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Despite the fact that scientists are aware that the streambed consists of various substrata in size, the estimation of benthic algal biomass has been conducted based almost exclusively on cobble sampling. This disparity in samples selected for the biomass estimation occurs because frame sampling collects all substrata, encompassed by the frame, including sand and stones, and is a time‐consuming method compared to single‐stone sampling. We conducted frame versus cobble sampling to test for sample selection disparity (SSD) in the estimation of benthic algal biomass. Estimates of algal biomass based on the frame sampling (area: 0.25 m2) were compared with those based on the cobble sampling taken at the same sampling points in a diatom‐dominated stream. Benthic algal biomass estimated based on cobble sampling was larger than the biomass estimated with frame sampling. The contribution of cobbles to the algal biomass encompassed by the frame was considerably higher than smaller substrata. These results suggest that cobble sampling tends to result in an overestimate of the benthic algal biomass in natural streams. Because the frame sampling requires intensive labor and time, we here proposed a general model based on quick visual assessments for percentage cover of cobbles on the streambed to calibrate estimates obtained by cobble sampling. [ABSTRACT FROM AUTHOR]

Crop height and biomass are the two important phenotyping traits to screen forage population types at local and regional scales. This study aims to compare the performances of multispectral and RGB sensors onboard drones for quantitative retrievals of forage crop height and biomass at very high resolution. We acquired the unmanned aerial vehicle (UAV) multispectral images (MSIs) at 1.67 cm spatial resolution and visible data (RGB) at 0.31 cm resolution and measured the forage height and above-ground biomass over the alfalfa (Medicago sativa L.) breeding trials in the Canadian Prairies. (1) For height estimation, the digital surface model (DSM) and digital terrain model (DTM) were extracted from MSI and RGB data, respectively. As the resolution of the DTM is five times less than that of the DSM, we applied an aggregation algorithm to the DSM to constrain the same spatial resolution between DSM and DTM. The difference between DSM and DTM was computed as the canopy height model (CHM), which was at 8.35 cm and 1.55 cm for MSI and RGB data, respectively. (2) For biomass estimation, the normalized difference vegetation index (NDVI) from MSI data and excess green (ExG) index from RGB data were analyzed and regressed in terms of ground measurements, leading to empirical models. The results indicate better performance of MSI for above-ground biomass (AGB) retrievals at 1.67 cm resolution and better performance of RGB data for canopy height retrievals at 1.55 cm. Although the retrieved height was well correlated with the ground measurements, a significant underestimation was observed. Thus, we developed a bias correction function to match the retrieval with the ground measurements. This study provides insight into the optimal selection of sensor for specific targeted vegetation growth traits in a forage crop. [ABSTRACT FROM AUTHOR]

Rice is one of the major food crops in China. It is important to timely, accurately, efficiently, and rapidly monitor the rice biomass. In this study, ten multispectral vegetation indices were extracted from Unmanned Aerial Vehicles (UAV) multispectral images. The data source was obtained for the correlation analysis with rice biomass. The results showed that the correlation between biomass and vegetation index was higher for the full reproductive period than for a single period. The correlation between biomass and vegetation index was also improved to divide the data for the full reproductive period into a nutritive and a reproductive period, according to the vegetation cover up to 100%. All ten vegetation indices were significantly correlated throughout the reproductive and nutrient growth period, with the highest DVI and RE-RVI correlation of 0.689 and 0.894, respectively; Except the DVI, the vegetation indices were significantly correlated in the productive growth period, with the highest RE-NDVI correlation of -0.794 for the rice spike portion and 0.629 for the stem and leaf portion. The biomass estimation models were then constructed using vegetation indices for the nutritive and reproductive growth periods. Quadratic regression and exponential models were also obtained in the nutritive growth period. The optimal multispectral index was achieved in the RERVI, with the validation accuracies of R2 =0.90, and RMSE=119.36 g/m2 ; In the reproductive growth period, the rice spike was a quadratic regression model with an optimal multispectral index of RVI and validation accuracies of R2 = 0.78 and RMSE = 124.98 g/m2, respectively. Overall, the accuracy of the model was improved in the biomass estimation with the full-birth period data using the vegetation cover close to 100%. The accuracy of biomass estimation was also improved to separate the rice spike from the stalk and leaf during the reproductive growth period. Relatively less saturation was suffered in the early growth period of rice, whereas, the accuracy was slightly higher than that in the late growth period; Moreover, the experimental data significantly improved the estimation precision and accuracy of the model. The rice biomass was segmented for modeling and accuracy validation during the whole life cycle, in terms of plant nutritional and reproductive growth. The finding can also provide effective information and references for field crop growth monitoring and decision-making on farmland production. [ABSTRACT FROM AUTHOR]

