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Hyperspectral Imaging of Florida's Coastline

A smart investment in the quality of Tomorrow's Florida
by

Nathan Heiderich

on 11 March 2013

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Transcript of Hyperspectral Imaging of Florida's Coastline

photo credit Nasa / Goddard Space Flight Center / Reto Stöckli A smart investment in the quality of
Tomorrow's Florida Hyperspectral Imaging of Florida's Coastline What's at Stake - Florida is the 4th largest economy in the U.S.
- 1,197 Miles of coastline and 663 Miles of beach brings in 87.3 million visitors
- Our water resources and tourism represent an economic impact of $67 billion to Florida's economy How to monitor
1,197 Miles of Coastline Currently Available Data Land Use Data Step 3
Merge and Visualize Data - Join the previously created risk level grids (LU, LC, LT, and SD) together using the Raster Calculator in Map Algebra
+ Calculate using the following formula and export to create Land use Total (LUT):
“LC” * 5 + “LU” * 3 + “SD” * 2 + “LT” * 1
+ Change the symbology to graduated colors based on the newly identified weighted risk levels

- For follow on and large projects, use the interpolation IDW tool to fill in the sampling data across vacant space allowing a more visual representation of the total area Step 1
Prepare Risk Data Step 2
Manipulate Risk Data - Reclassify OLU (2012) based on the following criteria to create Land Use (LU) grid and export:
+ Seawall in close proximity to commercial or high traffic areas – 5
+ Riprap seawall in close proximity to commercial or high traffic areas – 4
+ Seawall in residential areas – 3
+ Riprap seawall in residential or park areas – 2
+ Natural wetlands in close proximity to commercial or high traffic areas – 3
+ Natural wetlands in residential or park areas – 1

- Reclassify OLU (1950, 1995, 2005, and 2012) and standardize based on the following criteria to create the Land Type (LT) grid and export:
+ Wetland areas have already been classified as a 1
+ Forest and park areas – 2
+ Agricultural areas – 3
+ Residential areas – 4
+ Commercial areas – 5

- Tabulate the area of LT (each year) using Spatial Analyst
+ Join all the tables to the wetlands buffer and export.
+ Add a new field and populate with the Calculate Geometry tool for Total Area
+ Add another field and calculate for each year using the formula:
(([Recent Year] – [Past Year]) / [Total Area]) *100
+ Change the symbology to graduated colors and manually classify to 5 risk levels to create Landuse Change (LC): newly developed commercial (high) to newly developed forest/parks (low)
+ Add a field and assign values based on risk level

- Reclassify Species Density to reflect 5 risk level based on species density to create SD - Hillsborough County, one of 67 Florida Counties, holds the Tampa Bay Estuary, the largest open-water estuary in Florida
- More than 100 tributaries flow into Tampa Bay
- Mangrove covered islands support the most diverse waterbird nesting colonies in the U.S.
- More than 4 billion gallons of oil, fertilizer, and other hazardous material passes through Tampa Bay every year - Tracking the progression of water quality is possibly the single greatest value for Florida conservation efforts
- Dynamic monitoring processes must be employed to stay ahead of evolving threats Florida Value Florida Economy Florida Cross-section 2. Existing Benthic reporting Study Boundary
in Red 3. Hyperspectral Imagery
analysis of large areas - Southwest Florida Water Management District (SWFWMD) has detailed vector data by year from 1950 to current
- SWFWMD also provides current boundary shapefiles for Florida and it's 67 Counties with detailed coastlines
- Floridadisaster.org is a GIS data clearinghouse for framework and disaster relief data with much data regarding wildlife species and even susceptibility ratings for species to contaminants such as oil spills and other pollutants - Environmental Protection Commission of Hillsborough County has detailed Benthic and biological monitoring data from the early 1990s to current
- Beginning in 1999 and due to the success of the project, the EPC expanded the sampling program as community developments and associated runoff pollution also had grown significantly Benthic Monitoring Current Status of Florida Hyperspectral Imagery - 12 October 2012 saw a request for information from the Center for the Advancement of Science in Space (CASIS) to gauge commercial interest in using the Hyperspectral Imager for the Coastal Ocean (HICO)
- in 2012, the Galileo Group flew an unmanned aircraft with a hyperspectral imager over Florida waters to study seagrass and coral reef biomes. Identify Land use risk Identify Land use Change risk Identify Land Type risk Identify Species Density risk Assigning a total risk level to individual cells allows us to relate the 'weighted' cells to easily understood color ramps identifying color hotspots of high risk areas - Import the Species Density data and identify key node species at higher risk to pollutants (Oyster, Seagrass, Manatee, Snook etc.), clip to the Study Area layer (Hillsborough County layer), and export selection

