E3S Web Conf.
Volume 211, 2020The 1st JESSD Symposium: International Symposium of Earth, Energy, Environmental Science and Sustainable Development 2020
|Number of page(s)||7|
|Published online||25 November 2020|
UAV application to estimate oil palm trees health using Visible Atmospherically Resistant Index (VARI) (Case study of Cikabayan Research Farm, Bogor City)
Department of Geography, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, 16424, Indonesia
* Corresponding author: firstname.lastname@example.org
This article describes the making of an oil palm tree health map using aerial photos extracted from UAV DJI Phantom 4. A DJI Phantom 4 was flown at 100 meters height at the Cikabayan Research Farm, Bogor City. Raw aerial photos from DJI Phantom 4 were processed using Agisoft Photoscan software to generate dense point clouds. These points were computed to produce a digital surface model (DSM) and orthophotos with a spatial resolution of 2.73 cm/pixel. Red, green, and blue bands of the photos were computed to provide the Visible Atmospherically Resistant Index (VARI). Also, orthophotos containing oil palm trees were digitized to create points in vector form. VARI pixel values were added to each point and classified into four classes: Needs Inspection, Declining Health, Moderately health, and Healthy. Resulted oil palm tree health map reveals that most of the oil palm trees in the study location are classified as Declining Health and Needs Inspection. Profitably, plantation workers can directly inspect oil palm trees whose health are declining, based on information derived from oil palm tree health map. The information that comes from this study will significantly save time and effort in monitoring oil palm trees’ healthiness.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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