Drought Management in Batam using Combined NDVI-TCT Algorithm to Create a Classification Level Map

Drought constitutes a significant natural disaster with profound implications for agricultural productivity, economic stability, and ecological balance. Batam is one of the cities experiencing a high level of drought. At the end of 2022, Batam is actually on the verge of drought. The purpose of this study is to find out information on the distribution of potential for drought in Batam and the dominant factors affecting the potential for drought occurred using NDVI and TCT algorithms. This research employed remote sensing and GIS techniques, using Landsat 8 images to acquire parameters from NDVI, TCT, and Rainfall data, which are then processed through scoring and overlaying. The final step was to validate the vegetation index parameter by taking the coordinates. The final result is a map of the potential for drought in Batam, consisting of 5 classes of potential for drought. The area with a very low potential for drought was located mostly in Sagulung, with an area of 2.661,89 Ha. The areas with low potential for drought were mostly located in Nongsa, Batam Center, Batu Ampar, Bengkong, Lubuk Baja, and Batu Aji, with an area of 7.175,22 Ha. The areas with a very high potential for drought were mostly located in Galang, Bulang, and Belakang Padang, with an area of 19.744,76 Ha. The area with moderate potential for drought was mostly located in Sungai Beduk, with an area of 22.122,71 Ha. The areas with high potential for drought were mostly located in Galang and Bulang, with an area of 35.663,89 Ha. It is concluded from the results of this research that the collective classification of high and very high drought potential levels covers up to 64% of the entire research area.

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Journal
Journal of Geoscience Engineering Environment and Technology
Published
2024-09-30
DOI
https://doi.org/10.25299/jgeet.2024.9.3.13033
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Drought Management in Batam using Combined NDVI-TCT Algorithm to Create a Classification Level Map

Sismanto Sismanto, Satriya Bayu Aji, Luthfiya Ratna Sari, Sudra Irawan et al.
Journal of Geoscience Engineering Environment and Technology
Data Mining and Machine Learning Applications
article

Drought Management in Batam using Combined NDVI-TCT Algorithm to Create a Classification Level Map

Sismanto Sismanto, Satriya Bayu Aji, Luthfiya Ratna Sari, Sudra Irawan, Suci Dayanti Natali, Tita Fitriania
article en

Abstract

Drought constitutes a significant natural disaster with profound implications for agricultural productivity, economic stability, and ecological balance. Batam is one of the cities experiencing a high level of drought. At the end of 2022, Batam is actually on the verge of drought. The purpose of this study is to find out information on the distribution of potential for drought in Batam and the dominant factors affecting the potential for drought occurred using NDVI and TCT algorithms. This research employed remote sensing and GIS techniques, using Landsat 8 images to acquire parameters from NDVI, TCT, and Rainfall data, which are then processed through scoring and overlaying. The final step was to validate the vegetation index parameter by taking the coordinates. The final result is a map of the potential for drought in Batam, consisting of 5 classes of potential for drought. The area with a very low potential for drought was located mostly in Sagulung, with an area of 2.661,89 Ha. The areas with low potential for drought were mostly located in Nongsa, Batam Center, Batu Ampar, Bengkong, Lubuk Baja, and Batu Aji, with an area of 7.175,22 Ha. The areas with a very high potential for drought were mostly located in Galang, Bulang, and Belakang Padang, with an area of 19.744,76 Ha. The area with moderate potential for drought was mostly located in Sungai Beduk, with an area of 22.122,71 Ha. The areas with high potential for drought were mostly located in Galang and Bulang, with an area of 35.663,89 Ha. It is concluded from the results of this research that the collective classification of high and very high drought potential levels covers up to 64% of the entire research area.

Journal of Geoscience Engineering Environment and TechnologyVol. 9(3)
Universitas Gadjah Mada (ID), Universitas Batam (ID), Politeknik Negeri Batam
Climate action
Openalex Percentile: Top 69%
Data Mining and Machine Learning Applications
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