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Modeling 2020 Desert Locust Outbreak in East Africa

08 May 2020

Gro’s 2020 Desert Locust Outbreak Model estimates where locusts are and the severity of their impact in each district. The model compares this season’s change in NDVI to average historical change at the district level to identify any significant impact to vegetation. We also quantified the areas impacted through a pixel-level analysis, which is then aggregated to the district level.

Locust Impact Based on NDVI

NDVI stands for Normalized Difference Vegetation Index, a satellite-based measure of plant greenness indicative of vegetation health. In this case, we used MODIS NDVI, which is reported every eight days and can signal stresses to plant health as much as two weeks before problems are visible to the naked eye.

To identify the districts in East Africa that have been impacted by locusts this year, we examined two metrics that analyze the decrease in NDVI compared to historical averages to see where there was a statistically significant difference. If a district met either metric, it was considered to be impacted by locusts.

The first metric calculated the difference between the maximum NDVI observed since December 1, 2019, and the average of the three most recent NDVI observations. We compare this number to the historical average. The greater the difference between this year’s number and the historical average, the greater the impact locusts have had on that district.

The second metric looked at the slope of a linear regression fit of NDVI readings. If the slope of the line in a particular district was steeper than the historical average by a statistically significant factor, the district was considered impacted by locusts.

Analyzing Locust Impact on Cropland at the District Level

Once we identified the districts impacted by locusts, we summed the districts’ areas to get the total area impacted by locusts for each country. We also computed the total cropland area impacted by locusts by summing the cropland area of these districts, using data from the Global Cropland Area Database (GFSAD). While we typically develop our own crop masks at a 30-meter resolution using satellite imagery, we used an external source for this model due to time constraints.

Using NDVI as a proxy for the leaf area, or the amount of biomass, we were able to identify the severity of locust damage by estimating the amount of NDVI drop that was greater than historical averages.

You can see the results of this version of the model in the Gro Platform here.

Analyzing Locust Impact on Cropland at the Pixel Level

We also performed a pixel-level analysis of NDVI data, and applied the first metric described above to identify the pixels showing locust impact. Using Google Earth Engine, we overlaid these areas with a cropland mask from the Global Cropland Area Database (GFSAD).

This approach provides a more granular picture of cropland impacted by locusts than the district-level estimate in the model above, which assumes that all cropland in a district impacted by locusts is affected. By aggregating the areas of individual affected pixels, we arrive at a bottom-up, rather than top-down, estimate of the total cropland area impacted.

Beyond Somalia, Kenya, and Ethiopia, the pixel-level analysis also includes Eritrea, Uganda, Iran, Yemen, Saudi Arabia, Sudan, South Sudan, and Pakistan.

Click on the image below to interact with the display in the Gro web app.

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