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

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 ima...

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Autores principales: Yulianto, Fajar, Kushardono, Dony, Budhiman, Syarif, Nugroho, Gatot, Chulafak, Galdita Aruba, Dewi, Esthi Kurnia, Pambudi, Anjar Ilham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8916897/
https://www.ncbi.nlm.nih.gov/pubmed/35281749
http://dx.doi.org/10.1155/2022/4894929
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author Yulianto, Fajar
Kushardono, Dony
Budhiman, Syarif
Nugroho, Gatot
Chulafak, Galdita Aruba
Dewi, Esthi Kurnia
Pambudi, Anjar Ilham
author_facet Yulianto, Fajar
Kushardono, Dony
Budhiman, Syarif
Nugroho, Gatot
Chulafak, Galdita Aruba
Dewi, Esthi Kurnia
Pambudi, Anjar Ilham
author_sort Yulianto, Fajar
collection PubMed
description 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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spelling pubmed-89168972022-03-12 Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data Yulianto, Fajar Kushardono, Dony Budhiman, Syarif Nugroho, Gatot Chulafak, Galdita Aruba Dewi, Esthi Kurnia Pambudi, Anjar Ilham ScientificWorldJournal Research Article 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%. Hindawi 2022-03-04 /pmc/articles/PMC8916897/ /pubmed/35281749 http://dx.doi.org/10.1155/2022/4894929 Text en Copyright © 2022 Fajar Yulianto et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yulianto, Fajar
Kushardono, Dony
Budhiman, Syarif
Nugroho, Gatot
Chulafak, Galdita Aruba
Dewi, Esthi Kurnia
Pambudi, Anjar Ilham
Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title_full Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title_fullStr Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title_full_unstemmed Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title_short Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
title_sort evaluation of the threshold for an improved surface water extraction index using optical remote sensing data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8916897/
https://www.ncbi.nlm.nih.gov/pubmed/35281749
http://dx.doi.org/10.1155/2022/4894929
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