A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

Wang, Kepu; Wang, Zhihui; Liu, Xuehua

doi:10.3390/f10010001

Cited by 98 publications

(64 citation statements)

References 80 publications

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“…In the last decade, new approaches emerged to take advantage of the characteristics of active sensors, especially light detection and ranging (LiDAR) systems, which became a trend for tree crown detection [9]. More recently, the authors in [10] concluded that combining LiDAR data with optical imagery generally leads to better classification accuracy. Although this conclusion might be generalized, the authors focused on classifying tree species in urban environments.…”

Section: Introductionmentioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

101

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This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

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Section: Introductionmentioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

101

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show abstract

“…According to the data source used, the existing individual tree species identification can be divided into five categories: multispectral data based, LiDAR data based, multispectral + LiDAR data based, multi-spectral + hyperspectral based, hyperspectral + LiDAR based. The general workflow is to first use ground surveys or high spatial resolution data or high-density LiDAR data to obtain individual tree canopy, and then use universal classification methods to classify individual tree species [2,4].…”

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 fractions of clay, silt, and sand were obtained from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute [45].All of these spatial datasets were employed in the UTM WGS-84 coordinate system and interpolated to 1 km resolution. Detailed descriptions of datasets were reported elsewhere [46,47].…”

Section: Model Inputsmentioning

confidence: 99%

Impacts of Change in Atmospheric CO2 Concentration on Larix gmelinii Forest Growth in Northeast China from 1950 to 2010

Wang

Fan

et al. 2019

Forests

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Although CO2 fertilization on plant growth has been repeatedly modeled to be the main reason for the current changes in the terrestrial carbon sink at the global scale, there have been controversial findings on the CO2 fertilization effects on forests from tree-ring analyses. In this study, we employed conventional dendrochronological tree-ring datasets from Northeast China, to detect the effect of CO2 fertilization on Larix gmelinii growth from 1950 to 2010. Among four sites, there were two sites exhibiting a significant residual growth enhancement at a 90% confidence level after removing the size, age and climaterelated trends of tree-ring indices. In addition, we found consistency (R from 0.26 to 0.33, p < 0.1) between the high frequency CO2 fluctuation and residual growth indices at two of the four sites during the common period. A biogeochemical model was used to quantitatively predict the contribution of elevated atmospheric CO2 on accumulated residual growth enhancement. As found in the tree-ring data, 14% of the residual growth was attributed to the CO2 fertilization effect, while climate was responsible for approximately the remainding 86%.

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A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

Cited by 98 publications

References 80 publications

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

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

Impacts of Change in Atmospheric CO2 Concentration on Larix gmelinii Forest Growth in Northeast China from 1950 to 2010

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