Abstract
Monitoring water bodies by extraction using water indexes from remotely sensed images has proven to be effective in delineating surface water against its surrounding. This study tested and assessed the Normalized Difference Water Index, Modified Normalized Difference Water Index, Automated Water Extraction Index, and near infrared (NIR) band using Landsat 8 imagery acquired on September 3, 2016. The threshold method was adapted for surface water extraction. To avoid over and under-estimation of threshold values, the optimum threshold value of each of the water indexes was obtained by implementing a geoprocessing model. Examining images of Landsat 8, NIR band has the largest difference in reflectance values between water and non-water bodies. Thus, NIR band exhibits the highest contrast between water and non-water bodies. An optimum threshold value of 0.128 for NIR band achieved an overall accuracy (OA) and kappa hat (Khat) coefficient of 99.3% and 0.986, respectively. NIR band of Landsat 8 as water index was found more satisfactory in extracting water bodies compared to the multi-band water indexes. This study shows that the optimum threshold values of each of the water indexes considered in this study were determined conveniently, where highest value of OA and Khat coefficient were obtained by creating and implementing a graphical modeler in Quantum Geographic Information System that automates from setting threshold value to accuracy assessment. This study confirms that remote sensing can extract or delineate water bodies rapidly, repeatedly and accurately.
Highlights
Remote sensing is an observation method in obtaining information about several objects on Earth’s surface, without having contact with the use of sensors [1]
Images of colour composites The surrounding water bodies of the Municipality of Cordova and southwestern part of Lapu-Lapu City were selected from Landsat 8 operational land imager (OLI) imagery for surface water extraction
Results of this study have indicated that NIR band of Landsat 8 OLI can be adapted more efficiently as a single-band water index compared to the multi-band water index introduced earlier by others [13,14,15,16]
Summary
Remote sensing is an observation method in obtaining information about several objects on Earth’s surface (that generally includes water, vegetation, built-up, and bare soil), without having contact with the use of sensors [1]. Clouds or haze and cloud shadows affect optical remote sensing images [5,6,7,8] which makes it challenging to discriminate them from dark objects like water and shadows [5, 7, 8]. Remote sensing is essential in several studies on surface water mapping including but not limited to water bodies extraction [13,14,15,16, 18], flood management [19, 20], and water quality [21,22,23]. Water body extraction by multi-band water index threshold methods was introduced by McFeeters [13] from Landsat 4 Multispectral Scanner using green and near-infrared (NIR) bands, by Rogers and Kearney [14] from Landsat Thematic Mapper (TM) using red and green and shortwave infrared (SWIR) bands, by Xu [15] from Landsat 5 TM and Landsat 7 Enhanced TM using SWIR bands, and by Feyisa et al [16] from Landsat 5 TM using green, blue, NIR, and SWIR
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