Valuation and production possibilities on a working forest using multi-objective programming, Woodstock, timber NPV, and carbon storage and sequestration

Roise, Joseph; Harnish, K.; Mohan, Midhun; Scolforo, Henrique Ferraço; Chung, Joo-Sang; Kanieski, Bruno; Catts, Glenn P.; McCarter, James B.; Posse, J.; Shen, Tongde

doi:10.1080/02827581.2016.1220617

Cited by 19 publications

(12 citation statements)

References 28 publications

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“…They also noticed that interpolating the intensity raster was not very helpful for improving classification results; even though using intensity rasters with both first and last returns slightly benefited the study. In essence, our strategy of using intensity features for classification resulted in accuracies similar to related studies, and considering the boost it gave to the overall classification (18.62%) obtained through the combination of intensity image with PseudoNDVI, nDSM, and HMP features, this method may be efficient for future multispectral LiDAR endeavors; e.g., plant species classification [ 61 – 63 ], urban change detection [ 64 , 65 ], flood inundation mapping [ 66 ] and even carbon sequestration modeling [ 67 ].…”

Section: Discussionsupporting

confidence: 68%

Supervised spatial classification of multispectral LiDAR data in urban areas

et al. 2018

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Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%.

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Section: Discussionsupporting

confidence: 68%

Supervised spatial classification of multispectral LiDAR data in urban areas

et al. 2018

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“…The observed and computed tree density in the study area from the UAV-derived CHM were 305 and 300 trees per hectare (TPH; trees·ha −1 ), respectively. The most accurate results in the ITD were obtained primarily in test subplots with TPH ranging from 150 to 325 trees·ha −1 (Plot FID: 4,13,16,19,22,24,25,26). On average, 93.2% of trees were detected correctly, with commission and omission errors limited to 8.7% and 6.8%, respectively, with a F-score of 0.92.…”

Section: Resultsmentioning

confidence: 82%

“…Over the years, remote sensing techniques have been increasingly used for assessing forest resources, both directly and indirectly [10][11][12][13]. Aerial photography, light detection and ranging (LiDAR) and airborne multispectral, and hyperspectral images had been perceived as potential tools for observing forest areas and for performing broad-scale analysis of forest systems.…”

mentioning

confidence: 99%

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

325

232

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Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

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“…Secondly, tracking the dynamics of the largest trees can account for a large carbon fraction in tropical forests that are especially vulnerable to drought, acting as an early warning signal to climate change [62,63]. Frequent LiDAR surveys could be valuable for this, but only the area-based approach has been used to date [63,64]. Additionally, the ITC approach may best match field-based techniques to measure forest carbon and has a strong theoretical basis in minimising bias and associated uncertainty [65].…”

Section: Discussionmentioning

confidence: 99%

Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Jaafar

Woodhouse

Silva

et al. 2018

Forests

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Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

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Valuation and production possibilities on a working forest using multi-objective programming, Woodstock, timber NPV, and carbon storage and sequestration

Cited by 19 publications

References 28 publications

Supervised spatial classification of multispectral LiDAR data in urban areas

Supervised spatial classification of multispectral LiDAR data in urban areas

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

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