Background and Aims Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate above-ground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (e.g. cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from, for example, beam footprint compared to the structure diameter. Method We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the above-ground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory. Key Results The model accurately predicted the biomass with 1.6 % normalized root mean square error (nRMSE) at the segment scale from a k -fold cross-validation. It also gave satisfactory results when scaled up to the branch level with a significantly lower error (13 % nRMSE) and bias (−5 %) compared to conventional cylinder fitting to the point cloud (nRMSE: 92 %, bias: 82 %), or using the pipe model theory (nRMSE: 31 %, bias: −27 %). The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7 km of structures on average and that 96 % of that length was coming from the twigs (i.e. <5 cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree above-ground woody biomass (−21 %). Conclusions The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Summary Free‐air CO2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments. Long‐term field experiments to elucidate forest responses to rising atmospheric CO2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO2. At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation. [ABSTRACT FROM AUTHOR]

Background: This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols in the context of late-stage variety selection programs. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing programs. Results: The research uncovered that integrating geometric and spectral traits led to an increase in prediction accuracy, whilst a recursive feature elimination (RFE) based variable selection workflowled to slight reductions in accuracy with the benefit of increased interpretability. Models, tailored to specific experiments were more accurate than those modelling all experiments together, while models trained for broad-growth stages did not significantly increase accuracy. The comparison between a permanent and a precise region of interest (ROI) within the plot showed negligible differences in biomass prediction accuracy, indicating the robustness of the approach across different sampling locations within the plot. Significant differences in the within-season repeatability (w2) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction. Conclusions: The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes. [ABSTRACT FROM AUTHOR]

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R2 = 0.62), outperformed models driven solely by spectral bands (R2 = 0.33) or vegetation indices (R2 = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m2). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. [ABSTRACT FROM AUTHOR]

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model's estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables. [ABSTRACT FROM AUTHOR]

Fry counting can play a pivotal role in the aquaculture industry. However, manual counting cannot fully meet the large-scale production in recent years, due to the inefficient, low precision, and even damage to the fry. In this study, a new fry counting was proposed using improved YOLOv8 and multi-target tracking. The objects were taken as the grass carp fry with the body lengths ranging from 20 to 50 mm. Firstly, the test platform was designed, according to the test requirements. Eight fry videos were then captured to extract and screen 961 valid images as the initial dataset. Then augmentation operations were performed on the data, including motion blur, brightness adjustment, noise increase, and image flipping. 2285 images dataset was divided into the training set and testing set in the ratio of 9:1. P2 small layer of target detection was then added into the YOLOv8, due to the small fry targets and high detection speed. While the GAM (Global Attention Mechanism) was introduced in front of the detection head, and the Inner-SIoU (Inner-SCYLLA- Intersection over Union) loss function was used to accelerate the convergence speed of the model for the better recognition accuracy of small and overlapping targets. The YOLOv8 detector was trained with the following settings: initial learning rate of 0.01, moments of 0.9, weight decay of 0.0005, batch size of 16, and 300 training rounds. The image processing computer was a 12th Gen Intel(R) Core (TM) i7-12700H 2.70 GHz, with an operating system of 64-bit Windows 11 system with 24 G of system memory. After that, a tracking and counting approach was realized for fry counting using BoT-SORT multi-target tracking with motion feature matching, eight-element Kalman filter, and camera motion compensation and discarding the appearance feature matching module. The tracking of fry targets was realized for the fry targets. Finally, the performance of the improved model was also evaluated, in terms of merits and performance. The ablation tests showed that the improved model was significantly promoted the counting performance, compared with the pre-improved model; Comparative test results showed that the average counting accuracy (ACP), average absolute error (MAE), and root mean square error (RMSE) were 97.16%, 3.27, and 5.16, respectively. The indexes of improved model were better than those of YOLOv5+DeepSORT, YOLOv8+DeepSORT, YOLOv8+StrongSORT, YOLOv8+ByteTrack, and YOLOv8+BoT-SORT; The comparative tests showed that the counting performance was negatively correlated with the speed of the fry through the shooting area. Once the speed was too high, the tracker was caused the insufficient inference speed, leading to a decline in the counting accuracy. The number of small fries were found in the video with the higher speed and accuracy than before. The finding can lay the strong foundation for the rapid and accurate counting of fry on the biomass estimation in factory aquaculture. [ABSTRACT FROM AUTHOR]

