Abstract
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%.
Highlights
McFeeters [7] developed the normalized difference water index (NDWI) based on the green and near-infrared (NIR) bands to delineate features of open water by using a threshold value greater than zero as a delimiter for extracting surface water. is means that the positive values are classified as water and negative values are classified as nonwater. e NDWI approach was modified by Xu [8] to become the modified normalized difference water index (MNDWI) by replacing the NIR band with the shortwave-infrared (SWIR) band. is was done because the application of NDWI in some water areas adjacent to builtup land produces noise extraction of water information e Scientific World Journal mixed with the built-up land
The automatic water extraction index (AWEI) was developed with the aim of (a) increasing the accuracy of automatic lake surface water mapping by improvising the determination of threshold values based on analysis and calculations using the split-based approach (SBA) and (b) conducting test case studies of AWEI threshold improvement results on the surface water of several lakes, which has a variety of different or heterogeneous characteristics
2.35°–2.88° North and 98.52°–99.1° East (Figure 1), was used as a test case study area to develop an AWEI threshold improvement model to detect lake surface water on remotely sensed optical data that represent the characteristics of the type of volcanic-tectonic lake [23, 24]. e AWEI threshold improvement to detect lake surface water was tested at several other locations representing different lake characteristics in Indonesia. e Ministry of Environment of the Republic of Indonesia (KLHK) states that 15 national priority lakes in Indonesia need to be saved to restore, preserve, and maintain lake functions based on the principle of ecosystem to balance the environment’s carrying capacity
Summary
Remote sensing plays a role in providing information on large-scale monitoring of surface waters, with the advantages of high spatiotemporal resolution, multisensors, and nearreal-time operational data [1–3]. Is approach was developed to improve the extraction results of water information from several previous water indices, which was insufficient to use only two satellite imagery bands to achieve high accuracy. E SBA was used by Bovolo and Bruzzone [20] to identify the impact of land changes due to the tsunami disaster, applied to multitemporal imagery To address this challenge, one of the index approaches to water, AWEI, was selected and used in this study. The AWEI was developed with the aim of (a) increasing the accuracy of automatic lake surface water mapping by improvising the determination of threshold values based on analysis and calculations using the SBA and (b) conducting test case studies of AWEI threshold improvement results on the surface water of several lakes, which has a variety of different or heterogeneous characteristics
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