Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data

GEOMATICA, Год журнала: 2025, Номер unknown, С. 100047 - 100047

Опубликована: Янв. 1, 2025

Язык: Английский

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Drone LiDAR Occlusion Analysis and Simulation from Retrieved Pathways to Improve Ground Mapping of Forested Environments

Drones, Год журнала: 2025, Номер 9(2), С. 135 - 135

Опубликована: Фев. 12, 2025

Drone-mounted LiDAR systems have revolutionized forest mapping, but data quality is often compromised by occlusions caused vegetation and terrain features. This study presents a novel framework for analyzing predicting occlusion patterns in forested environments, combining the geometric reconstruction of flight paths with statistical modeling ground visibility. Using field collected at Unzen Volcano, Japan, we first developed an algorithm to retrieve drone from timestamped pointclouds, enabling post-processing optimization, even when original are unavailable. We then created mathematical model quantify shadow effects obstacles implemented Monte Carlo simulations optimize parameters different stand characteristics. The results demonstrate that lower-altitude flights (40 m) narrow scanning angles achieve highest visibility (81%) require more paths, while higher-altitude wider offer efficient coverage (47% visibility) single paths. For 250 trees per 25 hectares (heights 5–15 m), analysis showed above 90 degrees consistently delivered 46–47% visibility, regardless height. research provides quantitative guidance optimizing surveys though future work needed incorporate canopy complexity seasonal variations.

Язык: Английский

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Comparative evaluation of machine learning models for UAV-derived biomass estimation in Miombo Woodlands

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 19, 2025

Язык: Английский

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Modeling LiDAR-Derived 3D Structural Metric Estimates of Individual Tree Aboveground Biomass in Urban Forests: A Systematic Review of Empirical Studies

Forests, Год журнала: 2025, Номер 16(3), С. 390 - 390

Опубликована: Фев. 22, 2025

The aboveground biomass (AGB) of individual trees is a critical indicator for assessing urban forest productivity and carbon storage. In the context global warming, it plays pivotal role in understanding sequestration regulating cycle. Recent advances light detection ranging (LiDAR) have enabled detailed characterization three-dimensional (3D) structures, significantly enhancing accuracy tree AGB estimation. This review examines studies that use LiDAR-derived 3D structural metrics to model estimate AGB, identifying key influence estimation accuracy. A bibliometric analysis 795 relevant articles from Web Science Core Collection was conducted using R Studio (version 4.4.1) VOSviewer 1.6.20 software, followed by an in-depth 80 papers focused on forests, published after 2010 selected first second quartiles Chinese Academy Sciences journal ranking. results show following: (1) Dalponte2016 watershed are more widely used among 2D raster-based algorithms, point cloud-based segmentation algorithms offer greater potential innovation; (2) height crown volume important estimation, indices integrate these parameters can further improve applicability; (3) machine learning such as Random Forest deep consistently outperform parametric methods, delivering stable estimates; (4) LiDAR data sources, cloud density, types factors affect Future research should emphasize applications improving structure extraction complex environments. Additionally, optimizing multi-sensor fusion strategies address matching resolution differences will be crucial developing accurate applicable models.

Язык: Английский

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Urban Tree Carbon Storage Estimation Using Unmanned Aerial Vehicles Remote Sensing

Urban forestry & urban greening, Год журнала: 2025, Номер unknown, С. 128755 - 128755

Опубликована: Март 1, 2025

Язык: Английский

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Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 1010 - 1010

