# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Feature Extraction
#### 2.4.3 Textural Features  
Rubber plantations, which are usually artificially cultivated, exhibit distinct spatial distribution characteristics compared to natural forests, especially in the rubber foliation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formulation** & **Reference** \\ \hline NDVI (normalized difference vegetation index) & \(\frac{NIR-RED}{NIR+RED}\) & [35] \\ EVI (enhanced vegetation index) & \(2.5\times\) & \\ \(\left(\frac{(NIR-RED)}{(NIR+6\times RED-7.5\times BLUE+1)}\right)\) & [36] \\ DVI (difference vegetation index) & \(NIR-RED\) & [37] \\ GDVI (green difference vegetation index) & \(NIR-GREEN\) & [38] \\ GNDVI (green normalized difference vegetation index) & \(\frac{(NIR-GREEN)}{(NIR+CRES)}\) & [39] \\ MSR (modified simple ratio) & \(\frac{\left(\frac{NIR-1}{NIR-1}\right)}{(NIR+1)^{65}}\) & [40] \\ CI (chlorophyll index) & \(\frac{NIR}{NIR-1}\) & [41] \\ RVI (ratio vegetation index) & \(\frac{RED}{NIR-GREEN}\) & [42] \\ TVI (triangular vegetation index) & \(0.5[120\left(NIR-GREEN\right)-\) & [43] \\ SAVI (soil-adjusted vegetation index) & \(\frac{1.5(NIR-RED)}{(NIR+RED+0.5)}\) & [44] \\ OSAVI (optimized soil adjusted vegetation index) & \(\frac{1.16\times(NIR-RED)}{(NIR+RED+0.16)}\) & [45] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vegetation indices used in this study.  
stage. Textural features can effectively reflect these spatial distribution characteristics in remote sensing images. The introduction of textural features can offset the \"same object with different spectral features\" or \"different objects with the same spectral feature\" phenomena that can arise when using only spectral information. Therefore, it is worth introducing textural information in rubber mapping research to improve the classification accuracy.  
To fully utilize the information contained in the images taken in two periods and reduce the overlap of information among different bands, we derived the first principal component of the two images using the PCA method [46] to extract the textural features. Bands 1-4 of the PlanetScope images obtained on 8 June 2019 and 27 February 2019 were used as the inputs of PCA. The grey-level cooccurrence matrix (GLCM) algorithm [47] was used to extract the textural features of different land cover types. The textural features used in this study included homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), angular second moment (ANG), mean (MEAN), variance (VAR), and correlation (COR).  
It is worth noting that in the pixel-based classification procedure, it was necessary to determine a suitable texture extraction window size for the PlanetScope images because textural features must be calculated in a certain extraction window and different window sizes may influence the representativeness of the textural characteristics of different land cover types. In our work, the 8 textural features were extracted with window sizes of 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9, 11 \(\times\) 11, 13 \(\times\) 13, 15 \(\times\) 15, 17 \(\times\) 17, 19 \(\times\) 19, 21 \(\times\) 21, 23 \(\times\) 23, 25 \(\times\) 25, 27 \(\times\) 27, 29 \(\times\) 29, and 31 \(\times\) 31 (for a total of fifteen window sizes). Then, the optimal textural features were selected with a suitable window size based on random forest (RF) importance (detailed information can be found in Section 2.4.4) analysis to finally obtain HOM\({}_{\text{pixel}}\), CON\({}_{\text{pixel}}\), DIS\({}_{\text{pixel}}\), ENT\({}_{\text{pixel}}\), ANG\({}_{\text{pixel}}\), MEAN\({}_{\text{pixel}}\), VAR\({}_{\text{pixel}}\) and COR\({}_{\text{pixel}}\).  
In the object-based classification procedure, we extracted a total of 8 textural features corresponding to the first principal component of each segmentation object: HOM\({}_{\text{object}}\), CON\({}_{\text{object}}\), DIS\({}_{\text{object}}\), ENT\({}_{\text{object}}\), ANG\({}_{\text{object}}\), MEAN\({}_{\text{object}}\), VAR\({}_{\text{object}}\) and COR\({}_{\text{object}}\).