A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Lin, Yi; Hyyppä, Juha

doi:10.1016/j.jag.2015.11.010

Cited by 54 publications

(50 citation statements)

References 33 publications

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“…Different from previous studies [44,48,53], we aimed to develop a simple but efficient framework to propose and validate feature parameters from airborne laser data for tree species classification. Compared to only using the spectral characteristics of the orthophoto, the classification accuracy was improved in this study for all tree species using the ALS-derived features.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

Deng

Katoh

et al. 2016

Remote Sensing

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Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classes-Pinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved trees-using a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blue-RGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications.

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Section: Discussionmentioning

confidence: 99%

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

Deng

Katoh

et al. 2016

Remote Sensing

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“…A few studies have presented automated or semi-automated approaches for identifying useful features for the task of tree species classification (Bruggisser et al 2017;Li et al 2013;Lin and Hyyppä 2016). The previous work using traditional learning methods has required that the set of candidate features be assembled by an expert, with the intention of removing redundant and less useful information from the raw data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

Hamraz

Jacobs

Contreras

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees from airborne LiDAR data. To enable efficient processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM×4) and a set of four 2D views (4×2D). A training dataset of labeled tree crowns was generated via segmentation of tree crowns, followed by coregistration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples of augmented data (180 rotational variations per instance). The 4×2D representation yielded similar classification accuracies to the DSM×4 representation (~82% coniferous and ~90% deciduous) while converging faster. The data augmentation improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvements. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. Automatic derivation of optimal features via deep learning provide the opportunity for remarkable improvements in prediction tasks where captured data are not friendly to human visual systemlikely yielding sub-optimal human-designed features.

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“…Most studies have been limited to geometric or statistical features (Heinzel and Koch, 2011). Several point-based features have been developed to describe the structural properties of crowns of individual trees, such as crown shape and vertical foliage distribution (Li et al, 2013;Lin and Hyyppä, 2016). However, most of these studies found that it was difficult to classify mixed forests accurately based only on point clouds (Ørka et al, 2009;Heinzel and Koch, 2011;Yu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification of Broadleaved Forests in Nagano, Central Japan, Using Airborne Laser Data and Multispectral Images

Deng

Katoh

Takenaka

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This study attempted to classify three coniferous and ten broadleaved tree species by combining airborne laser scanning (ALS) data and multispectral images. The study area, located in Nagano, central Japan, is within the broadleaved forests of the Afan Woodland area. A total of 235 trees were surveyed in 2016, and we recorded the species, DBH, and tree height. The geographical position of each tree was collected using a Global Navigation Satellite System (GNSS) device. Tree crowns were manually detected using GNSS position data, field photographs, true-color orthoimages with three bands (red-green-blue, RGB), 3D point clouds, and a canopy height model derived from ALS data. Then a total of 69 features, including 27 image-based and 42 pointbased features, were extracted from the RGB images and the ALS data to classify tree species. Finally, the detected tree crowns were classified into two classes for the first level (coniferous and broadleaved trees), four classes for the second level (Pinus densiflora, Larix kaempferi, Cryptomeria japonica, and broadleaved trees), and 13 classes for the third level (three coniferous and ten broadleaved species), using the 27 image-based features, 42 point-based features, all 69 features, and the best combination of features identified using a neighborhood component analysis algorithm, respectively. The overall classification accuracies reached 90% at the first and second levels but less than 60% at the third level. The classifications using the best combinations of features had higher accuracies than those using the image-based and point-based features and the combination of all of the 69 features.

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A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Cited by 54 publications

References 33 publications

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

Tree Species Classification of Broadleaved Forests in Nagano, Central Japan, Using Airborne Laser Data and Multispectral Images

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