It’s hard not to have at least a grudging respect for the desert locust.
It has survived on Earth for millions of years, thriving in the heat and aridity
of the world’s most inhospitable deserts. For the most part, the insect
known as Schistocerca gregaria goes quietly about its inscrutable insect
business, a solitary and inconspicuous brown speck concealed in clumps of widely
scattered desert vegetation, subsisting on even the most noxious weeds when
Our admiration can only be carried so far, though, when this seemingly shy and inconspicuous insect reveals its surprising dark side. Throughout recorded human history, and surely long before, locust plagues have periodically poured forth from their arid confines and invaded areas where people live, farm, and graze their livestock. Their impact on human lives has often been so crippling that records of plagues have taken on religious significance and made their way into sacred and historical texts.
|A farmer in Madagascar watches a locust swarm form a menacing haze on the horizon. Locust swarms can destroy crops worth millions of dollars, and—more seriously—trigger famines among subsistence farmers. Working with the Food and Agriculture Organization of the United Nations, NASA scientists harnessed remote sensing satellites to help predict locust outbreaks. (Photograph Copyright Patrick Bollen)|
The Reach of the Desert Locust
Tens of millions of square kilometers of crops and rangeland in northern Africa,
the Middle East, and Asia are within the reach of the desert locust. The
livelihood of at least one-tenth of the world’s population can be affected
by the small insect with its voracious appetite. A single swarm can cover 1200
square kilometers (460 square miles) and can contain between 40 and 80 million
locusts per square kilometer. With each insect capable of eating its own body
weight (about 2 grams, or .07 ounces) in vegetation each day, a swarm that size
could consume 192 million kilograms of vegetation each day, or more than 423
million pounds. Now consider that in the last century alone, there were seven
periods of numerous plagues, the longest of which lasted intermittently for 13
Crop and rangeland damage due to locusts is a great threat to regional and
global food security, and efforts have been underway for decades to control the
formation of desert locust swarms. Over the years since World War II, the
United Nations Food and Agriculture Organization (FAO) has emerged as the leader
in the effort to predict, prevent, and control locust plagues. Along the way,
they have had help from remote sensing ecologist Jim Tucker at NASA’s
Goddard Space Flight Center. In the early 1980s, Tucker’s long-time
interest in desert ecology led him to collaborate with FAO scientists on using
satellite data to predict where and when locust plagues were likely to break
Locusts can consume roughly their own weight of vegetation each day—swarms of millions will strip crops bare in hours. (Photograph courtesy Compton Tucker, NASA GSFC)
During quiet periods, called recessions, locusts are confined to a 16-million-square-kilometer (6.2-million-square-mile) belt that extends through the Sahara Desert in northern Africa, across the Arabian Peninsula, and into northwest India. But when conditions are right (or perhaps ‘wrong’ would be the better word), swarms invade countries on all sides of the recession area, as far north as Spain and Russia and as far east as India and southwest Asia. As many as 60 countries can be affected. Swarms regularly cross the Red Sea between Africa and the Arabian Peninsula and are even reported to have crossed the Atlantic Ocean from Africa to the Caribbean. Monitoring locust habitat during recessions means monitoring a large, forbidding expanse of arid and semi-arid terrain, often in conflict-ridden, developing countries with little infrastructure or technology.
Normally, desert locusts live relatively solitary lives in the arid central Sahara, Arabia, and Persian Gulf regions (green). When conditions are right, however, they form swarms that migrate for thousands of kilometers (yellow). It is these migrating swarms that form plagues. (Map adapted from The Desert Locust in Africa and Western Asia: Complexities of War, Politics, Perilous Terrain, and Development, by Allan Showler)
Finding the Hot Spots
It was with these ideas in mind that Tucker and his colleague Jelle Hielkama, at
the FAO in Rome, pioneered a satellite-based approach to monitoring the desert
locust habitat in the early 1980s. The approach was based on a common satellite
indicator of vegetation called NDVI, for Normalized Difference Vegetation Index.
(The article Measuring Vegetation, NDVI
& EVI contains a thorough explanation of the remote sensing of vegetation.)
Vegetation has two key spectral characteristics: it absorbs light from the red
part of the electromagnetic spectrum for photosynthesis, and it reflects
electromagnetic energy from the near infrared (IR) part of the spectrum in order
to keep itself cool. If the satellite passes over an area that is heavily
vegetated, it should receive a reflected signal that is low in red light and
high in infrared light. If it passes over bare soil, the amount of red light
that is reflected increases significantly.
These relationships are calculated using a simple equation: the amount of red light received by the satellite subtracted from the amount of infrared light, divided by the combined signal received for both wavelengths. Values range from -1 to +1.
NDVI= (IR - red)/ (IR + red)
If the reflected infrared signal is high and red is low, then NDVI will be positive, signaling green vegetation. If no green vegetation is present, the amount of infrared light and red light reflected from the Earth’s surface will be comparable, and the NDVI will be near zero, signaling bare ground.
