Search

Tropical savannas have a patchy vegetation structure and heterogeneous composition that complicates their mapping and management. Land managers need detailed vegetation information, especially as tropical savannas often support extensive ranching systems or wildlife-based tourism and face specific challenges such as bush thickening, drought, bushfires and, in Africa, browsing by large game. Since existing methods to map savanna vegetation mosaics rarely provide the resolution or speed required, this study aimed to characterise savanna vegetation with sufficient detail for management purposes and sufficient generalisation for the assessment of processes at a landscape level, using an easy, quick, and cost-efficient system. The study area is a semi-arid savanna in a small game reserve south of Etosha National Park in Namibia. A rapid field assessment focused on the woody vegetation and used the Bitterlich method. Indicator species analysis and MRPP tests resulted in five mixed woody vegetation classes. Random Forest was used to model vegetation composition, structure and woody cover. The highest accuracy was obtained for vegetation composition (77 %) and the lowest for vegetation cover (71 %) with similar accuracies at a resolution of 10 m compared to 30 m. The most important predictors were a radar mosaic (ALOS PALSAR HV) and Sentinel-2 data representing days in wet and dry seasons, with MSAVI2 a more suitable vegetation index than NDVI. Other predictors such as land surface temperature during winter nights, geology, and distance to water points contributed to the models. The final vegetation map contains 10 classes based on woody vegetation composition and structure. The most dominant classes were Colophospermum mopane – Terminalia prunioides woodland (33 %) and bushland (18 %) with grassland only covering 2.5 %. The method described here was driven by management requirements and can be used for bush control monitoring, quantifying the carbon pool and carrying capacity. It combines an old field survey method with free state-of-the-art datasets and algorithms. The focus on woody vegetation minimises the dependence on the intermittent presence of grasses and herbs in semi-arid savannas.

No abstract available

Land cover change (LCC) is a complex and dynamic process influenced by social, economic, and biophysical factors that can cause significant impacts on ecological processes and biodiversity conservation. The assessment of LCC is particularly relevant in a country like Mozambique where livelihood strongly depends on natural resources. In this study, LCC was assessed using a point-based sampling approach through Open Foris Collect Earth (CE), a free and open-source software for land assessment developed by the Food and Agriculture Organization of the United Nations. This study aimed to conduct an LCC assessment using CE for the entire Mozambique, and according to three different land classifications: administrative boundaries (provinces), ecoregions, and protected vs unprotected areas. A set of 23,938 randomly selected plots, with an area of 0.5 hectares, placed on a 4 × 4 km regular grid over the entire country, was assessed using CE. The analysis showed that Mozambique has gone through significant loss of forest (− 1.3 Mha) mainly to the conversion to cropland. Deforestation is not occurring evenly throughout the country with some provinces, such as Nampula and Zambezia, characterized by higher rates than others, such as Gaza and Niassa. This result can be explained considering a combination of ecological and socio-economic factors, as well as the conservative role played by the protected areas. Our study confirmed that LCC is a complex phenomenon, and the augmented visual interpretation methodology can effectively complement and integrate the LCC analyses conducted using the traditional wall-to-wall mapping to support national land assessment and forest inventories and provide training data for environmental modeling. [ABSTRACT FROM AUTHOR]