Flooding is one of the major environmental problems facing the world. In recent years flooding has claimed thousands of lives and made hundreds of thousands of people homeless and caused several hundred billion dollars in economic losses. The severity of this problem has attracted the attention of the world community in recent years. In an attempt to prevent and lessen these disasters, the First World Conference on Natural Disasters was convened by United Nations’ General Assembly in Yokohama, Japan from 23 to 27 May 1994 with the goal and objectives among others geared towards disaster prevention, preparedness and mitigation . Yokohama conference led to follow-up conferences in Kobe (2nd) on 18-22 January 2005 and in Sendai (3rd) on 14-18 March 2015. These conferences were coordinated by the United Nations General Assembly and the United Nations Office for Disaster Risk Reduction (UNISDR). The main objectives of the Second conference in Kobe were to find ways to reduce the toll of disasters through preparation, and ultimately to reduce human casualties. In Kobe, Hyogo Framework for Action (HFA) from (2005-2015) was introduced with overall goal of building the resilience of Nations and Communities to Disasters . The (HFA) was the first plan to explain, describe and detail the work required from all different sectors and actors to reduce disaster losses. The Third UN World conference adopted the Sendai Declaration and Framework for Disaster Risk Reduction 2015-2030. The main aim of the conference was to update the landmark agreement reached in (HFA). The conference brought together all the stakeholders including government and civil society leaders from around the globe to discuss and strategized effective ways to compact and prepare for impact of disasters, and to agree on an updated global response framework  .
In the United States, more recent data shows that among natural hazards, flood is one of the most common and costliest disasters facing the nation. This is shown by the increases in natural hazards-related spending in the last two decades. Historical data show that U.S. spent $62 billion on disaster relief in fiscal years 2011 and 2012 . According to the National Oceanic and Atmospheric Administration’s (NOAA’s) 2018 Flood Damage Data report posted on their website; U.S. spent in 2014 about $2.8 billion on flood related damages . Recent Reuters report by Blake Brittain, indicated that weather and climate-related disasters cost the United States a record $306 billion in 2017 . Also, a report by NOAA indicated that the first three months of 2018, U.S. spent 3 billion-dollar on geohazards-related disasters .
2. Identification of Flood-Prone Areas Using GIS and Remote Sensing
Geospatial information is essential for an effective and quick response to emergency management, especially flooding. GIS and remote sensing technologies play an important role in understanding various disasters, their outcomes, and the damage they could inflict on a given area   . Early work by Twumasi and Asomani-Boateng  and Twumasi et al.  used GIS and other technologies to investigate urban flood zones in Accra, Ghana. Results of the study revealed notable flood risk zones and watercourses. Twumasi et al.  have used remote sensing and GIS technology to design an appropriate coastal database in six counties in Southern Mississippi. Results revealed that a greater part of the three counties along the coast lies less than 10 meters above mean sea level with exposure to coastal flooding disaster vulnerability. Twumasi et al.  employed remote sensing and GIS data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers’ plans for future occurrences. The study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geological Survey (USGS) and NASA’s Earth Observatory website. Results of the study revealed notable damages to social and natural environments as well as flood risk zones and watercourses in the study area. Other studies by Cox and James  used ArcGIS 3D Analyst extension’s ArcScene application software to create a 3D Visualization model that showed what parts of City of Davenport, Iowa would be submerged at deferent flood stages. Results of the study helped the city staff and council to visualize data about how floodwater could affect specific areas of the city at any flood stage. In the Izmir province of Turkey, Ozkan and Tarhan  used Shuttle Radar Topographic Mission (SRTM) Digital elevation model (DEM) data to predict potential flood hazard areas. Indeed, several research studies using GIS and Remote Sensing to identify flood-prone areas have been reported  - . Isma’il and Saanyol  coupled remote sensing and DEM data to map Kaduna River flood-prone areas using high resolution imagery obtained from Google Earth. Flood risk modeling using remote sensing and GIS has also gained considerable attention. For example, Lanza and Conti  have used remote sensing data to forecast flood hazard. Dang and Kumar  utilized remote sensing techniques combined with Geographic Information Systems-based hydrological modelling to identify flood risk in Ho Chi Minh City, Vietnam. Their study showed that rainfall-induced flood was not a serious problem with the flood depth of 2 - 10 cm while tidal flood was a substantial issue with 10 - 100 cm flood depths. The impacts of flooding will extend beyond relocation and food shortages and in the process, negatively affect local and national economies. Therefore, in order to reduce the cost and impacts associated with natural hazards such as flooding, it is necessary to investigate and understand the areas vulnerable to flooding. Perhaps the use of geographical information systems (GIS) and remote sensing technologies could assist policy makers in sustainable planning and management. Review of literature in the area shows that, there is a gap in information and knowledge on the use of spatial technology as decision support tool to aid flood management. It is therefore important to identify the areas vulnerable to flooding and determine the associated response. Thus, the primary objective of this paper was to identify flood-prone areas in the Southeast of Louisiana using GIS and remote sensing techniques. It is anticipated that the results of this study will be used to guide management and provide parishes with tools needed to plan for the predicted increase in flood events and mitigation of the effects on the community.
