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Article

Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

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Department of Earth and Ocean Sciences, University of North Carolina Wilmington, 601 S College Road, Wilmington, NC 28403, USA
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Department of Geography and Geosciences, Lutz Hall, University of Louisville, Louisville, KY 40292, USA
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Environmental Studies Program, Sustainability, Energy, and Environment Community, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, USA
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Department of Human Dimensions of Natural Resources, Graduate Degree Program in Ecology, Colorado State University, Campus Box 1480, Fort Collins, CO 80523-1480, USA
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Okavango Research Institute, University of Botswana, P/Bag 285, Maun, Botswana
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Department of National Parks and Wildlife, Private Bag 1, Kafue Road, Chilanga, Zambia
*
Author to whom correspondence should be addressed.
Academic Editors: Eva Ivits, Stephanie Horion, Roel Van Hoolst and Giuseppe Modica
Remote Sens. 2021, 13(4), 631; https://doi.org/10.3390/rs13040631
Received: 31 December 2020 / Revised: 5 February 2021 / Accepted: 5 February 2021 / Published: 10 February 2021
Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas. View Full-Text
Keywords: remote sensing; participatory mapping; NTFP; grazing; random forest; natural resources; drylands; savanna woodlands remote sensing; participatory mapping; NTFP; grazing; random forest; natural resources; drylands; savanna woodlands
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MDPI and ACS Style

Woodward, K.D.; Pricope, N.G.; Stevens, F.R.; Gaughan, A.E.; Kolarik, N.E.; Drake, M.D.; Salerno, J.; Cassidy, L.; Hartter, J.; Bailey, K.M.; Luwaya, H.M. Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping. Remote Sens. 2021, 13, 631. https://doi.org/10.3390/rs13040631

AMA Style

Woodward KD, Pricope NG, Stevens FR, Gaughan AE, Kolarik NE, Drake MD, Salerno J, Cassidy L, Hartter J, Bailey KM, Luwaya HM. Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping. Remote Sensing. 2021; 13(4):631. https://doi.org/10.3390/rs13040631

Chicago/Turabian Style

Woodward, Kyle D., Narcisa G. Pricope, Forrest R. Stevens, Andrea E. Gaughan, Nicholas E. Kolarik, Michael D. Drake, Jonathan Salerno, Lin Cassidy, Joel Hartter, Karen M. Bailey, and Henry Maseka Luwaya. 2021. "Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping" Remote Sensing 13, no. 4: 631. https://doi.org/10.3390/rs13040631

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