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Munawar, H.S. Remote Sensing Methods for Flood Prediction. Encyclopedia. Available online: https://encyclopedia.pub/entry/20727 (accessed on 13 April 2024).
Munawar HS. Remote Sensing Methods for Flood Prediction. Encyclopedia. Available at: https://encyclopedia.pub/entry/20727. Accessed April 13, 2024.
Munawar, Hafiz Suliman. "Remote Sensing Methods for Flood Prediction" Encyclopedia, https://encyclopedia.pub/entry/20727 (accessed April 13, 2024).
Munawar, H.S. (2022, March 18). Remote Sensing Methods for Flood Prediction. In Encyclopedia. https://encyclopedia.pub/entry/20727
Munawar, Hafiz Suliman. "Remote Sensing Methods for Flood Prediction." Encyclopedia. Web. 18 March, 2022.
Remote Sensing Methods for Flood Prediction
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Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). 

Multispectral LIDAR Radar Flood Prediction

1. Multispectral

Multispectral remote sensing stores the emitted or reflected energy from objects present on the surface of the Earth through sensors that can recognize specific spectral bands [1]. The spectral bands form a thin portion of the electromagnetic spectrum, specified by the lowest and highest wavelength that is recognizable by the sensor. As a result, one raster image is saved for each of the spectral bands [1][2][3][4]. Examples of current satellites making use of such sensors include Sentinel-2, Landsat 7, Landsat 8, and MODIS. Wieland and Martinis presented a framework to perform flood prediction on multispectral data gained from Landsat TM and Sentinel-2 images [5][6][7]. A convolutional neural network (CNN) was trained using these data to perform segmentation to determine water extent levels. Biases that may occur during downstream analysis are overcome by especially handling noise data such as clouds, shades, and frost. It outperforms the Random Forest classifier and a Normalized Water Index (NDWI) threshold function. Massari et al. [8] retrieved readings of soil moisture using the Advanced Scatterometer (ASCAT) to develop a rain-fall-runoff model that forecasts floods. The direct association between the satellite, soil moisture and rainfall are utilized in the model to make decisions regarding the future occurrence of floods. A study took place in the Mediterranean Sea, where readings from ten catchment locations in the ocean were recorded. These observations were acquired using the ASCAT satellite. These data are given as input to a rainfall-runoff calculation method called MISDc to obtain rainfall estimates. The rainfall data were used to predict the high-water flows in the Mediterranean Sea. Shahabi et al. [9] identified flood-prone areas using multispectral data acquired from the Sentinel-1 satellite of the Haraz watershed located in Iran. A machine learning-based ensemble method was used to perform flood susceptibility mapping. This model was composed of a combination of K-Nearest Neighbour (KNN), bagging, and a cubic classifier. Ten conditioning factors were gathered to train the model. Validation of the model showed that this ensemble method performs well and outperforms many other ensembles. The bagging approach significantly improved the accuracy of the KNN-cube ensemble for flood management and mapping problems. Noymanee [10] experimented with linear regression, ANN, boosted decision trees, Bayesian linear model, and decision forest to forecast floods in the district of Pattani in Thailand [10]. Bayesian Linear model demonstrated the best performance among all selected models, and was, therefore recommended for flood detection. A mathematical model was designed to model the upper and lower portions of the river stream. For example, to model the upstream part of a river, the following formula was used:
