Observing the Inundated Area Using Landsat-8 Multitemporal Images and Determination of Flood-Prone Area in Bandung Basin

Yulianto, Fajar; Suwarsono, Nfn; Sulma, Sayidah; Khomarudin, M. Rokhis

doi:10.30536/j.ijreses.2018.v15.a3074

Cited by 8 publications

(6 citation statements)

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“…Research conducted in Indonesia by Yulianto et al (2018), who attempted to observe inundated areas using Landsat-8 multitemporal images in Bandung basin, found that MNDWI was the most appropriate parameter to use to detect flooded areas in the Bandung basin which had heterogeneous land surface conditions. In other studies, by using Landsat-7 ETM+ image data, the extent of inundated areas caused by floods in lowland regions could be identified and separated based on NDWI, MNDWI, and NWI.…”

Section: Resultsmentioning

confidence: 99%

Detecting the Surface Water Area in Cirata Dam Upstream Citarum Using a Water Index From Sentinel-2

Suwarsono

Yulianto

Fitriana

et al. 2020

IJReSES

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This paper describes the detection of the surface water area in Cirata dam, upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir.

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Section: Resultsmentioning

confidence: 99%

Detecting the Surface Water Area in Cirata Dam Upstream Citarum Using a Water Index From Sentinel-2

Suwarsono

Yulianto

Fitriana

et al. 2020

IJReSES

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“…In this area, there is also a meeting location of Citarik river with its tributaries; those are Cimande river and Cikijing river. Such a river meeting area is prone to flooding as happened in floods in the Melong village of Cimahi city in the branch area of Cilember and Cibeureum river with Citarum river [16]. The meeting cross-section of Cisangkan river and Citarum river is in Leuwigajah Village, Cimahi City.…”

Section: The Materials and Methodsmentioning

confidence: 99%

Flood Quantification Index Based on Inundation Indicators and Risk Scale

Safarina

Karnisah

Permana

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Flood management requires an assessment of flood levels that occur appropriately. Determining flood levels quantitatively can help control floods more efficiently. This study aims to make flood quantification based on the inundation indicators and scale of risk. The case study is in Citarik, Cilember, Cimahi, Cisangkan, and Cibeureum, located in the Citarum watershed, West Java, Indonesia. The method used is a mathematical model of flow verified by the river's physical dimensions. Four indicators were analyzed based on unsteady one-dimensional river flow and overland flow models. The four flood quantity indicators with different weights are namely percentage of inundation area (Ai), percentage of area activity on the inundation area (Aac), inundation duration (D), and inundation depth (H) which have consecutive indexes 0.3, 0.2, 0.2 and 0.3. The initial discharge was analyzed based on unit hydrograph at the upstream river cross-section of the inundation region. The study's results by verification of field observations showed that the highest peak inundation was 3.0 m in Cilember, inundation duration was a maximum of 18 hours in Citarik and Cilember, the highest inundation area was 329.23 % of the watershed area of Cilember. Results of calculations with the formulations, the highest quantity is 16.18 in Cilember flood that occurs in Melong village with a risk scale of 5. These results conclude that the Cilember flood is high-level damage so that it is a priority to be overcome.

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“…is was done because noise cannot distinguish between water and darker pixel (nonwater) surfaces, such as the shadow. Implementation of several indices of such water has been used in various other aspects, such as analysis and flood monitoring [10][11][12], environment analysis [13], water surface mapping [14][15][16], water-quality assessment [17], and agriculture [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data

Yulianto

Kushardono

Budhiman

et al. 2022

The Scientific World Journal

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In this study, we proposed an automatic water extraction index (AWEI) threshold improvement model that can be used to detect lake surface water based on optical remote sensing data. An annual Landsat 8 mosaic was created using the Google Earth Engine (GEE) platform to obtain cloud-free satellite image data. The challenge of this study was to determine the threshold value, which is essential to show the boundary between water and nonwater. The AWEI was selected for the study to address this challenge. The AWEI approach was developed by adding a threshold water value based on the split-based approach (SBA) calculation analysis for Landsat 8 satellite images. The SBA was used to determine local threshold variations in data scenes that were used to classify water and nonwater. The class threshold between water and nonwater in each selected subscene image can be determined based on the calculation of class intervals generated by geostatistical analysis, initially referred to as smart quantiles. It was used to determine the class separation between water and nonwater in the resulting subscene images. The objectives of this study were (a) to increase the accuracy of automatic lake surface water detection by improvising the determination of threshold values based on analysis and calculations using the SBA and (b) to conduct a test case study of AWEI threshold improvement on several lakes’ surface water, which has a variety of different or heterogeneous characteristics. The results show that the threshold value obtained based on the smart quantile calculation from the natural break approach (AWEI ≥ −0.23) gave an overall accuracy of close to 100%. Those results were better than the normal threshold (AWEI ≥ 0.00), with an overall accuracy of 98%. It shows that there has been an increase of 2% in the accuracy based on the confusion matrix calculation. In addition to that, the results obtained when classifying water and nonwater classes for the different national priority lakes in Indonesia vary in overall accuracy from 94% to 100%.

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Observing the Inundated Area Using Landsat-8 Multitemporal Images and Determination of Flood-Prone Area in Bandung Basin

Cited by 8 publications

References 0 publications

Detecting the Surface Water Area in Cirata Dam Upstream Citarum Using a Water Index From Sentinel-2

Detecting the Surface Water Area in Cirata Dam Upstream Citarum Using a Water Index From Sentinel-2

Flood Quantification Index Based on Inundation Indicators and Risk Scale

Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data

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