Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

Abstract

Most research on forest tree species classification based on optical image data uses information such as spectral reflectance, vegetation index, texture, and phenology data. However, owing to the limited spectral resolution of multispectral images and the high cost of hyperspectral data, there is room for improvement in the classification of tree species in large areas based on optical images. The combined application of multispectral images and other auxiliary data can provide a new method for improving tree species classification accuracy. Hence, Sentinel-2 images were used to extract spectral reflectance, spectral index, texture, and phenological information. Data for topography, precipitation, air temperature, ultraviolet aerosol index, NO2 concentration, and other variables were included as auxiliary data. Models for forest tree species classification were constructed through feature combination and feature optimization using the random forest (RF), gradient tree boost (GTB), support vector machine (SVM), and classification and regression tree (CART) algorithms. The classification results of 16 feature combinations with the 4 classification methods were compared, and the contributions of different features to the classification models of forest tree species were evaluated. Finally, the optimal classification model was selected to identify the spatial distribution of forest tree species in the study area. The model based on feature optimization gave the best results among the 16 feature combination models. The overall accuracy and kappa coefficient were increased by 18% and 0.21, respectively, compared with the spectral classification model, and by 17% and 0.20, respectively, compared with the spectral and spectral index classification model. By analyzing the feature optimization model, it was found that terrain, ultraviolet aerosol index, and phenological information ranked as the top three features in terms of importance. Although the importance of spectral reflectance and spectral index features was lower, the number of feature variables accounted for a large proportion of the total. The importance of commonly used texture features was limited, and these features were not present in the feature optimization model. The RF algorithm had the highest classification accuracy, with an overall accuracy of 82.69% and a kappa coefficient of 0.80, among the four classification algorithms. The results of GTB were close to those of RF, and the difference in overall classification accuracy was only 0.14%. However, the results of the SVM and CART algorithms were relatively weaker, with overall classification accuracies of about 70%. It can be concluded that the combined application of Sentinel-2 images and auxiliary data can improve forest tree species classification accuracy. The model based on feature optimization achieved the highest classification accuracy among the 16 feature combination models. The spectral reflectance and spectral index data extracted from optical images are useful for tree species classification, but the effect of texture features was very limited. Auxiliary data, such as topographic features, ultraviolet aerosol index, phenological features, NO2 concentration features, topographic diversity features, precipitation features, temperature features, and multi-scale topographic location index data, can effectively improve forest tree species classification accuracy. The RF algorithm had the highest accuracy, and it can be used for tree species classification space distribution identification. The combined application of Sentinel-2 images and auxiliary data can improve classification accuracy, but the highest accuracy of the model was only 82.69%, which leaves room for improvement. Thus, more effective auxiliary data and the vertical structural parameters extracted from satellite LiDAR can be combined with multispectral images to improve forest tree species classification accuracy in future research.

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

“…Overall, SVM performance is superior to the RF algorithm which is consistent with previous studies [109,110]. However, other research studies comparing SVM and RF have reported contrasting results [111][112][113] possibly due to the application of a small training dataset. The RF model used a huge number of decision trees for the training data of high dimensional tree selection [85]; hence, it is crucial to train the RF model with a small dataset [114].…”

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

“…These features include the Normalized Difference Vegetation Index (NDVI), Normalized Water Index (NDWI), Normalized Built-up Index (NDBI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and Spectral Ratio (SR). Different from sensors such as Sentinel, the dataset from Landsat 8 does not include the red-edge band and related vegetation indices mentioned by YOU et al [14] during feature selection. When extracting texture features from the images, we used the gray-level co-occurrence matrix to compute the following 16 features: the entropy (ENT); inverse difference moment (IDM); angular second moment (ASM); variance (VAR); contrast (CONTRAST); correlation (CORR); dissimilarity (DISS); sum average (SAVG); shade (SHADE); difference variance (DVAR); profile (PROM); inertia (INTERTIA); sum variance (SVAR); spectral entropy (SENT); direction entropy (DENT); and maximum correlation (MAXCORR).…”

A Study on the Difference of LULC Classification Results Based on Landsat 8 and Landsat 9 Data

“…Forty test areas were selected in different regions of China between November 2021 and June 2022; the corresponding months, when the images were obtained from various test areas, are provided in Table 2, and the spatial distribution of the 40 test areas is depicted in Figure 1. February 2022 17,18,19,20,21,22,23,24 March 2022 25,26,27,28,29,30,31,32,33,34,35,36 April 2022 37 May 2022 38, 39, 40 June 2022…”

“…To understand the difference in forest tree species classification between Landsat 8 and Landsat 9 images, a total of 1481 sample points were selected by field survey and high-resolution images in Liuzhou, Guangxi Zhuang Autonomous Region, China. The specific description of the study area and sampling data can be found in [21]. The spatial distribution and detailed description of sample data are depicted in Figure 3 and Table 3, respectively.…”