A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

Abstract

The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water (γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

“…This may be attributed to NDWI using the NIR band, which is very sensitive to the vegetation water content [55]. In addition, because of the low resolution of MODIS data, the inundation area contained many mixed pixels, thus increasing the sensitivity of the NDWI to vegetation [56]. Comparing the results of the water index methods with those of the visual interpretation, the MNDWI provided more stable results than the other methods.…”

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

“…Satellite data can provide real-time, dynamic, and cost-effective information, and Earth observation procedures can be set up to provide operational (autonomous) monitoring of water resources [9,10]. Several methods have been proposed to classify surface water areas using either multispectral [9,11,12] or SAR remotely sensed data [13,14]. Popular techniques are image thresholding (rule-based classification) and supervised/unsupervised classification [15].…”

“…An alternative approach to finding a single "optimal" threshold that will work in multiple situations is to make use of automated, image-specific, threshold identification methods. Several such techniques have been proposed, among which Otsu's simple and robust algorithm [22] is one of the most utilised techniques for surface water mapping [9,12,15]. The Otsu algorithm finds a threshold by maximising the inter-class variance and minimising the weighted within-class variance [22].…”

Geospatial Analysis of Horizontal and Vertical Urban Expansion Using Multi-Spatial Resolution Data: A Case Study of Surabaya, Indonesia

“…The rapid development had addressed the increase of built-up area for commercial use along artery roads. The economic indicator became the driving force for the urban development [53]. Thus, the industrialization and urbanization had stimulated economic growth demand on manufactory and housing [54].…”

“…Level 2 in classification was applied using rule-based in accordance with Normalized Difference Water Index (NDWI) to delineate the waterbody, and Normalized Difference Vegetation Index (NDVI) to determine the green space. NDWI can effectively augment the water information in most cases [53] as a dimensionless quantity used as an indicator of the surface wetness [54]. NDVI is most widely used as the indicator to differentiate the vegetation in forests, agricultural land cover, and built environment [17,55].…”