The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB. [ABSTRACT FROM AUTHOR]

Carbon sequestration through tree-based systems has been well recognized due to its potential to mitigate climate change. The lack of site-specific and species-specific biomass estimation models is a challenge to accurately estimating forest biomass at local and regional scales. Agarwood (Aquilaria malaccensis) trees form an essential component of agroforestry systems in Assam and Tripura state of India. In this study, trees with stem girth range of 10–80 cm and stand age of < 10 to > 20 years old in smallholder agarwood stands classes were destructively harvested to develop biomass estimation models. Different allometric relationships were compared using either diameter alone or a compound variable, including diameter and height, to predict above ground biomass (AGB), below ground biomass (BGB) and total biomass (TB). Based on the model fit criteria (R2, RMSE, AIC and BIC), the best models for estimating above ground, below ground and total biomass of the agarwood trees were lnAGB = − 3.13 + (0.89 × lnD2H), lnBGB = − 2.40 + (1.49 × lnD) and lnTB = 0.29 + (0.96 × lnAGB), respectively. Stand biomass in the study area showed a significant increase from 6.92 Mg ha−1 in < 10 years old stands to 65.90 Mg ha−1in > 20-year-old stands, and is proportionally related with the increase in tree density of the stands ranging from 930–4470 stems ha−1. The continuous harvesting of various-sized trees and the re-plantation and regeneration of agarwood trees in the study area affected the increase in stand-level biomass and the distribution of biomass carbon across the size classes. The proper implementation of the Assam Agarwood Promotion Policy 2020 and similar policies of Tripura need to be supported to promote the cultivation and conservation of the species across the region. [ABSTRACT FROM AUTHOR]

Multispectral data were used to estimate the above-ground forest biomass in the newly established carbon polygon in Bashkortostan. Field measurements were compared with Normalised Difference Vegetation Index (NDVI) and Normalised Green-Red Difference Index (NGRDI) obtained from the Global Inventory Monitoring and Modelling System (GIMMS) dataset. The relationship between measured biomass values and vegetation indicators was linear; NDVI has the highest correlation coefficient with above-ground biomass and greatest significance at all sites, so regression relationships with NDVI were used to map the above-ground biomass. [ABSTRACT FROM AUTHOR]

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models' performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. [ABSTRACT FROM AUTHOR]

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha−1) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha−1) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gases in the atmosphere is due to carbon dioxide (CO2), which has affected climate change. Therefore, the forest plays an essential role in carbon storage which absorbs the CO2 and releases oxygen (O2) to stabilize the earth's ecosystem. This research aims to estimate aboveground biomass (AGB) using a combination of airborne hyperspectral and LiDAR data with field observation in a tropical forest. The objective of this study is to test the ability of vegetation indices and topographic features derived from hyperspectral and LiDAR data using machine learning for AGB estimation and to identify the best machine learning algorithms for estimating AGB in tropical forest. In this research, artificial neural network (ANN) and random forest (RF) algorithm were used to predict the AGB using different models with different combinations of variables. During model selection, the best model fit was selected by calculating statistical parameters such as the residual of the coefficient of determination (R2) and root mean square error (RMSE). Based on the statistical indicators, the most suitable model is Model 4 using anRF algorithm with mtry = p, and a combination of field observation, LiDAR, hyperspectral, vegetation indices (VIs), and topography. This model produced R2 = 0.997 and RMSE = 30.653 kg/tree. Therefore, using a combination of field observation and remote sensing data with machine learning techniques is reliable in forest management to estimate AGB in tropical forest. [ABSTRACT FROM AUTHOR]