Among the first things Tucker and Hielkama needed to find out was how much spatial detail they would need in their satellite images to track significant changes in NDVI. With satellite data, there is always a trade off between how much spatial detail is contained in the data, called spatial resolution, and the area that can be seen at one time, called spatial coverage. The greater the resolution, the smaller the spatial coverage, and the more time it takes to obtain information on a big area like the desert locust recession area. In the desert, rainfall is unpredictable, and the impacts can be fleeting. If you had to wait two or three weeks to see the same area twice, you might miss an important change.
“Given the technology available in the 80s—before worldwide digital networks and before everybody had powerful computers for data processing—we knew that to create something operationally useful, it would have to be simple, both conceptually and technically. It also needed to be low-cost,” says Tucker. Since a higher amount of detail leads to bigger data file sizes, the researchers would want to pick the lowest, or coarsest, spatial resolution data they could while still capturing the important information.
At that time, scientists were already using Landsat data to map ecological
conditions throughout the world, and on first glance that might have seemed a
natural choice. But Tucker knew that to cover the entire recession area using
30-m-resolution data from the Landsat Thematic Mapper would require acquisition
of at least 700 separate scenes. At $2800 a scene, that option was well outside
practical operating costs. Not to mention, Landsat typically only imaged the
exact same area once every 16 days (under favorable, cloud-free conditions),
which is not frequently enough to detect the more fleeting changes in desert
vegetation. To make the project work, Tucker and Hielkama’s first task was
to demonstrate that adequate monitoring could be done with coarser,
1-km-resolution data available from the National Oceanic and Atmospheric
Administration’s Advanced Very High Resolution Radiometer (AVHRR).
|Normalized Difference Vegetation Index data from satellites show the greening of the desert. In August and September 2001, patches of vegetation appeared in the desert of Mali and Niger. Satellite observations occur regularly even over the most remote areas, allowing locust control organizations to monitor potential outbreaks. (Image by Robert Simmon, NASA GSFC, based on SPOT Vegetation Products)|
First, they selected numerous test sites throughout desert regions in Mali and Algeria in Africa and the Thar Desert in India and Pakistan that were known to be high-frequency breeding sites for the desert locust. Then they put on their safari hats and took to the desert, collecting ground-based observations of vegetation cover at the sites, some of which were wadis, or water channels. It was these sites that represented the greatest challenge for coarse resolution data. Having vegetation clumped together in a wadi is a perfect way to bring sufficient numbers of locusts close enough together for them to begin rubbing each other the wrong way. Their field work showed that wadis were anywhere from 30 meters to 1000 meters wide (98 to 3281 ft). Would 1 kilometer AVHRR data be sufficient to track changes there, or would it be like looking for a needle in a haystack?
To demonstrate that their idea would work, Tucker and colleagues created a map
of the test sites using 30-meter-resolution data from Landsat. With the wadi
channels well identified on the satellite imagery and confirmed by field work,
the scientists created a grid overlay simulating the data that would come from
AVHRR. The map revealed that although in many cases the green vegetation took
up so little of an AVHRR-sized grid box (or pixel of data) that it might be
missed by the sensor, a sufficient number of grid boxes were at least 40 percent
filled with vegetation, which would be enough to detect significant change in
the amount of vegetation in the desert locust habitat.
|Water in the desert flows through channels called wadis. These wadis are often green with life when the wider expanses of desert are bone dry and barren. (Photograph courtesy Compton Tucker, NASA GSFC)|
“There was opposition initially to the use of coarse resolution data. The important point we were trying to make was that with the frequent satellite coverage of the area, there was a lot of remotely sensed information over time. It matters less if conditions are changing at lower resolution, and more that conditions are changing over time.” Tucker showed that if they used high-resolution data initially to map out the terrain and locate wadis or other small patches of vegetation, then they could recognize the changes in vegetation that appear over time in the coarse resolution data, even if they were somewhat subtle.Signs of Good Breeding
An Insect’s Alter Ego
Dry wadis appear beige against a darker background in this satellite image of the Sahara desert near the border of Mauritania and Algeria. Each pixel is 30 by 30 meters. The grid overlay represents the 1-km resolution of the Advanced Very High Resolution Radiometer data used for monitoring locusts by the FAO. AVHRR data show vegetation in channels that cover 40 percent or more of a pixel. (Image by Robert Simmon, NASA GSFC, based on Landsat Thematic Mapper data archived by the Global Land Cover Facility)
Signs of Good Breeding
Now that they had proven that coarse-resolution data could be used for routine monitoring, the next step was to streamline the process even further to make it useful to ground survey crews and resource managers in remote and low-tech sites. Hielkama decided to come up with a way to combine the daily NDVI values in any given area into a single locust indicator.
