Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

Uciechowska-Grakowicz, Anna; Herrera-Granados, Oscar; Biernat, Stanisław; Bac-Bronowicz, Joanna

doi:10.3390/rs15245776

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Accurate forest biomass estimation is essential for forestry management [10], enabling the determination of forest productivity, wood resources, and bioenergy potential. This involves monitoring changes in tree crowns and biomass quantity with respect to variations in air pollution, carbon dioxide (CO 2 ) levels, temperature, and precipitation [11][12][13][14]. Based on tree biomass, the absorption of carbon dioxide from the atmosphere can be calculated using indicators, like Leaf Area Index (LAI) [15][16][17][18] and other coefficients extensively described in [19].…”

Section: Introductionmentioning

confidence: 99%

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

Tarsha Kurdi,

Lewandowicz,

Gharineiat

et al. 2024

Remote Sensing

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This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, each of which is further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. For a tree dimensions estimation, the study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees. The maximum absolute quantitative accuracy equals 0.49 m, and the maximum relative absolute error equals 0.29%.

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Section: Introductionmentioning

confidence: 99%

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

Tarsha Kurdi,

Lewandowicz,

Gharineiat

et al. 2024

Remote Sensing

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LiDAR and maps blend for rural decision support

Marković,

Potić,

Đorđević

et al. 2024

Transactions in GIS

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This study integrates aerial LiDAR data and 2D cartographic information to rapidly develop an advanced non‐photorealistic rendering (NPR) model for rural environment analysis. The focus is enhancing decision support in crises and assessing potential hazards in these territories. The methodology involves capturing LiDAR data from high altitudes and classifying it as Ground, Vegetation, and Buildings. The integration of this data with 2D cartographic information, augmented with attribute data from a GIS database, is achieved through a semi‐automatic process. This process facilitates the creation of detailed 3D models, providing a more nuanced, visually and semantically rich representation of the rural landscape. The study underscores the benefits of combining LiDAR, photogrammetric, and cartographic data for creating accurate and detailed models of the rural environment, which are crucial for effective decision‐making and threat assessment.

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Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

Koszyk,

Jasińska,

Pargieła

et al. 2024

Remote Sensing

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Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

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Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

Cited by 3 publications

References 27 publications

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

LiDAR and maps blend for rural decision support

Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

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