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Giusti Ghivarry

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Proceedings Article | 22 December 2021 Paper

Application of random forest algorithm to Sentinel-1 for plantation detection: case study of Tesso Nilo ecosystem

Giusti Ghivarry, Adhera Sukmawijaya

Proceedings Volume 12082, 1208208 (2021) https://doi.org/10.1117/12.2618285

KEYWORDS: Polarization, Synthetic aperture radar, Vegetation, Composites, Radar, Image classification, Detection and tracking algorithms, Backscatter

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Investigation of possible variations of the combination of VV and VH polarization available for SENTINEL-1 Synthetic Aperture Radar (SAR) imagery has been conducted for plantation detection. The dual-polarization (VV and VH) Interferometric Wide Swath (IW) along descending orbit over Tesso Nilo Ecosystem collected between 1st to 25th March 2021 stored inside Google Earth Engine dataset catalog is used for this objective. Thus initial preprocessing is excluded. The Random Forest method is implemented for two purposes, (1) to identify variable importance of variable used in the classification process, (1) the supervised pixel-based classification. Training samples used in the classification process were collected from visual interpretation of SENTINEL-1 composite image whereas the validation sample is obtained from the Google earth high-resolution imagery. The result shows that variable (VV/VH), (VV-VH), and RVI has the highest degree of importance for oil palm, pulpwood, and forest detection respectively. There is a pattern where the derivative variables of the VV and VH polarization have a high degree of importance. The same pattern appears in the classification results, where scenario 13 ((VV-VH), (VV/VH), ((VV+VH)/2), RVI) has the highest overall accuracy value of 91.74%. Scenario 13 produces the user accuracy of 94.22%, 93.89%, 81.82%, and 95.45% for oil palm, pulpwood, forest, and other land use respectively. The scenario also produces a high producer accuracy of 93.86%, 93.89%, 81.82%, and 97.67%. The combination of available polarization derivative variables with SAR data capabilities can be utilized to build wide-scale plantation monitoring and management systems.

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