Figure S1. Spectral and spatial information for Sentinel-2 MSI and Landsat-8 OLI (Changed from Sentinel-2 MSI Level 2A Products Algorithm Theoretical Basis Document)

3.Method

3.1Random Forest

As a relatively new machine learning model, random forest can predict the role of up to several thousand variables and is regarded as one of the best machine learning algorithms. The random forest classification algorithm is an algorithm based on the classification and regression tree (CART) invented by Breiman et al., which integrates multiple decision trees through the idea of integrated learning. Its basic unit is the decision tree. If the CART decision tree is seen as an expert in the classification task, random forests are the experts that classify a task together (Iverson et al. 2008; Breiman 2001).
The steps for establishing a random forest are as follows:
(1) In the original sample, N training samples are randomly and regressively extracted, which is called the bootstrap method. This method is used to form a training sample set, and the data for each training sample set are approximately two-thirds of the original sample data set.
(2) Based on the extracted training sample set, N CART decision trees are constructed to form a random forest. During the decision tree growth process, m features are randomly selected at each node of each tree (the total number of features is M, m≤M). According to the principle of minimum Gini coefficient, a feature with the most classification ability was selected to perform node splitting within the decision tree.
(3) Multiple decision trees are generated to form a random forest classifier, which is used to classify remote sensing images and determine the category by voting.
The random forest algorithm not only enables the classification of remote sensing images, but it also plays an important role in feature selection and dimensionality reduction. Since approximately one-third of the original sample data is not extracted during the sampling process, this part of the data is called the Out-Of-Bag data (OOB data). Out-Of-Bag-Error (OOB Error) generated by OOB data evaluates classification accuracy and also calculates the importance of different feature variables (Variable Important, VI) for feature selection (Genuer et al. 2010; Sandri et al. 2012). The characteristic variable importance assessment model is as follows:
\(\text{VI}\left(M_{A}\right)=\frac{1}{N}\sum_{t=1}^{N}{(B_{n_{t}}^{M_{A}}-B_{O_{t}}^{M_{A}})}\),
where VI indicates the importance of the characteristic variable,M is the total number of features of the sample, N is the number of trees in the generated decision tree, \(B_{O_{t}}^{M_{A}}\) is the OOB error of the t-th decision tree when any eigenvalue \(M_{A}\) is not added with noise interference, and \(B_{n_{t}}^{M_{A}}\) is the OOB error of the t-th decision tree when any eigenvalue \(M_{A}\) is added with noise interference. If a certain feature \(M_{A}\) is randomly added with noise, and the accuracy of the OOB data is greatly reduced, this indicates that the feature \(M_{A}\) has a great influence on the classification result, and it also indicates that its importance is relatively high.
In the current study, the EnMAP-BOX tool developed by the German environment mapping and analysis program project team was used for band optimization and native and invasive species extraction. There are two important parameters in the process of constructing the random forest algorithm, namely, the number N of decision trees in the random forest and the number m of features extracted during the node-splitting process. When extracting feature variables, we selected the arithmetic square root of the total number of features in the EnMAP-BOX tool as the number of features. In theory, the greater the number of decision trees N, the higher the classification accuracy, but the higher the time cost. Based on the determination of the extracted feature m, we found that when the number of decision trees is N≥20, the OOB error gradually converges and tends to be stable. Therefore, we chose N=20 as the number of generated decision trees.

3.2Accuracy assessment of species classification

The confusion matrix is also called the error matrix. It is mainly used to compare the degree of confusion between the classification result and the actual measured value for accuracy evaluation. In the current study, the overall accuracy (OA), Kappa coefficient, producer accuracy (PA), and user accuracy (UA), which are commonly used at present, were selected as evaluation indicators to evaluate the classification results of different remote sensing images.

3.3Landscape index

The landscape index refers to the highly concentrated landscape pattern information, which reflects the simple quantitative indicators of its structural composition and spatial configuration, and is suitable for quantitative spatial analysis of the relationship between landscape pattern and ecological process. Due to the large number of landscape indices, previous research was consulted, and the class area (CA), patch density (PD), largest patch index (LPI), splitting index (SPLIT), and aggregation index (AI) were selected for obtaining the spatial and temporal changes in the habitat pattern of native and invasive species (Zhen et al. 2012; Liu et al. 2017).
Table S1. Description of landscape index