DETECTING SURFACE WATER AREAS AS ALTERNATIVE WATER RESOURCE LOCATIONS DURING THE DRY SEASON USING SENTINEL-2 IMAGERY (CASE STUDY: LOWLAND REGION OF BEKASI-KARAWANG, WEST JAVA PROVINCE)
Abstract
In Indonesia, drought is a type of disaster that often occurs, especially during the dry season. What is most needed at such times is the availability of sufficient water sources to meet shortages. Therefore, water source locations are vital during the dry season in order to meet needs. To meet this information need, remote sensing data offer a precise solution. This research proposes a rapid method of detecting surface water areas based on remote sensing image data. It focuses on the use of remote sensing satellite imagery to detect objects and the location of surface water sources. The purpose of the study is to rapidly identify objects and locate surface water sources using Sentinel-2 MSI (MultiSpectral Instrument), one of the latest types of remote sensing satellite data. Several water index (WI) methods were applied before deciding which was most suitable for detecting surface water objects. The lowland region of Bekasi-Karawang, a drought prone area, was designated as the research location. The results of the research show that by using Sentinel-2 MSI imagery, MNDWI (Modified Normalized Water Index) is the appropriate parameter to detect surface water areas in the lowland region of Bekasi-Karawang, West Java Province, Indonesia, during times of drought. The method can be employed as an alternative approach based on remote sensing data for the rapid detection of surface water areas as alternative sources of water during the dry season. The existence of natural water sources (swamps, marshes, ponds) that remain during this time can be used as alternative water resources. Further research is still needed which focuses on different geographical conditions and other regions in Indonesia.
Keywords
Full Text:
PDFReferences
Acharya TD, Yang IT, Subedi A et al., 2016. Change detection of lakes in Pokhara, Nepal using Landsat data, Proceedings 1(17): doi:10.3390/ecsa-3-E005
AghaKouchak, A, Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D. and Hain, C. R., 2015. Remote sensing of drought: Progress, challenges and opportunities, Reviews of Geophysics 53, 452–480, doi:10.1002/2014RG000456
Ashraf M, Nawaz R, 2015. A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi-Temporal Landsat Dataset, Journal of Geographic Information System 17:1-19
Chavez, Jr. P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24:459–79
Chavez, Jr. P.S., 1989. Radiometric Calibration of Landsat Thematic Mapper Mutispectral Images. Photogrammetric Engineering and Remote Sensing 55(9):1285-1294
Ding, F. 2009. “A New Method for Fast Information Extraction of Water Bodies Using Remotely Sensed Data.†Remote Sensing Technology and Application 24 (2): 167–171
Du Z, Linghu B, Ling F et al., 2012. Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China, Journal of Applied Remote Sensing 6(1), 063609, https://doi.org/10.1117/1.JRS.6.063609
Du Z, Li D, Zhou L, et al., 2014. “Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sensing Letters 5 (7): 672–681.doi:10.1080/2150704X.2014.960606
ESA, 2013. Sentinel 2, The Operational Copernicus Optical High Resolution Land Mission. Https://www.esa.int/ copernicus
ESA, 2015. Sentinel-2 Products Specification Document. Ref : S2-PDGS-TAS-DI-PSD, Issue : 13.1, Date : 18/11/2015. Https://sentinel.esa.int/web/sentinel/user-guides/document-library
Feng D, 2009. A new method for fast information extraction of water bodies using remotely sensed data, Remote Sensing Technology and Application 24(2). http:// www.oalib.com/paper/1468694#.XgQY7EczZ1s
Gao BC, 1996. NDWI a normalized difference water index for remote sensing of vegetation liquid water from Space, Remote Sensing of Environment 58:257-266
Godfray, H. C. J., J. R. Beddington, I. R. Crute, L. Haddad, D. Lawrence, J. F. Muir, J. Pretty, S. Robinson, S. M. Thomas, and C. Toulmin, 2010. Food security: The challenge of feeding 9 billion people, Science, 327(5967), 812–818, doi:10.1126/science.1185383
Kwang C, Osei jr EM, Amoah AS, 2018. Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River, Journal of Geography and Geology 10(1); 2018, doi:10.5539/jgg.v10n1p1
Li, W., Z. Du, F. Ling, D. Zhou, H. Wang, Y. Gui, B. Sun, and X. Zhang. 2013. “A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI.â€Remote Sensing 5 (11): 5530–5549. doi:10.3390/rs5115530
Li P, Jiang L, Feng, 2014. Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors, Remote Sensing 6:310-329, DOI:10.3390/ rs6010310
McFeeters SK, 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing 17(7): 1425-1432, DOI: 10.1080/ 01431169608948714
Szabó S, Gácsi Z, Balász, 2016. Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories, Landscape & Environment 10(3-4): 194-202, DOI: 10.21120/LE/10/3-4/13
Sekertekin, A., Cicekli, S.Y., & Arslan, N., 2018. Index-Based Identification of Surface Water Resources Using Sentinel-2 Satellite Imagery. 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, 2018, pp. 1-5, doi: 10.1109/ISMSIT.2018 .8567062
Suwarsono, Nugroho J.T, Wiweka, 2013. Identification of Inundated Area Using Normalized Difference Water Index (NDWI) on lowland region of Java Island, International Journal of Remote Sensing and Earth Sciences 10(2): 114-121
Suwarsono, Yulianto, F., Fitriana, H.L., Nugroho, U.C., Sukowati, K.A.D., and Khomarudin, M.R., 2020. Detecting the Surface Water Area in Cirata Dam Upstream Citarum Using A Water Index From Sentinel-2, International Journal of Remote Sensing and Earth Sciences 17(1): 1-8
Wang X, Liu Y, Ling et al., 2017. Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015, International Journal of Geo-Information 6(68), doi:10.3390 /ijgi6030068
Wilhite, D. A., 2005.Drought and Water Crises: Science, Technology, and Management Issues, pp. 432, vol. 86, CRC Press
Yang J, Du X, 2017. An enhanced water index in extracting water bodies from Landsat TM imagery, Annals of GIS, 23:3, 141-148, DOI:10.1080/19475683.2017.1340339
Yulianto F, Suwarsono, Sulma S, et al., 2018. Observing the inundated area using Landsat-8 multitemporal images and determination of flood-prone area in Bandung basin. International Journal of Remote Sensing and Earth Sciences 15(2): 131-140
Xu H, 2006. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery, International Journal of Remote Sensing 27(14): 3025–3033
Refbacks
- There are currently no refbacks.