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

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 303 ◽  
Author(s):  
Dan Zhao ◽  
Yong Pang ◽  
Lijuan Liu ◽  
Zengyuan Li
Keyword(s):  
Tree Species ◽  
Northeast China ◽  
Dominant Species ◽  
Mixed Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Support Vector ◽  
Individual Tree ◽  
Individual Trees ◽  
Better Than

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%.

scholarly journals CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1697
Author(s):  
Hui Li ◽  
Baoxin Hu ◽  
Qian Li ◽  
Linhai Jing
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Individual Tree ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used.

scholarly journals Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2725
Author(s):  
Qixia Man ◽  
Pinliang Dong ◽  
Xinming Yang ◽  
Quanyuan Wu ◽  
Rongqing Han
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Urban Vegetation ◽  
Individual Tree ◽  
Cloud Data ◽  
Random Forest Classification ◽  
The Individual ◽  
Individual Trees

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

scholarly journals Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 571 ◽  
Author(s):  
Chen ◽  
Xiang ◽  
Moriya
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Lidar Data ◽  
Individual Tree ◽  
Matching Algorithm ◽  
Crown Width ◽  
Individual Trees ◽  
Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

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

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

Mixture of Triclopyr and Imazapyr More Effective than Triclopyr Alone for Hardwood Forest Stand Improvement

2020 ◽  
Author(s):  
Mark A Turner ◽  
William D Gulsby ◽  
Craig A Harper
Keyword(s):  
Coastal Plain ◽  
Tree Species ◽  
Significant Proportion ◽  
Forest Stand ◽  
Liquidambar Styraciflua ◽  
Minimal Risk ◽  
Herbicide Mixture ◽  
Soil Activity ◽  
Individual Trees ◽  
Better Than

Abstract Treatment of individual trees in hardwood stands typically is conducted with herbicides that have no soil activity, such as triclopyr. However, triclopyr is not effective on some tree species. Applying a mixture of triclopyr and imazapyr would broaden the spectrum of species controlled, but nontarget mortality may be problematic as imazapyr may affect other trees through soil activity. We applied herbicide via girdle-and-spray as part of a forest stand improvement treatment in four upland hardwood stands in the Upper Coastal Plain of Alabama. We compared effects of using triclopyr alone with a mixture of triclopyr and imazapyr 18 months posttreatment. Only one untreated sweetgum (Liquidambar styraciflua) out of 440 trees was killed in the stands treated with the herbicide mixture (0.5 percent nontarget mortality rate). Nontarget mortality did not differ between treatments. However, the herbicide mixture controlled hickory (Carya spp.) and sourwood (Oxydendrum arboreum) better than triclopyr alone, with 56 percent of hickory treated with triclopyr still alive 18 months later, compared with 0 percent of hickory treated with the mixture. Our results indicate a mixture of triclopyr and imazapyr provides better control than triclopyr alone, and there is minimal risk to nontarget tree species in hardwood stands when used according to label recommendations. Study Implications Forest stand improvement (FSI) is a noncommercial practice typically conducted by cutting and using herbicide to kill undesirable trees. Consideration must be given to herbicide selection, which is based on efficacy on target species while minimizing nontarget mortality of residual trees. We found that a mixture of triclopyr and imazapyr applied via girdle-and-spray was more effective for FSI than triclopyr alone, which failed to control a significant proportion of treated hickory, and resulted in essentially no nontarget mortality in mixed hardwood stands 18 months after application.

scholarly journals Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1218 ◽  
Author(s):  
Julia Maschler ◽  
Clement Atzberger ◽  
Markus Immitzer
Keyword(s):  
Random Forest ◽  
Scots Pine ◽  
Tree Species ◽  
Near Infrared ◽  
Mean Shift ◽  
Hyperspectral Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.

Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral Data

2016 ◽  
Vol 44 (4) ◽  
pp. 595-603 ◽  
Author(s):  
Yuanyong Dian ◽  
Yong Pang ◽  
Yanfang Dong ◽  
Zengyuan Li
Keyword(s):  
Tree Species ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Urban Tree ◽  
Species Mapping

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343 ◽  
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

scholarly journals Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guang Yang ◽  
Yaolong Zhao ◽  
Baoxin Li ◽  
Yuntao Ma ◽  
Ruren Li ◽  
...  
Keyword(s):  
Tree Species ◽  
Vegetation Indices ◽  
Airborne Lidar ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Textural Features ◽  
Integrated Sensors ◽  
Species Classification ◽  
Multiple Features ◽  
Tree Species Classification

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

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