The folder contains two georeferenced TIFF images, Before_Storm.tif and After_Storm.tif, along with their auxiliary files. The images were captured on August 4 and August 8, 2019, before and after the hailstorm in the Taber and Barnwell area.

Below is a quick preview of the images: Before_Storm.tif on the left and After_Storm.tif on the right.

You'll visualize them by yourself later in the tutorial.

This tutorial uses Map Viewer Classic.

The files start uploading. You can monitor the progress in the Upload status column.

When all files show as 100% uploaded, you'll define a title template.

Since you are uploading more than one image at once, you can define a common prefix and suffix that will be applied to the titles of all images in the batch. This can be useful to make the images easily recognizable and retrievable in your ArcGIS Enterprise account.

You'll also add a suffix that contains your initials.

All the images uploaded will have the same prefix and suffix, for instance, Corn_Fields_Before_Storm_YN.

• For Tags, type Agriculture, Imagery, Damage assessment and press Enter.
• For Summary, type Imagery for the Taber-Barnwell, Alberta region.
• For Save in folder, accept the default location or choose a folder of your choice in your ArcGIS Enterprise account.

The process completes.

At the top of the content list, the two image layers appear.

This tutorial uses Map Viewer Classic.

A new map opens in Map Viewer Classic.

For now, the map contains only the Topographic basemap layer.

The two imagery layers you created appear in the result list.

The layer is added to the map, even though you can't see it yet because you are too zoomed out. You'll add the second layer, then you'll zoom in.

In the Contents pane, the two imagery layers are now listed.

The map zooms in to the Taber-Barnwell region extent. The top imagery layer, Corn Fields After Storm, is visible.

You'll rename the layers, removing your initials (for instance, YN) for a cleaner look.

In the Contents pane, the layer now appears as Corn Fields Before Storm.

You'll now compare both images.

On the Corn Fields Before Storm image, you can see many fields; many are circular in shape, and others rectangular. The fields tend to be bright or dark green, because in August, before the storm, many crops are getting close to maturity.

At first glance, the Corn Fields After Storm image has some lighter tones in some areas. In particular, there are light-tone traces crossing the image in the northwest to southeast axis. However, it is difficult to gather more precise information about crop damage at this point.

• Click the down arrow next to Band_1 and choose Band_3.
• Click the down arrow next to Band_3 and choose Band_1.

This new band combination will display the red band through the red channel, the green band through the green channel, and the blue band through the blue channel.

The Corn Fields Before Storm image updates to a natural color display. Most strikingly, the bare soil is now brown.

Similarly, you'll update the Corn Fields After Storm display.

You can now return to inspecting the before and after imagery, looking at them in the natural color combination.

Next, you'll save the map.

• For Title, type Hail Damage in corn fields.
• For Tags, type Agriculture, Imagery, Damage assessment and press Enter.
• For Summary, type Assessment of the damage caused by a hail storm to corn fields in the Taber - Barnwell region. or a more detailed summary of your choice.
• For Save in folder, accept the default or choose a folder of your choice.

The Band Arithmetic raster function opens in the side pane.

• Under Select the input data and set parameters, for Raster, choose Corn_Fields_Before_Storm.
• For Method, choose SAVI.
• For Band Indexes, type B4 B3 0.5.
• Under Result layer name, type SAVI Before.
• For Save result in, choose a folder or keep the default.
• Uncheck Use current map extent.

After a few moments, the preview appears. The preview is satisfactory, so you'll proceed with running the tool to generate a result layer.

After some time, the new result layer appears in the Contents pane and on the map.

The white and light gray areas represent healthy vegetation. The darker areas represent unhealthy or dead vegetation, or barren land.

Similarly, you'll apply SAVI to Corn Fields After Storm.

The Band Arithmetic raster function opens in the side pane.

• Under Select the input data and set parameters, for Raster, choose Corn_Fields_After_Storm.
• For Method, choose SAVI.
• For Band Indexes, type B4 B3 0.5.
• Under Result layer name, type SAVI After.
• For Save result in, choose a folder or keep the default.
• Uncheck Use current map extent.

