Paper
29 January 2024 Time series pattern of radar vegetation index over rubber tree infected by Pestalotiopsis sp. leaf fall disease
Ariq Anggaraksa Riesnandar, Masita Dwi Mandini Manessa, Supriatna ., Charlos Togi Stevanus, Farida Ayu
Author Affiliations +
Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129771Z (2024) https://doi.org/10.1117/12.3009632
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia
Abstract
In recent years, rubber plantations in Indonesia have been attacked by the Pestalotiopsis sp. leaf fall disease, which has caused a significant leaf fall that then creates a decline in latex production. Remote sensing data can be used to monitor the health of rubber plants. Sentinel-1 imagery is used to overcome cloud cover constraints in optical imagery at the Rubber Plantation. This study aims to analyze the spatial distribution and monthly rubber plant health trends using the radar vegetation indexes in Sembawa Rubber Plantation, South Sumatera, Indonesia. Furthermore this study compares the performance of two radar vegetation Index namely the RVI (Radar Vegetation Index) and NDRVI (Normalize Different Radar Vegetation Index) algorithms. The results of this study indicate that the vegetation index indicates the health of the rubber plant based on the leaf fall cycle due to Pestalotiopsis sp. within 5 months. A low index value indicates leaf fall, and a high value indicates regrowth.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ariq Anggaraksa Riesnandar, Masita Dwi Mandini Manessa, Supriatna ., Charlos Togi Stevanus, and Farida Ayu "Time series pattern of radar vegetation index over rubber tree infected by Pestalotiopsis sp. leaf fall disease", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129771Z (29 January 2024); https://doi.org/10.1117/12.3009632
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KEYWORDS
Vegetation

Radar

Diseases and disorders

Data processing

Polarization

Remote sensing

Correlation coefficients

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