Simien Mountains National Park (SMNP) provides valuable ecosystem services. Despite its ecological values, the effect of livestock-free grazing on habitat quality has not been investigated. The present study aimed to examine how livestock grazing within the Simien Mountains National Park affects herbaceous plant diversity and biomass. The study was carried out from February 2019 to July 2019 in livestock-grazed and ungrazed sites. In each grazing site, ten transects with a length of 25 m were set monthly. Along each transect line, five temporary sample plots (1 m2) were established. For aboveground biomass estimation, grass and forbs were clipped from 0.25 m2 in every sample plot, whereas below-ground biomass was estimated from two soil core (diameter = 6.35 cm, depth =20 cm) samples in each transect. Species diversity and evenness were estimated using the Shannon-Wiener diversity and evenness index. A total of 43 plant species belonging to 20 families were identified. Aboveground biomass and below-ground biomass were significantly higher in ungrazed areas than in the grazed ones. Similarly, species diversity and richness were relatively lower in the grazed areas than in ungrazed areas. The study concluded that livestock grazing has affected the habitat by degrading the vegetation diversity and biomass production. Therefore, stakeholders should be encouraged to innovate appropriate grazing management technologies for the local livestock ranchers to support the conservation efforts of the park. [ABSTRACT FROM AUTHOR]

Monitoring mangrove forest biomass is vital for assessing their carbon sequestration potential. This study uses airborne LiDAR data to estimate the aboveground biomass (AGB) of an old‐growth mangrove forest in the Katunggan It Ibajay Ecopark (KII Ecopark) on Panay Island, Philippines. To establish a relationship between the LiDAR canopy height profile with the field observed AGB at the plot level, we tested 20 LiDAR derived relative height (RH) metrics. First, we tested a relationship between field observed Lorey's mean canopy height (Hm) and RH metrics, which were then used to estimate AGB by applying a previously established allometric model. Second, we tested the direct relationship between RH metrics and observed AGB. Among RH metrics, RH95 showed the best correspondence with the Hm (R2 = 0.79) and when it was applied to the previously developed allometric for AGB estimation, the results showed a large underestimation of AGB (R2 = 0.46) for plots with higher canopy heights. Conversely, the direct method using a power regression model with RH95 and observed AGB provided a better estimate (R2 = 0.58). However, both models still underestimated AGB at the KII Ecopark. We conclude that, LiDAR‐based AGB estimation using Hm as a single variable can result in considerable underestimation, especially in old‐growth mangrove forests such as KII Ecopark. Further studies are necessary to develop accurate models for estimating AGB in such special types of mangroves which is important for mangrove monitoring, reporting and verification (MRV). [ABSTRACT FROM AUTHOR]

The carbon content of microbial biomass in soil serves as one of the indicators of its biological activity and is often used in assessing the impact of anthropogenic activity and natural changes on the soil microbiome. Significant spatial and temporal variation of the indicator at the level of ecosystems, soil typological units, land use types, etc., makes it difficult to interpret the data obtained during mass monitoring. The aim of the study was to determine the background values of microbial biomass content in the surface sub-litter layer of soils in protected forests of the Moscow region. A reconnaissance survey of various podzolic soils typical for the region and differing in their genesis and texture and formed under mixed southern taiga forests in four protected natural territories was carried out. The soils of autonomous positions and the soils experiencing slight additional moistening were studied. The microbial biomass carbon was estimated by two methods: by the content of phospholipids and by substrate-induced respiration of microbial communities. Significant spatial variability of microbial biomass was noted both at the site level and at the ecosystem level. For the autonomous soils of similar classification positions and textures, it was comparable with the local variability at the experimental sites. The value of microbial biomass depended mainly on the character of soil water regime and soil texture, which largely determined the sorption capacity of the soil and its provision with biophilic elements, and also correlated with the richness of the ground cover providing the soil microbiome with nutrients. In order to obtain correct results of monitoring soil biological activity, unification of the methodology and depth of sampling in soils of background and anthropogenically transformed ecosystems is of great importance. [ABSTRACT FROM AUTHOR]

Mixed subtropical forests possess a high amount of carbon pool owing to their rich species diversity and carbon sequestration potential. The Dhaulasidh forest is located in Himachal Pradesh within the subtropical Himalayan region. This research aimed to identify: (1) Optimal satellite-derived Sentinel-2A indices for predicting biomass, (2) the best-fitting model for biomass estimation, and (3) changes in above-ground carbon stock due to biomass loss, using satellite remote sensing and quadrat-based approaches. Results indicated that Band 3 (Green), Band 5 (Red edge), the vegetation (VEG) index, and the Carotenoid reflectance index (CRI) were suitable for estimating above-ground biomass (AGB). Shannon and Simpson's diversity indices were calculated as 0.89 and 0.73, respectively. Significant contributors to AGB included Mallotus philippensis, Emblica officinalis, Cassia fistula, Acacia catechu, Ehretia laevis, Kydia calycina, and Lannea coromandelica. The AGB prediction model based on vegetation indices demonstrated a strong correlation between observed and predicted biomass (R²=0.65, p<0.001), with a mean absolute percentage error of 20% and root mean square error of 7.33 tonnes per pixel. The study predicted a total loss of 22,917.15 tonnes of CO2 in mixed subtropical forests, representing a 12.04% reduction in carbon stock within the study area. These findings offer critical baseline data for environmental management and carbon balance in the forest ecosystem, recommending that forest management practices after deforestation should be reviewed for remedial measures for any developmental activities. [ABSTRACT FROM AUTHOR]

