Use of GIS and Remote Sensing Technology as a Decision Support Tool in Flood Disaster Management: The Case of Southeast Louisiana, USA

The primary objective of this paper was to identify flood-prone areas in Southeast of Louisiana to help decision-makers to develop appropriate adap-tation strategies and flood prediction, and mitigation of the effects on the community. In doing so, the paper uses satellite remote sensing and Geographic Information System (GIS) data for this purpose. Elevation data was obtained from the National Elevation Dataset (NED) produced by the United States Geological Survey (USGS) seamless data warehouse. Satellite data was also acquired from USGS Earth explorer website. Topographical information on runoff characteristics such as slope, aspect and the digital elevation model was generated. Grid interpolation TIN (triangulated irregular network) was carried from the digital elevation model (DEM) to create slope map. 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. Results of the study revealed that major-ity of the study area lies in low-lying ments was made for the study area.


Introduction
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 [1]. Yokohama conference led to follow-up conferences in Kobe with overall goal of building the resilience of Nations and Communities to Disasters [1]. 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 [1] [2].
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 [3]. 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 [4]. Recent Reuters report by Blake Brittain, indicated that weather and climate-related disasters cost the United States a record $306 billion in 2017 [5]. Also, a report by NOAA indicated that the first three months of 2018, U.S. spent

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 [7] [8] [9]. Early work by Twumasi 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 Journal of Geographic Information System 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.

The Study Area
The focus areas of this study were eleven parishes of Southeast Louisiana. These  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'   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) [7]). Due to low elevation, most areas get flooded during hurricane and tropical storms [24].       (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

Demographic Analysis in the Study Area
and 2000 ( Table 1)

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 [36].

Elevation Data
Topographical information on runoff characteristics was generated using Twumasi and Asomani-Boateng [10] 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. [8] approach was used. This method employs image drape technique to visualize the land-scape 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.

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.

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.

Results and Discussion
Results  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. 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    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 [8].

Policy Options and Conclusion
With a total population of 1,638,857 in 2020, which is almost a third of the State's population (Table 3, Figure 6), there is the need for an appropriate policy  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. Journal of Geographic Information System 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.