Paper
22 December 2021 Spatial and temporal analysis of drought in rice fields using Normalized Difference Drought Index (NDDI) in Indramayu Regency
Author Affiliations +
Proceedings Volume 12082, Seventh Geoinformation Science Symposium 2021; 120820A (2021) https://doi.org/10.1117/12.2617353
Event: Seventh Geoinformation Science Symposium (GSS 2021), 2021, Yogyakarta, Indonesia
Abstract
Drought explains a stage of water scarcity at a particular time in a certain location. In land, drought has impacts in many ways. Crop failure or “puso” will occur from a short-term drought, while economic disruption might happen in a long-term drought period. Several parts of Indonesia were periodically impacted by drought during the dry season, including Indramayu Regency. As one of the largest rice barns in West Java, 63% of Indramayu is covered by the rice field. Therefore, monitoring agricultural drought in such locations is challenging due to the extensive damage caused by the event. This study aims to analyze the spatial and temporal distribution of drought in Indramayu’s rice fields. This study utilizes the Normalized Difference Drought Index (NDDI) generated from Landsat 8 OLI-TIRS to identify drought locations. Observation is carried out during the dry months of July, August, September, and October. Most severe drought areas were found in October, especially in three sub-districts, including Gantar, Kroya, and Haurgeulis. Statistical-based correlation analysis shows a significant relationship between NDDI and distance from the irrigation network, although a relatively stronger correlation was found between NDDI and crop productivity. Conclusively, NDDI was successfully applied to identify drought areas in Indramayu Regency.
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Alinda Roihanna Fariesta, Iqbal Putut Ash Shidiq, and Muhammad Dimyati "Spatial and temporal analysis of drought in rice fields using Normalized Difference Drought Index (NDDI) in Indramayu Regency", Proc. SPIE 12082, Seventh Geoinformation Science Symposium 2021, 120820A (22 December 2021); https://doi.org/10.1117/12.2617353
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KEYWORDS
Agriculture

Electroluminescence

Vegetation

Data processing

Earth observing sensors

Java

Clouds

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