Search Results (132)

Abies nebrodensis L. is a critically endangered conifer endemic to Sicily (Italy). Its residual population is confined to the Madonie mountain range under challenging climatological conditions. Despite the good adaptation shown by the relict population to the environmental conditions occurring in its habitat, Abies nebrodensis is subject to a series of threats, including climate change. Effective conservation strategies require reliable and versatile methods for monitoring its health status. Combining high-resolution remote sensing data with reanalysis of climatological datasets, this study aimed to identify correlations between vegetation indices (NDVI, GreenDVI, and EVI) and key climatological variables (temperature and precipitation) using advanced machine learning techniques. High-resolution RGB (Red, Green, Blue) and IrRG (infrared, Red, Green) maps were used to delineate tree crowns and extract statistics related to the selected vegetation indices. The results of phytosanitary inspections and multispectral analyses showed that the microclimatic conditions at the site level influence both the impact of crown disorders and tree physiology in terms of water content and photosynthetic activity. Hence, the correlation between the phytosanitary inspection results and vegetation indices suggests that multispectral techniques with drones can provide reliable indications of the health status of Abies nebrodensis trees. The findings of this study provide significant insights into the influence of environmental stress on Abies nebrodensis and offer a basis for developing new monitoring procedures that could assist in managing conservation measures. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R², R_a² = 0.79) and AGB (R_a² = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated. Full article

Accurate detection and delineation of individual tree crowns (ITCs) are essential for sustainable forest management and ecosystem monitoring, providing key biophysical attributes at the individual tree level. However, the complex structure of mixed-wood forests, characterized by overlapping canopies of various shapes and sizes, presents significant challenges, often compromising accuracy. This study presents a two-stage deep learning framework that integrates Canopy Height Model (CHM)-based treetop detection with three-dimensional (3D) ITC delineation using high-resolution airborne LiDAR point cloud data. In the first stage, Mask R-CNN detects treetops from the CHM, providing precise initial localizations of individual trees. In the second stage, a 3D U-Net architecture clusters LiDAR points to delineate ITC boundaries in 3D space. Evaluated against manually delineated reference data, our approach outperforms established methods, including Mask R-CNN alone and the lidR itcSegment algorithm, achieving mean intersection-over-union (mIoU) scores of 0.82 for coniferous plots, 0.81 for mixed-wood plots, and 0.79 for deciduous plots. This study demonstrates the great potential of the two-stage deep learning approach as a robust solution for 3D ITC delineation in mixed-wood forests. Full article

Mangroves are critical ecosystems that provide essential environmental services, such as climate regulation, carbon storage, biodiversity conservation, and shoreline protection, making their conservation vital. High-resolution remote sensing using unmanned aerial vehicles (UAVs) offers a powerful tool for detailed mapping of mangrove species and structure. This study applies geographic object-based image analysis (GEOBIA) combined with machine learning (ML) to classify mangrove species at two ecologically distinct sites in Brazil: Cardoso Island (São Paulo State) and Suape (Pernambuco State). UAV flights at 50 m and 120 m altitudes captured multispectral data, enabling species-level classification of Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. By integrating field measurements and advanced metrics such as texture and spectral indices, the workflow achieved precise delineation of tree crowns and spatial distribution mapping. The results demonstrate the superiority of this approach over traditional methods, offering scalable and adaptable tools for ecological monitoring and conservation. The findings highlight the potential of UAV-based multispectral imaging to improve mangrove conservation efforts by delivering actionable, fine-scale data for policymakers and stakeholders. Full article

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set. Full article

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. High-resolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R² = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R² = 0.89, RMSE = 0.85 m). The accuracy of oak (R² = 0.11–0.49) and OBL (R² = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R² = 0.57, RMSE = 0.08 m² tree⁻¹) and volume (R² = 0.51, RMSE = 0.27 m³ tree⁻¹). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest. Full article

Individual tree segmentation is crucial to extract forest structural parameters, which is vital for forest resource management and ecological monitoring. Airborne LiDAR (ALS), with its ability to rapidly and accurately acquire three-dimensional forest structural information, has become an essential tool for large-scale forest monitoring. However, accurately locating individual trees and mapping canopy boundaries continues to be hindered by the overlapping nature of the tree canopies, especially in dense forests. To address these issues, this study introduces CCD-YOLO, a novel deep learning-based network for individual tree segmentation from the ALS point cloud. The proposed approach introduces key architectural enhancements to the YOLO framework, including (1) the integration of cross residual transformer network extended (CReToNeXt) backbone for feature extraction and multi-scale feature fusion, (2) the application of the convolutional block attention module (CBAM) to emphasize tree crown features while suppressing noise, and (3) a dynamic head for adaptive multi-layer feature fusion, enhancing boundary delineation accuracy. The proposed network was trained using a newly generated individual tree segmentation (ITS) dataset collected from a dense forest. A comprehensive evaluation of the experimental results was conducted across varying forest densities, encompassing a variety of both internal and external consistency assessments. The model outperforms the commonly used watershed algorithm and commercial LiDAR 360 software, achieving the highest indices (precision, F1, and recall) in both tree crown detection and boundary segmentation stages. This study highlights the potential of CCD-YOLO as an efficient and scalable solution for addressing the critical challenges of accuracy segmentation in complex forests. In the future, we will focus on enhancing the model’s performance and application. Full article