Simple Summary: Dragonflies and damselflies are invaluable components of freshwater ecosystems, acting as dominant predators and facilitating the exportation of matter and energy from aquatic to terrestrial environments thanks to their powerful flight. They are also crucial as a food source for various animals and, in some cases, for humans. Through a comprehensive review of the literature, we estimated the biomass, density, and secondary production of these insects, assessing their potential significance in terrestrial fertilization. Our findings indicate that dragonfly larvae are particularly abundant in lentic habitats. Overall, the evidence suggests that dragonflies and damselflies may make a substantial contribution to the exportation of materials to terrestrial systems, especially considering the adults' ability to migrate and inhabit different types of water ecosystems. Freshwater insects are highly significant as ecosystem service providers, contributing to provisioning services, supporting services, and cultural services. Odonates are dominant predators in many freshwater systems, becoming top predators in fishless ecosystems. One service that odonates provide is the export of matter and energy from aquatic to terrestrial ecosystems. In this study, we provide a review of the literature aiming to estimate the density, biomass, and secondary production of odonates and discuss to what extent this order of insects is relevant for the fertilization of terrestrial ecosystems. We found published data on 109 species belonging to 17 families of odonates from 44 papers. Odonata larvae are abundant in freshwater systems, with a mean density of 240.04 ± 48.01 individuals m−2 (±SE). Lentic habitats show much higher densities (104.40 ± 55.31 individuals m−2, N = 118) than lotic systems (27.12 ± 5.09, N = 70). The biomass estimations for odonates indicate values of 488.56 ± 134.51 mg m−2 y−1, with similar values in lentic and lotic habitats, which correspond to annual secondary productions of 3558.02 ± 2146.80 mg m−2 y−1. The highest biomass is found in dragonflies of the Aeshnidae, Corduliidae, and Gomphidae families. The available evidence suggests a significant potential contribution of Odonata to the exportation of material from water bodies to land. This is further strengthened by the ability of adult odonates to migrate and to colonize different types of water bodies. [ABSTRACT FROM AUTHOR]

Simple Summary: This research addresses the problem of biomass estimation and fish development monitoring by developing and meticulously validating a computer-vision-interpretable methodology for fish biometrics based on the extraction of statistical features of shape and size through a signature function. This is done by comparing manually extracted features in machine learning predictions against the computer vision (image feature extraction) machine learning predictions. This research shows promising results in statistical accuracy and error metrics, especially the computer vision methodology plus artificial neural networks for biomass prediction. It is concluded that the signature-function-based methodology plus the neural networks regression is competitive for biomass estimation and provides a powerful interpretable tool for species morphology development research. As a result, from a better understanding of fish development and biomass estimation, the aquaculture sector can improve production, wasting less animal food and detecting fish welfare problems through deformity detection, which translates into reduced costs and improved quality of animal meat. 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. [ABSTRACT FROM AUTHOR]

Since 2007, the Yellow Sea green tide has broken out every summer, causing great harm to the environment and society. Although satellite remote sensing (RS) has been used in biomass research, there are several shortcomings, such as mixed pixels, atmospheric interference, and difficult field validation. The biomass of green tide has been lacking a high-precision estimation method. In this study, high-resolution unmanned aerial vehicle (UAV) RS was used to quantitatively map the biomass of green tides. By utilizing experimental data from previous studies, a robust relationship was established to link biomass to the red-green-blue floating algae index (RGB-FAI). Then, the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements. Results show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas. The study provided an effective complement to the traditional satellite RS, as well as high-precision quantitative techniques for decision-making in disaster management. [ABSTRACT FROM AUTHOR]

