ESTIMATION OF OIL PALM PLANT PRODUCTIVITY USING SENTINEL-2A IMAGERY AT CIKASUNGKA PLANATION PTPN VIII, BOGOR, WEST JAVA

Authors

  • Afifah Nur Rahmasari Department of Geography, Faculty of Mathematics & Natural Sciences, University of Indonesia
  • Supriatna Department of Geography, Faculty of Mathematics & Natural Sciences, University of Indonesia
  • Andry Rustanto Department of Geography, Faculty of Mathematics & Natural Sciences, University of Indonesia

DOI:

https://doi.org/10.30536/j.ijreses.2022.v19.a3775

Keywords:

estimated productivity, oil palm plants, vegetation index algorithm, sentinel-2A imagery

Abstract

Palm oil is one of the commodities that is growing well in Indonesia with a high commercial value which makes the demand for processed palm oil products increase, it is necessary to have data and technology to estimate the productivity of oil palm plantations more efficiently. Remote sensing technology is one of the technologies that can be used to decision problems spatially and accurately, efficiently, and dynamically. One of them is remote sensing using Sentinel-2A imagery. This study aims to analyze the distribution and the accuracy of the NDVI and ARVI algorithms for the estimation of oil palm productivity at the Cikasungka Plantation PTPN VIII. The estimated productivity of oil palm plantations at Cikasungka Plantation varies in each block with an estimated productivity of oil palm plantations of 35,061 Kg/Ha/Month using the algorithm NDVI and ARVI algorithm is 35,431 Kg/Ha/Month. Oil palm productivity was regressed by vegetation index and plant age to generate a model. Based on modeling with these two algorithms, the accuracy of the ARVI algorithm model has a lower RMSE value than NDVI, so it can be said that it is better in estimation of oil palm plant productivity  at the Cikasungka Plantation.

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Published

2025-11-25

Issue

Vol. 19 No. 1 (2022)

Section

Articles

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