In the above equation, M represents the machine learning operation, W is the water level, the symbol * denotes predicted value, TX347 and TX33 are labels of the stations which are assigned to various river portions and R represent the rain value [10]. The flood mapping results in an input multispectral aerial image. The system classifies the flooded (Red) and non-flooded (Blue) regions and highlights them using different colours in the output such that the rescue workers can easily distinguish between them. Zhang et al. compared the flood prediction results for both spatial and temporal resolutions of existing sensors [11]. Landsat and MODIS images were collected for real-time prediction of floods. The models achieved a high level of accuracy which proved that for Landsat images both spatial and temporal models generate similar results for real-time prediction of floods. Cenci et al. evaluated the ability of Sentinel-1 to acquire soil moisture data for flood forecasting [12]. The soil moisture readings recorded by the satellite were used in a hydrological model called “Continuum” to predict flash floods. The study area was the Mediterranean Sea. The hydrological assimilation of different GEO SAR-like soil moisture products was evaluated using the SAR images. The results showed the effectiveness of Sentinel-1 derived soil moisture data to improve the flood predictions, especially for heavy flows. The Sentinel-1 data need the application of proper pre-processing methods before assimilating the data. Another finding was that apart from the need for high spatial resolution of the satellite, the temporal resolution of the satellite also plays an important role in the acquisition of correct data for the hydrological model. Ogilvie et al. [13] combined flood events data and satellite imagery to build a numerical model that monitors the water level in reservoirs. For this purpose, the rainfall run-off model and water level models were built for seven reservoirs. The data were collected between 1999 and 2014. An Ensemble Kalman Filter was applied to reduce the rainfall run-off errors and classification outliers. This method was able to reduce the root mean squared error by 54% when compared with flood forecast results provided by the previous hydrological model. Optical imagery was used for the measurement of water levels which helps in defining the scope of a flooded area [14]. The water level of a wide area can be measured in consecutive events. Analysing the change in water level can help in the easy prediction of flood events. This technology also takes the data of absolute water elevations. The data help to develop protocols for flood management and gives immense information for environmental science research. Remote sensing measures the accuracy of water up to decimeter level and shows real-time transmission [15]. Meng et al. [16] presented an approach to predict snowmelt floods in the Juntanghu watershed in China. A weather research and forecasting (WRF) model were used along with a snowmelt run-off model known as Tianshan Snowmelt Runoff Model (TSRM) which contains the snowmelt readings recorded during multiple years. Image data gathered from MODIS and DEM were used to predict floods using these hydrological models. The TSRM model driven by WRF was able to achieve 80% of condition ratios and determination coefficients of 0.85 and 0.82 for 2 years, respectively [16]. Boni et al. [17] combined data collected from Sentinel-1 and SAR to monitor floods in the Po River situated in Northern Italy. Image processing techniques such as thresholds, classification, and Region Growing Algorithm (RGA) were applied for the mapping of flood-prone areas [17]. The model achieved an overall user accuracy ranging from 60% to 80%. Li et al. used Sentinel-2 data along with data obtained from DEM having 90 m of spatial resolution. The noise data produced due to the presence of clouds, shadows, and frost were reduced using a Modified Normalized Difference Water Index (MNDWI), Revised Normalized Difference Water Index (RNDWI), Automated Water Extraction Index (AWEI), and Otsu threshold [18]. Google Earth Engine framework was used to calculate the water index and to extract water features. A root means a square error of 16.148 m was recorded using the proposed approach. Airborne SAR was studied for real-time flood area observation as well. Mason et al. [19] studied a method for selecting a subset automatically and in near real-time, which would allow the SAR water levels to be used in a forecasting model. Distributed water levels may be estimated indirectly along the flood extents in SAR images by intersecting the extents with the floodplain topography. It is necessary to select a subset of levels for assimilation because adjacent levels along the flood extent will be strongly correlated.

