A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Nguyen, Hoang Minh; Demir, Begüm; Dalponte, Michele

doi:10.3390/rs11242948

Cited by 12 publications

(7 citation statements)

References 33 publications

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“…Some studies have suggested that the accuracy of tree species detection using multispectral or hyperspectral data could improve when combined with LiDAR data [31][32][33]. However, multispectral or hyperspectral data have many constraints in their acquisition and require complex data processing [34]. In this case, the spectral information included in very high-resolution UAV-DAP data would be a cost-effective alternative data source.…”

Section: Introductionmentioning

confidence: 99%

Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

et al. 2020

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High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

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Section: Introductionmentioning

confidence: 99%

Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

et al. 2020

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“…Brandtberg [19] used LiDAR to classify individual tree species under leaf-off and leaf-on conditions and the accuracy of major species are around 60%. Nguyen et al [20] presented a wSVM-based approach for major tree species classification at ITC level using LiDAR data in a temperate forest and the accuracy was over 70%. Obviously, using LiDAR only is difficult to obtain high quality individual tree species, but multispectral LiDAR largely improves this condition by add spectral information to point cloud.…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

Zhao

Pang

Liu

et al. 2020

Forests

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This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction (“crown-based ITC” for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results (“pixel-based ITC” for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%.

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“…The normalized lidar point cloud were used to delineate individual tree crowns (ITCs) by means of Dalponte and Coomes' [46] algorithm implemented in the itcLiDAR function of the R library itcSegment. This algorithm is based on an adaptive local maxima filter and a region-growing method, and it has been successfully used in many previous studies [47][48][49][50]. The algorithm follows these steps: (1) generate a raster canopy height model (CHM) of spatial resolution defined by the user; (2) apply a Gaussian low-pass filter of fixed size of 3 × 3 pixels to the rasterized CHM in order to smooth the surface and to reduce the number of potential local maxima; (3) apply a moving window of variable size to the smoothed CHM in order to find a set of potential treetops (local maxima).…”

Section: Lidar Data Processingmentioning

confidence: 99%

“…Tree species classification and bark beetle attack detection were carried out using a class-weighted support vector machine (wSVM) classifier [49]. SVM is a well-known classifier that has been widely used in many studies in forestry and ecology [49,[72][73][74].…”

Section: Tree Species Classification and Bark Beetle Detectionmentioning

confidence: 99%

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Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

et al. 2022

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Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetles.

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A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Cited by 12 publications

References 33 publications

Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

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