3.1. The Study Area
The focus areas of this study were eleven parishes of Southeast Louisiana. These parishes are Washington, Tangipahoa, St. Tammany, St. John the Baptist, Orleans, St. Charles, St. Bernard, Jefferson, La Fourche, Plaquemines, and Terrebonne (Figure 1).
In the last two decades, the study area has experienced tremendous natural disasters which are related to flooding. Data from National Hurricane Center in Table 1 and Table 2 show the area has been impacted by tropical cyclones and is very vulnerable to strikes by major hurricanes. In 2005, Hurricane Katrina floods cost billions of dollars, destroying businesses, homes, and taking many lives (Table 1). In 2012 alone, the area and other parts of the state experienced more than 15 different storms between May and October destroying millions of Dollars’
Figure 1. The study area.
Table 1. Chronology of Louisiana tropical hurricanes and storms and their impacts (1997-2005)     .
Table 2. 2012 Atlantic hurricane season statistics  .
aTropical depression (TD), maximum sustained winds 33 kt or less; tropical storm (TS), winds 34 - 63 kt; hurricane (H), winds 64 - 95 kt; major hurricane (MH), winds 96 kt or higher; bDates begin at 0000 UTC and include all tropical and subtropical cyclone stages; non-tropical stages are excluded.
worth of property and loss of lives (Table 1). Casualty tally from Table 1 shows 195 deaths related to hurricanes in 2012 alone with Sandy being the highest. The area, receives rainfall throughout the year particularly during the winter months. The area, like the rest of Louisiana, experiences hot and humid summer, with elevated temperatures from mid-June to mid-September averaging 90˚C (32˚C) or more and overnight lows averaging above 70˚F (22˚C) ). Due to low elevation, most areas get flooded during hurricane and tropical storms .
Recent floods in August 2016 and July 2019 destroyed parts of surrounding coastal areas in southeast and southern Louisiana . In early July 2019, Hurricane Barry caused flood in many parts of Southeast Louisiana. Low lying areas such Orleans, St. Bernard and Plaquemines experienced heavy flooding. Excessive rainfall led to widespread flooding in southeast and southern Louisiana, as rivers swelled high above their banks. Representative samples of these floods are shown below in Figures 2-5.
Figure 2. Flooded road near Lake Pontchartrain, LA: Photo Courtesy of CNN .
Figure 3. Swollen Mississippi river caused by intense rainfall: Photo Courtesy of the advocate .
Figure 4. Inundated areas in downtown by New Orleans caused hurricane Barry: Image Courtesy of San Francisco Gate .
Figure 5. Inundated houses caused by Hurricane Katrina in August 2005: Image Courtesy of Public Radio International (PRI) .