After some time, the new result layer appears in the Contents pane and on the map.

You'll compare the two SAVI layers.

More fields are represented in darker tones after the storm than before, which means that they have lower SAVI values and less healthy vegetation. However, it is still difficult to see whether some fields were more affected than others by the storm. Next, you'll compute the difference between the two SAVI layers to measure the change in vegetation more precisely.

The Compute Change raster function opens in the side pane.

• Under Select the input data and set parameters, for From Raster, choose SAVI_After.
• For To Raster, choose SAVI_Before.
• For Compute Change Method, verify that Difference is selected.
• Under Result layer name, type Loss of healthy vegetation.
• For Save result in, choose a folder or keep the default.
• Uncheck Use current map extent.

After a few moments, the preview appears, and it looks satisfactory.

After some time, the new result layer appears in the Contents pane and on the map.

The white and light gray tones indicate a loss of healthy vegetation (positive numeric values). A visual inspection indicates quite clearly that the hailstorm crossed this area diagonally in the northwest to southeast axis, damaging the most fields in that diagonal area. The fields in the upper right and lower left side of the image appear much less affected.

The Remap raster function opens in the side pane. You'll enter the parameter values.

You'll express with a remap rule that all negative values in the raster should be changed to 0. To make sure you capture all negative values, you'll use -2 as the minimum value.

Any raster cell values between -2 and 0 will be changed to NoData.

• Under Result layer name, type Loss of healthy vegetation cleaned.
• For Save result in, choose a folder or keep the default.
• Uncheck Use current map extent.

After a few moments, the preview appears, and it looks satisfactory.

After some time, the new result layer appears in the Contents pane and on the map. You'll change its symbology to better see the results.

• For Stretch Type, choose Percent Clip.
• Under Trim extreme pixel values, for Exclude top, type 1.
• Under Trim extreme pixel values, for Exclude bottom, type 1.
• For Gamma, use the slider to choose the value 1.25.
• For Color Ramp, choose the Blue Bright ramp.

The map updates.

In the new layer, the deep shades of purple indicate the deeper loss of healthy vegetation. The empty areas indicate no loss of healthy vegetation.

The field boundaries appear on the map, symbolized in red.

You'll now compute the average loss of healthy vegetation in each field, using the Zonal Statistics As Table raster function. For each Taber field boundaries polygon, the tool will calculate the mean value of all the Loss of healthy vegetation cells that fall within that polygon.

The raster function appears.

• For Choose zone raster or features, choose Taber field boundaries.
• For Zone field, verify that Field_ID is selected.
• For Choose value raster, choose Loss_of_healthy_vegetation_cleaned.
• For Ignore NoData in calculations, verify that the Ignore check box is checked.
• For Statistic type, choose Mean.
• For Result table name, type Vegetation loss table.
• Uncheck Use current map extent.

After some time, the new result table appears. This is a stand-alone table, not a layer. You'll take a look at the table's content.

The table opens. It contains one row for each cultivated field polygon. The Mean column provides the mean of healthy vegetation loss for each polygon.

You now need to join this table back to the Table field boundaries layer using the common Field_ID, and output the result as a new layer named Vegetation loss per field.

The tool opens.

• For Choose target layer, choose Taber field boundaries.
• For Choose layer to join to target layer, choose Vegetation_loss_table.
• For Select the type(s) of join, click Choose the fields to match.
• For Target field, choose Field_ID.
• For Join field, choose Field_ID.
• For Choose join operation, verify that Join one to one is selected.
• For Result layer name, type Vegetation loss per field.
• Uncheck Use current map extent.

After some time, the new result layer appears. It now contains the Mean vegetation loss value for each cultivated field.

• For Fill, scroll down, and choose the cream to dark red color ramp.
• Set the Transparency slider to 0%.

• In the color palette, choose a dark gray.
• Set the Transparency slider to 0%.

