass Estimation Research Articles

Quantifying uncertainty in the contribution of mesopelagic fishes to the biological carbon pump in the Northeast Atlantic Ocean

Abstract Mesopelagic fishes may contribute substantially to marine carbon export and sequestration. However, uncertainty in this contribution due to limited precision of mesopelagic biomass and bioenergetic rate estimates has not been thoroughly quantified for any study site. Datasets that can confront these challenges are rare, particularly for comparing fish-mediated carbon flux to other biological carbon pump pathways. Using data from a unique three-ship expedition in spring 2021 in the subarctic Northeast Atlantic Ocean, we compare carbon transported by adult fish, zooplankton, and sinking particles, and calculate uncertainty in the relative contribution of fishes. Results indicate biomass- and bioenergetic-based uncertainty contributed roughly equally to variance in estimated carbon transport. The plausible range of mesopelagic fish carbon flux spans an order of magnitude: 1.6–21 mg C m−2 d−1 to 200 m depth and 0.52–9.6 mg C m−2 d−1 to 500 m. Fishes contributed ∼0.52%–18% at 200 m to the total biological carbon pump, and ∼0.43%–13% at 500 m. Of the fish-mediated carbon transport to 200 m, ∼8%–30% is sequestered on climate-relevant time scales (>100 years). This reinforces that carbon transport should not be conflated with carbon sequestration. These findings have implications for prioritizing future empirical measurements, evaluating trade-offs in fisheries management, and understanding the role of fishes in the biological carbon pump.

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.

Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models

Abstract. This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.

Estimating the Greenhouse Gas Emission Potential Due to Construction Work on the 225 kV Segou - Bamako High Voltage BI-Ternal Electric Line in Mali in The Context of Climate Change

The aim of this study is to assess the carbon emissions resulting from the construction of the power line linking Bamako to Ségou. It focuses on direct emissions, more specifically those linked to the degradation and desctruction of the vegetation cover in the project right-of-way. To collect the data, surveys were set up on the project right-of-way, through which 3302 trees were inventoried in situ at the three sites. Circumference was measured at 1.30 m from the ground surface. Allometric equations for direct biomass estimation were used as a predictor o f tree diameter. Emission factors were then used to estimate the potential greenhouse gas emissions resulting from tree felling during power line construction. The standard error associated with the carbon fraction in dry biomass was calculated. In the Ségou, Koulikoro and Dioïla project areas, there are a number of plant species that provide local populations with various ecosystem services, including micro-climate regulation through carbon sequestration or absorption of atmospheric CO2. The implementation of the project will undoubtedly result in the felling of these trees. This felling will generate emissions of 22.11 t.eqCO2/ha in Ségou, 16.56 t.eqCO2/ha in Koulikoro and 15.43 t.eqCO2/ha in Dioïla. Across Ségou, Koulikoro and Dioïla, the trees in the project right-of-way represent a reservoir of 34.04 t/ha of above-ground woody biomass. This biomass represents a carbon stock of around 16.58 t.C/ha. This carbon stock will be transformed into a carbon source with emissions of 60.78 t.eqCO2/ha if the trees in the power line right-of-way are systematically felled. Mitigation measures are therefore urgently needed, with compensatory reforestation areas planted with species with high carbon sequestration potential.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model’s R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

STUDI POTENSI BIOMASSA HUTAN SAGU (Metroxylon Sp) DI DESA TULEHU KECAMATAN SALAHUTU KABUPATEN MALUKU TENGAH

Sago forests (Metroxylon sp) in Tulehu Village, Salahutu District, Central Maluku Regency are one of the staple food sources of the Maluku community that have been consumed for generations. Various parts of sago from leaf stalks to trunks have been widely studied. However, research related to sago biomass has not been done much. In this regard, this paper aims to determine the potential biomass and carbon reserves stored in sago forests in Tulehu Village for the purposes of handling climate change. The biomass and carbon content reserves to be determined are sago at the pole and tree levels. The method used was 3 survey routes with 30 measurement plots using purposive sampling, observation, then analysis and estimation of biomass and stored carbon reserves. The calculation results at the pole level of sago obtained a biomass of 27 tons/ha and stored carbon reserves of 14 tons/ha. At the tree level of sago, a biomass of 42 tons/ha and stored carbon reserves of 20 tons/ha were obtained.

