TY - JOUR
T1 - UAV application to estimate oil palm trees health using Visible Atmospherically Resistant Index (VARI) (Case study of Cikabayan Research Farm, Bogor City)
AU - Nur Anisa, Medina
AU - Rokhmatuloh,
AU - Hernina, Revi
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences, 2020.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/25
Y1 - 2020/11/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097646777&partnerID=8YFLogxK
U2 - 10.1051/e3sconf/202021105001
DO - 10.1051/e3sconf/202021105001
M3 - Conference article
AN - SCOPUS:85097646777
SN - 2555-0403
VL - 211
JO - E3S Web of Conferences
JF - E3S Web of Conferences
M1 - 05001
T2 - 1st International Symposium of Earth, Energy, Environmental Science and Sustainable Development, JESSD 2020
Y2 - 28 September 2020 through 30 September 2020
ER -