Inundation Detection Research Articles

Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images

AbstractExtreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short‐wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record‐setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high‐resolution full‐polarized L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood‐resilience goals.

A deep learning technique-based automatic monitoring method for experimental urban road inundation

Abstract Reports indicate that high-cost, insecurity, and difficulty in complex environments hinder the traditional urban road inundation monitoring approach. This work proposed an automatic monitoring method for experimental urban road inundation based on the YOLOv2 deep learning framework. The proposed method is an affordable, secure, with high accuracy rates in urban road inundation evaluation. The automatic detection of experimental urban road inundation was carried out under both dry and wet conditions on roads in the study area with a scale of a few m2. The validation average accuracy rate of the model was high with 90.1% inundation detection, while its training average accuracy rate was 96.1%. This indicated that the model has effective performance with high detection accuracy and recognition ability. Besides, the inundated water area of the experimental inundation region and the real road inundation region in the images was computed, showing that the relative errors of the measured area and the computed area were less than 20%. The results indicated that the proposed method can provide reliable inundation area evaluation. Therefore, our findings provide an effective guide in the management of urban floods and urban flood-warning, as well as systematic validation data for hydrologic and hydrodynamic models.

First spaceborne demonstration of BeiDou-3 signals for GNSS reflectometry from CYGNSS constellation

The full constellation of Chinese Global Navigation Satellite System (GNSS) BeiDou-3 has been deployed completely and started fully operational service. In addition to providing global Positioning, Navigation and Timing (PNT) services, the BeiDou-3 satellites transmissions can also be used as the sources of illumination for Earth Observation (EO) with a bistatic radar configuration. This innovative EO concept, known as GNSS reflectometry (GNSS-R), allows to measure the Earth surface characteristics at high resolution via the reflected L-band radar signals collected by a constellation of small, low cost and low Earth orbiting satellites. For the first time in orbit, earth reflected BeiDou-3 signal has been detected from the limited sets of raw data collected by the NASA’s Cyclone GNSS (CYGNSS) constellation. The feasibility of spaceborne BeiDou-3 reflections on two typical applications, including sea surface wind and flooding inundation detection, has been demonstrated. The methodology and results give new strength to the prospect of new spaceborne GNSS-R instruments and missions, which can make multi-GNSS reflectometry observations available to better capture rapidly changing weather systems at better spatio-temporal scales.

Detection of Flood Inundation in the Progo Watershed using the SHETRAN Model

Floods are the most frequent disasters in Indonesia in the past 20 years. Based on the Indonesian Disaster Information Data (DIBI) compiled by BNPB, there are considered to be 9053 events (37.5%) of the total natural disaster events in Indonesia. The flood disaster has threatened and disrupted people’s lives and even resulted in fatalities, environmental damage, property losses, and psychological impacts. Therefore several efforts of flood risk reduction are required. One of them is to develop a model to detect the depth of flood inundation. The purpose of this study is to apply the SHETRAN Model for flood inundation detection in the Progo Watershed. The SHETRAN model is a distributed hydrological model. The data required in this model are hydro-climatological data, including rainfall and evaporation, land use land cover, soil type, and topography data obtained from remote sensing. The results of this study indicate that in the range from 2001 to 2017, the maximum height of flood inundation between the data on flood events in the field and the phreatic depth of the results from the SHETRAN Model were 0.606 m which occurred on January 17, 2013 in Tirta Rahayu Village, Sub-district Galur, Kulon Progo Regency. While the maximum height of flood inundation results from the RStudio analysis on the Progo Watershed range from 2001 to 2017 were 3,856 m which occurred in 2010.

High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data

To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation.

Utilizing Landsat Satellite Data (1990-2018) to Detect Water Inundation for the Management of Human Settlements in Coastal Zones

Objective: This study investigates waterinundation in Semarang and Demak and Kendalregencies in Java, Indonesia, utilizingLandsat 5, 7 and 8 satellite imagery, in combination withthe Seamless Digital Elevation Model and National Bathymetry (DEMNAS) data for 50, 100 and 150 year projections. Methods: Water inundation detection using optical methods (passive sensors) such as Landsat is an effective tool, more so when combined with the Normalized Difference Water Index (NDWI) method in Green Near Infrared (NIR) bands. Combining imagery from these remote sensing sources with DEMNAS land elevation data may strengthen future water inundation predictions and gauge land loss or degradation in regions subject to land inundation and sea level rise. Findings/Application: Semarang is currently subjected to coastal water inundation associated with losses of coastal infrastructure, resulting in the relocation of human settlements to more elevated areas. Sayung is a sub-distric, the most severely affected sub-district has previously expierienced an increase of water inundationfrom 1434.7ha (1990), 3489.1ha (2002) to4923.8ha (2002), an approximate 1.5 % of land loss annually. This average water inundation increase equivalent to 32 cm annually is based on DEMNAS data from 1990 to 2018, which may be due to coastal infrastructure which supports inundationin surrounding coastal areas such as Sayung, Demak regency. Keywords: Coastal Management, Land Inundation, Landsat, Normalized Difference Water Index (NDWI), Water Detection

