Our area of interest is Palo, Iowa. We filter to this city and create a 5 km buffer. We then extract flood data to this extent.
Dimensions of the stacked image
## [1] 7811 7681 6
Coordinate System of the stacked image
## CRS arguments:
## +proj=utm +zone=15 +datum=WGS84 +units=m +no_defs
Resolution of the stacked image
## [1] 30 30
Dimensions of the cropped image stack
## [1] 340 346 6
Coordinate System of the cropped image stack
## CRS arguments:
## +proj=utm +zone=15 +datum=WGS84 +units=m +no_defs
Resolution of the cropped image stack
## [1] 30 30
Applying stretch = “lin”
Applying stretch = “hist”
Applying a color stretch helps make differences in landscapes more visibly clear to assist in identifying patterns or features.
The images are similar in that they highlight the river region as well as other surface water areas. They also all use the the same blue, white, and red color scheme that we created in our palette. They differ in that the surface water areas are not all highlighted in the same color, and some images direct more attention towards the landscape rather than water regions. The final plot, the simple water index, highlights only the surface water captured in the image.
By applying five different raster thresholds assessing surface water features, we are apply to plot flooding areas in blue.
In this method of creating a flood raster, we calculated the cluster of most flooded cells in our kmeans data to create a flood mask.
## [1] 340 346 6
x | |
---|---|
ndvi | 5999400 |
ndwi | 6490800 |
mndwi | 10745100 |
wri | 7622100 |
swi | 13680900 |
layer | 434700 |