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Rekonstruksi tiga dimensi pohon memiliki peran penting dalam berbagai bidang, seperti studi pengelolaan hutan, ekologi, perhitungan emisi karbon, serta aplikasi perencanaan perkotaan. Namun, metode tradisional yang telah digunakan selama bertahun-tahun umumnya membutuhkan tenaga kerja intensif, memakan waktu lama, dan rentan terhadap kesalahan pengukuran. Untuk mengatasi kendala ini, metode berbasis point cloud mulai digunakan sebagai solusi yang lebih efektif. Point cloud tersebut dapat dihasilkan melalui teknologi LiDAR maupun fotogrametri. Teknologi LiDAR biasanya diintegrasikan dengan drone sebagai wahana untuk pengambilan data. Namun, dalam beberapa tahun terakhir, penggunaan LiDAR berbasis handheld-SLAM (Simultaneous Localization and Mapping) telah menjadi alternatif metode survei modern. Handheld-SLAM LiDAR adalah perangkat LiDAR yang dilengkapi dengan teknologi SLAM, memungkinkan pemindaian tiga dimensi secara real-time. Penelitian ini akan membandingkan dua metode akuisisi data, yaitu menggunakan drone LiDAR dan handheld-SLAM LiDAR, yang akan diaplikasikan untuk analisis karakteristik pohon, seperti bentuk tajuk (tree crown) dan batang pohon (tree trunk). Hipotesis awal penelitian ini adalah bahwa sensor LiDAR pada drone memiliki keterbatasan dalam merekonstruksi bentuk tiga dimensi pohon, khususnya pada bagian batang, karena akuisisi data yang dilakukan dari atas. Dengan adanya teknologi handheld-SLAM LiDAR, diharapkan keterbatasan tersebut dapat diatasi, sehingga menghasilkan pemodelan pohon yang lebih akurat. Lokasi survei penelitian ini adalah sebagian kecil area Taman Hutan Raya di Bandung, dengan tutupan lahan yang terdiri dari pepohonan. Akuisisi data dilakukan menggunakan dua perangkat LiDAR: handheld LiDAR SATLAB Lixel X1 dan DJI Zenmuse L1 yang diterbangkan menggunakan DJI Matrice 300RTK. Data yang diperoleh dari kedua alat ini kemudian diolah menggunakan perangkat lunak pengolah point cloud untuk menghasilkan data bebas noise. Hasil pengolahan ini digunakan untuk menganalisis karakteristik pohon, termasuk tree crown dan tree trunk. Penelitian ini diharapkan dapat menyimpulkan perbandingan efektivitas kedua metode akuisisi LiDAR dalam merekonstruksi bentuk nyata pohon, serta menunjukkan sejauh mana data dari kedua metode tersebut dapat diintegrasikan untuk menghasilkan model pohon yang lebih akurat dan komprehensif.

Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness of tree crowns within predefined clipping boundaries (e.g., rectangles), we develop a tree crown edge-aware (E-A) point cloud clipping algorithm, named E-A algorithm. Firstly, the crown edge and distance features are enhanced and quantified using mathematical morphology and nearest neighbor pixel methods. Then, these two features are linearly weighted and integrated for cutting line detection. Finally, the optimal cutting lines are detected by exploring a set of edges with the minimum sum of integrated feature values. E-A algorithm was tested with airborne LiDAR point clouds collected from China’s Saihanba Forest Farm, comparing it against regular clipping methods. The results indicate that E-A algorithm can automatically and effectively emphasize preserving tree crown completeness within predefined clipping boundaries. It reduces crown fragmentation errors by 73.29% on average and maintains an average area difference of 6.42% compared to regular clippings. This algorithm provides a crucial tool for forest point cloud applications.

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.

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.

