## 2 Materials and Methods
### Methods
#### 2.3.1 Sampling Strategies  
The collection of accurate training and validation samples is a necessary condition for accurate land cover classification [34]. Unrepresentative and/or inadequate samples will result in significant uncertainty of the land cover classification results [35]. Taking into account the actual situation in the QLM and referring to the existing land cover classification system and products, the classification system of this study is shown in Table 2. The classification system used in this study was the land cover classification system based on ecosystem types, and was divided into 9 categories: croplands (CO), forests (FO), grasslands (GL), shrublands (SL), wetlands (WL), water bodies (WB), construction land (CL), bare land (BL), and permanent snow and ice (PSI). The high-resolution satellite  
Figure 2: Flow diagram of data processing and analysis. imagery of the Google Earth Engine was used as a base map; the sample points were selected from the areas where land cover changes have not occurred for many years, and the areas that were relatively uniform and had little interference from human activities to ensure the accuracy and authenticity of the sample data. Furthermore, the collected sample data were compared and verified with the 10 m ecosystem-type data of the Qilian Mountain Nature Reserve (part of the Qilian Mountains), which was obtained by artificial interpretation and field verification. The unit of each sample point was a pixel. The quality and quantity control of sample point data was realized by the method of area estimation adjustment error [36; 37; 38; 39]. In this way, the deviation caused by stratified sampling could be adjusted. Using sample data, the confusion matrix \(n_{ij}\) of the classification result could be calculated. A more informative presentation of the error matrix is the unbiased estimation \(p_{ij}\) of the area ratio in the unit \(i\) and \(j\) of the error matrix:  
\[p_{ij}=W_{i}\frac{n_{ij}}{n_{i}}\] (1a) where \[W_{i}\] is the area ratio of land cover types to the total area of the study area in the classification results, and the proportion of the area classified as type \[i\] is: \[W_{i}=\frac{A_{i}}{A_{tot}}\] (1b) where \[A_{tot}\] is the total area of the map, and \[A_{i}\] is the mapped area of land cover type \[i\]. An unbiased estimator of the total area of type \[j\] is then: Figure 2:0 (2a) \begin{table}
\begin{tabular}{c c c c} \hline
**Code** & **Class** & **Abbreviation** & **Description** \\ \hline
1 & Cropland & CO & A land cover type that is greatly affected by intensive human activities. It varies greatly from bare field to seeding to crop growing to harvesting in the course of a year. It includes paddy fields, greenhouse farming, and other arable and tillage land. \\ \hline
2 & Forest & FO & Areas in which the tree cover percentage is \textgreater{}15\% and the tree height is \textgreater{}3 m, including natural forests, plantations, and fruit trees. \\ \hline
3 & Grassland & GL & Areas in which the herbaceous cover percentage is \textgreater{}15\%, including natural grassland and pastures. \\ \hline
4 & Shrublands & SL & Area in which the shrublands’ height range is 0.3–5 m, and cover percentage is \textgreater{}15\%, have unique texture. \\ \hline
5 & Wetlands & WL & Usually has obvious high reflectivity in the NIR band; marshland covered with aquatic herbaceous plants; mudflats are also included. \\ \hline
6 & Water bodies & WB & All inland waterbodies; dominated by natural waterbodies and artificial waterbodies. \\ \hline
7 & Construction land & CL & Includes urban areas, rural areas, and industrial and mining land greatly affected by human activities. \\ \hline
8 & Bare land & BL & Areas without vegetation cover, including wasteland, deserts, and the Gobi Desert. \\ \hline
9 & Permanent snow and ice & PSI & Perennial snow and ice distributed in the high mountains. \\ \hline \end{tabular}
\end{table} 
Table 2: Land cover classification system.