## 4 Outline and Methodology
### C4.5 Decision Tree Classification  
The decision tree classification technique is one of the important toolsets in data mining and has been widely used in many fields, such as biology, computer science and technology, clinical medicine, geology, management science and engineering [43; 44; 45; 46; 47]. In accordance with the top-down induction of decision tree, a DT consists of some root nodes, which are then split into more branches [48]. The univariate decision trees like C4.5 algorithm, using only one feature at an internal node, are the most popular methods due to their low computational complexity [49]. In this study, we adopted the C4.5 algorithm to achieve our goal based on expert knowledge.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **IGBP** & **UMD** & **LAI/FPAR** & **NPP** & **PFT** & **GlobeLand30** \\ \hline \multirow{8}{*}{Woodland} & 1. Evergreen Needleleaf forest & 1. Evergreen Needleleaf forest & 1. Shrubs & 1. Evergreen Needleleaf trees & 1. Forest \\  & 2. Evergreen Broadleaf forest & 2. Evergreen & 2. Broadleaf & 2. Evergreen & 2. Evergreen Broadleaf trees & 2. Shrubland \\  & 3. Deciduous Needleleaf forest & 3. Deciduous & 3. Needleleaf & 3. Deciduous & 3. Deciduous & \\  & 3. Deciduous Needleleaf forest & 3. Needleleaf forest & 3. Needleleaf vegetation & Needleleaf trees & \\  & 4. Deciduous Broadleaf forest & 4. Deciduous & 4. Deciduous & 4. Deciduous & \\  & 4. Deciduous Broadleaf forest & Broadleaf forest & & Broadleaf vegetation & Broadleaf trees & \\  & 5. Mixed forests & 5. Mixed forests & & & 5. Shrub & \\  & 6. Closed shrublands & 6. Closed shrublands & & & & \\  & 7. Open shrublands & 7. Open shrublands & & & & \\ \hline \multirow{8}{*}{Grassland} & 1. Woody savannas & 1. Woody savannas & Grasses/Creal & 1. Annual Broadleaf vegetation & 1. Grass & 1. Grassland \\  & 2. Savannas & 2. Savannas & 2. Savannas & 2. Annual grass vegetation & & \\  & 3. Grasslands & 3. Grasslands & & & & \\ \hline \multirow{8}{*}{Cropland} & 1. Croplands & 1. Croplands & 1. Broadleaf crops & & 1. Cereal crops & 1. Cultivated land \\  & 2. Croplands/Natural vegetation & & & & 2. Broad-leaf crops & \\ \hline \multirow{8}{*}{Wetland} & 1. Water bodies & 1. Water bodies & 1. Water bodies & 1. Water bodies & 1. Water bodies & 1. Water bodies & bodies \\  & 2. Permanent wetlands & & & & 2. Snow and ice & 2. Wetland \\  & 3. Snow and ice & & & & & 3. Permanent snow and ice \\ \hline Artificial \multirow{3}{*}{Wetfaces} & 1. Urban and built-up & 1. Urban and built-up & 1. Urban & 1. Urban & 1. Urban and built-up & 1. Artificial Surfaces \\ \cline{1-1}  & 1. Barren or sparsely vegetated & 1. Barren or & 1. Non-vegetated land & 1. Non-vegetated land & 1. Barren or sparse vegetation & 1. Barren land \\ \cline{1-1}  & & & & & vegetation & \\ \hline \hline \end{tabular}
\end{table}
Table 4: A total of five harmonized land cover types between MCD12Q1 and GlobeLand30.  
The classification process is divided into four steps: defining classification rules, constructing decision tree, implementing decision tree and evaluating the classification results. Here, the classification rules are obtained using the KR in Section 4.2 (Table 4). The new land cover types are, respectively, extracted by the supervised decision tree operation. Specially, DT is built by the corresponding DN values in the datasets and the classification scheme is performed in ENVI.