“In the beginning,” says Tucker, “we were processing the data here at Goddard every ten days. Because there was no digital network, we created the vegetation maps here and took photos of them. Then I got in my car and drove into downtown D.C. to the FAO office where the photos would go into the FAO diplomatic pouch to be transmitted overnight to Rome and FAO. This went on for almost seven years.” The results would be interpreted by Tucker’s colleagues in Rome, who sent the information out via teletype machines. They needed to simplify the information as much as possible.
One approach to combining NDVI data is simply to average all the NDVI values within a certain area. The trouble with that is that when the changes you are looking for occur on a small scale, such as in a narrow wadi surrounded by desert, averaging might cause your vegetation to “disappear.” Instead, Hielkama developed what he called the Potential Breeding Activity Factor. This breeding factor considered the NDVI value of each pixel in a given area and placed it into one of four categories from high NDVI to low. Then he created a weighted score for all the pixels in the area by multiplying the total number of pixels in each category by a different number. The highest NDVI category was multiplied by 1000, the next by 100, the next by 10, and the lowest category by 1. All these scores were added up and divided by the total number of pixels in the area. By assigning progressively more importance to areas with higher amounts of vegetation (higher NDVI), the calculation produced a measure of how likely a particular area was to become a hot spot for plague formation. The key advantage was that it reduced the volume of data that needed to be transmitted operationally.
To demonstrate that their streamlined approach worked, the researchers applied their remote-sensing techniques to a retrospective analysis of a locust plague that had occurred in West Africa between 1980 and 1981. Using even coarser, 4-km-resolution data from the NOAA-6 AVHRR instrument, they calculated the potential breeding factor for numerous areas in the southern Sahara Desert in West Africa where locust upsurges were known to have begun.
Comparing their results to rainfall observations and ground surveys of locust populations conducted during the upsurge, the scientists found that not only did the breeding factor calculated using the coarse-resolution data correlate with the documented field locations of upsurge populations, but they also discovered that the initial upsurge was quite likely localized to a small area. Controlling the upsurge in this 600-square-kilometer (230-square-mile) area with spraying would likely have been very successful at preventing a plague that lasted several months and ultimately affected 600,000 square kilometers (230,000 square miles)!Locust Recon Today
Finding the Hot Spots
Tucker and Hielkama used AVHRR data from 1982, which corresponded with a locust outbreak, to validate their observations. These images show a ‘flush’ of vegetation (orange and yellow), which appeared and subsided in only a month. (Images courtesy Compton Tucker)
Locust Recon Today
Since the early 1980s, the collaboration between Goddard and the FAO has continued. Satellite data combined with weather information, aerial surveys, and ground truth information have been used operationally by the FAO’s Global Information and Early Warning System and more recently by the Desert Locust Information Service, operated by the FAO Plant Protection Service. The Desert Locust Information Service provides the data on their web site and uses them in creating their weekly bulletins on the latest locust situation in the desert locust recession area. Over the 20 years since the project was first initiated, the collaborators have made use of routine data from several generations of satellites: the Landsat series, the AVHRR series, France’s SPOT satellites, and the Sea-viewing Wide Field-of-View Sensor (SeaWiFS). In 1999, the FAO signed an agreement with NASA headquarters to receive data from the Moderate Resolution Imaging Spectroradiometer (MODIS) flying aboard NASA’s Terra satellite as part of its ongoing environmental monitoring in the desert locust recession area.
Says Tucker, “Remote sensing of vegetation in the desert locust habitat is now the fundamental reconnaissance source for monitoring and predicting upsurges and swarms.” Given that most control methods involve chemical pesticides, identifying potential outbreaks early in development decreases the volume of pesticides required and minimizes collateral environmental contamination. The near-daily coverage provided by the most recent generation of satellite sensors, including MODIS, should be advantageous considering that a benign, solitary-phase desert locust can switch to the swarm-forming gregarious phase with as little as four hours of “rubbing elbows” with its neighbors. Getting these locust-fighting tools into the hands of the people who need them still presents some challenges in the face of civil unrest throughout the desert locust recession area, but Tucker is confident in the ability of satellite data to prevent future devastation. “With modern remote sensing capabilities and today’s advanced communication networks,” he says, “desert locust plagues should be a thing of past.”
Hielkama, J.U., Roffey, J., Tucker, C.J. (1986). Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data. International Journal of Remote Sensing, 7 (11): 1609-1622.
Sword, G.A., Simpson, S.J., 1,.El Hadi, O.T.M., and Wilps, H. (2002) Density dependent aposematism in the desert locust. Proceedings of the Royal Society of London B, 267:63-68.
United Nations Food and Agriculture Organization Locusts and Migratory Pests Group Web Site. Accessed online August 2002.
Britain’s Biotechnology and Biological Science Research Council. One of the crowd: The amazing biology of the desert locust. Accessed online August 2002.Signs of Good Breeding
The FAO Desert Locust Information Service now publishes regularly updated maps showing the distribution of locusts based in part on satellite imagery. (Map courtesy FAO Desert Locust Information Service)