Detection of Areas Associated with Flash Floods and Erosion Caused by Rainfall Storm Using Topographic Attributes, Hydrologic Indices, and GIS

Bannari, A.; Ghadeer, A.; El-Battay, A.; Hameed, N. A.; Rouai, M.

doi:10.1007/978-3-319-51844-2_13

Cited by 22 publications

(26 citation statements)

References 23 publications

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“…Using spectral processing to acquire indices necessitates the analysis of the spectral characteristics of remote sensing imagery to determine their quantitative relationship with wetness/water and dryness in land changes. Albedo, normalized difference water index (NDWI), normalized multi-band drought index (NMDI), and normalized difference moisture index (NDMI) are the principal spectral indices used to characterize moisture in land changes [3,4,33]. Hydrological processing involves acquiring indices to analyze the hydrological characteristics of a digital elevation model (DEM) to detect areas associated with flash floods and erosion caused by rainstorms and sediment transport and accumulation [4,25].…”

Section: Methodsmentioning

confidence: 99%

“…Albedo, normalized difference water index (NDWI), normalized multi-band drought index (NMDI), and normalized difference moisture index (NDMI) are the principal spectral indices used to characterize moisture in land changes [3,4,33]. Hydrological processing involves acquiring indices to analyze the hydrological characteristics of a digital elevation model (DEM) to detect areas associated with flash floods and erosion caused by rainstorms and sediment transport and accumulation [4,25]. However, even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric, and temporal characteristics, most existing land-cover datasets have been derived from categorical processing based on pixel or object approaches using conventional or advanced pattern recognition techniques, such as random forests (RFs) [34,35], neural networks (NNs) [6,[36][37][38][39][40][41][42][43][44], and support vector machines (SVMs) [6][7][8][45][46][47][48].…”

Section: Methodsmentioning

confidence: 99%

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A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

et al. 2019

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This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

et al. 2019

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“…SPI also determines the erosion potential of the surface [43] and is considered as an essential predictor of landslides [37,38]. Areas with high SPI values indicate a higher erosion potential, while negative values suggest no predicted erosion [44,45]. In this study, a layer of SPI was derived using the following equations in GIS:…”

Section: Landslide Conditioning Factormentioning

confidence: 99%

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

et al. 2020

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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

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“…It is required for drought and flood forecasting, natural disaster management, and assistance effort coordination during flooding periods. Moreover, it plays a fundamental role in the prediction of erosion and sediment loads in watershed streams according to topography and lithology [2][3][4]. In this context and in contrast with many conventional methods, remote sensing science and technology significantly contributed in the activities of SM estimation, flood damage assessment, and landscape management [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, Morocco is experiencing the longest dry episode of its contemporary history characterized by a reduction of precipitation and a rise in temperatures. In the southern regions of Morocco, the rainfall rarely occurs but with high intensity during a short period of time, which causes flooding problems and accelerated the erosion phenomenon and land degradation [3,4]. According to Erskine and Saynor [12], catastrophic floods are defined as events with a flood peak discharge at a rate of at least 10 times greater than the mean annual flood.…”

Section: Introductionmentioning

confidence: 99%

Synergy between SMOS-MIRAS and Landsat-OLI/TIRS Data for Soil Moisture Mapping before, during, and after Flash-Flood Storm in Southwestern Morocco

Bannari¹,

Rhinane²,

Bahi³

2019

Topics in Hydrometerology

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This chapter deals with soil moisture (SM) characterization over the Guelmim city and its neighborhood in the Southwestern Morocco that has been flooded several times over the past 50 years. To achieve this, space-borne SMOS and Landsat-8 OLI/TIRS data were preprocessed to correct several radiometric anomalies, and they were used. The SMOS brightness temperature data acquired before, during, and after the storm with 1-day temporal resolution and coarse spatial resolution (25 km) were transformed to the SM maps. OLI and TIRS data with moderate spatial and temporal resolutions were converted to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to retrieve the Soil Moisture Index (SMI) maps. The results obtained were analyzed, intercompared, and validated against the compiled SM values from rainfall database (SM-RFE) delivered by NOAA climate prediction center Rainfall Estimator (RFE) for Africa. SMOS results show how the spatial variation of SM changes extremely at the regional scale before, during, and after the flash flood day-today. The SMI results converge toward the same conclusions showing a drastic SM change before and after flash flood highlighting the impact of inundation and the mud accumulation. By reference to the measured SM-RFE datasets, the validation of the derived SM maps exhibits a significant correlation (R 2 ≥ 0.89). Globally, we observe a good complementarity among the considered data sources and processing methods for SM spatial information extraction, and the potential of their integration for the development of a prediction and monitoring model for flash flooding at the regional and local scales.

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Detection of Areas Associated with Flash Floods and Erosion Caused by Rainfall Storm Using Topographic Attributes, Hydrologic Indices, and GIS

Cited by 22 publications

References 23 publications

A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

Synergy between SMOS-MIRAS and Landsat-OLI/TIRS Data for Soil Moisture Mapping before, during, and after Flash-Flood Storm in Southwestern Morocco

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