Опубликована: Март 13, 2025

Unpiloted aerial systems (UAS) and light detection ranging (lidar) sensors provide users with an increasingly accessible mechanism for precision forestry. As these technologies are further adopted, questions arise as to how select processing methods influencing subsequent high-resolution modelling analysis. This study addresses specific individual tree (ITD) impact the successful of trees varying sizes within complex forests. First, while many studies have compared ITD over several sites, algorithms, or sets parameters based on a singular validation metric, this quantifies 10 perform across tree-height size quartiles diameter at breast height (dbh) quartiles. In total, 1000 reference from 20 species three temperate forest sites were analyzed average point density 826.8 pts/m2. The results indicate that four classes, highest overall F-score (0.7344) was achieved F-scores 0.857 largest 0.633 smallest class. To expand analysis, generalized linear models used compare top performing worst method each variable site along continuous gradient. analysis suggests clear distinctions in performance (true positive false rates) method. UAS-lidar must ensure demonstrated validated ways communicate their relative effectiveness all sizes. Without such consideration, show surveys management conducted using may not accurately characterize present

Язык: Английский

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Comparing LiDAR-generated above-ground biomass with field data in an old-growth native urban forest in Australia

Sustainable Horizons, Год журнала: 2025, Номер 15, С. 100147 - 100147

Опубликована: Май 13, 2025

Язык: Английский

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Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Agriculture, Год журнала: 2025, Номер 15(10), С. 1072 - 1072

Опубликована: Май 16, 2025

Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands suboptimal model accuracy. To address these limitations, we proposed an integrated framework combining UAV imagery, simulated data, E2D-COS feature selection, deep neural network (DNN), transfer learning (TL). The algorithm data was used identify structure-resistant strongly correlated LCC: Big trumpet stage: 418 nm, 453 506 587 640 688 767 nm; Spinning 541 559 723 nm. Combining the selection TL DNN significantly improves accuracy: R2 Maize-LCNet is improved by 0.06–0.11 RMSE reduced 0.57–1.06 g/cm compared LCNet-field. Compared existing studies, this study not only clarifies that are able estimate chlorophyll, but also presents high-performance, lightweight (fewer input) approach achieve accurate in maize, which can directly support growth monitoring nutrient management at specific stages, thus contributing smart agricultural practices.

Язык: Английский

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Quantifying the Carbon Stocks in Urban Trees: The Rio de Janeiro Botanical Garden as an Important Tropical Carbon Sink

Journal of Zoological and Botanical Gardens, Год журнала: 2024, Номер 5(4), С. 579 - 589

Опубликована: Окт. 4, 2024

The rapid urbanization process in recent decades has altered the carbon cycle and exacerbated impact of climate change, prompting many cities to develop tree planting green area preservation as mitigation adaptation measures. While numerous studies have estimated stocks urban trees temperate subtropical cities, data from tropical regions, including botanic gardens, are scarce. This study aimed quantify aboveground biomass (AGB AGC, respectively) at Rio de Janeiro Botanical Garden arboretum, Janeiro, Brazil. Our survey included 6793 stems with a diameter breast height (DBH) ≥ 10 cm. total AGB was 8047 ± 402 Mg, representing 4024 201 Mg AGC. density 207 Mg·ha−1 (AGC = 104 5 Mg·ha−1), which is slightly lower than stored Brazil’s main forest complexes, Atlantic Amazon forests, but much higher worldwide. results suggest that, addition their global importance for plant conservation, gardens could function significant sinks within matrix.

Язык: Английский

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Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images

Remote Sensing, Год журнала: 2024, Номер 16(22), С. 4287 - 4287

Опубликована: Ноя. 17, 2024

Although linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based methods exhibit several limitations: (1) They are often sensitive noise and demonstrate weak robustness; (2) these ignore local information inherent in data; (3) number of extracted features is restricted by classes. To address drawbacks, this paper proposes a novel joint sparse (JSLLDA) method integrating embedding regression locality-preserving regularization into LDA model for feature dimensionality reduction HSIs. In JSLLDA, row-sparse projection matrix can be learned, uncover structure data imposing L2,1-norm constraint. The also employed measure reconstruction error, thereby mitigating effects occlusions. A locality preservation term incorporated fully leverage geometric structural data, enhancing discriminability learned projection. Furthermore, an orthogonal introduced alleviate limitation on acquired features. Finally, extensive experiments conducted three datasets demonstrated that performance JSLLDA surpassed some related state-of-the-art methods.

Язык: Английский

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