Multi-platform LiDAR approach for detecting coarse woody debris in a landscape with varied ground cover

Shokirov, Shukhrat; Schaefer, Michael; Levick, Shaun R.; Jucker, Tommaso; Abdurahmanov, Ilhom; Youngentob, Kara N.

doi:10.1080/01431161.2021.1995072

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…Compared to previous research, the proposed approach produced a robust result in classifying CWD material with one of the highest accuracy values, whereas the performance in detecting stumps was fairly good, falling just outside of the range of values described by Puliti et al (2018), maybe due to the difference in tree species as object of investigation. Compared to only the studies reporting the use of the same Random Forest model to classify CWD (Queiroz et al 2019;Shokirov et al 2021), the accuracy values presented in this study show higher results in both overall accuracy and class accuracy.…”

Section: Classi Cation and Validationmentioning

confidence: 56%

Enhancing precision in quantification and spatial distribution of logging residues in plantation stands

Udali,

Talbot,

Ackerman

et al. 2023

Preprint

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Forests, essential components of ecosystems, are managed for sustainable timber production in forest plantations to meet the growing demand for wood products. The intricate balance between sustainable forest management and logging residue practices is crucial for ecological integrity and economic viability. Logging residues, byproducts of timber harvesting, significantly influence carbon and nutrient cycling, soil structure, and overall ecosystem health. Recent technological advancements, particularly the use of drones integrated with artificial intelligence, enable the processing of large datasets, providing meaningful insights into logging residues and forest dynamics. This study aims to evaluate the quantification and distribution of logging residues in forest plantations, utilizing machine learning classification models fed with drone-based images. The classification was performed using a Random Forest model fed with spectral and terrain variables, whereas the volume estimations were derived from field measurements and from the drone classification. Overall the classification achieved solid results (Overall Accuracy of 0.89), and the volume estimation resulting in solid comparison with field estimation (ratio 0.43–1.37), but poor correlation (R2 of 0.17 and 0.31). We concluded that the proposed methodology is suitable for classifying and assessing residues distribution over recently harvested areas, but further improvement of the volume estimation methodology is necessary to ensure comprehensive and precise assessment of residue distribution over recently harvested areas.

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Section: Classi Cation and Validationmentioning

confidence: 56%

Enhancing precision in quantification and spatial distribution of logging residues in plantation stands

Udali,

Talbot,

Ackerman

et al. 2023

Preprint

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“…These are attributable to deadwood, because deforestation processes took place in the Kall valley in the past years [4]. Other studies confirm that laser scanning is a widely used remote sensing method to detect coarse woody debris [75], [76], however, improper filtering of the LiDAR data can be problematic when trying to separate ground from deadwood [77]. After applying the splines to the filter result (Fig.…”

Section: Discussionmentioning

confidence: 88%

Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

Storch¹,

Lange²,

Jarmer³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42 percentage points in the area with dense low vegetation and by up to 14 percentage points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1score for each filter. Therefore, our concept can support on-site field prospection.

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“…Assuming that the adjacent points have a higher probability of belonging to the same class, we adopted the majority filtering strategy to minimize the inconsistencies associated to roof ridges and those located in the middle of canopies. This filter is typically used in post-processing of images subject to classification and is available in some image processing softwares [42]. In our approach, it is adapted to be applied in the 3D point cloud.…”

Section: Building Detection Refinementmentioning

confidence: 99%

Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach

dos Santos,

Galo

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Building detection plays an important role in urban applications and is usually a prerequisite for contour extraction and building modelling. Over the last decades, airborne LiDAR data have been used due to its capability to represent terrestrial surfaces and objects with high geometric quality. In this study, it is proposed a novel building detection approach based on geometric/morphological object characteristics. The proposed strategy is divided into three main stages: i) selection of candidate points based on height; ii) building detection using geometric feature (omnivariance) and K-means clustering algorithm; and iii) refinement based on majority filter and mathematical morphology. The experiments were conducted using airborne LiDAR datasets with varying point density acquired in different urban environments. The results indicated the robustness of the proposed approach for all datasets and environment complexities, presenting average Fscore around 96%. In addition, the results pointed out that point density can impact the building detection, producing better results for higher point density datasets. Compared with related approaches, the proposed strategy result in better performance in terms of completeness, producing an omission error rate smaller than 3%.

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Multi-platform LiDAR approach for detecting coarse woody debris in a landscape with varied ground cover

Cited by 10 publications

References 44 publications

Enhancing precision in quantification and spatial distribution of logging residues in plantation stands

Enhancing precision in quantification and spatial distribution of logging residues in plantation stands

Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach

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