## 2 Materials and Methods
### Methods  
OBIA has shown great advantages in processing VHR images [31; 32]. Therefore, a two-level workflow based on the OBIA was customized to extract land-cover changes in the cultural landscapes of the CHS. At level I, an unsupervised method was applied to extract the potentially changed objects; at level II, a supervised machine-learning method was used to refine the result and obtain the from-to land cover-change information. The flowchart of the proposed workflow is shown in Figure 2, and the specific steps are as follows. 1. Image co-registration: The bi-temporal Google-Earth images were co-registered. Since change detection on VHR images has a high-quality requirement, image co-registration was applied using the Image Registration Workflow tool in ENVI 5.3 [33] to ensure the root mean square error (RMSE) of geometrical accuracy was less than two pixels. 2. Change vector analysis (CVA): The CVA [34] was applied to the bi-temporal images to derive a pixel-wise difference image (Equation (1)). \[V=\sqrt{\sum_{i=1}^{K}(p_{i}-q_{i})^{2}}\] (1) \[V\] is the Euclidean distance between the spectral vectors of two pixels \[p_{i}\] and \[q_{i}\] at the same location from bi-temporal images. \[K\] is the number of multispectral bands. 3. Thresholding: The Otsu thresholding [35] was applied to the difference image. This algorithm can conform a threshold \(t\) by minimizing intra-class intensity variance or equivalently by maximizing inter-class variance, so as to divide changed and unchanged pixels. 4. Image segmentation: Independently, the spectral bands of bi-temporal images were stacked to combine information from both times, and image segmentation based on \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Acquisition Time** & \begin{tabular}{c} **Data Acquisition Time and** \\ **Satellites** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Core Area** \\ **(ha)** \\ \end{tabular} &
\begin{tabular}{c} **Buffer Area** \\ **(ha)** \\ \end{tabular} \\ \hline Yungang Grottoes & 2015/01/11 & 2020/04/12 & 1.07 m & 115 & 800 \\  & WorldView-2 & WorldView-3 & & & \\ \hline Wooden Pagoda of Yingxian & 2015/11/01 & 2020/04/25 & 0.27 m & 7 & 395 \\  & GeoEye-1 & GeoEye-1 & & & \\ \hline Chengcun Ancient City Ruins & 2015/04/05 & 2020/03/15 & 1.07 m & 59 & 1418 \\  & WorldView-3 & WorldView-2 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data information of the three cultural-heritage properties. the fractal net evolution algorithm (FNEA) method [36] was performed on the stacked spectral bands. As a key step of OBIA, image segmentation can group pixels into multiple image segments (objects) according to some pre-defined homogeneity criteria. The pixels inside an object were assumed to be with high homogeneity. Therefore, the basic unit is an object rather than a pixel in OBIA (an illustration is shown in Figure 3). Figure 2: The flowchart of the developed workflow. 5. Extracting potentially changed objects: Since the same element can exhibit great spectral heterogeneity in VHR images, extracting changed elements at the object level rather than the pixel level can avoid introducing the salt-and-pepper noise (i.e., isolated pixels). The changed objects were extracted according to the segmentation and Otsu thresholding results (Equation (2)). \[O=\left\{ \begin{array}{ll}1&\text{if }N_{c}>N\cdot r\\ 0&\text{if }N_{c}\leq N\cdot r\end{array} \right.,\] (2)  
\(O=1\) indicates that an object is a potentially changed object; otherwise, \(O=0\). \(N_{c}\) is the number of changed pixels in this object, and \(N\) is the total pixels in this object. \(r\) is an empirical threshold. In the study, \(r\) was set to 50% through trial-and-error tests, which means if the changed pixels exceed a half in an object, this object is then defined as a changed object. This step converts the processing unit of the imagery from pixels to objects. 6.