Using GIS and remote sensing to explore the influence of physical environmental factors and historical land use on bushland structure

Abstract

Bushland is a vegetation type with a dense cover of woody plants of low stature and a grass understory. We explored the influence of environmental factors (precipitation, topography, geology, hydrology and waterhole density) and historical land use in the Tsavo ecosystem bushland. We mapped vegetation patterns in the region using remote sensing, GIS and field data and tested relationships with physical factors. We used regression analysis to explore the interaction of physical factors and human influence by including a protected and a nonprotected area, representing contrasting historical land use policies, as factors in the regression model. Bushland vegetation had the largest proportion in the region as a whole, but its proportion was notably greater in the nonprotected area. Precipitation, elevation, geology and historical land use were significant predictors of vegetation patterns in the regression models. Higher precipitation and elevation make woody plants superior competitors over grasses, resulting in predominance of bushland in such areas. Geologically, marine deposits result in shallow calcareous soils that favour grasses over trees, hence, absence of bushland. Wildlife management policies such as providing water for wildlife, protective boundaries and controlling wildfires influence distribution of wildlife and result in conditions that prevent regeneration and recruitment of trees.

A geospatial approach to assess habitat diversity and inform conservation efforts using a case study from North Africa

“…This demonstrated their importance for the planning and management of protected areas, as they allow for cost and time effective monitoring of large and diverse areas and spatial characterisation of habitat types. These techniques were largely used to conduct various ecological and conservation studies in other parts of Africa (Aynekulu et al, 2008; Betbeder et al, 2014; Fasona & Omojola, 2009; Mutiti et al, 2017; Ngwenya & Marambanyika, 2021; Orimoloye et al, 2019; Senouci et al, 2021; Symeonakis & Drake, 2004), showing great potential for future application in developing countries. Similarly, RS and GIS‐based techniques are being increasingly applied in a wide variety of environmental and biodiversity conservation studies conducted in other regions outside Africa (Groom et al, 2006; Liu et al, 2012; Menon & Bawa, 1997; Osborne et al, 2001; Palacios‐González et al, 2015; Tavares et al, 2019).…”