2. Light Detection and Ranging (LIDAR)

LIDAR stands for Light Detection and Ranging. It is an active remote sensing technology that uses laser pulses to measure the distance of an object present on Earth from the sensor [20][21][22]. The Lidar system records other data from the Earth’s surface and along with the returned light pulses, the data obtained are used to create a 3D model which represents the properties of the Earth’s shape and surface. A LIDAR system thus consists of a laser scanner and a GPS. This technology has been used in applications that monitor and examine the Earth’s surface. A more common application of LIDAR technology is to generate DEM to be used in GIS which facilitates the emergency response operations. Hence, it has an immense potential to monitor water levels in water bodies to predict any future occurrence of a flood. Recently, several research articles have proposed methods for flood risk assessment and prediction using LIDAR remote sensing. Webster et al. [23] employed LIDAR to acquire details related to the rise of sea level to produce food risk maps. LIDAR data are used to construct a Digital Surface Model (DSM) and DEM which show the ground and non-ground regions and highlight the elevated and normal sea levels in the study area. The results were validated using GPS technology which shows accuracy that exceeds 30 cm [23]. Lamichhane and Sharma [24] developed a flood warning system using a DEM derived from LIDAR. The acquired LIDAR data were also integrated with some field data related to flooding in the target area to determine the evacuation time required by the people. Flood risk maps were produced by an HEC-GeoRAS, a software that allows the processing of geospatial data in ArcGIS [24]. The flood risk maps were then combined with digital orthographic maps to construct a real-time online flood warning system for the public [25]. Fadi et al. [26] used three channels of geometrical data derived from LIDAR. The first channel consists of survey data, the second channel is based only on the data acquired by LIDAR and the third one consists of a combination of the riverbank locations derived from survey and cross-sections data acquired by LIDAR technology [26]. It aimed to predict the return period of the storm in the target area. The data were processed in the HEC-RAS tool to make flood-related predictions. The results showed that geometries obtained from LIDAR predicted floods with higher widths as compared with the predictions made by survey-derived geometries. Makinano and Santillan [27] integrated data from several resources to construct an early flood warning system. These sources include LIDAR, an open-source flood model, meteorological data, real-time hydrologic data and geographic visualization tools [27]. The acquired data are used to construct a two-dimensional (2D) hydraulic model using the HEC-RAS tool that produces accurate flood risk maps and provide early flood warnings. Stoleriu et al. [28] used high-density LIDAR derived data to improve the accuracy of flood risk maps generated by DEM [28]. HEC-RAS software was used to construct the flood hazard maps. The system was used to predict the flood reoccurrence probabilities in the durations of 33, 100, and 1000 years. The system can measure water levels up to an accuracy of 0.5 m.

3. Radar

Radar (Radio Detection and Ranging) [29][30][31] was first used in the year 1940 by the navy department of America. As its name suggests, this remote sensing technology makes use of radio waves to find various characteristics of objects such as their direction, speed, location, and range. The organization of radar is composed of a transmitter that generates electromagnetic waves in the domain of radio or microwaves, an antenna for transmission, an antenna for receiving, a receiver, and a workstation that processes the object characteristics. The transmitter emits radio waves which are reflected by the object and then return to the receiver, where it is analysed by the processor to determine different object properties [30].
Once the detection through optical remotely sensed data fails, the synthetic aperture radar (SAR) comes into action. A high-resolution synthetic aperture radar (SAR) has been frequently used in the detection of areas affected by floods [32]. The technology provides real-time assessment of devastated and flooded areas. The prime quality of this technology is its penetration capacity to clouds, rain, and haze [33]. It does not matter whether it is a bad or drastic environment or too much sunlight, the technology provides effective expertise. The technology can easily distinguish between light and water. Radar uses microwaves; thus, flooding surfaces can be easily detected by its sensors. The flat surface of the water reflects the signals away from the sensor. This causes a decrease in the intensity of returned radiation as compared to the incident radiation causing a darker pixel in the image [34]. Thus, areas with water show dark pixels as compared to the pixels formed by the deflection through land areas.
Mitigation and management of floods require the analysis of the spatial extent and progressive pattern of remotely sensed images. The spatial extents of flooding are necessary to save lives and to avoid destruction. Combining this information with GIS and satellite data can help in estimating the damage caused by a flood [35]. Satellite transmissions involving microwaves revolutionized data extraction even in bad weather conditions and sunlight [17][18]. Data assimilation techniques facilitated the real-time integration of SAR-derived water levels and developed forecast models for disasters [36]. The integration of sensing data with data assimilation provided 3D reports of the flood used for the prediction of the flood as well as organizing the warning system for the flood. A problem faced by this technology is its inability to measure the long-term and real-time water level at fixed points. This is because of the orbital cyclic movement of the satellite. Thus, regardless of its high accuracy and real-time monitoring, it does not fit as the best technology for urban flood prediction. However, it works best for large water bodies including oceans and rivers [37]. Garcia-Pintado developed a flood prediction model that used SAR-derived water level observations in the Severn and Avon rivers situated in the United Kingdom (UK). To overcome this problem, a spatial filter localization method is proposed. Overall results showed that this model is feasible to work as an independent flood forecasting model that uses Earth Observations (EO) [38].

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