This study gives the first data on the body and beak morphometric characteristics, age, and genetic structure of neon flying squid, a rarely caught cephalopod in the Adriatic Sea. We identified specimens as recently resurrected Ommastrephes caroli species using two mitochondrial markers, 16S ribosomal RNA gene and cytochrome c oxidase subunit I gene. Overall, 23 juveniles (3 females, 3 males, and 17 unsexed), with a dorsal mantle range of 65–152 mm, were caught in September 2020 in the waters of the Korčula Channel, island of Palagruža, and island of Jabuka, thus providing the most abundant sample of this species in the Mediterranean waters. The length–weight relationship showed an isometric growth. The results of the beak/length regressions suggest hood length is a useful characteristic for biomass estimation studies, as it showed a good linear fit to the dorsal mantle length. Statolith growth increments were easily visible and statolith microstructure analysis was successfully used to determine the age of 22 individuals. The estimated age ranged from 36 to 64 days (mean = 48 days). The back-calculation analysis showed that the squid hatched during July and August 2020, indicating that O. caroli spawns during the warmer, summertime period. Considering the size and age of the caught individuals, the Adriatic Sea could represent a potential feeding ground for this species. The genetic structure analyses indicate the existence of separate Atlantic and Mediterranean/Adriatic subclusters; however, this warrants further investigation. [ABSTRACT FROM AUTHOR]

Microalgae reactors provide an efficient and clean alternative for the production of biofuels, nutritional and cosmetic bioproducts, wastewater treatment, and mitigation of industrial gases to reduce greenhouse gas emissions. The main control objective in these systems is productivity optimisation. For this reason, real-time monitoring of key biological performance indicators affecting microalgae production such as microalgae growth rate, biomass concentration, dissolved oxygen, pH level or total inorganic carbon is crucial. However, there are no sufficiently robust solutions on the market to estimate or measure all of these variables, especially for open reactors on an industrial scale. This paper presents a new online state estimator, based on a robust sliding mode observer combined with a nonlinear dynamic model endowed with a minimum number of states to capture dynamics of key biological performance indicators. This soft-sensor has been verified with a realistic reactor model that has been experimentally tested. Simulations showed promising results in terms of accuracy (with mean values of the state estimation errors in the order of 10−4 g m −3 for the biomass concentration, 10−5 to 10−13 mol m −3 for the other states and deviations in the order of 10−4 g m −3 for the biomass concentration, 10−5 to 10−10 mol m −3 for the other states) and robustness with respect to signal noise, state deviations, initial errors and parametric uncertainty. • Architecture and design of a soft-sensor for industrial raceway photobioreactor. • Sliding modes techniques for on-line robust monitoring main biological indicators. • Proposal and verification of a reduced model as mathematical reactor replica. • Soft-sensor tested by simulation with a experimentally verified reactor model. [ABSTRACT FROM AUTHOR]

A novel method for the mechanical detection of wheat biomass, based on the mechanical properties of wheat plants, is proposed to enable the quick assessment of wheat biomass. The mechanical model developed for the wheat plants, based on the variable cross-section beam elastic bending theory, can be used to analyse the interactive forces between the reel and wheat plants, and predict wheat biomass based on the magnitude of the force. The influence of wheat ears on deflection was incorporated into the model. The accuracy of wheat plant deflection forces obtained using the model was confirmed through theoretical analyses, simulations and experimental measurements. Moreover, deflection tests and posture analysis were performed on the wheat plants for different locations at which the deflection forces were acting and for different plant densities. Experiments focusing on reel operation demonstrated that the deflection forces exerted by the reel rod on wheat plants could be used to predict the number of bent plants, which would subsequently help in wheat biomass estimation. The study found that the influence of the wheat ear on the deflection force significantly increased as the plant deflection increased. The deflection force was most effective at two-thirds of the height of the wheat plant. Moreover, the higher the plant density, the greater the deflection force, which was closely correlated with wheat biomass. A model was established based on the results of the linear regression performed to determine the relationship between the deflection force acting on a wheat plant and its biomass. The model with a determination coefficient of 0.9155 provided a theoretical basis for detecting the feed quantity of the combine harvester. • Analysed the interaction force between the combine harvester reel and wheat plants. • A mechanical model of wheat plant deflection was established. • The method of bending theory of elastic beam with variable cross-section is used. • Prediction of wheat biomass based on interaction force. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m3 ha−1 year−1, MBE = −0.45 m3 ha−1 year−1). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests. [ABSTRACT FROM AUTHOR]

Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem. [ABSTRACT FROM AUTHOR]

Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here, we demonstrate the limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in an effort to support more accurate and reliable estimation of LCC and ASR. The results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. The findings from a smaller-scale study that incorporate phenology timings into the estimation of annual aboveground biomass reveal that summer pastures in Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000 to 2005. The methods proposed can be extended to map grassland grazing pressure by predicting the LCC and tracking the ASR, thereby improving sustainable resource use in alpine grasslands. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson's correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is a critical criterion for assessing the ecological value and productivity of grasslands. Quickly and accurately estimating the AGB of grasslands has become a matter of great concern in grassland ecosystem research. By combining ground ASD hyperspectral data and airborne hyperspectral data, this study successfully extracted the sensitive bands of canopy reflectance and calculated the various vegetation indexes, thus providing information of more dimensions for above-ground biomass estimation. Biomass inversion-based yield modeling results reveal that the 377 nm raw band reflectance of the ASD data and the narrow-band vegetation index of the Resonon data exhibit outstanding performance in yield modeling. Comparative analysis indicates that the Resonon hyperspectral data demonstrate a distinct advantage in assessing meadow steppe yield, and the quadratic polynomial model based on its narrow-band SAVI achieves an R² of 0.553 and an RMSE of 37.53. [ABSTRACT FROM AUTHOR]

Multispectral (MS) images offer essential spectral information for monitoring paddy crops' Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multilinear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) is one of the critical measures of forest resources. Therefore, it is crucial to identify a reliable method to estimate the AGB, especially in the tropics, where forest ecosystems are exposed to several depleting factors, including deforestation, climate change and replacing natural forests with palm oil tree plantations. We investigated the digital elevation data over the forest and uses an artificial intelligence-based approach to develop a method for quick and cost-effective assessment of the AGB. The study was conducted in the tropical peatland rainforest of Brunei Darussalam. The Shuttle Radar Topography Mission (SRTM) elevation data product and Light Detection and Ranging (LiDAR) digital elevation data were used. A linear regression (LR) model and three different machine learning (ML) algorithms, i.e., Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machines (SVM), were tested and compared. As model inputs, the SRTM elevation and distance from the peat dome’s center, a feature of a peatland swamp forest, were used. ML methods were trained on the samples taken from the LiDAR elevations. The validation results showed that the SVM was the best method to predict AGB in the study area with R² = 0.70, RMSE = 83.65 Mg ha-1, and MAE = 74.43 Mg ha-1, which in relative terms corresponds to approximately 6% of the AGB of the forest of interests. This study demonstrated the potential of ML algorithms in AGB estimation based on canopy height derived from the InSARbased DEM in tropical forests. [ABSTRACT FROM AUTHOR]

Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the field of crop research, the study of roots involves many challenges, particularly the lack of effective methods for identifying crop roots. To deal with this problem, this study proposes a solution strategy: in applying strontium to crop leaves, the roots of different crops are distinguished. In this study, we applied strontium ions to the leaves of hydroponically grown wheat, and these strontium ions were partially absorbed by the leaves and transported to the roots. Therefore, the strontium concentration of roots was significantly increased through the foliar application of strontium. After mixing the treated wheat with the untreated wheat root, the greater the biomass of the wheat root applied with strontium ions, the higher the strontium concentration in the mixed root. Based on this phenomenon, we can establish the relationship between wheat root biomass and strontium concentration in mixed roots through linear fitting. Furthermore, we can also use the relationship between root biomass and root length to establish the correlation between strontium concentration in mixed roots and the root length of wheat with strontium ions. After measuring the strontium concentration of the mixed roots to be distinguished, the root biomass and root length of wheat applied with strontium ions can be calculated according to the equation obtained through linear fitting. The accuracy of this method was verified through a comparison with the actual value and the existing root staining method. The results show that the coefficient of determination (R2) of the root biomass estimation equation obtained through linear fitting reached 0.83, which is statistically significant (p < 0.01). The Pearson correlation coefficient with the measured value was more than 0.9, showing a very high correlation and significance. The root length estimation equation derived from the relationship between the root biomass, strontium concentration and root length was compared with the real root length value and the root length value obtained using the staining method. The Pearson correlation coefficient also exceeded 0.8 and reached a statistically significant level (p < 0.01). This study confirms that strontium ions can be absorbed and transported to roots through wheat leaves and successfully developed a new method for predicting the length of wheat roots, providing an effective new tool for wheat root research. [ABSTRACT FROM AUTHOR]

