Term Project

Edge Enhancement and Vegetation Change of

the Tampa Bay Region from 1995 to 2010

Joel Weber      Introduction to Remote Sensing        December 14, 2014

 

Introduction

The Tampa Bay Region (Figure 1) is located on the West Central portion of the Florida peninsula. It consists of four adjacent counties surrounding Tampa Bay. The counties include Hillsborough County, Pinellas County, Pasco County, and Manatee County. The overall population of the region, according to the 2010 U.S. Census Bureau (2014), is 2,933,298, which is a 150% increase from the estimated population of 1,948,063 people in 1995. The population density varies significantly for each county. Pinellas County has the highest density of 3,300 people/mi² while Manatee County has a density of only 356 people/mi² (U.S. Census Bureau, 2014).

This significant density and population growth from 1995 to 2010 has led to various land use changes. It is important to monitor land use changes to determine environmental impacts in the area. Therefore, I will be using satellite imagery taken from Landsat 5 to determine some environmental impacts of the population growth and urban sprawl between 1995 and 2010. This will be achieved by downloading the Landsat 5 data from the USGS Global Visualization Viewer for each year, mosaicking the images together, creating a subset of the region. Then I will run a Normalized Difference Vegetation Index for both years to determine where vegetated areas are located. I will also run a nonlinear edge-enhancement for both years to determine where new roads and subdivisions have been added. Finally, I will calculate where land surface has significantly changed using binary change.

Remote Sensing Analysis

            The first step in the process of any remote sensing analysis is downloading data from an online source. For my analysis I used the USGS Global Visualization Viewer to download Landsat 5 data from 1995 and 2010. It is important to select a temporal resolution that is consistent between both images (i.e. using images from the same month of the given years). It is also important to select data that has a relatively low atmospheric distortion to provide a more accurate analysis. Since my study area covers more than one image scene I had to download three images from 1995 and three images from 2010. I found it frustrating to locate images from similar months that had high quality images for the given years. After finding acceptable images from both years I downloaded them and unzipped the files. The next step is to convert the individual .TIFF bands that were downloaded, into .img images that can be used in Erdas. This was done by importing bands 1-5 and 7 from the Landsat 5 satellite into Erdas and stacking the layers into one image.

Next, I had to mosaic the three images for 1995 together and the three images for 2010 together, using “MosaicPro” in Erdas (Figure 2). This was also frustrating because there are several different settings the user must set in order to create a seamless mosaic. For my mosaic (Figure 3) I used histogram matching of overlap areas with a feather function. I tried several other settings which didn’t provide quality mosaics.

           

After the mosaics were created the next step is to create a subset of the area of interest. To do this I imported a shapefile of the four counties in the Tampa Bay Region into Erdas (Figure 4). Then I needed to create an .aoi file by using the “Save as AOI Layer As…” function. Finally, this .aoi file was used to create a subset of the Tampa Bay Region (Figure 5), which will be used for further analysis.

 

            Next, I ran a Normalized Difference Vegetation Index (NDVI) on the Tampa Bay Region for 1995 and 2010 (Figure 6). NDVI uses an equation to calculate how much vegetation is in each pixel. The NDVI equation is

NDVI=(NIR-Red)/(NIR+Red)

Where NIR= reflectance in the near infrared band and Red= reflectance in the red band. Pixels with high vegetation will appear lighter in color and pixels with low vegetation will appear darker. It is important to use images with similar temporal resolution because images taken at different months will have significantly different NIR reflectance values, which will lead to inaccurate analysis.

             Next, I ran a non-directional edge enhancement technique on images from both years (Figure 7). This process shows where dramatic changes in brightness values occur in a relatively short distance. Edges are commonly found along river banks and along roadways. Comparing non-directional edges from two different years can provide detail about where new roads are being built and possibly where population is increasing. Figure 7 shows where new roads were built in the lower portion of Manatee County as urban sprawl occurs.

            Finally, I ran a binary change detection between 1995 and 2010 to determine where vegetation has changed (Figure 8). To determine where change has occurred we simply subtract NIR brightness values of one image from NIR brightness values of another image. Then we apply a change threshold to determine whether or not that area has changed. After the 1995 image has been subtracted from the 2010 image we use the histogram to determine the threshold. In this case we use 1.5 times the Standard Deviation plus the mean brightness value. Any areas that have NIR brightness values above the threshold have changed.

Conclusion

            In conclusion, all remote sensing projects begin with data acquisition, then images must be mosaicked together, then a subset of the study area must be created. Finally, you are able to run various analysis techniques. I found that the NDVI values were much lower in 2010 than in 1995 throughout the entire image. This could be caused by urban sprawl in some areas and differences in crop production in more rural areas. I also found a large increase in edges that occur in the lower portion of Manatee County and other suburban areas between 1995 and 2010 while areas that are already densely populated will have similar edges between the two years. This can definitely be attributed to urban sprawl as populations increase. Finally, the binary change detection between 1995 and 2010 shows us where vegetation has changed (Figure 9). Very little change has occurred in dense urban areas while significant changes have occurred in rural farmland and suburban areas.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

 

U.S. Census Bureau. (2014). State and county quickfacts: Florida. Retrieved December 12, 2014, from http://quickfacts.census.gov/qfd/states/12000.html.

 U.S. Geologic Survey. (2014). Global visualization viewer. Retrieved December 10, 2014, from http://glovis.usgs.gov/.