Quality analysis of SRTM and HYDRO1K: a case study of flood inundation in Mozambique

Mård, Johanna; Arnberg, Wolter

doi:10.1080/01431160903464112

Cited by 23 publications

(19 citation statements)

References 12 publications

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“…Therefore they are useful sources of topographic data, especially for low-lying coastal areas with gentle slopes (Gorokhovich and Voustianiouk, 2006;Schumann et al, 2008), and consequently applicable for inundation modelling (Karlsson and Arnberg, 2011). Figure 4 shows results of flood modelling undertaken for the lower Niger River (Nigeria) using SRTM 30 and 90 m. The Niger River overflowed its banks in 2007 and flooded a large part of the floodplain.…”

Section: Altimetrymentioning

confidence: 99%

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

Musa

Popescu

Mynett

2015

Hydrol. Earth Syst. Sci.

109

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Abstract. Hydrological data collection requires deployment of physical infrastructure like rain gauges, water level gauges, as well as use of expensive equipment like echo sounders. Many countries around the world have recorded a decrease in deployment of physical infrastructure for hydrological measurements; developing countries especially have less of this infrastructure and, where it exists, it is poorly maintained. Satellite remote sensing can bridge this gap, and has been applied by hydrologists over the years, with the earliest applications in water body and flood mapping. With the availability of more optical satellites with relatively low temporal resolutions globally, satellite data are commonly used for mapping of water bodies, testing of inundation models, precipitation monitoring, and mapping of flood extent. Use of satellite data to estimate hydrological parameters continues to increase due to use of better sensors, improvement in knowledge of and utilization of satellite data, and expansion of research topics. A review of applications of satellite remote sensing in surface water modelling, mapping and parameter estimation is presented, and its limitations for surface water applications are also discussed.

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Section: Altimetrymentioning

confidence: 99%

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

Musa

Popescu

Mynett

2015

Hydrol. Earth Syst. Sci.

109

View full text Add to dashboard Cite

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“…Assim, a rede de drenagem pode ser modelada incorretamente em áreas com abundância dessas feições. Karlsson & Arnberg (2011) afirmam que um dos maiores erros nos dados do SRTM é a presença de vegetação.…”

Section: Figura 5 Dados Resultantes Da Classificação Semissupervisiounclassified

Mapeamento de áreas aluvionares no semiárido brasileiro por meio de dados colaterais e imagens orbitais

Lopes

Cabral²,

Filho

et al. 2013

Rev. bras. eng. agríc. ambient.

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RESUMOA região semiárida do Brasil possui grande potencial para armazenamento de água em áreas aluvionares, podendo potencializar a pequena agricultura. Verifica-se a necessidade de uma metodologia para mapeamento dessas áreas, com o objetivo de futuros estudos in loco para implantação de barragens subterrâneas e manejo correto dos solos aluvionares. Neste sentido, objetivou-se a aplicação de imagens Landsat-Mapeador Temático 5 em conjunto com dados colaterais, como a rede de drenagem, mapa de classes de solo e mapa de relevo para auxiliar na classificação de terraços aluviais. Teve-se, como área de estudo, a bacia do Rio Pajeú, no sertão do estado de Pernambuco. Buscou-se também, por meio de dados SRTM (Shuttle Radar Topography Mission), a avaliação topográfica das áreas classificadas. Verifica-se que a utilização única de dados orbitais traz classificação incongruente mas com a inserção de dados colaterais é possível obter melhores resultados na classificação. A rede de drenagem é fundamental no mascaramento de classificações espúrias. Quando procede à análise topográfica por meio de dados do SRTM das áreas classificadas como terraços aluvionares, observa-se a inconsistência desses dados. Palavras-chave: solos aluviais, classificação supervisionada, sensoriamento remoto, relevoMapping alluvial areas in semi-arid region of Brazil through collateral data and satellite images ABSTRACT The semi-arid region of Brazil has great potential for storing water in alluvial areas, and may give support to small-scale family farming. There is a need to establish a methodology for mapping these areas with the objective of future studies to implement underground dams and suitable management of the soils that occur on alluvial terraces. In this way, the aim of this study was to apply Landsat-Thematic Mapper images together with collateral data such as the drainage network, map of soil classes and elevation data to assist in the classification of alluvial terraces. The study area was Pajeú River basin located in the 'Caatinga' ecosystem of the Pernambuco state. Topographic assessment of the alluvial areas was done by using SRTM (Shuttle Radar Topography Mission) data. It was found that the single use of orbital data brings incongruous classification, but with the inclusion of collateral data it was possible to obtain better results in classification. The drainage network was also important in masking the spurious classification. The analysis of the topography of the classified areas was inadequate using SRTM data.

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“…Hydro 1k data sets are currently being developed individually for each continent. The absolute vertical accuracy for HYDRO1k data, a hydrologically modified version of the elevation model GTOPO30, is not specified officially, but GTOPO30 has a vertical accuracy of 39 m in terms of linear error at the 90% confidence level, corresponding to a root mean square error of 18 m over the area of interest [41]. The Lambert Azimuthal Equal Area projection was selected for this data.…”

Section: Dem and In Situ Datamentioning

confidence: 99%

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

et al. 2017

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Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10 L2). Producer's accuracy, user's accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

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Quality analysis of SRTM and HYDRO1K: a case study of flood inundation in Mozambique

Cited by 23 publications

References 12 publications

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

Mapeamento de áreas aluvionares no semiárido brasileiro por meio de dados colaterais e imagens orbitais

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

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