Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856641294

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 5 Non-renewable resources: Geology, geomorphology and engineering projects. Chairman: J. V. Taranik, Liaison: B. N. Koopmans

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Operational satellite data assessment for drought/disaster early warning in Africa: Comments on GIS requirements. Hubertus L. Bloemer & Scott E. Needham, Louis T. Steyaert

Document type:: Multivolume work

Structure type:: Chapter

561 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. 1 INTRODUCTION 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. 2 BACKGROUND ON EARLY WARNING PROGRAM 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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