Rangelands Vegetation Mapping at Species Composition Level Using the <b>SPiCla</b> Method: <b>S</b>DM Based <b>Pi</b>xel <b>Cla</b>ssification and Fuzzy Accuracy. A New Approach of Map Making

Vegetation maps have been made since centuries. The vegetation cover was represented as homogeneous mapping units (polygons), representing different vegetation types, where each type consists a combination of different plant species (floristic composition). More recent, with the use of satellite imagery, the polygons have been replaced by pixels with similar content as the polygon maps. In both approaches, field-observations were linked to the mapping units (polygons or pixels) often resulting in a complex of different vegetation types per mapping unit. In our new approach field data (sample points) on presence and abundance of individual grass species are spatially extrapolated based on a set of environmental layers, using the species distribution modelling approach (SDM). When combined, each pixel will contain its own set of information about the vegetation structure and its floristic composition. This new methodology (SPiCla) results in a very accurate and detailed vegetation map at pixel level, allowing extraction of very detailed, accurate and easy to update spatial information on e.g., forage production and quality (palatability) for rangelands management. As no exact boundaries exist, but only gradients, we introduced fuzzy accuracy. The resolution mainly depends on the resolution of (or one of) the environmental layers used, scale of interest and workability. The methodology is generic and applicable to any other region in the world. Introduction Vegetation maps, especially in rangelands, are often based on complexes of different grass species with varying abundances and cover percentages. Variations in the spatial distribution of the rangelands vegetation types and species abundances and cover are often influenced by rainfall, soil and relief (Reed et al. 2009). These maps can be used for rough estimations of grasslands quality, but for more reliable estimations mapping units with complexes of different species with different palatability are often too general and coarse for accurate estimations of species composition, and palatable biomass production. Given the importance of these variables in management of rangelands, this paper describes a generic method for the compilation of a detailed map containing at pixel level both the structural – and the floristic composition of the vegetation. Methods and Study Site The Maara Middle Basin is a typical example of a savannah ecosystem (Braun and Mungai 1981) in East Africa. The vegetation of the Maara plains can roughly be divided into woodland, bushland and grassland (Pratt and Gwynne 1977). Grasslands are predominant in the plains, while forest is mostly confined to hill crests and along the main rivers. Chloris spp. and Sporobolus spp. dominate the grasslands on basement rocks, Pennisetum spp. dominate on the volcanic soils in the South East. The south western part receives more rainfall and is dominated by Themeda triandra, while Pennisetum mezianum is common on the floodplains. Several grassland types include dwarf shrubs and perennial herbs, important browse plants for sheep and goats (Wijngaarden, W. van 1985). First a preliminary stratification of the Maara Middle Basin was produced, by applying the hyper-temporal NDVI classification method (de Bie et al. 2012; Westinga et al. 2020), using 16-day composite of MODISTerra NDVI images (vegetation greenness index rescaled from 0 to 255 DN-Values) from January 2000 to December 2013. The classification result formed the basis for a stratified random field-sampling scheme, with emphasis on the grasslands. Fieldwork was done in October 2014 (65 samples), May 2015 (62 samples) and October 2016 (171 samples), covering both the wet and dry season. Validation of the final vegetation map was done in February 2019 (58 samples). In each sample site data on land cover, vegetation structure, grass species composition and abundance were collected. The field data were grouped in to floristic vegetation types according the Braun-Blanquet method (Braun-Blanquet 1932) and the classification result was summarized in a synoptic table (see table 1). To be able to produce a very detailed and accurate vegetation map at pixel level, the species distribution modelling (SDM) approach was applied (Elith and Leathwick 2009). It is pivotal to select those environmental parameters bearing significance for the environmental requirements of each grass species. The following