Full text: Remote sensing for resources development and environmental management (Vol. 2)

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
Operational satellite data assessment for drought/disaster 
early warning in Africa: Comments on GIS requirements 
Hubertus L.Bloemer & Scott E.Needham 
Ohio University, Athens, USA 
Louis T.Steyaert 
NOAA, NESDIS/AISC, Columbia, Mo., USA 
ABSTRACT: The National Oceanic and Atmospheric Administration (NOAA/National Environmental Satellite Data and 
Information Service (NESDIS) Assessment and Information Service Center (AISC) has developed operational climate 
impact assessment for improved drought/disaster early warning in semi-arid regions of Africa. The system is 
based on daily United States NOAA polar orbiting, Advanced Very High Resolution Radiometer (AVHRR) satellite 
data used in combination with ten-day rainfall reports from ground stations throughout the region. 
The current assessments are prepared using a "light table" G.I.S. approach for map/image overlay and 
statistical time series analysis. As part of the United States Agency for International Development's (AID) 
funded project, NOAA/AISC and Ohio University developed and defined requirements for a cost effective, reliable 
computer based G.I.S. In addition to describing the assessment process, computer hardware and software 
considerations to meet the needs of the spatial analysts are discussed. Remote sensing data processing and 
G.I.S. capabilities are assessed according to various data handling proficiency and applicability of data. 
This includes considerations of a variety of computer systems currently available, including "turn-key" 
stations with G.I.S. packages as well as a comparison of the obtainable G.I.S. software packages for different 
types of data sets. 
The National Oceanic Atmospheric Administration 
(NOAA), National Environmental Satellite Data, and 
Information Service (NESDIS) Assessment and 
Information Service Center (AISC) has developed a 
reliable and cost-effective program to support 
disaster early warning and technical assistance 
objectives of the Agency for International 
Development (AID). The operational Early Warning 
Program was developed at the request of the AID 
Office of U.S. Foreign Disaster Assistance (OFDA) 
and in cooperation with the University of Missouri- 
Columbia, US/AID Missions, and host countries. 
Qualitative climatic impact assessments routinely 
provide early warnings of weather impacts on 
subsistence agricultural crops and the potential for 
drought caused food shortages with a lead-time of 3- 
6 months before socio-economic impacts occur. 
Recent advances in the operational use of daily NOAA 
polar orbiting meteorological satellite data for 
agricultural assessment have significantly upgraded 
the existing system which is based primarily on 
daily weather station reports. Global 
meteorological satellite information increases the 
spatial resolution for analysis and presents an 
opportunity to incorporate a Geographic Information 
Systems approach into the assessments. This paper 
comments on some of the GIS requirements for the 
upgraded assessment system. 
Since July 1979, AISC has issued weekly and monthly 
assessments of climatic impacts on food security for 
developing countries in the Caribbean Basin, Africa, 
South and Southeast Asia, and more recently, the 
South Pacific Islands, Central America, and the 
Andes countries of South America. Decision makers, 
planners, and economists are provided with timely, 
reliable information based on continuous monitoring 
of environmental conditions. The AISC assessment 
cables are sent through NOAA international 
communication facilities to American Embassies, AIE 
including overseas missions, the U.S. Department of 
Agriculture, and various United Nations agencies 
(e.g., Food and Agriculture Organization). Climatic 
impact on potential food supplies, subsistence crop 
conditions and field operations are assessed. 
Information on crop calendars, estimated soil 
moisture, and unusual or severe weather events such 
as flooding, high winds and other meteorological 
extremes are reported. If available, information 
from ancillary sources is included to supplement 
AISC analysis. The result is a cost-effective 
increase in the lead-time for planning of food 
assistance strategies and mitigation measures to 
reduce the adverse impact of climate. 
In addition, AISC prepares special assessment 
reports for AID, e.g.: the 1981/82 drought impact 
in Bangladesh, Sri Lanka, Malaysia, Tanzania, 
Botswana; the 1981/83 drought problems in Nepal, 
Philippines, Ethiopia, Somalia, Uganda, Sudan, Sahel 
Region, southern Africa, Haiti, NE Brazil and the 
South Pacific; and more recently Kenya, Tanzania and 
Sahel countries. These were used by AID as one 
input to estimate the magnitude of drought impact, 
potential food shortage deficits and/or disaster 
assistance needs. The Early Warning Program is 
based on weekly rainfall/weather analysis and 
climatic impact assessment models for more than 350 
agroclimatic regions (i.e., regions which are 
generally homogeneous with respect to agricultural 
crops and climatic type). Regional rainfall 
estimates are determined from ground station reports 
received through an international communications 
network. Satellite cloud data (e.g., METEOSAT) are 
used to improve the accuracy of precipitation 
estimates, particularly in those regions where 
weather data are sparse and unreliable. Weather 
data are then interpreted by regional agroclimatic 
indices which indicate potential crop production in 
relative terms. Finally, climatic impact and the 
potential for abnormal food shortages are identified 
from these indices. 
Agroclimatic/crop condition indices are based on 
derived climatic variables (e.g., soil moisture, 
plant water deficit and moisture stress) which 
directly determine the plant's response to 
environmental conditions, hence productivity. The 
selection of the regionally appropriate indices is 
in part determined through the use of episodic event 
data. For example, candidate indices are

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