The crown area is a critical metric for evaluating tree growth and supporting various ecological and forestry analyses. This study compares three approaches, i.e., unsupervised clustering, region-based, and deep learning, to estimate the crown area of Pinus eldarica Medw. using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm³). The first approach applied unsupervised clustering techniques, such as Mean-shift and K-means, to directly estimate crown areas, bypassing tree top detection. The second employed a region-based approach, using Template Matching and Local Maxima (LM) for tree top identification, followed by Marker-Controlled Watershed (MCW) and Seeded Region Growing for crown delineation. The third approach utilized a Convolutional Neural Network (CNN) that integrated Digital Surface Model layers with the Visible Atmospheric Resistance Index for enhanced segmentation. The results were compared against field measurements and manual digitization. The findings reveal that CNN and MCW with LM were the most effective, particularly for small and large trees, though performance decreased for medium-sized crowns. CNN provided the most accurate results overall, with a relative root mean square error (RRMSE) of 8.85%, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a bias score (BS) of 1.00. The CNN crown area estimates showed strong correlations (R² = 0.83, 0.62, and 0.94 for small, medium, and large trees, respectively) with manually digitized references. This study underscores the value of advanced CNN techniques for precise crown area and shape estimation, highlighting the need for future research to refine algorithms for improved handling of crown size variability. Full article

The generation of aerial and unmanned aerial vehicle (UAV)-based 3D point clouds in forests and their subsequent structural analysis, including tree delineation and modeling, pose multiple technical challenges that are partly raised by the calibration of non-metric cameras mounted on UAVs. We present a novel method to deal with this problem for forest structure analysis by photogrammetric 3D modeling, particularly in areas with complex textures and varying levels of tree canopy cover. Our proposed method selects various subsets of a camera’s interior orientation parameters (IOPs), generates a dense point cloud for each, and then synthesizes these models to form a combined model. We hypothesize that this combined model can provide a superior representation of tree structure than a model calibrated with an optimal subset of IOPs alone. The effectiveness of our methodology was evaluated in sites across a semi-arid forest ecosystem, known for their diverse crown structures and varied canopy density due to a traditional pruning method known as pollarding. The results demonstrate that the enhanced model outperformed the standard models by 23% and 37% in both site- and tree-based metrics, respectively, and can therefore be suggested for further applications in forest structural analysis based on consumer-grade UAV data. Full article

Accurate individual tree crown delineation (ITCD), which can be used to estimate various forest parameters such as biomass, stem density, and carbon storage, stands as an essential component of precision forestry. Currently, raster data such as the canopy height model derived from airborne light detection and ranging (LiDAR) data have been widely used in large-scale ITCD. However, the accuracy of current existing algorithms is limited due to the influence of understory vegetation and variations in tree crown geometry (e.g., the delineated crown boundaries consistently extend beyond their actual boundaries). In this study, we achieved more accurate crown delineation results based on an expansion process. First, the initial crown boundaries were extracted through watershed segmentation. Then, a “from the inside out” expansion process was guided by a novel gradient feature to obtain accurate crown delineation results across different forest conditions. Results show that our method produced much better performance (~75% matched on average) than other commonly used methods across all test forest plots. The erroneous situation of “match but over-grow” is significantly reduced, regardless of forest conditions. Compared to other methods, our method demonstrates a notable increase in the precisely matched rate across different plot types, with an average increase of 25% in broadleaf plots, 18% in coniferous plots, 23% in mixed plots, 15% in high-density plots, and 32% in medium-density plots, without increasing over- and under- segmentation errors. Our method demonstrates potential applicability across various forest conditions, facilitating future large-scale ITCD tasks and precision forestry applications. Full article

Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively. Full article

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). Full article

Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. The approach utilizes hyperspectral, unmanned aerial vehicle laser scanning data, and ground survey data from Maoershan Forest Farm in Heilongjiang Province, China. The study consists of two main processes: (1) combining semantic segmentation algorithms (U-Net and Deeplab V3 Plus) with watershed transform (WTS) for tree crown detection (U-WTS and D-WTS algorithms); (2) resampling the original images to different pixel densities (16 × 16, 32 × 32, and 64 × 64 pixels) and inputting them into five 3D-CNN models (ResNet10, ResNet18, ResNet34, ResNet50, VGG16). For tree species classification, the MSFB combined with the CNN models were used. The results show that the U-WTS algorithm achieved a recall of 0.809, precision of 0.885, and an F-score of 0.845. ResNet18 with a pixel density of 64 × 64 pixels achieved the highest overall accuracy (OA) of 0.916, an improvement of 0.049 over the original images. After incorporating MSFB, the OA improved by approximately 0.04 across all models, with only a 6% increase in model parameters. Notably, the floating-point operations (FLOPs) of ResNet18 + MSFB were only one-eighth of those of ResNet18 with 64 × 64 pixels, while achieving similar accuracy (OA: 0.912 vs. 0.916). This framework offers a scalable solution for large-scale tree species distribution mapping and forest resource inventories. Full article

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come at the cost of increased complexity, which may deter the uninitiated from using these models. In this work, we present a framework to simplify the creation of neuro-symbolic models for tree crown delineation and tree species classification via the use of object-oriented programming and hyperparameter tuning algorithms. We show that models created using our framework outperform their non-neuro-symbolic counterparts by as much as two F1 points for crown delineation and three F1 points for species classification. Furthermore, our use of hyperparameter tuning algorithms allows users to experiment with multiple formulations of domain knowledge without the burden of manual tuning. Full article