Photoautotrophic picoplankton of the Kara Sea in the middle of summer: Effect of first-year ice retreat on carbon and chlorophyll biomass and primary production

The Arctic warming leads to a decline in sea-ice extent and thickness, rapid warming and freshening of the sea surface which impact the distribution of phytoplankton size composition. Picophytoplankton is an ecologically important component of Arctic pelagic marine ecosystems, and its role may be altered by global warming. In this study, the abundance and biomass, the chlorophyll a (Chl-a) and primary production (PP) of picophytoplankton, and its spatial and temporal distribution were investigated in the Kara Sea during the ice-melt season in July 2019. Picophytoplankton played a major role in the surface PP in the southern and western areas of the Kara Sea. In the surface layer, the contribution of picophytoplankton to total Chl-a increased insignificantly, and the contribution of picophytoplankton to total PP decreased significantly with the time of sea ice retreat. In the euphotic zone, the Chl-a concentration of picophytoplankton and its contribution to total Chl-a decreased with the time of sea ice retreat. The average picophytoplankton biomass determined in the present study (2.72 ± 5.10 mg C m−3) corresponded to the biomass estimates in the Arctic. The picophytoplankton community was strongly dominated by eukaryotes, cyanobacteria were only detected at 3 out of 11 stations, with maximum abundances (0.07 × 109 cells m−3) observed at depths below 15 m. The obtained results contribute significantly to the study of the picophytoplankton dynamics during the ice-melting season in the hard-to-reach Kara Sea.

Mapping predicted biomass in cereal rye using 3D imaging and geostatistics

Abstract Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

Improving AGB estimations by integrating tree height and crown radius from multisource remote sensing.

Precise estimation of forest above ground biomass (AGB) is essential for assessing its ecological functions and determining forest carbon stocks. It is difficult to directly obtain diameter at breast height (DBH) based on remote sensing imagery. Therefore, it is crucial to accurately estimate the AGB with features extracted directly from RS. This paper demonstrates the feasibility of estimating AGB from crown radius (R) and tree height (H) features extracted from multi-source RS data. Accurate information on tree height (H), crown radius (R), and diameter at breast height (DBH) can be obtained through point clouds generated by airborne laser scanning (ALS) and terrestrial laser scanning (TLS), respectively. Nine allometric growth equations were used to fit coniferous forests (Larix principis-rupprechtii) and broadleaf forests (Fraxinus chinensis and Sophora japonica). The fitting performance of models constructed using only "H" or "R" was compared with that of models constructed using both combined. The results showed that the quadratic polynomial model constructed with "H+R" fitted the AGB estimation better in each vegetation type, especially in the scenario of mixed tall and short coniferous forests, in which the R2 and RMSE were 0.9282 and 25.30 kg (rRMSE 17.31%), respectively. Therefore, using high-resolution data to extract crown radius and tree height can achieve high-precision, global-scale estimation of forest above ground biomass.

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.

