• Spatial Information Research
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• v.23 no.6
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• pp.31-41
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• 2015

This research is primarily intended to explore a novel way to monitor desiccation of inland wetland by combining MNDWI (Modified Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index). The changes for vegetation and water condition on Upo Wetland located at southeastern Korea were investigated by MNDWI and NDVI derived from 2002, 2010 and 2015 Landsat data. The integrated use of MNDWI and NDVI made it possible to identify area-wide vegetation cover changes and to assess water storage changes on multi-annual time scales simultaneously. Comparing MNDWI with NDVI reveals the quantitative evidences for anthropogenic and environmental influences (such as road, building, water) causing an accelerated wetland desiccation. In fact, our monitoring approach raises critical issues regarding the hydrological cycle and its inter-annual changes for inland wetland under threat of drying up and highlights the important role of MNDWI and NDVI integration for any urgent or long-term treatment plan. This research presents scientific and objective evidences to support integrated approach of NDVI and MNDWI in exploring drying up trends of wetlands.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.1-11
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• 2021

Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.144-144
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• 2022

Identifying waterbody from remote sensing images, namely water detection, helps understand continuous redistribution of terrestrial water storage and accompanying hydrological processes. It also allows us to estimate available surface water resources and help effective water management. For this problem, NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) are widely used. Although remote sensing indexes can highlight remote sensing image in the water, the noise and the spatial information of the remote sensing image are difficult to be considered, so the accuracy is difficult to be compared with the visual interpretation (the most accurate method, but it requires a lot of labor, which makes it difficult to apply). In this study, we attempt to improve existing NDWI and MNDWI to better water detection. We establish waterbody database of South Korea first and then used it for assessing waterbody indices.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.627-635
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• 2020

With the increasing severity of climate change, intense torrential rains are occurring more frequently globally. Flooding due to torrential rain not only causes substantial damage directly, but also via secondary events such as landslides. Therefore, accurate and prompt flood detection is required. Because it is difficult to directly access flooded areas, previous studies have largely used satellite images. Traditionally, water indices such asthe normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) which are based on different optical bands acquired by satellites, are used to detect floods. In addition, as flooding likelihood is greatly influenced by the weather, synthetic aperture radar (SAR) images have also been used, because these are less influenced by weather conditions. In this study, we compared flood areas calculated from SAR images and water indices derived from Landsat-8 images, where the images were acquired at similar times. The flooded area was calculated from Landsat-8 and Sentinel-1 images taken between the end of May and August 2019 at Lijiazhou Island, China, which is located in the Changjiang (Yangtze) River basin and experiences annual floods. As a result, the flooded area calculated using the MNDWI was approximately 21% larger on average than that calculated using the NDWI. In a comparison of flood areas calculated using water indices and SAR intensity images, the flood areas calculated using SAR images tended to be smaller, regardless of the order in which the images were acquired. Because the images were acquired by the two satellites on different dates, we could not directly compare the accuracy of the water-index and SAR data. Nevertheless, this study demonstrates that floods can be detected using both optical and SAR satellite data.

• Ecology and Resilient Infrastructure
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• v.5 no.2
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• pp.59-71
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• 2018

Lake Enriquillo, the largest lake in the Dominican Republic, recently has undergone unusual water area changes since 2001 thus it has been affected seriously by local community's livelihood. Earthquakes and seismic activities of Hispaniola plate tectonic coupled with human activities and climate change are addressed as factors causing the increasing. Thus, a thorough study on relationship between lake area changing, and those factors is needed urgently. To do so, this study applied MESMA on MODIS data to extract water area of Lake Enriquillo during 2001 and 2012 bimonthly, with six issues 12-year. MODIS provides high temporal resolution, and its coarse spatial resolution is compensated by MESMA fraction map. The increase in water area was $142.2km^2$, and the maximum lake area was $338.0km^2$ (in 2012). Water areas extracted by two Landsat scenes at two different times with three image classification approaches (ISODATA, MNDWI, and TCW) were used to assess accuracy of MODIS and MESMA results; it indicated that MESMA water areas are same as ISODATA's, less than 0.4%, while the highest difference is between MESMA and TCW, 2.4%. A number of previously formulated hypotheses of lake area change were investigated based on the outcomes of the present study, though none of them could fully explain the changes.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.45-53
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• 2013

Global warming causes sea levels to rise and global changes apparently taking place including coastline changes. Coastline change due to sea level rise is also one of the most significant phenomena affected by global climate change. Accordingly, Coastline change detection can be utilized as an indicator of representing global climate change. Generally, Coastline change has happened mainly because of not only sea level rise but also artificial factor that is reclaimed land development by mud flat reclamation. However, Arctic coastal areas have been experienced serious change mostly due to sea level rise rather than other factors. The purposes of this study are automatic extraction of coastline and identifying change. In this study, in order to extract coastline automatically, contrast of the water and the land was maximized utilizing modified NDWI(Normalized Difference Water Index) and it made automatic extraction of coastline possibile. The imagery converted into modified NDWI were applied image processing techniques in order that appropriate threshold value can be found automatically to separate the water and land. Then the coastline was extracted through edge detection algorithm and changes were detected using extracted coastlines. Without the help of other data, automatic extraction of coastlines using LANDSAT was possible and similarity was found by comparing NLCD data as a reference data. Also, the results of the study area that is permafrost always frozen below $0^{\circ}C$ showed quantitative changes of the coastline and verified that the change was accelerated.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.54-68
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• 2014

In order to analyze the land surface properties in Seoul and its surrounding metropolitan area, several indices and land surface temperature were calculated by the Landsat satellites (e.g., Landsat 5, Landsat 7, and Landsat 8). The Landsat data came from only in the fall season with Landsat 5 on October 21, 1985, Landsat 7 on September 29, 2003, and Landsat 8 on September 16, 2013. The land surface properties used are the indices that represented Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Wetness Index (MNDWI), Normalized Difference Wetness Index (NDWI), Tasseled cap Brightness, Tasseled cap Greenness, Tasseled cap Wetness Index, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) and the land surface temperature of the area in and around Seoul. Most indices distinguish very well between urban, rural, mountain, building, river and road. In particular, most of the urbanization is represented in the new city (e.g., Ilsan) around Seoul. According to NDVI, NDBI and land surface temperature, urban expansion is displayed in the surrounding area of Seoul. The land surface temperature and surface elevation have a strong relationship with the distribution and structure of the vegetation/built-up indices such as NDVI and NDBI. While the NDVI is positively correlated with the land surface temperature and is also negatively correlated with the surface elevation, the NDBI have just the opposite correlations, respectively. The NDVI and NDBI index is closely associated with the characteristics of the metropolitan area. Landsat 8 and Landsat 5 have very strong correlations (more than -0.6) but Landsat 7 has a weak one (lower than -0.5).