Investigating the Correlation between Rice Production and RGB Vegetation Index from Drone Imagery and NIR-Based Index from Sentinel Images

##plugins.themes.academic_pro.article.sidebar##

Published Nov 30, 2023
https://doi.org/10.70124/salaga.v1i1.1100
Download
PDF
Statistic
Salaga Jurnal, Vol. 01, Nomor 1, June 2023

##plugins.themes.academic_pro.article.main##

Hastina Hastina
Universitas Hasanuddin
Olly Sanny Hutabarat
Universitas Hasanuddin
Daniel Useng
Universitas Hasanuddin

Abstract

One of the basic needs, especially for the people of Indonesia, is rice. This causes the demand for rice to increase day by day in accordance with population growth. Therefore, rice production in Indonesia is expected to meet the basic needs of the Indonesian population. Estimating the level of rice production is important to determine the level of food availability in an area. Image data obtained from drones and sentinel images can be analyzed to obtain vegetation indices and used to predict rice production. The purpose of this research is to see the relationship between the RGB vegetation index of drone imagery and the NIR-based index of sentinel imagery with rice production. in this study, a regression analysis was conducted between the vegetation index values ​​obtained from drone imagery and sentinel-2 imagery with rice paddy production then the equation obtained from the regression analysis was used to estimate rice paddy production on the rice fields used as test samples. Correlation analysis was also conducted where this analysis was used to see how strong the relationship is between the vegetation index used and rice production. As for the results obtained, there are three levels of relationship obtained from the results of correlation analysis between several vegetation indices used, namely strong, medium and very strong levels. The TGI vegetation index shows a moderate level of relationship, while the strong level consists of the VARI, ExG, RGBVI and GLI vegetation indices. And at a very strong level shown by the NGRI, NDVI and NDRE vegetation indices. Estimation of rice production can be predicted with the NDRE vegetation index which has the highest determinant value, which is 84.06%. Validation of the NDRE vegetation index shows a result of 55.97%, where more data is under estimate which means the estimation results are smaller than the results obtained in the field.

##plugins.themes.academic_pro.article.details##

How to Cite
Hastina, H., Olly Sanny Hutabarat, & Daniel Useng. (2023). Investigating the Correlation between Rice Production and RGB Vegetation Index from Drone Imagery and NIR-Based Index from Sentinel Images. Salaga Journal, 1(1), 14–21. https://doi.org/10.70124/salaga.v1i1.1100 (Original work published November 23, 2023)

References

  1. Sudarsono N, Bambang S, Arwan P. Analisis Fase Tumbuh Padi dengan algoritma NDVI, EVI, SAVI dan LWSI Pada Citra Landsat 8. Jurnal Geodesi;5(1):127.
  2. Vitasari W, Daniel U, Ahmad M. Pendugaan Produksi dan Indeks Vegetasi Tanaman Padi Menggunakan Data Citra Platform Unmanned Aerial Vehicle (UVA) dan Data Satelit Landsat 8. Jurnal Agritechno. 2017;10(2):205-207.
  3. Wijayanto C. Pendugaan Produksi Tanaman Padi Berdasarkan Nilai Indeks Vegetasi Pada Musim Tanam Utama (Desember-Maret) Menggunakan Data Citra Drone. Skripsi. Universitas Hasanuddin.