Strategic monitoring of crop yields and rangeland conditions in Southern Africa with remote sensing

The monitoring of vegetation resources is of vital importance for Southern African countries because of the dominance of agriculture in the economy. The use of remote sensing techniques in a national or local planning context is particularly adapted to the Southern African conditions because large areas can be covered regularly with minimal requirements for field based infrastructure. Furthermore, relatively low-cost receiving stations have been installed in the meteorological departments and other local institutions, which makes satellite data available in realtime. Real-time acquisition is essential for the operational monitoring of vegetation development and remote sensing plays a significant role in three main areas: • Inventory or mapping of cover types • Monitoring of vegetation conditions relative to the norm • Estimates of biomass In this research operational techniques were developed in each of these areas with the participation and involvement of users. Remote sensing and field survey techniques for inventory and mapping of cover types were adapted and developed from existing experience in the European context to match the requirements in Southern Africa. The need for an unbiased sample of field observations, for the calibration of digital classification of satellite imagery was identified and methodology for its collection demonstrated. Methods developed for the inventory of crop types in Europe were successfully adapted to the African rangeland. The levels of classification accuracy achieved were similar to that obtained in the European context for a classification scheme of equivalent complexity. A Vegetation Productivity Indicator (VPI) was developed for monitoring vegetation conditions based on real-time acquisition of NOAA HRPT imagery from a local receiving station and a historical Normalised Difference Vegetation Index (NDVI) archive. The VPI maps show departure from normal vegetation response using methodology similar to the analysis of extreme events in hydrology, in near real-time. The method was successfully implemented in Zambia to monitor maize production and in Namibia to monitor rangeland. The VPI was significantly correlated with rainfall. The technique was successfully transferred to the Department of Meteorological Services in Botswana where VPI maps are produced routinely and presented to the inter-ministerial drought committee for assessing rangeland conditions. Methodology for rapid biomass assessment was developed using simple physiognomic plant parameters. Field Measurements were taken in four different cover types (grassland, steppe and shrub and tree savanna) and correlated with the NDVI derived from the satellite observations in Etosha National Park, Namibia in near real-time. The pooled regression relationship which was obtained was highly statistically significant. However, the regression model excluding the two savanna types exhibited a higher correlation suggesting that there might be a separate relationship between savanna biomass and NDVI. Biomass maps were produced using the pooled relationship and their potential for operational targeting of areas suitable for prescribed burning was illustrated. Although the methods and techniques in this work were developed using time series of NOAA-AVHRR and the NDVI, they can all be adapted to include data from new sensors systems and other vegetation indices as they become available. Methods demonstrated in this work can be integrated to form a suitable framework for a national vegetation resources monitoring system. This would assist Southern African governments in making decisions related to vegetation resources by providing sound and timely technical advice.