Applying Space Technology in South Africa for Early Warning and Decision Support system : a potential for Disaster Management.

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1 Applying Space Technology in South Africa for Early Warning and Decision Support system : a potential for Disaster Management. B M Petja, H.A.Mudau, P E Frost and Mellaart, E Institute for Soil Climate and Water, Agricultural Research Council (South Africa) Abstract. The Institute for Soil, Climate and Water developed an early warning and decision support system funded by the National Department of Agriculture. The system went operational in October The system is refered to as Umlindi ( Zulu word for the watchman) and provides information on fire, vegetation condition, water satisfaction index (crop yield prediction and drought monitoring model) and rainfall data. The system depends NOAA AVHRR Satellite data from 1985 up to date and rainfall data from 1920 up to date archived at the Institute for Soil, Climate and Water. There are some attempts to apply the system for the disaster management. Introduction South Africa s climate is characterised by severe spatial and temporal fluctuations in rainfall. This affects issues relating to food security, crop production and general agricultural economics. Policy makers and decision makers are often required to respond to drought and other disasters without having sound scientific information available. Therefore the Umlindi system attempts to inform decision makers of the current drought condition, fire risk and vegetation condition based on the interpreted NOAA satellite data and climate data. Umlindi is the Zulu word for the watchman. The system is also being expanded for use in disaster management. However the Umlindi system proved to be useful in predicting the yield of maize crops against the predicted extent of drought. The methodology The Umlindi system attempts to inform decision-makers of the current drought condition, fire risk and vegetation condition. This information is based on interpreted National Oceanic and Atmospheric Administration (NOAA) satellite and climate data. The data are processed in Arcview and Chips for Windows to derive the following four products. The procedures are fully automated with Avenue scripts. Vegetation condition The NOAA-AVHRR channel 1 and 2 data have been found sensitive indicators of the presence and condition of green vegetation. The Normalised Difference Vegetation Index (NDVI) is computed from the equation: NDVI = (IR-R)/(IR+R) Where: IR = Infra red band (Channel 2) and R = Red band (Channel 1) NDVI images describe the amount of green vegetation present. A dekadal NDVI image shows the highest possible greenness values that have been

2 measured during a ten-day period. Vegetated areas will generally yield high values because of their relatively high near infrared reflectance and low visible reflectance. For better interpretation and understanding of the NDVI images, Temporal image differencing approach for change detection was used in this case. NDVI images of this year (2001) were compared to the long term average ( ) in order to indicate how vegetation has changed over the given time. Processed satellite image to show the vegetation activity. The Normalised Difference Vegetation Index (NDVI) images are derived from the red and mid infra red bands of NOAA images using Chips. The image derived from Chips for Windows is then displayed according to the required legend with Arcview and exported to the web site. Fire mapping The sites where active fires occurred in a ten-day period. The active fires are derived from the mid infra-red band using Chips. The image derived from Chips for Windows is then displayed according to the required legend in Arcview and exported to the web site. The ARC-Institute for Soil, Climate and Water is the Southern African node for the World Fire Web. Active fires are detected on a daily basis and sent to an intern server that is available at Rainfall Rainfall for a ten-day period interpolated between the available station data. Arcview Spatial Analyst is used to interpolate the rainfall with the following procedure: Calculate percentages by dividing the rainfall value with the long-term average for each station. o Use Inverse Distance Waiting (IDW) to interpolate these o percentages. Multiply the percentage map with the long-term average map. The long-term average rainfall surfaces are very accurate. Attributes such as altitude, slope, aspect, distance to sea, position in local terrain, rain shadow effects, etc. were used to create these maps. Crop growth and drought monitoring The system calculates Water Satisfaction Index for a standard maize variety. It indicates whether this crop has received sufficient water over the season up to the present, or if it has suffered from insufficient water. The Water satisfaction Index is calculated with Arcview Spatial Analyst from evapotranspiration and rainfall data. Umlindi products. FIRE PRODUCTS The two fire maps are displayed on the Umlindi website. 1. Active fires over a period of ten 2. Fire risk during the following twenty

3 NDVI PRODUCTS The vegetation condition is displayed on the ndvi map per 10 day period. The long term vegetation condition over a period of 15 years is also displayed. The current map vegetation is compared with the long term vegetation map to calculate the comparison map. Figure 1 Fire map processed from NOAA AVHRR satellite showing fire spots (21-28 February 2001). RAINFALL Four maps are displayed : The rainfall during the previous month. The long term median of the previous month. A comparison between the rainfall during the prevoius month and the long term median. The rainfall over the prevoius ten The maps are a combination of measurements from radar stations and weather stations. The data from weather stations are interpolated and are only used fro the areas that are not covered by radar. Rainfall is measured in millimeters and is shaded from light to dark blue. Figure 2 NDVI image processed from NOAA AVHRR satellite March 2001

4 CROP GROWTH AND DROUGHT ( WATER SATISFACTION INDEX ). Assessment of the extent and severity of drought requires meteorological, soil and crop information. The Water Satisfaction Index (WSI) ( Frere and Popov, 1978 ) integrates the temporal distribution of rainfall, the particular crop water requirements and soil factors to determine the current status of the crop. The WSI is the index of the percentage of the crop s water requirements that was satisfied. The WSI for a standardises crop is calculated using available information. Any moisture stress leads to a reduction in potential yield. Under extreme stress crop death may occur. Climate data available for South Africa is used to calculate the WSI for a standardised crop. Interpretation of the Water Satisfaction Index Map. A large yield reduction has been experienced in the north eastern parts of the Eastern Cape, central to eastern parts of KwaZulu-Natal, central parts of the Free State with a patch of high probability of crop failure and the central parts of Gauteng and Mpumalanga. The central and north eastern parts of Limpopo province, together with the western parts of North West towards the northern of border of the Free State, have also experienced a large yield reduction. The remaining parts have little or no water stress with some spots of crop failure in the North West and northern Gauteng. Conclusion The Umlindi system has been very useful in providing with up to date vegetation condition, rainfall data and crop yield prediction and a drought monitoring. However the system need to be advanced in a way that it will provide with real time monitoring of fire and floods which will assist in developing a disaster management system. For more information visit ml). Acknowledgements The National Department of Agriculture is duly acknowledged for funding of the project. The contribution of Harold Weepener and Giovanni Narcisco in developing the system is also appreciated. Figure 3 Water Satisfaction Index map of January 2002.

5 Reference Frere, M. and Popov, G.M. (1986) Early Agrometeorological Crop Yield Forecasting. FAO, Plant Production and Protection Paper No. 73. FAO. Rome