Ms. Dwi Wahyu Triscowati Profile

Dwi Wahyu Triscowati

at BPS Of Banyuwangi Regency

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Proceedings Article | 21 November 2019 Open Access

Multitemporal remote sensing data for classification of food crops plant phase using supervised random forest

Dwi Wahyu Triscowati, Bagus Sartono, Anang Kurnia, Dede Dirgahayu Domiri, Arie Wahyu Wijayanto

Proceedings Volume 11311, 1131102 (2019) https://doi.org/10.1117/12.2547216

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Food crops monitoring in developing countries such as Indonesia plays an essential role to support national goals in food security and self-sufficiency. One of the fundamental challenges is plant phase classification task which could help to estimate yield before harvest. In contrast to the conventional field survey method which required a large amount of human and capital resources, we explore a more scalable, inexpensive and real-time method using publicly available remote sensing data, i.e. Landsat-8 satellite. Landsat-8 provides rich spatiotemporal features which could support the detection of numerous vegetation and crop-related indices. However, to accurately classify the plant phase, the existing features require additional spectral pattern from different seasons. We found out the existence of temporal autocorrelation among features of food crops plant phase. We propose a supervised random forest method to make features engineering to select best multitemporal features. In this study, we focus on the rice plant phase classification in Banyuwangi Regency, Indonesia as a case study. The ground truth data are the monthly frame area sampling of average rice plant phase at the regency level which officially released by BPS-Statistics Indonesia. The experimental result shows the accuracy of 0.573 with one temporal feature. Furthermore, incorporating four consecutive temporal features gives higher accuracy gain to 0.727 which shows the temporal autocorrelation. Based on the extensive evaluations, our findings and contributions in this study include: (1) insight to capture the temporal autocorrelation to increase the model accuracy (2) a machine learning classification model which is not sensitive to multicollinearity. Our proposed method provides the potential benefit for the government and statistical agencies towards a more scalable agricultural survey.

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