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of <0.4. Three different combinations of input datasets were used to predict the biomass, including spectral bands (SBs) only, vegetation indexes (VIs) only, and a combination of both spectral bands and vegetation indexes (SBVIs). Utilizing SBs, the biomass ranged between 150 and 286 mg/ha in site A and 99 and 376 mg/ha in site B. Meanwhile, using VIs indicated a biomass range of 163 Mg/ha–337 Mg/ha and 131–392 Mg/ha for sites A and B, respectively. The combination of spectral bands and vegetation indexes yielded AGB values of 145–290 Mg/ha in site A and 116–389 Mg/ha in site B. The northern and western regions of site A, characterized by higher altitudes and lower forest density, notably showed lower biomass values than other regions. Conversely, similar regions in site B, situated at lower latitudes, demonstrated different biomass ranges. The RF model exhibited robust accuracy, with R2 values of 0.74 and 0.83 for spectral bands and vegetation indexes, respectively. However, with a combination of both, an R2 of 0.79 was achieved. Furthermore, altitudinal gradients significantly influence the biomass distribution across both sites, with specific elevation ranges yielding optimal results. The AGB variation along the slope further corroborated these findings. In both sites, the western aspects showed the highest biomass across all combinations of input datasets. The variable importance analysis highlighted that ARVI8a, NDI45, Band12, Band11, TSAVI8, and ARVI8a are significant predictors in sites A and B. This comprehensive analysis enhances our understanding of AGB distribution in the mountainous forests of Pakistan, offering valuable insights for forest management and ecological studies.

Aboveground biomass estimation and mapping using Sentinel-2 data in a dry afromontane forest

ABSTRACT Forest biomass estimates are required for many applications, including accounting for the role of forests in the global carbon cycle, supporting sustainable forest management and making informed decisions. For all these applications and others, accurate and reliable forest biomass estimates are required. This study evaluated the potential of Sentinel-2 data for predicting and mapping aboveground biomass (AGB) of a dry Afromontane Forest in Tigray, Northern Ethiopia. Multiple linear regression was employed to model the relationship between AGB and Sentinel-2-derived spectral variables. The evaluation criteria for best-fit model selection were based on the coefficient of determination (R2), root mean square error (RMSE, %), and bias (Bias, %). All the spectral variables evaluated here were significantly correlated with AGB (p < 0.01). The model that includes a fraction of photosynthetically active radiation (FAPAR), leaf area index (LAI), Band 2 and Band 3 as predictor variables provided the best predictive performance for AGB in the study area (R 2 = 0.38, RMSE = 13.62 Mg ha−1 and Bias = -3.10 Mg ha−1). The predicted AGB of the study area ranges from 0.1 to 141.8 Mg ha−1, with a mean value of 12.4 Mg ha−1. The results from this study suggested that Sentinel-2 data can be potentially applied for estimating and mapping AGB in dry Afromontane forests.

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.

Lidar-derived estimates of boreal shrub biomass in Southcentral Alaska

Abstract Despite widespread observations of shrub proliferation and expansion (shrubification), few studies quantify shrub biomass at the regional scale. Here we describe and implement a two-part modeling approach to estimate and map tall shrub (diameter at root collar &gt; 2.5 cm) expected aboveground biomass, or E[SHB], across a 16.6 million ha boreal region where shrubification occurs in wetlands and subalpine ecosystems. Using n = 384 field plots nested within m = 11 study sites across southcentral Alaska, we constructed random forest models of the probability (pSH) that shrub wood volume surpasses tree wood volume, and generalized additive models (GAMs) of aboveground biomass of tall shrubs (SHB) using rasterized aerial lidar variables collected by NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, together with gridded climate data from a Parameter-elevation Regressions on Independent Slopes Model 30-year normal climatology (PRISM). Applying those models to G-LiHT tiles, then averaging across tiles within n = 843 watersheds covering 9.2 million ha, we estimated that below 1,000 m asl, the area-weighted mean value of E[SHB] = pSH x SHB = 6.6 Mg ha-1 with sd = 4.5 Mg ha-1. This is the first study to estimate current shrub biomass density at the regional scale in southcentral Alaska and serves as a biomass baseline for measuring and modeling aboveground carbon fluxes where plant communities are undergoing climate-driven change.