Individual tree crown delineation (ITCD) employing unmanned aerial vehicle light detection and ranging data can directly obtain high-precision tree-level structural information within a block, with this information being the foundation for monitoring and management of the forest, thus reducing time-consuming labor. Despite the fact that numerous ITCD algorithms have been proposed, there has not yet been a robust and comprehensive comparison of these algorithms in plantations. In this article, we evaluated the performance of seven classic ITCD methods under various stand densities and crown classes and analyzed the parameter sensitivity as well as the correlation of segmentation accuracy with optimal parameters and stand metrics. The results demonstrate that the segmentation and crown description accuracy, stability, and adaptability of the algorithm should be comprehensively considered when choosing an algorithm. The forest characteristics impact the accuracy of the algorithms, and the complexity of the forest canopy structure and omission error of suppressed trees are the key factors impacting ITCD accuracy. Furthermore, this study shows that it is feasible to control the parameters of the algorithm through stand measurement. These results will be helpful in guiding the selection of ITCD methods and will provide support for improving the ITCD algorithm in the future.

The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels.

Tree canopy plays an essential role in the biophysical activities in forest environment. During the past two decades, individual tree delineation using high-resolution imagery data has become a hot topic in forest sensing research. Individual Tree Crown (ITC) segmentation methods aim to generate masks that delineating the boundary of each ITC, which supports various tree parameter extractions. Thanks to the rapid development of deep learning, the ITC segmentation methods achieved remarkable improvement. However, existing research suffers from the limited availability of the datasets, and the lack of evaluation standards as well as task-orientated neural networks. In 2024, the International Society of Photogrammetry and Remote Sensing (ISPRS) launched the first International Contest of ITC Segmentation. The contest aims to reveal the state-of-the-art of the ITC method development using high-resolution images, to clarify the remaining barriers and challenges, and to guide further explorations in the field. This paper overviews the contest and reports the initial finding regarding the impact factors of the method performance.

The frequency of forest disturbances has increased in recent years, provoking widespread defoliation, crown dieback and tree mortality. The ICP Forests monitoring network offers a unique platform for observing the impacts on forests of heatwaves, droughts and other extreme climatic events, as well as the trends of defoliation and mortality. The Italian ICP Forests Level I network consists of 261 permanent plots where tree crown defoliation and damage symptoms are assessed visually each year by well-trained crews of the Corpo Forestale dello Stato (2001-2016) and the Carabinieri Forestale from 2017 onward. This paper aims to assess the main tree species’ responses, in terms of defoliation and mortality, to severe climatic events. The results are discussed in relation to species-specific physiological behaviour and bioclimatic regions. A significant trend toward increasing defoliation and mortality has been observed since 2010 in both conifers and broadleaves. Conifers (especially Picea abies), which are largely diffuse in the Alpine regions, have suffered from bark beetle outbreaks due to severe windstorms (such as Vaia in 2018) and recurrent dry years. In the temperate regions, characterised by deciduous broadleaved trees, the most relevant defoliation events coincided with the driest and hottest years, with low relative humidity (2012, 2017 and 2021-2022), only partially recovering in the subsequent years. Among them, Fagus sylvatica and Quercus cerris, along with increased defoliation, showed symptoms caused by fungi of the genus Biscogniauxia, causal agents of “charcoal canker”, in less favourable site conditions. Quercus pubescens was the most resilient species, able to restore its crown after defoliation. The Mediterranean forests, with evergreen broadleaved species, showed no significant trends but were impacted at the most drought-prone coastal sites. The findings evidenced that the current ICP Forests network in Italy represents a fundamental infrastructure for monitoring impacts and trends connected to climate change and species-specific responses. A local intensification of the grid would help to capture under-represented species or ecological conditions.

