Quantifying Sub-Pixel Surface Water Coverage in Urban Environments Using Low-Albedo Fraction from Landsat Imagery

Abstract

Abstract:The problem of mixed pixels negatively affects the delineation of accurate surface water in Landsat Imagery. Linear spectral unmixing has been demonstrated to be a powerful technique for extracting surface materials at a sub-pixel scale. Therefore, in this paper, we propose an innovative low albedo fraction (LAF) method based on the idea of unconstrained linear spectral unmixing. The LAF stands on the "High Albedo-Low Albedo-Vegetation" model of spectral unmixing analysis in urban environments, and investigates the urban surface water extraction problem with the low albedo fraction map. Three experiments are carefully designed using Landsat TM/ETM+ images on the three metropolises of Wuhan, Shanghai, and Guangzhou in China, and per-pixel and sub-pixel accuracies are estimated. The results are compared against extraction accuracies from three popular water extraction methods including the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and automated water extraction index (AWEI). Experimental results show that LAF achieves a better accuracy when extracting urban surface water than both MNDWI and AWEI do, especially in boundary mixed pixels. Moreover, the LAF has the smallest threshold variations among the three methods, and the fraction threshold of 1 is a proper choice for LAF to obtain good extraction results. Therefore, the LAF is a promising approach for extracting urban surface water coverage.

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

“…Temporal patterns of urban land expansion for each province and for the whole study [47]; GLCM mean is the mean value of the gray level co-occurrence matrix and is widely used in textural features extraction, calculated by the gray scale value of the images [48]. Elevation and slope are calculated from ASTER GDEM V1 data; NDWI is the Normalized Difference Water Index, calculated from bands three and five of Landsat OLI [49]; NDBI is the Normalized Difference Build-up Index, calculated from bands five and six of Landsat OLI [50]; and EVI/Phenology is Enhanced Vegetation Index varied in time, calculated from bands one, four, and five of Landsat OLI [51].…”

Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests

“…When subpixel-scale mixtures are of interest, most rely on the application of machine learning algorithms against large amounts of training data selected either from the imagery to be classified or from independent sources that are often higher spatial resolution image data [11][12][13]. However, spectral mixture modeling [14] has been applied to Landsat for subpixel wetland vegetation/condition assessment [15][16][17][18][19] and inland inundation monitoring [20][21][22][23][24][25][26]. Because it is a physically based approach [27], spectral mixture modeling may be used to establish theoretical reflectance data from which DSWE model decision rules could be drawn-thereby eliminating the need for extensive (and therefore costly) training data collection.…”

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

“…While the limited selection of available WorldView-2 bands prevented us from assessing model performance using an alternative index such as AWEI ns , we recommend that future studies evaluate model performance using water indices that respond linearly to the fractional coverage of land and water. Alternatively, sub-pixel waterline extraction could be applied directly to more physically meaningful water index surfaces, such as per-pixel estimates of fractional water coverage derived from spectral unmixing analysis [65][66][67]. This will ensure that sub-pixel resolution waterline positions can be compared reliably across environments with unique and contrasting spectral characteristics.…”