A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta

Abstract

River bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion and flooding. There have been several studies examining RB and coastal erosion in the VMD using remotely sensed satellite data, but the applied methodology was not adequately validated. Therefore, we developed a novel SRBED (Spectral RB Erosion Detection) method, in which the M-AMERL (Modified Automated Method for Extracting Rivers and Lakes) is proposed, and a new RB change detection algorithm using Landsat data. The results show that NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) using the M-AMERL algorithm (i.e., NDWIM-AMERL, MNDWIM-AMERL) perform better than other indices. Furthermore, the NDWIM-AMERL; SMA (i.e., NDWIM-AMERL using the SMA (Spectral Mixture Analysis) algorithm) is the best RB extraction method in the VMD. The NDWIM-AMERL; SMA performs better than the MNDWI, NDVI (Normalized Difference Vegetation Index), and WNDWI (Weighted Normalized Difference Water Index) indices by 35–41%, 70% and 30%, respectively. Moreover, the NDVI index is not recommended for assessing RB changes in the delta. Applying the developed SRBED method and RB change detection algorithm, we estimated a net erosion area of the RB of –1.5 km2 from 2008 to 2014 in the Tien River from Tan Chau to My Thuan, with a mean erosion width of –2.64 m and maximum erosion widths exceeding 60 m in places. Our advanced method can be applied in other river deltas having similar characteristics, and the results from our study are helpful in future studies in the VMD.

Assessment of Riverbank Erosion Hotspots along the Mekong River in Cambodia Using Remote Sensing and Hazard Exposure Mapping

“…Such approaches can provide accurate results, but they are typically used in small-scale study areas. Another remote sensing technique uses satellite images, such as those acquired by Landsat [18,[29][30][31] and Sentinel-2 [32]. The use of satellite images in this technique enables the reconstruction of historical changes in the riverbank of a large river with fewer resources and at minimal cost.…”

“…For example, the left riverbank of the Mekong River in Tan Chau and Sa Dec erodes landward at a high average rate of about 30 m/yr and 42 m/yr, respectively. In a study by Binh et al [30], the authors improved the method of riverbank detection, specifically in the Mekong Delta in Vietnam, using Landsat images and water indices in combination with a modified automated method for extracting rivers and lakes.…”

Assessment of the soil loss caused by riverbank erosion in Serbia

“…Worldwide, several different techniques have been used to quantify these processes and different time span, 1 to 10 years for field measurements and 10 to 200 years for maps and aerial photographs (Hooke 1979(Hooke , 1980(Hooke , 2003Hooke & Redmond 1989;Duan 2005). In recent years, there has been an increase of the usage of aerial and terrestrial photogrammetry in fluvial process studies (Yang et al, 2018;Hemmelder et al, 2018;Binh et al, 2020).…”

Spatial Validation of Spectral Unmixing Results: A Systematic Review

“…In order to minimized the errors, Arai et al [368], Cao et al [164], Li et al [107], Soenen et al [500], and Zurita-Milla et al [419] carefully chose the size of the reference maps; Bair et al [254], Cavalli [114,145], Ding et al [152], Fernandez-Garcia et al [256], Hamada et al [441], Hajnal et al [169], Lu et al [435], Ma & Chan [78], Rittger et al [262], Sun et al [263], Yang et al [488], and Yin et al [151] spatially resampled the reference fractional abundance maps; Estes et al [447] compared different windows of pixels (i.e., 3 × 3, 7 × 7, 11 × 11, 15 × 15, and 21 × 21); Pacheco & McNairn [480] selected the size and the spatial resolution of the reference maps; Ben-dor et al [507], Fernandez-Guisuraga et al [342], Kompella et al [328], Laamarani et al [343], and Plaza & Plaza [465] carefully co-localized the reference fractional abundance maps on the reference maps; Wang et al [366] expanded the windows of the field sample size; Zhu et al [64] resampled at "four kinds of grids" (i.e., 1100 × 1100 m, 2200 × 2200 m, 4400 × 4400 m, and 8800 × 8800 m) the reference fractional abundance map and compared the results. Bair et al [254], Binh et al [341], Cavalli [114,145], Cheng et al [543], and Ruescas et al [448] evaluated the errors in co-localization and spatial-resampling due to the comparison of different data at different spatial resolutions. Moreover, Cavalli [145] proposed a method to minimize the errors: the comparison of the histograms of the reference fractional abundance values with the histogram...…”