3.2. Demographic Analysis in the Study Area
From 1990, 2000, 2010 and 2020 population census, Southeast Louisiana Parishes which comprised of the study area had a combined population of 1,576,127; 1,534,433; 1,426,253 and 1,638,857 respectively (Table 3). Data shown in Table 3 indicates that the total population of the study area declined by −2.65% between 1990 and 2000, and −7.05% between 2000-2010 respectively. In the subsequent years between 2010 and 2020, the population in the area grew by 14.91%. The study area experienced intense hurricane activities between 1990 and 2000 (Table 1). As a result, some of the Parishes experienced steady to moderate and negative population growths. For example, Washington, St. Tammany, Terrebonne, Plaquemines, and St. Bernard had positive growth rates of (14%), (32.35%), (25.35%), (4.62%) and (0.89%) respectively. Others such as St. Charles posted the highest decline in population at the rate of (−30.67%), followed by La Fourche (−29.82%) and St. John the Baptist (−22.88%) in that same period. Some of the parishes in the study area witnessed turbulent years during the period 2000 and 2010 because of hurricane activities such as Allison, Ivan, Katrina and Rita (Table 1). The area was significantly impacted by the activities of these Hurricanes in 2005 causing some of the population to move out of the area to other parishes. The affected Parishes whose populations decreased in number were Orleans −27.53%, St. Bernard −43.82%, Plaquemines −6.17% and Jefferson −4.76%. The period between 2010 and 2020 has seen an impressive population growth rate of some of the parishes in the study area. Jefferson, Orleans, and Bernard which recorded negative growth rates between 2010 and 2010 showed positive increases in populations with growth rates at 3.9%, 11.33%, and 23.72% respectively. Furthermore, parishes such as Tangipahoa, St. John the Baptist, St. Charles, La Fourche and Terrebonne have seen the most rapid increases in population over the 2010-2020 period, at rates of (35.28%), (25.53%), (64.92%), (58.11%), and (42.48%) respectively since 2010. The study area’s spatial distribution of population in 2020 is shown in Figure 6.
Figure 6. The 2020 population of the study area.
3.3. Data Acquisition
In order to identify flood-prone areas in Southeast Louisiana, a set of different spatial data was acquired. These included elevation and satellite data. Elevation data was obtained from the National Elevation Dataset (NED) produced by the United States Geological Survey (USGS) seamless data warehouse . Satellite data was obtained from USGS Earth explorer website. This consisted of two pairs of Landsat Enhanced Thematic Mapper Plus (ETM+) WRS-2 satellite data acquired between November 18, 1999 and November 27, 1999 covering the eleven Southeast Louisiana parishes. The path and row of the satellite data are 22 and 39; and 21 and 39 respectively .
3.4. Data Processing
3.4.1. Elevation Data
Topographical information on runoff characteristics was generated using Twumasi and Asomani-Boateng  method. They included slope, aspect and the digital elevation model. Elevation data are invaluable for assessing and documenting flood risk and communicating detailed information. Grid interpolation TIN (triangulated irregular network) was carried from the digital elevation model (DEM) to create slope map. The slope and the DEM were classified into high and low values. The flood vulnerability area map was generated using the Boolean operation in Arc GIS Raster calculation tool. The idea of using Boolean operation is to detect areas where topography is simultaneously low slope and low elevation.
Additionally, in order to assess flood risk areas, Twumasi et al.  approach was used. This method employs image drape technique to visualize the landscape of the study area. To do that, both images (DEM and satellite image) were re-projected and co-registered using the projection of the study area and satellite data as a base. This procedure permits overlay of both images.
3.4.2. Satellite Data
Landsat ETM+ images were processed using ERDAS IMAGINE 2017 image processing software. The images were imported into ERDAS as a single band and housed into ERDAS using ERDAS native file format GEOTIFF. To convert the single panchromatic bands 1 - 12 into multispectral data, ERDAS Layer Stack modules were used to group the images. This was followed by radiometric correction of all the images for variation in sun angle and atmospheric effects. Additionally, all the images were geometrically corrected to remove, haze, scan lines and speckles and referenced to the Louisiana ground-based coordinate system and Datum. Landsat ETM+ data of November 18, 1999 and November 27, 1999 were mosaicked. This was followed by a histogram equalization enhancement technique performed on all the images. Later, a shape file of the study area parishes (Washington, Tangipahoa, St. Tammany, St. John the Baptist, Orleans, St. Charles, St. Bernard, Jefferson, LaFourche, Plaquemines and Terrebonne) Shape file was imported into ERDAS, and used as ERDAS Area of Interest Tool (AOI) file to subset the ETM+ image of the study area.