Morocco's argan trees, native to forests, are now cultivated in large orchards within the Argan Biosphere Reserve, transforming "Arganiculture". These orchards are anticipated to bolster carbon storage, but their precise contribution to carbon storage remains unclear. This study introduces for the first time allometric equations for estimating biomass in different components of argan plants within orchards. A total of 89 plant individuals, aged 2 to 6 years, were collected. Their diameter and total height were measured. The biomass, carbon content, and biomass carbon stock of each component were determined. The best-fit allometric equation incorporates diameter, height, growing years, and root-to-shoot ratio to estimate total biomass (R2 = 0.95). The estimated total carbon biomass stock ranged from 0.01 to 0.82 t CO2 ha−1 for plants, at a density of 200 plants ha−1. Between 2021 and 2023, the average annual carbon sequestration was 0.20 t CO2 ha−1 year−1. This model offers valuable tools for use when species-specific equations during the establishment growing stage are unavailable, enhancing carbon sequestration quantification for more reliable results and informing climate change mitigation strategies. The allometric parameters serve as benchmarks for trees resembling the argan tree. The methodology could be adapted for other forest plants undergoing conversion to orchard cultivation. [ABSTRACT FROM AUTHOR]

Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV–Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications. [ABSTRACT FROM AUTHOR]

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and allweather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of aboveground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB ≤ 80 t/ha. The overall RMSE for all vegetation with AGB ≤ 80 t/ha was 15.3 t/ha (R² – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands. [ABSTRACT FROM AUTHOR]

Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry. [ABSTRACT FROM AUTHOR]

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m2, reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m2 smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application. [ABSTRACT FROM AUTHOR]

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. [ABSTRACT FROM AUTHOR]

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Data acquisition for sustainable forest management has focused on obtaining high quality information to estimate biomass. Improving the quality of non-timber sustainability indicators, like deadwood volume, has been a minor interest. To explore how inventory approaches could be improved, we applied a Global Uncertainty and Sensitivity Analysis (GUSA) to evaluate which factors propagate more errors in deadwood modelling and how better data collection can minimize them. The impact of uncertainty on deadwood characteristics (diameter, collapse ratio, decay class, tree species, and position) was explored under stakeholders´ preferences, management actions, and climate change scenarios. GUSA showed that removing the prediction error in deadwood tree species and diameter would alter the most the total uncertainty in deadwood volume. We found that assessment of high deadwood volume was less uncertain for the scenarios where small deadwood items were left decaying on the forest floor (BAU) and for high-end climate change scenario (RCP8.5) which resulted in lower deadwood accumulation in forest stands and therefore also in lower likelihood of erroneous estimates. Reduced uncertainty in tree species and diameter class will elevate the certainty of deadwood volume to a similar level achieved in living biomass estimation. Our uncertainty and sensitivity analysis was successful in ranking factors propagating errors in estimate of deadwood and identified a strategy to minimize uncertainty in predicting deadwood characteristics. The estimation of uncertainty in deadwood levels under the scenarios developed in our study can help decision makers to evaluate risk of decreasing deadwood value for biodiversity conservation and climate change mitigation. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological–climatic and anthropogenic–technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations. [ABSTRACT FROM AUTHOR]

Understory small trees and shrubs play a crucial role in the biogeochemical cycles in forest ecosystems. However, their biomass in northeastern China is still uncertain and has been heavily disregarded due to the limited number of available allometric equations. For this study, 782 plants from 33 species obtained by the destructive method were used to develop species-specific and generalized aboveground allometric biomass equations based on collar diameter (D) and height (H) using weighted nonlinear seemingly unrelated regression. Each biomass component was shown to be well predicted by D alone, with R2 adj. values mostly greater than 0.80. The majority of species performed better in the models for wood and aboveground biomass when H was included as D2H. Furthermore, generalized equations for the two components showed a comparatively large coefficient of variation but comparable Bias to species-specific equations, especially for small trees. It is recommended to estimate biomass using generalized equations for mixed species only when species-specific equations are unavailable at a given site. However, in the event when precision is not the primary concern, generalized equations are also suggested. The developed equations will help to improve the accuracy of biomass assessment of understory woody plants in northeastern China forest ecosystems. [ABSTRACT FROM AUTHOR]