Final Project Video/Slide Show

http://students.uwf.edu/mpj2/juliette_2000_2009.avi

Final Project

Hydrilla (hydrilla verticillata) is considered to be one of the most problematic aquatic plants in the United States. Hydrilla is considered a noxious pest because it grows so rapidly, out competing and eliminating native species. Hydrilla is native to Europe and Asia and was probably brought to the Tampa and Miami, Florida areas as an aquarium plant in the late 1950s; by the 1970s, it was established throughout Florida. Hydrilla verticillata continues to be sold through aquarium supply dealers and over the Internet, even though the plant is on the U.S. Federal Noxious Weed List (IFAS). Hydrilla has been found in several locations in Lake Juliette and represents a threat of increasing cost of treatment and possible take over of the lake. The costs of not managing it early in its growth curve are extreme. Hydros and cooling water intakes can be shut down. Fish kills have occurred, and recreational use could be eliminated. Management methods include herbicides, grass carp (Ctenopharyngodon idella Val.), and mechanical removal (IFAS). The herbicide active ingredients, copper, diquat, endothall, and fluridone can be used to selectively control hydrilla to some extent, but do not destroy the tubers. Grass carp is a herbivorous fish that is effective for controlling hydrilla (Van Dyke et al. 1984). Possession of this fish is illegal in most states because of the potential environmental damage that could result if escaped fish establish a breeding population. Sterile, triploid grass carp (Malone 1984) are also effective (Cassani and Caton 1986) and are now available and legal by permit in some states in the U.S. In small ponds or lakes and canal systems, with adequate control structures, and where total removal of vegetation is acceptable, triploid grass carp stocking is highly recommended. The decision to be made is the cost effectiveness of continued chemical treatment versus the use of sterile grass carp as an alternate means of control based on the areas of hydrilla covered in the past 10 years, as well as, the hydrilla density level trend to be used as an indicator for treatment effectiveness.

Data was collected in late August, early September for the years 2000 – 2005, and 2007 – 2009 in the form of uncorrected GPS data and survey sheets (excluding 2006 due to GPS data mismanagement but data sheets and survey points were located) containing data point numbers and percentage of hydrilla coverage at each survey point. Survey points will be input using the corrected GPS coordinates taken at each survey location during each survey.
Other useful data sets are:
County - Georgia GIS Clearinghouse
Aerial Image – ESRI
Roads – Georgia GIS Clearinghouse
Rivers/Ponds/Lakes data – Georgia GIS Clearinghouse
Topography – USGS
The survey data was input into excels files using station numbers as a guide to match to the GPS data. Once this was done the points containing hydrilla were selected and removed into separate shape files and labeled by year. Next a polygon shape file was made to use as an extent for the spatial analysis and as a clip range to isolate the lake polygon. A reverse clip was then made to allow for the lake area to be seen as a void and the rest of the areas blanked out. A topographic map was then placed below this map. This allowed for the elevations for the lake area to be estimated with a new polygon named hydrilla range. All areas with a depth of 40 feet or less were included in the polygon allowing for deeper areas and islands to be removed from the growing range. The hydrilla range polygon was used as the mask for spatial analysis so that a more accurate depiction of the growing areas could be depicted. Next each year’s point data was placed on the map, one at a time, and the spatial analysis technique ‘kernel density’ was used as an estimate to extrapolate the data to the entire population over an area based on the density found and number of points in the vicinity. Once this was completed for each year the base map from above was used to make a map for each year and a group animation to illustrate the changes over the 10 year span.

Regular treatment has taken place since 2001 around boat ramps and the intake and from the results you can see a baseline plant levels present from year to year with the exception to the explosions of growth in 2004 and 2007. In 2000 the lake was allowed a draw-down in October and the water elevation receded almost to the point were Plant Scherer could no longer operate. This could account for the low levels that first year but each year after treatment took place at each of the 3 boat ramps and the plant intake thrice a year in the beginning, middle and end of the growing season. It is possible that regular treatment has retarded the growth of hydrilla each year and kept it at a manageable level but the explosions in growth make this seem unlikely. Also as the spread has continued almost the entire perimeter of the lake has been surrounded by the plant and most likely its tubers. . It is possible to continue with chemical treatment and allow for regular draw-downs to control hydrilla but the with the risk of a clogged intake and loss of valuable recreational areas of the lake, as well as, mounting expenses other options could be explored. With the extent of the spread and the risk of damage due to an imminent explosion of growth it would be wise to treat this reservoir with sterile grass carp to allow for regular and longer term control. This measure, while initially expensive, would allow for a savings in man-hours and chemical treatment cost after a few years and with the majority of water being pumped from the nearby Ocmulgee River only minimal reservoir control would need to take place to control the spread of the grass carp.