People of the Sea, or of the Soil? How the Balance of Marine and Terrestrial Resource Availability Informs Maximum Population on Four Polynesian Islands

AbstractMost studies of the food resource potential of early Polynesian populations focus exclusively on agricultural potential, and specifically starchy staples, despite the importance of marine resources to the Polynesians. To more accurately estimate total precontact food resource availability, we characterized the terrestrial and near-shore marine environments of four Polynesian islands: Moʻorea, Maupiti, Mangareva and Taravai. We estimate the agricultural potential of each island after a consideration of the ecological factors related to productivity. We also estimate the productivity of the near-shore marine environment as a function of surface area. Using a range of fish yield scenarios from Pacific subsistence systems we scale relative measures of faunal food biomass derived from local archaeological excavations to generate absolute biomass estimates. We convert our estimates of agricultural and fished/foraged food potential into a maximum population size based on calorie availability. Moʻorea’s fertile valleys and wetlands would potentially generate sufficient food energy, mostly from starchy staples, to support a considerable population, many times larger than the other islands. In the most likely fish yield scenario Moʻorea gets only 0.4% of its calories from marine sources, while the others range from 7–18%. These relative inputs reflect the vast superiority in productivity (in terms of calories per km2) of agricultural vs near-shore marine zones. We also find that population size on islands with smaller fringing reefs, such as Moʻorea, may have been limited by a lack of fish protein. Moʻorea’s maximum population is approximately halved when diet breadth is considered.

Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration’s (NASA’s) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency’s (ESA’s) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture

Real-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced with the Swin transformer to extract image features more efficiently. Additionally, a squeeze-and-excitation (SE) module is introduced to enhance feature representation by dynamically adjusting the importance of each channel through “squeeze” and “excitation”, making the extracted features more focused and effective. Finally, a multi-scale fusion (MSF) module is added after the back-end to fully utilize the multi-scale feature information, enhancing the model’s ability to capture multi-scale details. The experiment demonstrates that Swin-CSRNet achieved excellent results with MAE, RMSE, and MAPE and a correlation coefficient R2 of 11.22, 15.32, 5.18%, and 0.954, respectively. Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. Therefore, the proposed method not only counts the number of fish with higher speed and accuracy but also contributes to advancing the automation of aquaculture.

Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.

Enhancing highland ecosystems in East Hararge, Ethiopia: evaluating intervention strategies for species diversity, soil organic carbon stock, and carbon sequestration

Forest loss and soil degradation pose significant challenges globally, particularly in developing nations like Ethiopia. Various intervention strategies have been implemented to rehabilitate degraded ecosystems. However, research gaps exist in understanding the most effective approaches, particularly in highland ecosystems. This study aimed to evaluate the effectiveness of different intervention strategies on species diversity and carbon stock in the West Hararghe zone, specifically at the Doba district Hades site. The data was collected from 5 February 2022 to 10 December 2022. The study area was characterized by systematic plots and transects to assess vegetation diversity and soil properties across four intervention categories: (physical; only soil and water conservation structures were used, enclosure; only animal and human interaction was restricted, biological; only different tree species were planted, biophysical; both soil water conservation and tree planting were used). Soil samples were collected at different depths, and biomass estimation was conducted using established formulas. Statistical analyses, including one-way ANOVA and post-hoc tests, were performed to test the level of significance. Biophysical interventions exhibited the highest species diversity index (SDI) (4.65 ± 1.21), followed by enclosure interventions (1.68 ± 0.58), while physical interventions recorded the lowest SDI (0.63 ± 0.18). Enclosure interventions led to the highest soil organic carbon (SOC) stocks at 40.13 t C ha−1, followed by biophysical interventions at 39.26 t C ha−1. Physical interventions resulted in the lowest SOC stocks at 23.9 t C ha−1, underscoring their potential for carbon sequestration. Biophysical interventions, which include both above-ground and below-ground biomass, showed significant carbon stock accumulation, totaling 173.8 t C ha−1 (39.26 t C ha−1 from SOC and 114.6 t C ha−1 from biomass), highlighting their effectiveness in enhancing ecosystem resilience. Other studies support these findings, underscoring the importance of context-specific approaches and long-term monitoring for sustainable land management. Enclosure and biophysical interventions emerge as promising strategies for enhancing species diversity and soil health, as well as promoting carbon sequestration in highland ecosystems. These findings emphasize the need for informed decision-making and community involvement in ecosystem restoration efforts to achieve meaningful and lasting impacts.