Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Abstract

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.

A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

“…Based on the gathered results, the random forest and support vector machine classifiers generated the highest overall accuracy for species classification. In some studies, the SVM classifier has been proved to be more effective in tree species discrimination than other classifiers [3,66,87,101]; however, in other studies, the differences between RF and SVM results were marginal [61,67]. Unfortunately, a specific group of features dedicated to species classification and the particular classifiers used for the classification do not guarantee a high overall accuracy.…”

“…LiDAR technology, which is valued for its unique capabilities for detecting 3D structural information, is a great supplement to discrimination based on any optical remote-sensed imagery. It has been proven, in many publications, that the combination of LiDAR data with multispectral or hyperspectral imagery generates greater species classification accuracy than LiDAR data alone [42,59,[62][63][64][65][66][67][68]. LiDAR data also complements imagery data used for other purposes, such as mapping dead trees with crowns and snags (dead trees without crowns) [108,109], analyzing forest structural complexity [110,111], and determining bird species habitats [112], where passive imagery alone would not be as efficient as a set fused with LiDAR data.…”

“…We would like to point out that some reviewed publications described experiments based on LiDAR data combined with diverse sensor data (e.g., multispectral, hyperspectral, satellite imagery). We have included them in this review, but only within the scope that corresponds to LiDAR data [42,59,[62][63][64][65][66][67][68].…”

“…Species classification based on remote sensing data has evolved along with the methodological developments of statistical learning. In the reviewed studies, the following species classifiers have been applied: logistic regression [35,43,66], linear discriminant analysis [43,75,80,87], and kNN [43,66], with the most popular methods recently being random forest [3,34,43,61,64,81,88,[99][100][101] and support vector machines [3,5,61,66,87,[101][102][103][104].…”

“…Based on the reviewed publication results, random forest and support vector machine classifiers generated the highest overall accuracy for species classification, compared to other classifiers, while the differences in RF and SVM results can be treated as marginal [61,67]. Nevertheless, in some of the studies, the SVM classifier has been proved to be more effective in tree species discrimination than other classifiers [3,66,87,101]. Despite the high accuracy that random forest and support vector machine methods might generate, it should be emphasized that using a particular classifier always has advantages and disadvantages.…”