Satellite environmental monitoring for migrant pest forecasting by FAO: the ARTEMIS system

1990 ◽  
Vol 328 (1251) ◽  
pp. 705-717 ◽  
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
European Space Agency ◽  
Satellite Communications ◽  
Digital Data ◽  
Computer Hardware ◽  
Desert Locust ◽  
Food And Agriculture ◽  
Noaa Avhrr ◽  
Avhrr Data

Since 1975, the Food and Agriculture Organization of the United Nations (FAO) has been pioneering the development of the use of satellite remote sensing techniques for improving the surveillance and forecasting capabilities of the centralized Desert Locust Reporting and Forecasting Service at FAO Headquarters and, indirectly, those of Regional Organizations and National Plant Protection Services. On the basis of findings from experimental activities on the use of Landsat and NOAA satellite AVHRR data for Desert Locust habitat detection and monitoring through vegetation assessment, and the use of Meteosat data for rainfall monitoring, FAO defined an operational system for satellite environmental monitoring in support of the FAO Desert Locust Plague Prevention Programme and the FAO Global Information and Early Warning System on Food and Agriculture. The system, African Real Time Environmental Monitoring using Imaging Satellites (ARTEMIS) is an advanced computer hardware and software configuration, equipped for direct acquisition of hourly Meteosat digital data and for automated thematic processing of Meteosat and NOAA AVHRR data for large area precipitation and vegetation condition assessment, being the key environmental factors for supporting Desert Locust population development. Since August 1988, the ARTEMIS system has generated a number of operational products documenting the occurrence of rainfall and vegetation development in the Desert Locust recession area on a 10-day and monthly basis at spatial resolutions varying from 7.6-1.1 km. These products are being used by the FAO Emergency Centre for Locust Operations (ECLO), along with synoptic weather and locust data, for the preparation of the bulletins containing the Desert Locust situation summaries and forecasts. For making ARTEMIS output products and other relevant data available in a timely manner at regional and national levels, a dedicated satellite communications system, Data and Information Available Now in Africa (DIANA), is currently being developed by the European Space Agency in cooperation with the FAO Remote Sensing Centre. The DIANA system will, by mid-1991, provide a capability for high speed (64 kb s -1 ) two-way transfer of facsimile images of documents and maps, character- coded text documents and digital images in raw or processed form from computers at FAO Headquarters to personal computer based terminals of recipients, initially in Africa, by using the commercial Intelsat satellites.

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

2018 ◽  
Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs
Keyword(s):  
Remote Sensing ◽  
Radio Astronomy ◽  
Phased Arrays ◽  
Satellite Communications

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

Integrating remote sensing and machine learning into environmental monitoring and assessment of land use change

2021 ◽  
Vol 27 ◽  
pp. 1239-1254
Author(s):  
Hong Anh Thi Nguyen ◽  
Tip Sophea ◽  
Shabbir H. Gheewala ◽  
Rawee Rattanakom ◽  
Thanita Areerob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Environmental Monitoring

scholarly journals Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

2021 ◽  
Vol 11 (13) ◽  
pp. 5911
Author(s):  
Vanesa Martos ◽  
Ali Ahmad ◽  
Pedro Cartujo ◽  
Javier Ordoñez
Keyword(s):  
Remote Sensing ◽  
Sustainable Agriculture ◽  
Information Acquisition ◽  
Industrial Revolution ◽  
Structural Parameters ◽  
Special Focus ◽  
World Population ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Zero Hunger

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

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