# Uncertainty Analysis of Multisource Land Cover Products in China
## 2 Materials and Methods
### Method  
Three aspects were included in the uncertainty evaluation of land cover products: Area uncertainty, spatial location uncertainty, and spatiotemporal change uncertainty. Area uncertainty was used to evaluate the quantitative characteristics of land cover products, spatial uncertainty was used to evaluate the spatial distribution of land cover products, and spatiotemporal uncertainty was used to evaluate changes in land cover types over time. The methods used in this study mainly included the following:  
(a) Data preprocessing: Before the uncertainty analysis of land cover products, data preprocessing was carried out, including research area tailoring, upscaling transformation, and classification system merging. First, three kinds of land cover products were obtained based on the ArcGIS software, and land cover products with the same boundary were obtained. The maximum area aggregation method was used to unify products with different spatial resolutions to the same resolution for research. It aligned the raster of the output dataset (coarse raster) with the input data raster (fine raster) and assigned the type with the most occurrences in the input raster to the output raster. The three multisource land cover products have different land cover classification systems, they needed to be merged into a new unified and generalized classification system: Forest, nontree vegetation, and bare land. (b) Area uncertainty analysis: According to the forest and nontree vegetation in the study area from 2001 to 2012, the change in the area of the three products in the past 12 years was calculated. In order to explain the uncertainties and differences among the different land cover products, this study used relative deviation to express the difference of forest and nontree vegetation. The coefficient of deviation formula was used to calculate the coefficient of deviation of the three products [36]. The deviation coefficient \(D\) was an effective index to measure the classification accuracy of land cover products. \[D_{xk}=\left(\frac{x_{k}}{k}-1\right)\times 100 \tag{1}\]  
In formula (1), \(x\) is the land cover classification product; \(k\) is the land cover type of the product; \(\overline{k}\) is the mean value of the area of the three products of a certain type \(k\); and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class 1** & **Class 2** & **Class 1** & **Class 2** \\ \hline \multirow{4}{*}{Forest} & Evergreen & & Deciduous \\  & broad-leaved forest & & broad-leaved shrub \\  & Deciduous & & Evergreen \\  & broad-leaved forest & & needle-leaved shrub \\  & Evergreen & & Deciduous \\  & needle-leaved forest & & needle-leaved shrub \\  & Deciduous & & Grassland \\  & needle-leaved forest & & Crossland \\  & Evergreen & & Crossland \\  & broad-leaved shrub & & Cross \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PFT-based integrated land cover category for the ESA CCI land cover data. \(D_{xk}\) is the deviation coefficient of the area of the type \(k\) in product \(x\)[37]. The mean value (_MEAN_) and root mean square error (_RMSE_) were used to calculate the variation in the resolution consistency in the entire study area [38] using Formula (2). \[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{2}\]  
(c) Spatial uncertainty analysis: The area uncertainty analysis can quantitatively give the difference between different products, but the result was statistical information, and the spatial characteristics were removed. Therefore, this study adopted the method of spatial superposition with three products. The difference was made pixel-by-pixel, and the difference in different products was judged pixel-by-pixel. (d) Temporal and spatial uncertainty analysis: The changing trend of forest and nontree vegetation from 2001 to 2012 was evaluated, and the entire area was analyzed pixel-by-pixel. The 12-year trend of forest and nontree vegetation coverage of each pixel was analyzed by the least squares method.