Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models

Tu, Jingzhi; Yang, Guoxiang; Qi, Pian; Ding, Zengyu; Mei, Gang

doi:10.7717/peerj-cs.263

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…Prior to SFL estimation, we processed the dataset further. The point cloud covering each plot was extracted and height-normalized using the k-nearest neighbor approach with an inverse-distance weighting (kNN-IDW) [82]. Overlapping points derived from multiple flightlines were subsequently eliminated and the resulting point clouds were used for the extraction of last-of-many (lm), first-of-many (fm), and single (s) returns.…”

Section: Lidar Data Preprocessingmentioning

confidence: 99%

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.

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Section: Lidar Data Preprocessingmentioning

confidence: 99%

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

et al. 2020

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“…Therefore, it is essential to carefully choose the appropriate algorithm based on the specific characteristics of the interpolation scene, including the terrain type, initial data density, and distribution mode. These factors can affect both the accuracy and computational efficiency of the interpolation (Tu et al, 2020). However, traditional methods may not be able to handle the non-linear errors in elevation for large-scale datasets such as SDB and satellite LiDAR (Salah, 2021).…”

Section: Traditional Interpolation Algorithmsmentioning

confidence: 99%

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

Peng

Zhang

et al. 2023

Front. Mar. Sci.

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Building a high-precision bathymetry digital elevation model is essential for navigation planning, marine and lake resource planning, port construction, and underwater archaeological projects. However, existing bathymetry methods have yet to be effectively and comparatively analyzed. This paper comprehensively reviews state-of-the-art bathymetry methods, including data acquisition techniques, model accuracy, and interpolation algorithms for underwater terrain mapping. First, We assess the merits and drawbacks of novel data acquisition devices, such as single-beam/multi-beam echo sounders and light detection and ranging systems. After that, we analyze the accuracy of the ETOPO1, GEBCO_2022 and SRTM15 to provide valuable insights into their performance. Furthermore, we evaluate ANUDEM, Inverse Distance Weighting, Kriging and Nearest Neighbor interpolation algorithms in different underwater terrains by comparing their applicability, reliability, and accuracy in various underwater environments. Finally, we discuss the development trends and challenges in underwater bathymetry technology and offer a forward-looking perspective on the future of this essential field.

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“…The points of the filtered cloud were labeled as ground and non-ground using the cloth simulation algorithm [32]. Subsequently, the ground points were interpolated using the k-nearest neighbor approach with an inverse distance weighting, constructing the digital terrain model (DTM) [33]. The produced DTM was eventually used to eliminate the terrain effects, resulting in a height-normalized point cloud.…”

Section: Tree Registry Generationmentioning

confidence: 99%

Supporting Operational Tree Marking Activities through Airborne LiDAR Data in the Frame of Sustainable Forest Management

Georgopoulos,

Stefanidou,

Gitas

2023

Forests

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Implementing adaptation and mitigation strategies in forest management constitutes a primary tool for climate change mitigation. To the best of our knowledge, very little research so far has examined light detection and ranging (LiDAR) technology as a decision tool for operational cut-tree marking. This study focused on investigating the potential of airborne LiDAR data in enhancing operational tree marking in a dense, multi-layered forest over complex terrain for actively supporting long-term sustainable forest management. A detailed tree registry and density maps were produced and evaluated for their accuracy using field data. The derived information was subsequently employed to estimate additional tree parameters (e.g., biomass and tree-sequestrated carbon). An integrated methodology was finally proposed using the developed products for supporting the time- and effort-efficient operational cut-tree marking. The results showcased the low detection ability (R2 = 0.15–0.20) of the trees with low DBH (i.e., regeneration and understory trees), while the dominant trees were accurately detected (R2 = 0.61). The stem biomass was accurately estimated, presenting an R2 of 0.67. Overall, despite some products’ low accuracy, their full and efficient exploitability within the aforementioned proposed methodology has been endeavored with the aim of actively contributing to long-term sustainable forest management.

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Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models

Cited by 6 publications

References 38 publications

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

Supporting Operational Tree Marking Activities through Airborne LiDAR Data in the Frame of Sustainable Forest Management

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