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.

中文翻译：

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一种以牧场管理为导向的方法来绘制干燥的稀树草原 - 林地镶嵌


热带稀树草原具有斑驳的植被结构和异质组成，这使得它们的制图和管理变得复杂。土地管理者需要详细的植被信息，特别是因为热带稀树草原通常支持广泛的牧场系统或基于野生动物的旅游，并面临特定的挑战，例如灌木丛增厚、干旱、丛林火灾，以及在非洲浏览大型猎物。由于现有的绘制稀树草原植被镶嵌物的方法很少提供所需的分辨率或速度，因此本研究旨在使用简单、快速且具有成本效益的系统，为管理目的提供足够的细节来表征稀树草原植被，并为在景观层面评估过程提供足够的概括。研究区域是纳米比亚埃托沙国家公园以南一个小型野生动物保护区的半干旱稀树草原。快速实地评估侧重于木本植被，并使用了 Bitterlich 方法。指示物种分析和 MRPP 测试得出 5 个混合木本植被类别。随机森林用于模拟植被组成、结构和木本覆盖。在 10 m 分辨率与 30 m 分辨率下，植被组成的准确性最高 （77%），植被覆盖度 （71%） 的精度最低。最重要的预测因子是雷达镶嵌 （ALOS PALSAR HV） 和 Sentinel-2 数据，表示雨季和旱季的天数，其中 MSAVI2 是比 NDVI 更合适的植被指数。其他预测因素，例如冬夜的地表温度、地质和到水点的距离，都对模型做出了贡献。最终的植被地图包含基于木本植被组成和结构的 10 个类。 最主要的类别是 Colophospermum mopane – Terminalia prunioides 林地 （33%） 和灌木丛 （18%），草原仅覆盖 2.5%。此处描述的方法由管理要求驱动，可用于灌木控制监测，量化碳库和承载能力。它将旧的实地调查方法与免费的最先进的数据集和算法相结合。对木本植被的关注最大限度地减少了对半干旱稀树草原中草和草本植物间歇性的依赖。