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.

scholarly journals Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 818
Author(s):  
Yanbiao Xi ◽  
Chunying Ren ◽  
Zongming Wang ◽  
Shiqing Wei ◽  
Jialing Bai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Spectral Characteristics ◽  
Great Influence ◽  
Hyperspectral Images ◽  
Changbai Mountains ◽  
Species Classification ◽  
Tree Species Classification

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

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.

scholarly journals Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

2018 ◽  
Vol 10 (7) ◽  
pp. 1111 ◽  
Author(s):  
Edwin Raczko ◽  
Bogdan Zagajewski
Keyword(s):  
Tree Species ◽  
National Park ◽  
Accuracy Assessment ◽  
Research Area ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Species Classification ◽  
Tree Species Composition ◽  
Tree Species Classification ◽  
R Programming

Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest.

scholarly journals Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods

2020 ◽  
Vol 12 (23) ◽  
pp. 3926
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Tree Species ◽  
Learning Algorithms ◽  
Spectral Characteristics ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Species Classification ◽  
Tree Species Classification ◽  
Very High

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

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