OBJECTIVES:

1. Explain how remote sensing can be used to detect storm damage.

2. Describe how using remote sensing to detect storm damage can beneficial.

3. Explain how using remote sensing can benefit both farmers and the environment.

INTRODUCTION:

Remote Sensing can be used in agriculture to determine storm damage. For this tutorial we will use hail as the source of damage. Hailstorms destroy about 3% of crops grown in the prairies each year. These devastating storms can sweep across a field at 20 mph destroying all crops and vegetation in its wake (Fig. 1A vs. B). In response to the prospect of hail, many farmers carry hail insurance. Hail insurance coverage varies. However, in the advent of a severe hailstorm, hail insurance can be a lifesaver because the producer will recover some economic loss even though the crop may be completely destroyed. Traditional methods of claims adjusting are both time and labor intensive. Below is a picture of a severe hailstorm that struck near Britton, SD on 07/03/03. Damage to the soybean crop is shown in Fig. 1B.

USE OF IMAGERY

Remote sensing was obtained on 06/29/03 and 07/05/03 from Sky Hawk Consulting, Vermillion SD. The fields are shown in Fig. 2 and 3; entire field area is 320 acres (A). Soybean field is located on north 160 A and wheat field on south 160 A. Vegetative indexes of the fields were created as shown in Fig. 2 and 3. On this scale, more green equals better vegetation. In Fig. 2, the soybean field appears as to pink to dark red and the wheat field appears bright green. After the hailstorm, in Fig. 3, notice the reflectance of the soybean field has moved down the scale to brown and tan and the wheat field has changed to pink and red.

ECONOMIC BENEFITS

Remote sensing imagery may assist farmers in adjusting hail insurance claims by:

1. Documenting that crops were growing well before the storm.
2. Determine number of acres hail damaged.
3. Documenting damage after the storm.

Documenting damage is important when determining a financial solution as in the case of hail settlements. Landsat data can be used as a good source of 'before' imagery. The imagery is collected continuously every 16 days over the entire US. Hailstorms are unpredictable and Landsat may serve as a good data source for a 'before picture' after a storm.

To document after storm damage, the 'after picture' should be collected as soon as possible after the storm. This may be important because depending on storm extent and number of claims, plant re-growth may occur before the hail adjuster arrives. Using Landsat to document crop damage is not just limited to hail. Landsat before and after imagery may be used to document crop damage from other natural disasters such as heavy rains, flooding, fire, or even drought.

ACCURACY (Owen Chandler, Armando Apan, Rod Pullinger, and Ken Bullen)

There are several limitation and variations that can make a difference in the traditional form of crop assessment.
· It is difficult to provide accurate and reliable analysis (uses the "science of estimation")
· Variations between assessors can exist.
· It is often difficult to view the entire area due to size or access constraints
· It can be sometimes difficult to gain access to the property due to uncontrollable reasons
· Travel to claims area takes both time and money
· The overpayment and underpayment of claims

Remote sensing and GIS are techniques that are potentially useful in processing crop damage for loss adjustment. The following benefits can be expected through the use of remote sensing and GIS maps.

· Potentially scientifically more accurate
· Minimal variations between assessments
· Potentially improves efficiency
· Potential cost savings
· Ability to assess entire fields easily

When used together traditional crop assessment and remote sensing provide useful and accurate results.

Figure 4. The area calculations for five different fields from three different sources of calculation. Most adjustments are fairly close except for in fields one and four. The "Claim" is the area determined by claims adjustment, the light yellow is the estimate by the farmer, and the light blue is the calculated loss by remote sensing.

Remote sensing in some cases has been found to provide accuracy between 5% and 30% differences when compared to traditional methods. It can be used as an excellent method in combination with traditional hail adjustments to obtain the best results.

Figure 5. Behavior of sorghum before, during, and after hailstorm.

Generally, the stage of growth and the degree of damage to the plant determine yield loss predictions. Defoliation, the destruction of the plants leaves, affects the plants ability to effectively convert sunlight into energy through photosynthesis. Therefore, the loss in leaf area causes the plant to become stressed. These stresses cause damages that alter the properties of the plants and leaves, which change the way the plant reflects light. The percentage of defoliation is most accurate between 7 and 10 days after the storm. Then the most accurate window to collect damage data is between day 10 and 14. This provides time for the plant to show the signs of stress. These signs of stress are seen and measured by remote sensing satellites as in figures 2 and 3. After 28 days the plant has grown back some of what it is able too after being damaged.

