Increases in woody plant cover in savanna grassland environments have been reported on globally for over 50 years and are generally perceived as a threat to rangeland productivity and biodiversity. Despite this, few attempts have been made to estimate the extent of woodland increase at a national scale, principally due to technical constraints such as availability of appropriate remote sensing products. In this study, we aimed to measure the extent to which woodlands have replaced grasslands in South Africa's grassy biomes. We use multiseason Landsat data in conjunction with satellite L-band radar backscatter data to estimate the extent of woodlands and grasslands in 1990 and 2013. The method employed allows for a unique, nationwide measurement of transitions between grassland and woodland classes in recent decades. We estimate that during the 23-year study period, woodlands have replaced grasslands over ~57 000 km and conversely that grasslands have replaced woodlands over ~30 000 km , a net increase in the extent of woodland of ~27 000 km and an annual increase of 0.22%. The changes varied markedly across the country; areas receiving over 500 mm mean annual precipitation showed higher rates of woodland expansion than regions receiving <500 mm (0.31% yr and 0.11% yr , respectively). Protected areas with elephants showed clear loss of woodlands (-0.43% yr ), while commercial rangelands and traditional rangelands showed increases in woodland extent (>0.19% yr ). The woodland change map presented here provides a unique opportunity to test the numerous models of woody plant encroachment at a national/regional scale.
ABSTRACT:Recent advances in cloud-based technology has led to the rapid increase of geospatial web-based applications. The combination of GIS and cloud-based solutions is revolutionizing product development in the geospatial industry and is facilitating accessibility to a wider range of users, planners and decision makers. Accessible through an internet browser, web applications are an effective and convenient method to disseminate information in multiple formats, and they provide an interface offering interactive access to geospatial data, real-time integration and data processing, and application specific analysis tools. An example of such a web application is GeoTerraImage's monthly crop monitoring tool called GeoFarmer. This tool uses climatic data and satellite imagery processed through a complex rule-based algorithms to determine monthly climatic averages and anomalies, and most importantly the field specific crop status (i.e. is the field fallow, or is the crop emerging, or if the field has been harvested). Monthly field verification has formed a part of calibrating the growth classification outputs to further improve the accuracy of its monthly agricultural reporting. The goal of this application is to provide timely data to decision makers to assist them in field-level and regional crop growth monitoring, crop production and management, financial risk assessment and insurance, and food security applications. This web application has the unique advantage of being highly transportable to other regions, since it has been designed so it can easily be adapted to other seasonal growth response patterns, and up-scaled to regional or national coverages for operational use.
The global trend of transformation and loss of wetlands through conversion to other land uses has deleterious effects on surrounding ecosystems, and there is a resultant increasing need for the conservation and preservation of wetlands. Improved mapping of wetland locations is critical to achieving objective regional conservation goals, which depends on accurate spatial knowledge. Current approaches to mapping wetlands through the classification of satellite imagery typically under-represents actual wetland area; the importance of ancillary data in improving accuracy in mapping wetlands is therefore recognised. In this study, we compared two approaches -Bayesian networks and logistic regression -to predict the likelihood of wetland occurrence in KwaZulu-Natal, South Africa. Both approaches were developed using the same data set of environmental surrogate predictors. We compared and verified model outputs using an independent test data set, with analyses including receiver operating characteristic curves and area under the curve (AUC). Both models performed similarly (AUC>0.84), indicating the suitability of a likelihood approach for ancillary data for wetland mapping. Results indicated that high wetland probability areas in the final model outputs correlated well with known wetland systems and wetland-rich areas in KwaZulu-Natal. We conclude that predictive models have the potential to improve the accuracy of wetland mapping in South Africa by serving as valuable ancillary data.
Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.
ABSTRACT:For service providers developing commercial value-added data content based on remote sensing technologies, the focus is to typically create commercially appropriate geospatial information which has downstream business value. The primary aim being to link locational intelligence with business intelligence in order to better make informed decisions. From a geospatial perspective this locational information must be relevant, informative, and most importantly current; with the ability to maintain the information timeously into the future for change detection purposes. Aligned with this, GeoTerraImage has successfully embarked on the production of land-cover / land-use content over southern Africa. The ability for a private company to successfully implement and complete such an exercise has been the capability to leverage the combined advantages of cutting edge data processing technologies and methodologies, with emphasis on processing repeatability and speed, and the use of a wide range of readily available imagery. These production workflows utilise a wide range of integrated procedures including machine learning algorithms, innovative use of non-specialists for sourcing of reference data, and conventional pixel and object-based image classification routines, and experienced/expert landscape interpretation. This multi-faceted approach to data produce development demonstrates the capability for SMME 1 level commercial entities such as GeoTerraImage to generate industry applicable large data content, in this case, wide area coverage land-cover and land-use data across the sub-continent. Within this development, the emphasis has been placed on the key land-use information, such as mining, human settlements, and agriculture, given the importance of this geo-spatial land-use information in business and socio-economic applications and decision making. MANUSCRIPTThe Earth Observation (EO) industry has seen significant changes in terms of its data availability and processing abilities in the last 24-36 months. There is a clear shift from the conventional and long-term traditional data access and processing workflows (which were relevant merely a decade ago), to big data crunching algorithms, cloud-based computing and real-time information processing, supported by internet and technological giants. The changes seen and experienced in the EO industry have been daunting if unexpected, or exciting if prepared. This paper describes the short story of a commercial focused company, GeoTerra Image (GTI) in South Africa, that is set on proving its capability in adapting to the changing EO industry. In order to remain both relevant and competitive , GTI has embraced these changes, and rapidly adapted to a new way of thinking, thus proving that regardless of the size and location of any commercial EO organisation, there is still relevance and opportunity to their existence.GTI is a small commercially focussed company based in South Africa, which uses EO technologies to provide solution driven services to many market sectors ...
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