- Import Landuse (OLU) data (1950, 1995, 2005, and 2012), identify all wetland areas within the FLUCSDESC field (ie. Bay Swamps, Estuaries, Beaches, etc.), clip to the Study Area layer (Hillsborough County layer), export selection, and assign a value of 1 in a new field

- Create a 100 meter Buffer around the exported wetland area
+ 100m inland is the desired area for our land use risk model
+ 100m into the wetlands is the study area for the Benthic and Hyperspectral focus as this is the most immediate impact of pollution

- Convert the LU and SD layers to Raster, set the operating environment (mask and extent) to the wetlands buffer, and the cell size to 10m Step 4
Benthic Data - The Benthic data is collected from field samplings and delivered to the team in excel spreadsheets which are imported in tabular format
+ Join the Benthic table to wetlands buffer to project the coordinates with the specific Benthic data and export as (BD)
+ Group the Benthic readings into five risk levels
+ Perform a union of the BD and LUT layers to identify current trends and risk areas Step 5
Hyperspectral Imagery - Hyperspectral Imagery (HSI) Data is imported as raster data and will be limited to the study area with mask and extent and the data will be normalized

- Risk levels will be assigned using the Reclassify tool and then merged with a union to the BD and LUT layers

- Data from HSI must be matched with a spectral library to identify the spectral signature for the threat types of pollution to be searched for
+ As an emerging scientific tool, redundancy is a must in order to verify results
+ The key advantage is the immense areas that can be scanned for various invasive species, pollutants, or other emerging threats to the quality of our Florida water quality Light reflecting off particles in the water are caught in filters
at specifically defined wavelengths providing unique signatures
that can be applied to massive areas to identify new locations
of those signatures 1. Existing Land Use data Identify the value of Tomorrow Data workflow to understand
the evolving environment Tracking the Future The Florida coastline can be broken down by county with a yearly presentation to each county to formulate the best strategy for conservation efforts in that community

An oil spill, runoff after a damaging tropical storm, or a new development with pollution from thousands of new residents and businesses must not be allowed to crash into these priceless wetlands without mitigation processes in place.

The future depends upon the technological advances of today. Department of Natural Resources Florida Fish and Wildlife Conservation Commission Individual Port Authorities Local County and Beach Preservation Efforts Other State level Conservation Efforts Tampa Bay Estuary Program
http://www.tbep.org/portrait/fast_facts.html
Florida Chamber of Commerce
http://www.flchamber.com/article/floridas-real-gdp-grew-by-0-5-in-2011/
Florida Fast Facts
http://www.stateofflorida.com/Portal/DesktopDefault.aspx?tabid=95
Southwest Florida Water Management District - Shapefile Library
http://www.swfwmd.state.fl.us/data/gis/layer_library/category/indexes
Environmental Protection Commission of Hillsborough County – Benthic Data
http://www.epchc.org/index.aspx?nid=215
Hyperspectral Imaging in Space - Florida
http://www.spaceflorida.gov/press-room/2012/10/12/casis-announces-request-for-information-in-hyperspectral-imaging
Hyperspectral Imager for the Coastal Ocean – HICO
http://hico.coas.oregonstate.edu/
Galileo Group – Commercial HSI Imaging in Florida
http://www.galileo-gp.com
http://www.auvsi.org/112612/112712Galileo
Hyperspectral Techniques for Water Quality - AVIS
http://earth.esa.int/workshops/hyperspectral_2010/papers/s4_1friese.pdf Hyperspectral Imaging - Identify the High Risk Target Pollutants –
+ Benthic reporting indicates a current high risk pollutant in Hillsborough County Estuary (our study area) is Nitrogen and Phosphorus pollutants
+ As future reporting identifies further pollutants at the high risk level, the list of targets will grow with priority going to the most recent pollutants - Identify the Spectral Signature of the Target Pollutants
+ Hyperspectral Imaging scientists have already identified the spectral returns for Nitrogen and Phosphorus
+ New pollutants will require testing to add the new target pollutants to the spectral library
- As the imaging is completed, raster data can be ingested by the specific cells that match the targeted spectral signatures
+ Each target cell is assigned a visible color
+ A visible report can be made to identify the density and area of the identified pollutants and merged with the Landuse and Species data to highlight the depth of the threat - All data will be posted on ArcGIS.com as web apps
+ This will allow presenters, researchers, conservationists, or policymakers to access targeted and current data – data that can protect our future Florida
+ Posting all production to ArcGIS.com will further promote the advancement of new technology and applications, such as hyperspectral imaging – as well as the need for a centralized Florida GIS clearinghouse
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