# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Segmentation  
Image segmentation is the first step in the object-based classification workflow and a critical process for creating objects based on intrahomogeneity and interheterogeneity, which directly affect the image classification accuracy. The most widely applied method, the multiresolution segmentation (MRS) algorithm [34], was used in this study. MRS is a bottom-up region-merging technique that starts with one-pixel objects and iteratively merges them into larger objects based on the regionally minimized heterogeneity principle. In the MRS procedure, three key parameters (the segmentation scale, shape index and compactness index) affect the image segmentation accuracy. Different parameter combinations should be tested in the segmentation procurement step and validated by visual examinations to obtain good segmentation results. In the foliation stage of rubber trees, due to the similarities of spectral characteristics between rubber trees and forests, rubber plantations and forestlands can be easily confused, and their boundaries are not clear; therefore, undersegmentation easily occurs during the segmentation procedure. In the defoliation stage of rubber trees, different rubber trees in the same rubber forest field have different degrees of defoliation, resulting in heterogeneous spectral characteristics and causing the study objects to be prone to oversegmentation. The segmentation of a single image in the leafing or deciduous stage could lead to not only inaccurate segmentation results but also inconsistent segmentation results for other images in these two stages.  
Therefore, PlanetScope images taken during two different phenological phases were analyzed in the segmentation procedure, with a total of 8 bands, including the blue, green, red, and near-infrared bands of the PlanetScope images obtained on 8 June 2019, during the rubber foliation period, and on 27 February, during the rubber defoliation period. The weight of each band was set to 1. In the segmentation procedure, it is important to simultaneously consider images obtained in two periods to ensure the segmentation accuracy of the rubber plantations by visual interpretation. First, we set a small segmentation scale of 35 with a shape value of 0.1 and a compactness value of 0.5 to obtain finely divided objects and ensure that there were few undersegmented objects. The segmentation effect was good for small-area ground objects, but excessive segmentation occurred for large construction lands, farmlands, rubber plantations and forests. Second, based on the segmentation objects generated in the previous step, we set a large segmentation scale of 55 to ensure that small patches of large-area forests, farmlands, rubber plantations and water bodies were merged. However, some small fragmentation still occurred, so the shape index and compactness index were adjusted based on a shape value of 0.3 and a compactness of 0.5. Finally, based on the segmentation results from the previous layer, a larger segmentation scale (80) was applied, and the shape index and compactness index remained unchanged. Large-area ground objects such as rubber plantations, forests and buildings were combined, and the segmentation effect obtained at this step was the best.