Locust Watch

Earth observation

Locust monitoring using satellites

Currently available satellites cannot directly detect individual locusts or locust swarms. Some highly sophisticated satellites used by the military and forthcoming civilian satellites could potentially detect locust swarms but these images are not yet available. Current satellites can provide continuous estimates of rain-producing clouds and ecological conditions, such as vegetation development, which are important factors for monitoring Desert Locust habitats and forecasting locust development. The temporal, spectral and spatial characteristics of the sensor instruments onboard these satellites provide a wide range of sensing capabilities.


Rainfall estimates

DLIS uses rainfall estimates derived from METEOSAT, mainly infrared and visible channels, to understand better the spatial and quantitative distribution of rainfall in the Desert Locust breeding areas. Although imagery are available every 15 minutes and estimates every three hours, DLIS uses daily 24-hour cumulative estimates as well as decadal estimates of rainfall processed by Columbia University's International Research Institute for Climate and Society (IRI). DLIS combines satellite-derived estimates with those that originate from meteorological models. Whenever possible, these are verified with ground data.


Vegetation estimates

DLIS used to rely on 1 km resolution SPOT-VGT imagery to monitor ecological conditions in the breeding areas of the Desert Locust. Although the sensor is specifically designed for vegetation monitoring, it became clear that it is difficult to detect the sparse vegetation in the desert – vegetation that appears to be dry to the satellite yet sufficiently green for Desert Locust survival and breeding. Consequently, DLIS turned to higher resolution imagery, that of 250 metre resolution MODIS, consisting of 16-day cumulative images. Analysis of individual channels provides an even more accurate estimation of ecological conditions in Desert Locust habitats. Whenever possible, these are verified with survey results. DLIS is now shifting to 20 metre resolution Sentinel-3.


Soil moisture estimates

DLIS collaborates with NASA and Lobelia to produce two products on an operational basis that estimates soil moisture that is required for successful egg laying and hatching by Desert Locusts. The Lobelia product is the result of the SMELLS SM project that developed average soil moisture from daily estimates at 1 km spatial resolution during the most recent decade based on disaggregated SMOS soil moisture product. Typical surface soil moisture values are from 0-0.05 m3/m3 (dry) to 0.5 m3/m3 (wet). Relevant ranges for Desert Locust monitoring are 0.10-0.20 m3/m3.


Current research and progress

FAO DLIS collaborates with a variety of universities and other partner institutes such as the Italian Institute of Biometeorology (IBIMET), the European Commission Joint Research Centre (JRC), Columbia University's International Research Institute for Climate and Society (IRI), NASA's World Wind Project, and the Catholic University of Louvain (Belgium) in improving the application of remote sensing imagery for Desert Locust monitoring and forecasting.

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See also
Modelos

(inglés)

Estimación de las tasas de desarrollo y la migración de las langostas, modelos 3D y respuesta óptima.

Sistema de información geográfica (GIS)

(inglés)

Sistemas de información geográfica RAMSES (Sistema de reconocimiento y gestión del entorno de la Schistocerca) y SWARMS (Sistema de gestión de las alertas sobre la Schistocerca).

Operaciones de emergencia

(inglés)

EarthRanger: seguimiento de las operaciones aéreas de inspección y control.

Drones

(inglés)

dLocust: dron de gran alcance para la elaboración de mapas sobre la vegetación y la detección de langostas.

Herramientas digitales

(inglés)

Conjunto de instrumentos eLocust3: transmisión de datos en tiempo real del terreno a los centros nacionales de lucha contra la langosta.

Predicciones climáticas

(inglés)
Utilización de predicciones a seis meses sobre las precipitaciones y la temperatura para prever el desarrollo de langostas.