This satellite overview shows crop damage over large areas. The white arrow points to an area of damaged crops where a hailstorm hit. Large areas are easy to track and measure using remote sensing.

Figure 6. courtesy of Trent University

FLOOD DAMAGE

Figure 7. on the left shows flood damage in Manitoba, Canada. Area A designates one area of the flooding, area B designates the branch out to the right, area C is the left stream branching out and area D is the right stream branching out. This larger scale flooding can also be applied to storm damage on a field. Heavy rains can cause areas with poor drainage or shallow depressions to fill with water and damage crops. This crop loss due to storm damage can be measured and easily adjusted for in both small and large areas.

Figure 7. courtesy of Trent University

Figure 9. A GIS map gives fire crews the longitude and latitude of a small fire that also needs to be put out.

FIRE DAMAGE

Another natural disaster that can influence agriculture is a fire. Thousands of acres of forest and farmland are lost to fire. Figure 9 on the right shows a fire in Montana. Notice the bright yellow spot in the upper right. This area shows another fire that without satellite imagery may have been overlooked when compared to the big area and caused more damage before being put out. Similar application can be used on a field that is burned. The random and uncontrollable pattern of fires makes it hard to determine exactly how much crop was lost to fire. By drawing lines on remote sensing images, shapes can be created and the area calculated by the computer. This provides timely accurate information. The burned area can also be compared to unburned areas of that same field and potential yield loss can be calculated.

Figure 11.(left): Drought impact

DROUGHT

In the Midwest drought is also another way crop damage can occur. If areas of fields do not receive water when needed crop growth may be stunted and yields will be lower. Figure 11 on the left displays an image of a drought-impacted area. Maps such as these can then be used in combination with "ground truthing" to efficiently identify damages for insurance adjustments or other necessary information.

QUESTIONS

Download and print questions here

1. How much of the crops grown on the prairie are destroyed each year by hail?

2. How does the soybean field change from figure 2 to figure 3?

3. How does the wheat field change from figure 2 to figure 3?

4. Explain how a farmer can use the data from figures 2 and 3.

5. In what three ways can remote sensing assist farmers?

6. What are some of the limitations in traditional crop adjustment techniques?

7. What are some of the possible benefits of remote sensing data?

8. In figure four, which field shows the greatest difference between the different methods?

9. In Claim one, which of the three estimates the highest amount of damage? Which is it the lowest?

10. What is the best way to get useful crop damage information?

11. In some cases the percentage between the calculated value and the claims value has differed between what two percentages?

12. When is the percentage of defoliation most accurate?

13. When is the most accurate time frame to collect the data from hail damage?

14. How can remote sensing be used to detect flood damage?

15. In what ways can remote sensing be used to detect fire damage?

16. How can remote sensing detect damage caused by drought?

17. How can remote sensing detect problems with irrigation or other problems in a field?

MAP SECTION

1. Right click on the following link and select open in new window. Map page

2. Select both hyperlinks as visible. The active layer is not important.

3. Select Moody, July 17 2002, before the storm as a visible layer. Refresh the map.

4. Now unselect before the storm and select August 18 2002, after the storm. Refresh the map.

5. Use the tool to determine how many acres the field is for question three.

MAP QUESTIONS

1. What changes do you observe from the before storm picture to the after storm picture?

2. What changes do you observe from the before storm falsely colored map to the after storm falsely colored map?

3. How many acres are in the field?

4. If the farmer lost an average of 70 bushels per acre from the storm how many bushels (to the nearest bushel) did he lose?

5. If corn is selling for $2 per bushel, how much money would he have lost from question 4?

EMAIL QUESTIONS (Message board not available)

1. Have you ever suffered crop damage from storm, wind, flood, fire, drought or any other natural disaster?

2. Did you use a satellite image before or after the storm? If so, how?

3. Make up a third question as a class or small group.