# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Rubber Plantation Mapping and Accuracy Assessment  
Four rubber plantation maps were finally derived using the four classification approaches (Figures 10 and 11). The rubber plantation area in the study region was estimated to be 152.35 km\({}^{2}\), 139.37 km\({}^{2}\), 162.67 km\({}^{2}\), and 159.21 km\({}^{2}\) according to the pixel-based RF approach, pixel-based SVM approach, object-based RF approach and object-based SVM approach, respectively. The pixel-based approaches yielded smaller rubber plantation areas than the object-based approaches. We also found that significantly fewer fragmentation patches were derived in the object-based approaches than in the pixel-based approaches. As shown in Tables 4 and 5, the object-based approaches achieved higher accuracies than the pixel-based approaches, according to the obtained confusion matrix; the overall accuracy and KIA increased by more than 1.66-4.25% and 2.22-5.75%, respectively, between these two approaches. Higher user accuracies for rubber were obtained with the object-based RF and SVM approaches (95.18%) than with the pixel-based RF and SVM approaches (91.25% and 93.83%, respectively), indicating that object-based methods can effectively reduce omission errors when estimating rubber plantation characteristics. For rubber, the producer's accuracies of the object-based approaches were higher than those of the pixel-based approaches, exhibiting increases of 3.94-8.22%, suggesting that object-based methods excel in obtaining lower commission errors. For the easily confused farmland and forestland cover types, the producer's accuracies and user's accuracies of the object-based methods were also higher than those of the pixel-based methods. Figure 10: Rubber plantation maps generated by the pixel-based RF/SVM approaches. (**a**) the classification result of the pixel-based RF approach. (**b**) the classification result of the pixel-based SVM approach. Figure 11: Rubber plantation maps generated by the object-based RF/SVM approaches. (**a**) the classification result of the object-based RF approach. (**b**) the classification result of the object-based SVM approach. According to the comparative analysis performed between the RF and SVM classifiers, the accuracy of identification with the pixel-based SVM classifier was slightly better than that of the pixel-based RF classifier, but there was no significant difference found between the classification accuracy of the object-based RF classifier and that of the object-based SVM classifier. When identifying rubber plantations, the pixel-based SVM approach displayed better producer's accuracy and user's accuracy than the pixel-based RF approach, but the object-based RF approach and the object-based SVM approach exhibited equal capacity with the same producer's accuracy (95.18%) and user's accuracy (95.18%). In order to show the differences of different classification results in detail, we selected two rubber intensive planting areas (site A and B) in the research area as the key analysis objects for in-depth discussion (see Figure 12). Compared to pixel-based classification approaches, object-based classification approaches can effectively reduce the degree of fragmentation. As shown in Figure 12, the fragmentation degrees of the rubber plantation maps derived using the pixel-based approaches were apparently higher than those obtained with the object-based approaches, mainly because the pixel-based classification methods consider a single pixel as the identification unit. The rubber trees in the same forestland area can have different canopy structures, especially during the deciduous stage, which leads to heterogeneous remote sensing recognition of the characteristics of rubber trees. Therefore, the classification results obtained with the pixel-based classifier are influenced by serious salt-and-pepper phenomena. In addition, the pixel-based classifiers performed comparatively poorly in distinguishing rubber tree, farmland and forest areas.