Abstract
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.
Highlights
Rural communities in southern Africa face a variety of climatic and environmental challenges that contribute toward their vulnerability
We address three research questions: (RQ1) Can Landsat satellite remote sensing data reliably contribute toward accurate prediction of resource use intensity when trained with data derived from participatory mapping? (RQ2) How useful are population proxy variables for predicting resource use intensity in this region? (RQ3) How do prediction performance and predicted resource use intensity patterns compare between resource types and communities? remotely sensed data have been used to map and quantify valuable natural resources in communally managed areas [46,50,78], we are unaware of any attempt to spatially characterize the intensity at which communities engage in these subsistence resource use activities
This study presented a unique integration of remote sensing methods with participatory mapping to map spatial patterns of natural resource use intensity in a communally managed African landscape
Summary
Rural communities in southern Africa face a variety of climatic and environmental challenges that contribute toward their vulnerability. People in this region often rely on rain-fed agriculture for their livelihoods. Crop loss from wildlife, pests, and disease can further constrain food and economic resources for a household [8,9,10]. To overcome such hardships, diversify their livelihood base, and buffer themselves from climatic shocks, many households raise livestock and collect natural resources from surrounding lands [11,12,13,14,15]. The natural resource bases that allow for these additional livelihood activities are susceptible to overuse through natural resource exploitation and environmental changes
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