Stem and root assessment in mangrove forests using a low-cost, rapid-scan terrestrial laser scanner

Kargar, Ali Rouzbeh; MacKenzie, Richard A.; Apwong, M.; Hughes, Ethan G.; Aardt, Jan van

doi:10.1007/s11273-020-09753-w

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…Significant variation in DBH was noticed because thin trees (below 7 cm DBH) were omitted during field survey. As the mangroves of India including our study area are prohibited for biophysical parameter estimation using destructive methods, we could compare the AGB estimation of our study against the estimation using allometric equation as we could see from other research by Kargar et al (2020) [61]. In their study, the mangroves of Federated States of Micronesia have been surveyed using TLS for the detection of stems and roots using integrated RF and SVM methods and constructing tree structure using alpha shapes and convex hull.…”

Section: Discussionmentioning

confidence: 95%

Estimation of Above Ground Volume of Mangrove Forest Trees from Terrestrial LiDAR Data using Supervised Machine Learning Algorithms

Adimoolam¹,

Pillai²,

Gnanappazham³

et al. 2022

Preprint

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Accurately quantifying the above-ground volume (AGV) and thus above-ground biomass (AGB) of forest stands is an important aspect in the conservation of mangrove ecosystem owing to their ecological and economic benefits. However, the number of studies focusing on quantifying mangrove forests’ biomass has been relatively low due to their marshy terrain, making exploratory studies challenging. In recent times, the use of LiDAR technologies in forest inventory studies has become increasingly popular, due to the reliability of LiDAR as a highly accurate means of 3D spatial data acquisition. In this study, we propose an end-to-end methodology for estimating AGV of mangrove forest stands from terrestrial LiDAR data. Many of the recent studies on this topic effectively employ machine learning algorithms such as multi layer perceptron, random forests, etc. for filtering foliage in the point cloud data of single trees. This study further extends that approach by incorporating the impact of class imbalance of forest point cloud data in a weighted random forest classifier. For the task of segmentation of wood/foliage points in a single tree point cloud, this approach yielded an average increase of 2.737% in the balanced accuracy score, 0.007 in the Cohen’s kappa score, 2.745% in the ROC AUC score and 0.857% in the F1 score. For the task of AGV estimation of a single tree, this approach resulted in an average coefficient of determination of 0.93 with respect to the ground truth volumes. For the task of counting pneumatophores in a plot-level point cloud, the proposed breadth-first searching method yielded an average coefficient of determination of 0.9391. Also, the machine learning classifier and geometric features used in this study were invariant to tree species and hence could be generalised for the classification of point clouds of other tree species as well. Finally, a breadth-first graph-search segmentation based approach is also proposed as part of this pipeline to estimate the contribution of pneumatophores to the AGB of mangrove forest stands. Since pneumatophores are a special adaptation of mangrove forests for gaseous exchange in marshy environments, this study aims to incorporate the detection and AGB estimation of pneumatophores in the inventory of mangrove forest stands. Studying the contribution of pneumatophores to the AGB of mangrove forest plots could also aid future mangrove forest inventory studies in modeling the underlying root network and estimating the below-ground biomass of mangrove trees.

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Section: Discussionmentioning

confidence: 95%

Estimation of Above Ground Volume of Mangrove Forest Trees from Terrestrial LiDAR Data using Supervised Machine Learning Algorithms

Adimoolam¹,

Pillai²,

Gnanappazham³

et al. 2022

Preprint

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“…Filter discussion; Section S5: Correlation between Topographic and Field Wetness; Figure S1: Gravimetric Soil Water Content (SWC) as a function of topography-based Saga Wetness Index (SWI) and the fitted regression lines. References [99][100][101][102][103][104][105][106][107][108][109][110][111] are cited in the Supplementary Materials. Institutional Review Board Statement: Not applicable.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

et al. 2022

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Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

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“…Terrestrial laser scanning (TLS) is an effective tool for observing vegetation objects (e.g., tree stems) at the forest holding level because it can acquire detailed three-dimensional (3D) point clouds in a nondestructive way [1,2]. For forest resources inventory, stem detection is the foundation of many applications such as stem mapping [3,4], the estimation of the diameter at breast height (DBH) [5], stem volume prediction [6], and stem reconstruction [7].…”

Section: Introductionmentioning

confidence: 99%

Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation

Chen,

Liu,

Pan

et al. 2023

Forests

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Terrestrial laser scanning (TLS) is an effective tool for extracting stem distribution, providing essential information for forest inventory and ecological studies while also assisting forest managers in monitoring and controlling forest stand density. A feature-based method is commonly integrated into the pipelines of stem detection, facilitating the transition from stem point to stem instance, but most studies focus on feature effectiveness from the point level, neglecting the relationship between stem point extraction and stem detection. In this paper, a feature-based method is proposed to identify stems from TLS data, with features selected from stem levels. Firstly, we propose a series of voxel-based features considering the stem characteristics under the forest. Then, based on the evaluation of some commonly used and proposed features, a stem-based feature selection method is proposed to select a suitable feature combination for stem detection by constructing and evaluating different combinations. Experiments are carried out on three plots with different terrain slopes and tree characteristics, each having a sample plot size of about 8000 m2. The results show that the voxel-based features can supplement the basic features, which improve the average accuracy of stem point extraction and stem detection by 9.5% and 1.2%, respectively. The feature set obtained by the proposed feature selection method achieves a better balance between accuracy and feature number compared with the point-based feature selection method and the features used in previous studies. Moreover, the accuracies of the proposed stem detection methods are also comparable to the three methods evaluated in the international TLS benchmarking project.

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Stem and root assessment in mangrove forests using a low-cost, rapid-scan terrestrial laser scanner

Cited by 11 publications

References 44 publications

Estimation of Above Ground Volume of Mangrove Forest Trees from Terrestrial LiDAR Data using Supervised Machine Learning Algorithms

Estimation of Above Ground Volume of Mangrove Forest Trees from Terrestrial LiDAR Data using Supervised Machine Learning Algorithms

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation

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