Methodology
Mapping Impervious Surface | Mapping Tree Canopy
Mapping Impervious Surface
| This example shows two images are the same area in southwestern Kennesaw, Georgia. Vegetation in these images is red. | |
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 1 meter color infrared aerial photo |  30 meter pixel satellite imagery Landsat ETM+ sensor |
| Step one: We create a binary map of impervious surface using the 1 meter aerial photograph. A 30 meter grid is placed over the photograph and the percentage of impervious surface for each cell is calculated. This represents the 30 meter cell size of the Landsat image. | |
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| Step two: We then build a regression model using the photo interpreted impervious surface to predict the percentage of impervious surface found in each grid cell of the Landsat image. | |
 Yellow area is actual impervious surface |  Predicted impervious surface |
Animation of Process  |
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Mapping Tree Canopy
| This example shows two images of the same area in northern Newton county. | |
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 1 meter color infrared aerial photo |  30 meter pixel satellite image of same area |
| Step one: We created a binary map of tree canopy and no tree canopy, then placed a 30m grid over the new map to calculate percentage of tree canopy for each 30 meter grid cell. |
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 Actual tree canopy based on aerial photo interpretation. |  Predicted percentage of tree canopy. The darker the grid cell, the higher the percentage of tree canopy. |
| Step two: We then build a regression model using the photo interpreted tree canopy to predict to percentage of tree canopy found in each Landsat grid cell. | |
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Animation of process  |
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