References
Cassani, J. R. and W. E. Caton. 1986. Growth comparisons of diploid and triploid grass carp under varying conditions. Progr. Fish-Cult. 48:184-187.
Van Dyke, J. M., A. J. Leslie, Jr. and L. E. Nall. 1984. The effects of grass carp on the aquatic macrophytes of four Florida lakes. J. Aquat. Plant Manage. 22:87-95.
http://plants.ifas.ufl.edu/node/183. IFAS Extension, University of Florida. Center for Aquatic and Invasive Plants. 2009.
Thanks to Georgia Power Environmental Affairs for access to data. Other data sources included by map.

Map 1 shows the heliport and the area around it; as well as, where it lies in the overall security area. This map went smoothly and other than waiting 20 min.s every time the roads had to render it was easy.

Map 2 is show a bit better in the 1st map but here the routes are made into point features to show where barricades could be set up to secure the perimeter.

This map was guerrilla warfare on my poor brain. It shows the line of sight points and the graph illustrating areas that are not completely able to be seen from the entrance. I had to restart a new map and reimport my data to make this work.

The final map also gave me fits. Projection errors and disappearing data ruled the day. Off to the final project.

http://students.uwf.edu/mpj2/mod7poster.ppt

Well that went a lot better than i thought. Other than my road layer taking a full hour and a half to clip I had very few issues.

Week 6 Project 2

The purpose of this project is to illustrate the decision making tools available in the ArcGis program. This was shown by using Arc to narrow down search areas for an older couple to look for housing after the move to Alachua County/Gainesville City area with a set of given criterion. The criterion are as follows: proximity to grandchildren, North Florida Regional Hospital (work), University of Florida (work), community centers, bus routes, areas with a larger percentage of 65 and older and areas with higher home values. All of these criterions, except home values and age/area percentages, are illustrated in the Alachua Area Map with public lands included to show the number of parks in the area.

This second map shows the 65 and over percentage, property value, and community center proximity maps. The first two show the highest amounts in the lighter colors. You can see that just along the outer areas of Gainesville there are areas of higher property values and higher percentages of those over 65 years of age. The community center map shows the cluster of centers in the city area and some of those in the area around. This map was done by importing the property value figures from the 2000 census into the program and joining it with the census tracts by the standard id number. Once the data was converted the feature was converted into a raster and illustrated by the value data. The same was done with the age data with data already possessed from that same census.

The third map shows the proximity of four of the initial criterion by using Euclidian direction and standardizing the interval to provide a target like proximity map. In it you can see the areas closest to each spot that the couple would like to be near to.

This fourth map shows the first weighted overlay map. This map shows several possible housing areas that meet four of the seven core criterion. Each was weighted equally at 25%.

This final map shows an alternately weighted map with house values at 15%, 65 and over % at 15%, bus and community centers at 5% each, University proximity at 15%, Hospital at 20% and family proximity at 25%. Two of the same areas are highlighted here as in the first overlay.

In the Alachua County area the best matches lay within the Gainesville City area and all criterions can be met in these areas presented.

This first map shows the university student occupancy rate. I didn't have much if any problems here. I enjoy learning how to manipulate the data and show it in different ways. Also the query functions are pretty intuitive.



This second map shows the buffer area around a proposed construction site and the proposed traffic vs existing traffic values. I have to use the buffer and merge tools a fair amount with my work so this was easy to start but the adding of the tables and merging of tables by specific data was new to me and will be useful.

Deep Horizon Oil Spill Participation project.

animated file: http://students.uwf.edu/mpj2/oilparticipation.avi


GIS is used in disaster response to provide accurate data to disaster relief workers and victims. This is done in a wide variety of ways: Extent and nature of destruction patterns, damage locations, density and assessments, infrastructure closures. All this data allows for people to get into and out of a disaster area safer and make better decisions to improve the efficiency of the response. Maps can provide a clear way to bring in equipment and aid, as well as, the areas most in need. As the response time moves from the initial rescue stage into recovery and rebuilding, GIS provides insurers and government aid agencies with ‘hotspots’ of damage so that applications for aid from those locations can be pushed through with priority. As clean up continues damage extents can be used to calculate clean up and rebuilding cost and provide for initial projections on the overall investment required to get the effected area recovered.

GIS is being used currently in these same ways to provide data for those at work on the Deepwater Horizon Oil Spill. Maps projecting the extent (both current and projected) of the oil spread, effects from both current and weather patterns, sensitive shoreline, fisheries and other wildlife at risk, staging areas, ports, command post and aid activity. This data allows whoever is currently in charge the ability to send the fish and wildlife people where the greatest numbers of animals are at risk. It also allows for beach and fishery closures to be made based on where the oil spread has drifted. As the spill is abated and the full clean up effort is begun GIS will be used to estimate damage and clean up cost. GIS is a useful decision making tool that can provide a more efficient means of response to disaster relief.