Search Results (143)

Forest structural parameters, such as canopy closure, stand density, diameter at breast height, tree height, leaf area index, stand age, and biomass, are fundamental for quantifying forest ecosystem functioning and supporting sustainable forest management. Remote sensing has become an indispensable tool for forest structural parameter estimation. Commonly used data sources include optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs), and multisource data fusion. Correspondingly, modeling approaches have evolved from empirical and statistical methods to machine learning, deep learning, and hybrid physical-data-driven models, enabling improved characterization of nonlinear and complex forest structures. Each data source and modeling strategy offers unique strengths and limitations with respect to accuracy, scalability, interpretability, and transferability. This review provides a concise synthesis of recent advances in remote sensing data sources and model algorithms for forest structural parameter estimation, evaluates the strengths and limitations of different sensors and algorithms, and highlights key challenges related to uncertainty, scalability, transferability, and model interpretability. Finally, future research directions are discussed, emphasizing cross-scale integration, multisource data fusion, and physically informed deep learning frameworks as promising pathways toward more accurate, robust, and ecologically interpretable forest structural parameter modeling at regional to global scales. Full article

Brazil nut (Bertholletia excelsa Bonpl.) is a major non-timber forest product in the Amazon, supporting extractivist communities in Brazil, Bolivia, and Peru and contribute to forest conservation. Unlike other extractive products, Brazil nut production has not declined under commercial use and is recognized for its socioeconomic and environmental importance. Precision agriculture has been transformed by the use of unmanned aerial vehicles (UAVs) and artificial intelligence (AI), which enable monitoring efficiency and yield estimation in several crops, including the Brazil nut. This study assessed the potential of using UAV-based imagery combined with YOLOv8 object detection model to identify and quantify Brazil nut fruits in a native forest fragment in eastern Acre, Brazil. A UAV was used to capture canopy images of 20 trees with varying diameters at breast height. Images were manually annotated and used to train the YOLOv8 with an 80/20 split for training and validation/testing. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP). The model achieved recall above 90%, with an F1-score of 0.88, despite challenges from canopy complexity and partial occlusion. These results indicate that UAV-based imagery combined with AI detection provides an approach for estimating Brazil nut yield, reducing manual effort and improving market strategies for extractivist communities. This technology supports sustainable forest management and socioeconomic development in the Amazon. Full article

Accurate Digital Terrain Models (DTMs) are essential for managing forest drainage networks as a crucial element of water management, yet dense canopies and complex micro-topography challenge Airborne Laser Scanning (ALS) precision. This study evaluates the vertical accuracy of an ALS-derived DTM specifically within forest drainage ditches, utilizing 706 GNSS and total station measurements for validation. The results indicate a positive elevation bias, with a mean elevation error of 0.415 m and an RMSE of 0.464 m, 54.7% higher than the 0.3 m declared in the DTM technical report. Forest height, acting as a proxy for forest structural density and canopy closure, was significantly associated with a reduction in ground reflection density and an increase in the distance to the nearest ground reflection (p < 0.05). Mixed-effects ANOVA confirmed that there are significantly more ground reflections in low vegetation (0–1 m). Crucially, multiple regression analysis revealed that forest height was not the primary driver of elevation error; instead, ditch geometry was the most significant predictor. Narrower ditches exhibited substantially higher errors than wider ones, regardless of the canopy height. Furthermore, while ground reflection density decreased in mature stands, this reduction did not significantly diminish DTM vertical accuracy, suggesting that some of the LiDAR reflections of low vegetation could be misclassified as ground reflections, decreasing accuracy. These findings suggest that while ALS is effective for general forest topography and mapping drainage infrastructure, its application may require corrections for ditch dimensions rather than vegetation height alone to mitigate systematic overestimation of ditch bed elevations. Full article

Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R²) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

As an essential blue carbon ecosystem, mangroves play a vital role in coastal protection, biodiversity conservation, and climate regulation. However, their complex and variable growth environments pose challenges for precise monitoring. Hainan Island represents a region within China where mangrove forests are the most concentrated and diverse in type. In recent years, ecological restoration efforts have led to the recovery of their coverage areas. This study analyzed the spatial distribution, canopy height, and aboveground carbon storage variations in Hainan mangrove forests. Deep-learning and multiple machine-learning algorithms were used to integrate multitemporal Sentinel-2 remote sensing imagery from 2019 to 2023 with unmanned aerial vehicle observations and field survey data. Multi-rule image fusion and deep-learning techniques effectively enhanced mangrove identification accuracy. The mangrove classification achieved an overall accuracy exceeding 90%. The mangrove area in Hainan increased from 3948.83 ha in 2019 to 4304.29 ha in 2023. Gradient-boosted decision tree (GBDT) models estimated average canopy height with a high coefficient of determination (R² = 0.89), and Random Forest (RF) models yielded the best estimations of total above-ground carbon stock with strong agreement to field observations. Integrating multisource remote sensing data with artificial intelligence algorithms enabled high-precision dynamic monitoring of mangrove distribution, structure, and carbon storage to provide scientific support for the assessment, management, and carbon sink accounting of Hainan mangrove ecosystems. Full article

Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems. Full article

Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection. To that end, imagery acquired using unmanned aerial systems (UASs) and classified using pixel-based, object-based, or neural network-based approaches provides researchers a promising avenue. However, in scenarios where spectral differences cannot be used to distinguish between crop and weed foliage, where physical overlap between crop and weed foliage obstructs object-based detection, or where large datasets are not available to train neural networks, alternative methods may be required. For instances where there is a consistent difference in height between crop and weed plants, a mask can be applied to a canopy height model (CHM) such that pixels are determined to be weed or non-weed based on height alone. The CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86% as well as explain significant variation in sweetpotato yield (p < 0.01). The CHMM approach contributes to the diverse methodologies needed to conduct weed detection in different agricultural settings. Full article

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article

The utilization of photography imagery captured using cameras mounted on unmanned aerial vehicles (UAVs) for aboveground biomass (AGB) inventory has seen rapid growth in recent years. Existing research has predominantly focused on utilizing spectral and textural features for biomass inversion. However, estimating the AGB of trees remains a great challenge using stereoscopic imagery without the help of a digital terrain model (DTM). This study introduces five DTM-independent crown metrics using a digital surface model (DSM) and a canopy height model (CHM) derived from UAV stereoscopic imagery. The accuracy of the five metrics was evaluated against field measurements. The results indicate that the relationship between the crown cross-sectional area (CCSA) and AGB is stronger than that between tree height (TH) and AGB, with R² = 0.62 and RMSE = 69.22 (kg/tree) for Larix gmelinii and R² = 0.93 and RMSE = 142.06 (kg/tree) for Pinus sylvestris. Moreover, these DTM-independent crown metrics could be used to estimate the AGB of forests in the Greater and Lesser Khingan Mountain, with R² = 0.77 and RMSE = 77.10 (kg/tree) for coniferous trees and R² = 0.78 and RMSE = 72.46 (kg/tree) for all other trees. The results of this study demonstrate that UAV stereoscopic imagery can capture forest canopy information, and DTM-independent crown metrics can be used for AGB inversion where information on terrain under forest is unavailable. Full article

The integration of LED lighting enables precise radiation control in plant factory cultivation systems. While LEDs offer energy efficiency and spectral tuning, achieving a uniform photosynthetic photon flux density (PPFD) remains a critical technical challenge. This study evaluated the impact of three spatial LED configurations on irradiance uniformity using commercial horticultural LEDs and a light recipe of 75% red and 25% blue. Optical simulations in TracePro^® 2017 were conducted to analyze radiant flux, optical efficiency, and uniformity, along with LED quantity, system cost, and electrical consumption under two environmental scenarios: open (without reflective walls) and closed (with reflective walls). Results show that distribution 3, which featured reduced central LED density, achieved 4–8% higher homogeneity in the open scenario, and 2.7–6.5% in the closed scenario, compared to symmetric layouts (distribution 1 and 2). Reflective walls increased average PPFD by up to 20% and optical efficiency by around 9%, with a minimal effect on uniformity. Lowering the lamp-to-canopy distance from 35 cm to 30 cm resulted in a 10% increase in PPFD. Despite a reduction in total photon flux, distribution 3 exhibited superior irradiance homogeneity. One-way ANOVA confirmed significant effects of environment, height, and LED model (p < 0.05), but not of spatial alone. This simulation-based methodology offers a robust framework for optimizing energy-efficient lighting systems. Future work will explore the integrating of non-visible wavelengths and experimental validations to extend practical applicability. Full article

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation. Full article

Satellite Light Detection and Ranging (LiDAR) systems produce high-resolution data essential for confronting critical environmental challenges like climate change, disaster management, and ecological conservation. A HyperHeight Data Cube (HHDC) is a novel representation of LiDAR data. HHDCs are structured three-dimensional tensors, where each cell captures the number of photons detected at specific spatial and height coordinates. These data structures preserve the detailed vertical and horizontal information essential for ecological and topographical analyses, particularly Digital Terrain Models and canopy height profiles. In this paper, we investigate lossless compression techniques for large volumes of HHDCs to alleviate constraints on onboard storage, processing resources, and downlink bandwidth. We analyze several methods, including bit packing, Rice coding (RC), run-length encoding (RLE), and context-adaptive binary arithmetic coding (CABAC), as well as their combinations. We introduce the block-splitting framework, which is a simplified version of octrees. The combination of RC with RLE and CABAC within this framework achieves a median compression ratio greater than 24, which is confirmed by the results of processing two large sets of HHDCs simulated using the Smithsonian Environmental Research Center NEON data. Full article

Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R² values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R² of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R² of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article