# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods
### Image Classification and Accuracy Assessment  
There are several classifier packages for supervised classification in GEE, such as Random Forests (RF), Classification and Regression Trees, Support Vector Machine, and Naive Bayes [9]. Many researchers reported higher accuracy was achieved using RF classifier algorithm [18; 33; 34]. The RF algorithm is an ensemble learning method, which consists of many individual decision trees [5]. Each individual decision tree has several nodes, and the final result is determined by the majority vote. The advantage of RF is that it can produce a highly accurate classifier and handle thousands of input variables [35]. Thus, RF was used to obtain the classification maps in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Layer** & **Source** & **Spatial Resolution (m)** & **Year** \\ \hline Landsat 5 Surface Reflectance & GEE dataset (USGS) & 30 & 1990, 2007 \\ Landsat 7 Surface Reflectance & GEE dataset (USGS) & 30 & 2000 \\ Landsat 8 Surface Reflectance & GEE dataset (USGS) & 30 & 2019 \\ Administrative boundary & Fujian Forestry Bureau & - & 2003, 2016 \\ Forest inventory data & Fujian Forestry Bureau & - & 2003, 2016 \\
1:10,000 topographic map & Fujian Forestry Bureau & 1 & - \\ Socio-economic data & Fuzhou Statistical Yearbook & - & 1990–2019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data sources used in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land Cover Classes** & **Description** \\ \hline Sandy land (SL) & Beaches \\ Built-up land (BL) & Residential areas and other built-up land, such as roads, industrial land \\ Cultivated land (CL) & Cultivated, fallow farmland and terraced \\ Forest land (FL) & Forest, grassland, orchard, and urban greening land \\ Other land (OL) & Unused land or hard-to-use land, including rocks and bare soil \\ Water (W) & Rivers, reservoirs, lakes, dams and oceans \\ \hline \hline \end{tabular}
\end{table}
Table 2: The description of land cover classes on Haitan Island, China.  
For the year of 2007, 114 field samples (with GPS locations) were collected in December of 2007, which were additional samples for validation. The field samples covered each type and were distributed as uniformly as possible in the accessible areas of Haitan Island. The GPS coordinate was collected three times in each sample, and the mean (X, Y coordinate) was taken as the location of sample. All the samples were unloaded in GEE. Since the early Google Earth satellite images (1990 and 2000) that cover Haitan Island were lower resolution than the image in the year 2019, topographic map and forest inventory data were used as auxiliary data to delineate the reference points. Finally, all the reference data was uploaded in GEE for accuracy assessment. According to the confusion matrix for each year in GEE, user's accuracy, producer's accuracy, overall accuracy, Kappa coefficient, and F1 score [18; 36] were calculated to evaluate the classification results [9]. F1 score ranged from 0 to 1, which was calculated based on the combination of producer's and user's accuracy. The higher value of F1 score means higher accuracy achieved. Finally, the land use classified maps were downloaded from GEE.