3.4.3. Image Drape
Image Drape was performed using ERDAS IMAGINE Virtual GIS. The output image was then draped over the NED elevation data for visualization purposes with vertical exaggeration of 16 feet.
4. Results and Discussion
Results of elevation and satellite image processing are shown in Figures 7-10. Figure 7 shows image drape of Southeast Louisiana. The image was generated from a Landsat 1999 ETM+ satellite image draped over an elevation model produced from USGS 30 m elevation data downloaded from the website. In order to enhance the topographic expression, the image was exaggerated 3-times in height. Colors of the scene were enhanced by use of a combination of visible red, green, and blue wavelengths and infrared bands with RGB 4, 3, 2. Figure 8 shows the classified image of DEM of the study area. Figure 8 shows classified DEM of Southeast Louisiana. Areas with the low elevation below sea level are shown in the dark and light green color. Figure 9 displays the flow direction of the elevation. Flow direction determines which direction water will flow in a given cell. Figure 10 shows elevation aspect of the study area. Flat surfaces have no aspect and are given a value of −1. If the highest cell value is located at the top-left of the window (“top” being due north) and the lowest value is at the bottom-right, it can be assumed that the aspect is southeast. Figure 11 represents 3D Triangular Irregular Network (TIN) elevation showing both low and higher elevation of the study area. Figure 12 shows the census block of the study area. Results displayed
Figure 7. Image drape of Southeast Louisiana. From the image, Southeast Louisiana appears in the foreground of this perspective view generated from a Landsat satellite image and elevation data from USGS.
Figure 8. Classified DEM of southeast Louisiana.
in Figures 7-11 have shown that, the population of most of the study area lives in low-lying coastal urban centers except Washington, Tangipahoa and St. Tammany which are in the upper elevations. Low-lying areas especially in the study area could experience, increased flooding activities and sea level rise if hurricanes and severe storms became stronger. The rise in sea level could lead to submergence. This could also damage substantially coastal infrastructure.
This study can enhance decision-making as a support tool in flood disaster management by showing the exact locations of flood risk areas in the parishes. For example, one of the things environmental managers would regularly need, is
Figure 9. Flow direction of the elevation in the study area.
Figure 10. Aspect of the elevation.
to access simulations of complex spatial data pictures in simplified ways to optimize quick assessments of areas at which the damage is going to be concentrated during flooding disasters and inclement weather debacles. Access to such a tool can improve the capability of planners in the formulation of effective procedures to follow with regard to location of the damage, evacuation plans to help emergency service workers and first responders to direct the population at risk in the most efficient manner to safer grounds during crisis .
5. Policy Options and Conclusion
Figure 11. Southeast Louisiana TIN elevation.
Figure 12. Study area census block group.
that focuses on dealing with a flooding or other coastal-zone environmental crisis. Results of GIS and remote sensing imagery shown in this study can serve as a powerful motivating factor instructional and sensitizing tool for the population at large, which may not appreciate the dangers experienced in the coastal areas of Southeast Louisiana as a result of overpopulation. There is also the need to design and build a comprehensive Regional Information Systems (RIS) in the form of periodic inventorying, monitoring and evaluation with full support of the governments in the study area. RIS would entail combining remote sensing data, climate data, field survey data, national and local-level weather forecast, and hydrological data including information on the river flow into one system.
Table 3. Population of the Southeast Louisiana Parishes from 1990-2010 .
Developing such a system would offer the decision makers access to the appropriate temporal-spatial data for monitoring the pressures mounted on the areas’ socio-economic systems and ecosystems by seasonal floods. Such a tool could act as an effective decision support system in order to keep development in harmony with environmental sustainability.
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