Creating Raster Information Product using Raster Analytics

Raster Analytics

ArcGIS Enterprise at 10.5 provides you with the ability to perform large raster analysis using distributed computing. This capability is provided in the arcgis.raster.analytics module and includes functionality to summarize data, analyze patterns, images, terrain and manage data. This sample show the capabilities of imagery layers and raster analytics.

Imagery layers

Input
import arcgis
from arcgis.gis import GIS
from IPython.display import display

gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')

Here we're getting the multispectral landsat imagery item:

Input
items = gis.content.get('4ca13f0e4e29403fa68c46d188c4be73')
imglyr = items.layers[0]
items
Output
Landsat 8 Views
Landsat 8 multispectral and multitemporal imagery with on-the-fly renderings and indices for visualization and analysis. The imagery in this layer is updated daily and is directly sourced from the Landsat on AWS collection.Imagery Layer by esri_livingatlas
Last Modified: May 13, 2020
0 comments, 0 views

The code below cycles through and lists the Raster Functions published with the imglyr:

Input
for fn in imglyr.properties['rasterFunctionInfos']:
    print (fn['name'])
Agriculture with DRA
Bathymetric with DRA
Color Infrared with DRA
Natural Color with DRA
Short-wave Infrared with DRA
Geology with DRA
Agriculture
Bathymetric
Color Infrared
Geology
Natural Color
Short-wave Infrared
NDVI Colorized
Normalized Difference Moisture Index Colorized
NDVI Raw
NBR Raw
None

Let us create a map widget and load this layer

Input
marthasbasin = arcgis.geocoding.geocode("Marthas Basin, Montana")[0]
Input
map1 = gis.map(marthasbasin, zoomlevel=12)
Input
map1
Output
Input
map1.add_layer(imglyr)

The utility of raster functions is better seen when we interactively cycle through these raster functions and apply them to the map. The code below cycles through the first 6 raster functions stored with the Imagery Layer and a small time delay to illustrate. The image processing occurs on-the-fly at display resolution to show how the layer can be visualized using these different raster functions published with the layer.

Input
import time
from arcgis.raster.functions import apply

for fn in imglyr.properties['rasterFunctionInfos'][:6]:
    print(fn['name'])
    map1.remove_layers()
    map1.add_layer(apply(imglyr, fn['name']))
    time.sleep(4)  
Agriculture with DRA
Bathymetric with DRA
Color Infrared with DRA
Natural Color with DRA
Short-wave Infrared with DRA
Geology with DRA

Raster functions

Developers can create their own raster functions, by chaining different raster functions. For instance, the code below is doing an Extract Band and extracting out the [4,5,3] band combination, and applying a Stretch to get the land-water boundary visualization that makes it easy to see where land is and where water is. Its worth noting that the raster function is applied at display resolution and only for the visible extent using on the fly image processing.

Input
from arcgis.raster.functions import stretch, extract_band

def process_bands(layer, bands):
    return stretch(extract_band(layer, bands), 
                    stretch_type='percentclip', min_percent=0.1, max_percent=0.1, gamma=[1, 1, 1], dra=True)

Let us apply this raster function to the image layer to visualize the results.

Input
map2 = gis.map(marthasbasin, zoomlevel=12)
map2
Output
Input
map2.add_layer(process_bands(imglyr, [4, 5, 3]))

Creating a Raster Information Product using Landsat 8 imagery

This part of the notebook shows how Raster Analytics (in ArcGIS Enterprise 10.5) can be used to generate a raster information product, by applying the same raster function across the extent of an image service on the portal. The raster function is applied at source resolution and creates an Information Product, that can be used for further analysis and visualization.

Input
montana_landsat = gis.content.get('09a4edaefae74ec6a2cb114f155091e3')
montana_landsat
Output
ImgSrv_Landsat_Montana_2015
Show the burned area from the montana firesImagery Layer by api_data_owner
Last Modified: August 08, 2018
0 comments, 2 views

In the code below, we use extract and stretch the [7, 5, 2] band combination. This improves visibility of fire and burn scars by pushing further into the SWIR range of the electromagnetic spectrum, as there is less susceptibility to smoke and haze generated by a burning fire.

Input
montana_lyr = montana_landsat.layers[0]
Input
fire_viz = process_bands(montana_lyr, [7, 5, 2])
fire_viz

fire_visualization_MT

We can use the save method to apply the raster function across the entire extext of the input image layer at source resolution, and presist the result in an output image layer. This creates a raster product similar that can be used for further analysis and visualization.

Input
arcgis.env.verbose = True
Input
from datetime import datetime as dt
montana_fires_lyr = fire_viz.save('Montana_Burn_scars'+str(dt.now().microsecond))
Submitted.
Executing...
Input
montana_fires_lyr
Output
Montana_Burn_scars
Analysis Image Service generated from GenerateRasterImagery Layer by arcgis_python
Last Modified: August 06, 2018
0 comments, 0 views
Input
base_map = gis.map(marthasbasin, 12)

natural_color_map = gis.map(marthasbasin, 12)
natural_color_map.add_layer(montana_landsat)

false_color_map = gis.map(marthasbasin, 12)
false_color_map.add_layer(montana_fires_lyr)

We can compare the natural color and false color images uaing a tabbed widget.

In the false color image the red and brownish pixels correspond to burn scars of the fire:

Input
import ipywidgets as widgets

tab = widgets.Tab([base_map, natural_color_map, false_color_map])
tab.set_title(0, 'Basemap')
tab.set_title(1, 'Natural Color')
tab.set_title(2, 'False Color')
tab
Output

Thus using the same raster function, we were able to both visualize on the fly and derive a persisted image service using distributed raster analysis.

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