The effect of the parent tree on seedling recruitment has been studied in various research studies. The Janzen–Connell (JC) hypothesis states that the closer the seedlings are to the source tree, the greater the risk of mortality and/or impact from pathogens and herbivores. Despite the extensive existing literature, there are not many studies that evaluate the influence of crown area, as well as the effects on leaf asymmetry, an important measure of biotic and abiotic stress. (1) This study evaluates the effect of distance from the parent tree and the crown’s area of influence on mortality, growth, and leaf asymmetry of Manilkara zapota seedlings, as well as insect herbivory and damage from leaf pathogens in a Mexican neotropical forest. (2) We selected 10 reproductive adult trees (Diameter at breast height, DBH ~ 10–25 cm) and established four 10 m × 1 m transects around each tree in four directions (north, south, east, and west). Each transect produced 10 quadrants of 1 m², and the quadrant where the shadow of the parent tree extended was marked as either under crown or crown-free. All M. zapota seedlings were counted in each quadrant. For one seedling in each quadrant, we recorded height, leaf asymmetry (LA), insect herbivory, and damage from leaf pathogens. Herbivory by insects, damage from leaf pathogens, and LA were only measured on the newest leaves. Mortality was determined after 9 months per quadrant, as well as light availability (photosynthetic photon flux density), temperature, and relative humidity. (3) We found that mortality and relative growth rate (RGRHeight) increased near and under the parent tree. Furthermore, LA decreased at greater distances from the parent tree and only outside the crown’s influence. Additionally, LA had a strong positive influence on damage caused by insect herbivory and leaf pathogens, impacting both more strongly under the crown. A high dependency of leaf pathogens on damage from insect herbivory was also recorded. Finally, the most frequent type of herbivory was that caused by chewing insects. (4) To our knowledge, we present one of the few studies that has addressed the JC hypothesis, considering not only the distance from the parent tree and seedling density but also the influence of the crown on the performance of M. zapota seedlings. Studies that consider the influence of the microenvironment are of fundamental importance for a comprehensive understanding of the JC hypothesis.

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.

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (Ar) of 96.60%. Compared to the two conventional ITC delineation methods, the Ar of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time.

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data.

For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites.

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.

Single-tree segmentation can greatly contribute to the extraction of forest structure parameters. The LiDAR point cloud from unmanned aerial vehicle (UAV) was a commonly used data source for the single tree segmentation. However, the point cloud data was large, and the processing was complex. This study aimed to accurately segment the single-tree crown from the point cloud data using deep learning. The main purpose was to improve the processing of point cloud data and the accuracy of single tree segmentation in complex stands. Point cloud rasterization was combined with deep learning to segment the canopy of a single tree. Firstly, the D2000 UAV (Feima Robotics company) platform equipped with the LiDAR sensor (DLiDAR2000) was used to acquire the point cloud data of a mixed coniferous and broadleaf forest. Lidar360 software was then employed for point cloud denoising, ground point classification, and point cloud normalization preprocessing. Subsequently, the top-down rasterization of the sample plot cloud was performed to calculate the maximum height, maximum intensity, and density information per unit rasterized area of the point cloud. The RGB channels were then mapped corresponding to the rasterized image pixels, in order to make the tree canopy clearer in the rasterized image. Secondly, according to the Mask RCNN model within the Detectron2 framework, the number of layers and iterations of different backbone networks were compared to select a backbone network that provided superior segmentation performance. Thirdly, the Global Context Network (GC) and Attention Mechanism modules were integrated into the ResNet network. A comparison was made with the simultaneous introduction of the GC Net and Attention Mechanism modules to enhance the segmentation accuracy of the Mask R-CNN model. To validate the practicality of the improved Mask R-CNN model, its segmentation accuracy was compared with that of similar deep learning networks (U-Net and DeepLabv3+). Finally, the tree crown masks that were segmented by the improved Mask R-CNN were used to segment the point cloud of individual tree crowns. The segmentation of the test plot was compared and evaluated using the watershed, K-means, and the improved Mask R-CNN. Among the three backbone networks of Mask R-CNN, the R50-FPN-3X network saved some training time and computational resources, compared with the R101- FPN-3X network. An average accuracy of 76.72% was achieved to increase by 1.01 percentage points higher than that of the R50-FPN-1X network. In the R50-FPN-3X backbone model, after introducing the Squeeze-and-Excitation (SE), Coordinate Attention (CA), and Convolutional Block Attention Module (CBAM) mechanisms, the average accuracy increased by 1.41, 2.14, and 4.65 percentage points compared to the original model, respectively. The GC Net module was integrated into the ResNet network, and the model accuracy was 80.35%, which was an improvement of 3.63 percentage points over the original model. While both CBAM and GC Net attention mechanisms achieved the highest accuracy of 82.91%, thus increasing by 1.54 percentage points and 2.56 percentage points, compared with the two modules alone. The improved Mask R-CNN model achieved an average accuracy that was 7.27 percentage points higher than the U-Net model and 4.62 percentage points higher than the DeepLabv3+ model. The improved Mask R-CNN outperformed the Watershed and K-means algorithms in single-tree crown point cloud segmentation, indicating the highest recall, precision, and F-score of 81.19%, 78.85%, and 80.00%, respectively. The point cloud segmentation with the improved Mask R-CNN network demonstrated the robustness of the tree crown segmentation in mixed coniferous and broadleaf forests. Point cloud data processing was also integrated with deep learning models. The accuracy of single-tree crown segmentation was enhanced significantly, thereby providing reliable foundational data and technical references to assess the forest resource, biomass, and carbon stock. [ABSTRACT FROM AUTHOR]