The map shows the average vegetation loss per field, the most affected fields appearing in dark red.

The fields that appear to have experienced the highest loss are clearly concentrated in the same northwest to southeast axis observed before. The fields with the lowest loss are mostly concentrated in the upper right and lower left corners.

You may observe some neighboring fields with different damage levels. This could have many causes. For instance, different crop types could be affected differently by a hailstorm. Also, two fields with the exact same crop, but each at a different level of plant maturity, could also be affected differently.

The map provides a first assessment of the damage caused by the hailstorm. It could be used to guide a more detailed inspection on the ground.

An optional next step would be to share the map you created with the farming community and other stakeholders.

The Function Editor appears. This is where you'll create your change analysis raster function template. You'll add the first raster function, Band Arithmetic, to the template.

The Band Arithmetic function appears in the editing window.

You'll now choose the Band Arithmetic settings, so that it computes the SAVI index.

• For Method, choose SAVI.
• For Band Indexes, type B4 B3 0.5.

You'll now give an informative name to this function. This function will be used specifically to process the before imagery, so you'll name it SAVI Before.

Next, you'll choose the function's input settings.

You declared the Raster input as public, which means that it will be exposed in the tool, and the user can choose the imagery layer that should go into that input.

The editing window updates showing the SAVI Before element.

You'll now add a second Band Arithmetic element to compute the SAVI on the after imagery.

• For Method, choose SAVI.
• For Band indexes, type B4 B3 0.5.

The editing window updates, showing the SAVI Before and SAVI After elements.

The editing window organizes all the elements in a pleasing order.

The Compute Change function appears. You'll draw arrows from the two Band Arithmetic functions to Compute Change, to indicate that the output of the two functions should be used as the input to Compute Change.

Both SAVI After and SAVI Before are now connected to Compute Change.

You'll now add the Remap function to the raster function template.

The Compute Change output will be used as the input to Remap.

As previously in the manual workflow, you'll remove the negative values.

The layout updates.

The raster function template is now ready. You'll save it and give it a name.

• For Title, type Compute loss of healthy vegetation.
• For Type, verify that Mosaic is selected.
• For Tags, type Agriculture, Imagery, SAVI, Damage assessment, and press Enter.
• For Folder, accept the default or choose your folder of choice.
• For Share, verify the level is set to Owner.

The Success window appears.

For now, you'll apply the template on the imagery in your current Hail damage in corn fields web map.

In the web map, the raster function template appears next to the map. It is ready to use on your data.

Note:

If at any point you want to go back to editing the raster function template, you can click the miniature window in the bottom corner of the map. When you are done, click OK.

• For SAVI After, select Corn_Fields_After_Storm.
• For SAVI Before, select Corn_Fields_Before_Storm.
• For Result layer name, type Loss of healthy vegetation cleaned 2.
• Uncheck Use current map extent.

The preview appears.

The preview is satisfactory, so you'll proceed with running the tool to generate a result layer.

After some time, the new result layer appears in the Contents pane and on the map. Running the Compute loss of healthy vegetation template, you went directly from the original imagery layers to the Loss of healthy vegetation cleaned 2 layer. Note that Loss of healthy vegetation cleaned and Loss of healthy vegetation cleaned 2 are identical, which is what you would expect.

Note:

This raster function template focuses only on the raster analysis part of the workflow. Optionally, you can apply the final steps of the workflow manually: symbolize Loss of healthy vegetation cleaned 2, apply Zonal Statistics as Table and Join Features, and symbolize the end result. You should obtain a layer identical to the Vegetation loss per field layer.

In the future, you could apply this same raster function template to other imagery. To retrieve this raster function template from a new web map, you would do the following. On the ribbon, click Analysis, Raster Analysis, and Browse Raster Function Templates. In the Custom Analysis Tools and Raster Functions window, click the down arrow next to System and choose My Content. In the list of raster function templates, locate the Compute loss of healthy vegetation template.