Location
Parker-Reed, SSWAC
Start Date
1-5-2014 9:00 AM
End Date
1-5-2014 10:00 AM
Project Type
Poster
Description
This study attempts to recreate existing datasets of land-use classifications for Brevard County in east-central Florida from Landsat 5 satellite images using two different classification tools provided by the GIS program ArcMap. After acquiring the Landsat images from the USGS for the years 1995, 2000, 2004, and 2009, the unsupervised classification tool was used in order to identify 6 different land-use classes: urban, agriculture, non-forested land, forest, water, and wetland. Then, 6 sample polygons for each class were used as a basis for the software to recognize different land-use types, and the classification was repeated. In order to measure the effectiveness of one tool versus the other, this study compared the classifications from both tools to the existing datasets for land-use in Brevard County. Because the existing datasets used several hundred distinct land-use classes, they had to be condensed into the same 6 classes as above. After converting the polygons into a raster layer (a continuous image consisting of pixels rather than lines and polygons), a binary (match or no match) analysis was used to assess the values of the pixels in the land-use datasets versus the unsupervised and the trained classifications. The analysis shows that the trained classifications consistently display a higher percent match; with differences as high as 17% between 50% and 67%, and as low as 1% between 53% and 54% for the unsupervised and trained classifications, respectively.
Faculty Sponsor
Manny Gimond
Sponsoring Department
Colby College. Environmental Studies Program
CLAS Field of Study
Interdisciplinary Studies
Event Website
http://www.colby.edu/clas
ID
817
Included in
Recreating Ground-Truth Land-Use Classifications from Landsat Images
Parker-Reed, SSWAC
This study attempts to recreate existing datasets of land-use classifications for Brevard County in east-central Florida from Landsat 5 satellite images using two different classification tools provided by the GIS program ArcMap. After acquiring the Landsat images from the USGS for the years 1995, 2000, 2004, and 2009, the unsupervised classification tool was used in order to identify 6 different land-use classes: urban, agriculture, non-forested land, forest, water, and wetland. Then, 6 sample polygons for each class were used as a basis for the software to recognize different land-use types, and the classification was repeated. In order to measure the effectiveness of one tool versus the other, this study compared the classifications from both tools to the existing datasets for land-use in Brevard County. Because the existing datasets used several hundred distinct land-use classes, they had to be condensed into the same 6 classes as above. After converting the polygons into a raster layer (a continuous image consisting of pixels rather than lines and polygons), a binary (match or no match) analysis was used to assess the values of the pixels in the land-use datasets versus the unsupervised and the trained classifications. The analysis shows that the trained classifications consistently display a higher percent match; with differences as high as 17% between 50% and 67%, and as low as 1% between 53% and 54% for the unsupervised and trained classifications, respectively.
https://digitalcommons.colby.edu/clas/2014/program/4