Detection, quantification and monitoring Prosopis spp. in the Northern Cape Province of South Africa using remote sensing and GIS
Van den Berg, Elzie Catharina
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Invasive Prosopis trees pose significant threats to biodiversity and ecosystem services in the Northern Cape Province of South Africa. Several estimates have been made of the spatial extent of alien plant invasion in South Africa. The South African Plant Invaders Atlas (SAPIA) suggested that about 10 million hectares of South Africa has been invaded. However, the rate and spatial extent of Prosopis invasion has never been accurately quantified. The objective of the study is to use Remote Sensing and Geographic Information System (GIS) techniques to: (i) reveal areas susceptible to future invasion, (ii) describe the current extent and densities of Prosopis, (iii) to reveal the spatial dynamics and (iv) establish the extent of fragmentation of the natural vegetation in the Northern Cape Province. Image classification products were generated using spectral analysis of seasonal profiles, various resolution image inputs, spectral indices and ancillary data. Classification approaches varied by scene and spatial resolution as well as application of the data. Coarse resolution imagery and field data were used to create a probability map estimating the area vulnerable to Prosopis invasion using relationships between actual Prosopis occurrence, spectral response, soils and terrain unit. Multi-temporal Landsat images and a 500m x 500m point grid enabled vector analysis and statistical data to quantify the change in distribution and density as well as the spatial dynamics of Prosopis since 1974. Fragmentation and change of natural vegetation was quantified using a combined cover density class, calculating patch density per unit (ha) for each biome The extent of Prosopis cover in the Northern Cape Province reached 1.473 million hectare or 4% of the total land area during 2007. The ability of the above mentioned Remote Sensing and GIS techniques to map the extent and densities of Prosopis in the Northern Cape Province of South Africa demonstrated a high degree of accuracy (72%). While neither the image classification nor the probability map can be considered as 100% accurate representations of Prosopis density and distribution, the products provide use full information on Prosopis distribution and are a first step towards generating more accurate products. For primary invasion management, these products and the association of a small area on a map with Prosopis plants and patches, mean that the management effort and resources are efficiently focused. Further studies using hyper-spectral image analysis are recommended to improve the classification accuracy of the spatial extent and density classes obtained in this study.