MODIS-Based Investigation of Flood Areas in Southern Cambodia from 2002–2013

In Cambodia and the Vietnamese Mekong Delta, floods commonly occur during the rainy season, and a better understanding of their spatio-temporal distribution is important for both disaster prevention and the improvement of agricultural production. This study investigated spatio-temporal flood inundation and land cover change from 2002 to 2013 in the southern part of Cambodia using Terra satellite on-board Moderate Resolution Imaging Spectroradiometer (MODIS) images. The algorithm for flood inundation detection, WFFI (Wavelet-based Filter for detecting spatio-temporal changes in Flood Inundation) was used, and the parameters were modified to fit the present study. The estimated inundation areas were validated using eight Landsat images. In a comparison between the original and modified WFFIs, the modified WFFI (70–96%) exhibited better accuracy than the original WFFI (30–70%). Overall, the temporal change in the flood inundation area presented a decreasing trend, and a link to the in-situ observed water level showed a decreasing trend during the rainy season. Furthermore, the estimated flood inundation exhibited a significant delay since 2008. Based on the yearly land cover MODIS product, the permanent water body and wetland areas decreased, whereas the cropland areas increased. This was as a result of increased agricultural productivity. However, water shortage was the major obstacle to increasing agricultural productivity, and it also had a negative impact on aquatic ecology, such as fish spawning grounds.

Deteksi Kerawanan Banjir Genangan Menggunakan Topographic Wetness Index (TWI)

Flood is the most frequent disaster occured in Indonesia. Flood events result in loss and damage to communities and the environment. Floods are triggered by several factors including hydrometeorological factors, topography, geology, soil and human activities. Topographic factor is one of the flood trigger control factors. Topographic calculation for flood inundation detection can be done by Topographic Wetness Index (TWI) method. The TWI method focuses on topographic conditions of the region, especially the upper slopes and lower slopes to assess the trend of water accumulation in a region. TWI calculations are based on the topography of an area represented by DEM (Digital Elevation Model) data in the form of DTM (Digital Terrain Model). The high value of TWI is associated with high flood vulnerability. Based on the calculation of TWI value, flood-prone areas in Kebumen District include Adimulyo Subdistrict, Puring Subdistrict, Ambal Subdistrict, Rowokele Subdistrict and Buayan Subdistrict.

Cyber surveillance for flood disasters.

Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.

Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization

The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM+images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.

DEM-based modification of pixel-swapping algorithm for enhancing floodplain inundation mapping

Floodplain inundation plays a key role in riparian ecosystems. Remote sensing provides an advanced technology for detecting floodplain inundation, but the trade-off between the spatial and temporal resolutions of remotely sensed imagery is a well-known issue. Sub-pixel mapping is an effective way to mitigate the trade-off by improving the spatial resolution of image classification results while keeping their temporal resolution. It is therefore useful for improving the mapping of highly dynamic flood inundation using coarse-resolution images. However, traditional sub-pixel mapping algorithms have limitations on delineating the extent of floodplain inundation that reveals linear and complex characteristics. A modified pixel-swapping (DMPS) algorithm which is based on a digital elevation model (DEM) is thus developed in this study. It is built on the widely accepted pixel-swapping (PS) algorithm and one of its derivatives, the linearized pixel-swapping (LPS) algorithm. A Landsat image recording a significant flood inundation event in the Chowilla Floodplain of the Murray–Darling Basin in Australia was used as a case study. The results show that the DMPS algorithm outperformed the original PS and LPS algorithms both in accuracy and rationality of the resultant map. It improves the accuracy and the kappa coefficient by about 5% and 0.1, respectively, in comparison with the PS algorithm. The spatial pattern of inundation derived from the DMPS algorithm reveals fewer breakpoints and errors along the river channels. Moreover, it is observed that the DMPS algorithm is less sensitive to some critical parameters compared with the PS and LPS algorithms. It is hoped that the proposed DMPS algorithm will broaden the application of coarse-resolution sensors in floodplain inundation detection, which would thereby benefit the ecological studies in floodplains.

Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery

Flood inundation is crucial to the survival and prosperity of flora and fauna communities in floodplain and wetland ecosystems. This study tried to map flood inundation characteristics in the Murray-Darling Basin, Australia, utilizing hydrological and remotely sensed data. It integrated river flow time series and Moderate Resolution Imaging Spectroradiometer (MODIS) images to map inundation dynamics over the study area on both temporal and spatial dimensions. Flow data were analyzed to derive flow peaks and Annual Exceedance Probabilities (AEPs) using the annual flood series method. The peaks were linked with MODIS images for inundation detection. Ten annual maximum inundation maps were generated for water years 2001–2010, which were then overlaid to derive an inundation frequency map. AEPs were also combined with the annual maximum inundation maps to derive an inundation probability map. The resultant maps revealed spatial and temporal patterns of flood inundation in the basin, which will benefit ecological and environmental studies when considering response of floodplain and wetland ecosystems to flood inundation.

Limitations and Potential of Satellite Imagery to Monitor Environmental Response to Coastal Flooding

RAMSEY, E. III; WERLE, D.; SUZUOKI, Y.; RANGOONWALA, A., and LU, Z., 2012. Limitations and potential of satellite imagery to monitor environmental response to coastal flooding.Storm-surge flooding and marsh response throughout the coastal wetlands of Louisiana were mapped using several types of remote sensing data collected before and after Hurricanes Gustav and Ike in 2008. These included synthetic aperture radar (SAR) data obtained from the (1) C-band advance SAR (ASAR) aboard the Environmental Satellite, (2) phased-array type L-band SAR (PALSAR) aboard the Advanced Land Observing Satellite, and (3) optical data obtained from Thematic Mapper (TM) sensor aboard the Land Satellite (Landsat). In estuarine marshes, L-band SAR and C-band ASAR provided accurate flood extent information when depths averaged at least 80 cm, but only L-band SAR provided consistent subcanopy detection when depths averaged 50 cm or less. Low performance of inundation mapping based on C-band ASAR was attributed to an apparent inundation detection limit (>30 cm deep) in tall Spartina alterniflora marshes, a possible canopy collapse of shoreline fresh marsh exposed to repeated storm-surge inundations, wind-roughened water surfaces where water levels reached marsh canopy heights, and relatively high backscatter in the near-range portion of the SAR imagery. A TM-based vegetation index of live biomass indicated that the severity of marsh dieback was linked to differences in dominant species. The severest impacts were not necessarily caused by longer inundation but rather could be caused by repeated exposure of the palustrine marsh to elevated salinity floodwaters. Differential impacts occurred in estuarine marshes. The more brackish marshes on average suffered higher impacts than the more saline marshes, particularly the nearshore coastal marshes occupied by S. alterniflora.

Lidar intensity for improved detection of inundation below the forest canopy

Wetland hydrology is an important factor controlling wetland function and extent, and should therefore be a vital part of any wetland mapping program. Broad-scale forested wetland hydrology has been difficult to study with conventional remote sensing methods. Airborne Light Detection and Ranging (LiDAR) is a new and rapidly developing technology. LiDAR data have mainly been used to derive information on elevation. However, the intensity (amplitude) of the signal has the potential to significantly improve the ability to remotely monitor inundation — an important component of wetland hydrology. A comparison between LiDAR intensity data collected during peak hydrologic expression and detailedin situ data from a series of forested wetlands on the eastern shore of Maryland demonstrate the strong potential of LiDAR intensity data for this application (>96% overall accuracy). The relative ability of LiDAR intensity data for forest inundation mapping was compared with that of a false color near-infrared aerial photograph collected coincident with the LiDAR intensity (70% overall accuracy; currently the most commonly used method for wetland mapping) and a wetness index map derived from a digital elevation model. The potential of LiDAR intensity data is strong for addressing issues related to the regulatory status of wetlands and measuring the delivery of ecosystem services.

Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR

An important practical application of orbital SAR data is the detection of flood inundation beneath forest canopies. C-band microwaves (especially C-VV) are generally considered inferior to longer wavelengths (especially L-band) for mapping flooded forests. Nevertheless C-band data have been shown to exhibit sensitivity to inundation beneath forest canopies. Because of this, the application and interpretation of C-band images requires identification of the forest conditions under which C-band microwaves detect inundation. Images from ERS-1 and RADARSAT representing nearly identical hydrological conditions were used to classify flooded and nonflooded forests for the Roanoke River floodplain, North Carolina, USA. Flooded forests were detected accurately using RADARSAT, and were distinguishable but not mapped accurately using ERS-1. This poses potential problems for the accurate interpretation of C-VV images (i.e. ERS-1/ERS-2) in such environments. To examine this issue, biophysical data from forest plots were used to assess the effect of forest structure on the ability of ERS-1 to detect flooded forests. Basal area (BA) and height to the bottom of the canopy exhibited statistically significant relationships to the detection of inundation using ERS-1, while leaf area index, canopy height, canopy depth, and crown closure were not statistically significant. The results suggest that forest structure and scattering processes at the trunk layer (due to higher BA and open understories with greater heights to the bottom of the canopy) most strongly influence double-bounce scattering of C-VV microwaves from flooded forests.