A method for mapping flood hazard along roads

Kalantari, Zahra; Nickman, Alireza; Lyon, Steve W.; Olofsson, Bo; Folkeson, Lennart

doi:10.1016/j.jenvman.2013.11.032

Cited by 69 publications

(50 citation statements)

References 39 publications

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“…Principal component analysis (PCA) was performed on ten S2 bands per image date and the first two components (PC1 and PC2) with the largest "percent of Eigenvalues" were retained [45]. Two types of textural measures, namely the grey level co-occurrence matrix (GLCM) and grey level difference vector (GLDV), were generated from each PC1.…”

Section: Index Equationmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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S.M.alfieri-1@tudelft.nl (S.M.A.); M.Menenti@tudelft.nl (M.M.)Abstract: Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polari...

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Section: Index Equationmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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“…The STI can provide vital information on the potential of sediment transport in the stream network of a given watershed [57]. It considers the effect of topography on erosion [58] and is, as such, used for characterizing erosion and deposition processes [59], and as a flood conditioning factor that defines the movements of waterborne sediments due to the water movement:…”

Section: Sediment Transport Index (Sti)mentioning

confidence: 99%

“…In addition, DD affects the magnitude of peak flow by changing the concentration time; therefore, it can provide useful information for site selection of check dams. The DD value is calculated by dividing the total length of all rivers in a watershed by the watershed area [59]. In the present case, drainage density was calculated using the line density tool in ArcGIS software.…”

Section: Drainage Density (Dd)mentioning

confidence: 99%

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

et al. 2019

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Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

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“…Também para esse fim,Dans et al (2013) mensuraram níveis de impermeabilização do solo por meio de imagem de altíssima resolução espacial IKONOS datada de 2003, com um metro de resolução.A delimitação geográfica potencial da inundação é o procedimento inicial, ou seja, o mapeamento de áreas que podem ser afetadas. A magnitude de perdas materiais e de vidas ocasionadas pelo fenômeno nessas áreas está ligada à vulnerabilidade(KALANTARI et al, 2014). Assim, a potencial perda material e de vidas é evidentemente ligada ao grau e tipo de ocupação.…”

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Gestão integrada dos recursos hídricos e a problemática das inundações urbanas

Peixoto

Rodrigues

Albuquerque

2019

GEO

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As inundações e enchentes urbanas têm sido um dos principais problemas em muitas cidades. O presente ensaio busca delimitar esse fenômeno através das abordagens metodológicas mais utilizadas: perigo (hazards), susceptibilidade e vulnerabilidade socioambiental, além de indicar medidas de controle, possíveis caminhos e desafios para soluções propostas pela evolução e desenvolvimento da Gestão Integrada dos Recursos Hídricos - GIRH. Concepções integradas das inundações e enchentes urbanas vêm apontando para a necessidade de gestão dos riscos e identificação das vulnerabilidades socioambientais para a minimização das perdas de vidas e materiais. A GIRH possui potencial de fundamentar meios eficazes de equacionamento dos problemas das inundações e enchentes urbanas, tendo capacidade axial de integração entre políticas de recursos hídricos e uso do solo. No nível institucional, no caso brasileiro, o principal desafio é articular os interesses do município nas políticas de uso e ocupação do solo aos planos estaduais ou nacional dos recursos hídricos. Para superar esse desafio são necessários estudos na perspectiva holística do problema, de modo a subsidiar e justificar perante às autoridades a necessidade de políticas de uso e ocupação do solo vinculadas à gestão hídrica gerando maior participação dos municípios nos processos de efetivação da GIRH.

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A method for mapping flood hazard along roads

Cited by 69 publications

References 39 publications

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

Gestão integrada dos recursos hídricos e a problemática das inundações urbanas

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