Remote Sensing Technology for Land Farm Mapping Based on NDMI, NDVI, and LST Feature

Ahmad Fauzi Mabrur(1*), Noor Akhmad Setiawan(2), Igi Ardiyanto(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author


Remote Sensing is a reliable and efficient data acquisition techniques. This technique is widely used for land image processing. This technique has many advantages, especially in terms of cost and time. In this study, the classification between dry and irrigated land from irrigation canals is presented. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) values obtained from satellite imagery data are used in this process. It is expected that through this method, the distribution and control of irrigation water can optimize existing agricultural potential. Ground Check (GC) is used for validation process. The results showed that the error rate based on the moon was not so large, i.e., 18%. The highest errors occur in February and March. This happens because those months are the rainy season, so the measured temperature is mostly the temperature above the cloud layer. On the other hand, the lowest error occurs in November. Also, it can be seen that this method can function optimally when detecting residential areas or highways.


Remote Sensing; Classification; NDVI; NDMI; LST

Full Text:



R. Shofiyanti, “Teknologi Pesawat Tanpa Awak untuk Pemetaan dan Pemantauan Tanaman dan Lahan Pertanian,” Inform. Pertan., Vol. 20, No. 2, pp. 58–64, 2011.

M.S. Pervez, M. Budde, and J. Rowland, “Remote Sensing of Environment Mapping Irrigated Areas in Afghanistan Over the Past Decade Using MODIS NDVI,” Remote Sens. Environ., Vol. 149, pp. 155–165, 2014.

S. Senturk, S. Bagis, and B.B. Ustundag, “Application of Remote Sensing Techniques in Locating Dry and Irrigated Farmland Parcels,” The Third International Conference on Agro-Geoinformatics, 2014, pp. 1-4.

W. Li, S. Wang, Y. Zhou, Q. Xu, F. Wang, and Y. Han, “Remote Sensing Methods for Surveying and Extracting Abandoned Farmlands,” 2012 5th Int. Congr. Image Signal Process (CISP 2012), 2012, pp. 1086–1090.

X. Zhu, W. Zhu, J. Zhang, and Y. Pan, “Mapping Irrigated Areas in China from Remote Sensing and Statistical Data,” J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol. 7, No. 11, pp. 4490–4504, 2014.

A. Hafizh S, A.B. Cahyono, and A. Wibowo, “Penggunaan Algoritma NDVI dan EVI pada Citra Multispektral untuk Analisa Pertumbuhan Padi,” Geoid, Journal of Geodesy and Geomatics, Vol. 9, no. 1, pp. 7–10, 2013.

Wahyunto, Widagdo, and B. Heryanto, “Pendugaan Produktivitas Tanaman Padi Sawah Melalui Analisis Citra Satelit,” Inform. Pertan., Vol. 15, pp. 853–869, 2006.

Badan Pusat Statistik Kab. Kebumen, Kebumen Regency in Figures 2016, Kebumen, Indonesia: BPS Kab. Kebumen, 2017.

E. Achmad, H. Hamzah, A. Albayudi, and B. Bima, “Indeks Kelembaban Taman Nasional Bukit Tiga Puluh Menggunakan Citra Satelit Landsat 8,” Proc. of Semin Nas. Geomatika, 2018, pp. 425-432.

Wahana Komputer, Mengolah Data Citra Satelit Menggunakan ENVI, 1st ed., Semarang, Indonesia: Andi Offset, 2017.


Article Metrics

Abstract views : 1393 | views : 1613


Copyright (c) 2019 IJITEE (International Journal of Information Technology and Electrical Engineering)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ISSN  : 2550-0554 (online)

Contact :

Department of Electrical engineering and Information Technology, Faculty of Engineering
Universitas Gadjah Mada

Jl. Grafika No 2 Kampus UGM Yogyakarta

+62 (274) 552305

Email :