Mapping natural resource collection areas from household survey data in Southern Africa. (December 2020)

• Record Type: Journal Article
• Authors: Bailey, Karen M.; Drake, Michael D.; Salerno, Jon; Cassidy, Lin; Gaughan, Andrea E.; Stevens, Forrest R.; Pricope, Narcisa G.; Woodward, Kyle D.; Luwaya, Henry Maseka; Hartter, Joel
• Is Part Of: Applied geography. Volume 125(2021)
• Publication Date: 2020-12
• Subjects: Resource mapping -- Transfrontier conservation area -- Southern Africa -- Fuelwood -- Participatory mapping -- Human livelihoods; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2020.102326
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: As conservation landscapes are threatened by global change, there is a growing need to understand relationships between human livelihoods and environmental processes. This often involves integrating multiple data sources capturing different scales of measurement. Participatory methods have emerged as a means to accomplish this, but are hampered by a wide range of challenges associated with data collection and translation. Here, we present a novel methodology for mapping human use of natural resources that overcomes many of the difficulties faced in participatory mapping. Based in the world's largest terrestrial transfrontier conservation area, we couple household surveys with in-situ fine-scale mapping to identify key resource areas that support local livelihoods. This allows for a spatially referenced human use 'footprint' that can be combined with remotely-sensed data measuring environmental impact. This methodology is applicable across contexts and has implications for landscape management and conservation. Highlights: There is a need to understand relationships between human livelihoods and environmental processes. Participatory methods are hampered by challenges associated with data collection and translation. We present a novel methodology for mapping human use of natural resources. We pair household surveys with in-situ mapping to identify natural resource collection patterns. This method integrates remotely-sensed data with community knowledge in a minimally biased way.