Accurate segmentation of individual tree crowns (ITC) segmentation is essential for investigating tree-level based growth trends and assessing tree vitality. ITC segmentation using remote sensing data faces challenges due to crown heterogeneity, overlapping crowns and data quality. Currently, both classical and deep learning methods have been employed for crown detection and segmentation. However, the effectiveness of deep learning based approaches is limited by the need for high-quality annotated datasets. Benefiting from the BaKIM project, a high-quality annotated dataset can be provided and tested with a Mask Region-based Convolutional Neural Network (Mask R-CNN). In addition, we have used the deep learning based approach to detect the tree locations thus refining the previous Marker controlled Watershed Transformation (MCWST) segmentation approach. The experimental results show that the Mask R-CNN model exhibits better model performance and less time cost compared to the MCWST algorithm for ITC segmentation. In summary, the proposed framework can achieve robust and fast ITC segmentation, which has the potential to support various forest applications such as tree vitality estimation.

Urban tree classification enables informed decision-making processes in urban planning and management. This paper introduces a novel data reformation method, pseudo tree crown (PTC), which enhances the feature difference in the input layer and results in the improvement of the accuracy and efficiency of urban tree classification by utilizing artificial intelligence (AI) techniques. The study involved a comparative analysis of the performance of various machine learning (ML) classifiers. The results revealed a significant enhancement in classification accuracy, with an improvement exceeding 10% observed when high spatial resolution imagery captured by an unmanned aerial vehicle (UAV) was utilized. Furthermore, the study found an impressive average classification accuracy of 93% achieved by a classifier built on the PyTorch framework, with ResNet50 leveraged as its convolutional neural network layer. These findings underscore the potential of AI-driven approaches in advancing urban tree classification methodologies for enhanced urban planning and management practices.

Accurate mangrove biomass measurement is necessary for the management and protection of mangrove ecosystems. Sonneratia apetala was the first high-quality mangrove species to be introduced for mangrove restoration in China. Compared with other mangrove species, Sonneratia apetala has higher productivity and can store large amounts of carbon in its living biomass. However, accurate depiction of the single-wood canopy of Sonneratia apetala is challenging because of its high clumping density and intricate canopy structure. While traditional satellite remote sensing focuses on regional or larger-scale monitoring needs, the newly emerged Unmanned Aerial Vehicle (UAV) remote sensing has significant advantages for monitoring mangroves at finer scales. However, few studies have used UAV data for mangrove biomass analyses. In this study, we successfully used consumer-grade UAV data to estimate the height and aboveground biomass (AGB) of Sonneratia apetala on Qi'ao Island, Zhuhai, Guangdong Province. We used a variable window filter algorithm to detect the treetops. Individual tree canopy segmentation was performed using the seed region-growing algorithm. Additionally, we constructed a regression equation for height (H) and diameter at breast height (DBH) of Sonneratia apetala in the study area and optimized the traditional allometric equation. Finally, mangrove AGB was estimated at the tree level using the optimized allometric equation, and the results indicated that the AGB of Sonneratia apetala could be accurately extracted using UAV images. The accuracy of the tree delineation was 67%, the correlation between H and DBH was DBH = 2.2726H-6.4415, and the correlation coefficient R2 was 0.8713. The aboveground mass of a single wood of Sonneratia apetala in the study area ranged from 29.60 to 388.44 kg, with a mean value of 145.72 kg and a total aboveground mass of 368.97 t. Partial spatial clustering was observed in the distribution of the aboveground mass of Sonneratia apetala in the study area, with a Moran's I index value of 0.594. The aboveground mass of Sonneratia apetala at the edge of the area and in the window part of the area was found to be smaller, mainly for two possible reasons. First, the natural death of Sonneratia apetala in part of the study area created a window, but its surrounding seedlings were still in the biomass accumulation stage. Second, the aboveground mass of Sonneratia apetala at the edges of the study area is often lost due to its vulnerable nature and anthropogenic factors. The average number of Sonneratia apetala in each quadrat was 6.3 and the AGB in the study area ranged from 2.99 to 247.24 t/hm2, with a mean value of 92.14 t/hm2. The estimated AGB based on UAV data was consistently lower than that generated from field data. The methods described in this study offer the possibility of easily repeatable, low-cost UAV surveys, providing a faster and more economical approach for monitoring mangrove forests than traditional ground surveys. These results may assist in decision-making regarding ecological monitoring, resource use, mangrove introduction, and scientific advancement of mangroves in China.

Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade-off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.

Urbanisation is one of the most severe land use changes with significant negative impacts on several biological groups. However, the response of arthropods to this process is still unclear, especially in cities located in arid regions, which represent an important part of global urban ecosystems. Here, we examined variations in abundance, richness, diversity and taxonomic composition of the tree crown‐dwelling arthropods in 10 Brazilian cities of semiarid climate located in the dry forest region (Caatinga), taking into account temperature gradients and number of street trees along the cities. We expected that cooler (in a hot range of temperature) and more forested cities would present richer, more abundant and distinct communities than warmer cities. This hypothesis is supported by the large amount of evidence showing the negative effects of temperature on the local structuring of biological communities. We used the method of the arboreal arthropod collector to sample the arthropod community inhabiting trees crowns up to 10 m in height. We collected a total of 22,911 arthropod specimens belonging to two classes (Insecta and Arachnida) and 24 orders. As expected, temperature (min 21.7°C, max 26.8°C) proved to be a significant predictor of arthropod diversity in semiarid cities. Cities with higher temperatures reduce taxonomic unit richness (0D) by 33% and diversity (1D and 2D) in up to 75% and affect composition of arthropod orders composition. On the other hand, the effect of tree numbers showed distinct responses among the sampled orders, positively contributing to the abundance of Psocoptera, while exerting a negative effect on the abundance of Thysanoptera. Overall, our findings highlight the importance of temperature and number of trees in determining urban arthropod fauna. [ABSTRACT FROM AUTHOR]

Urban trees provide vital ecosystem services, and assessing their health is crucial for managing urban infrastructure. Traditional methods of assessing crown density, an indicator of tree vitality, involve horizontal perspectives of unobstructed canopies. This study presents a novel method for estimating crown density in urban street trees that are surrounded by obstructing objects like buildings. The approach is based on photographs of the tree crown from defined positions using a smartphone. The method was validated on eight small-leaved lime trees in Leipzig during the 2021 vegetation period, demonstrating that crown density can be estimated by analyzing smartphone-photographs from various perspectives. The method provides data to quantify crown development and can be used to compare the vitality status of individual trees. The different perspectives are consistent in their estimates of crown density throughout the annual plateau phase of crown development. During the initial greening phase, crown photographs taken from angularly oriented positions showed a higher slope value than those taken from other positions. The method can also estimate the effect of blue-green infrastructures on tree vitality compared to regular urban tree planting methods. The approach is a practical and cost-effective tool for assessing tree vitality in spatially confined urban areas. HIGHLIGHTS Crown densities were consistently estimated for eight street trees in 2021.; The method can compare vitality, quantify temporal shift, and study climate change effects.; Smartphone photography was used to estimate crown densities of street trees using different perspectives.; The number of photos can be considerably reduced to optimize monitoring efforts.;