Your search found 3 records
1 Cho, M. A.; Onisimo, M.; Mabhaudhi, Tafadzwanashe. 2023. Using participatory GIS and collaborative management approaches to enhance local actors’ participation in rangeland management: the case of Vulindlela, South Africa. Journal of Environmental Planning and Management, 66(6):1189-1208. [doi: https://doi.org/10.1080/09640568.2021.2017269]
Rangelands ; Participatory approaches ; Geographical information systems ; Collaboration ; Planning ; Stakeholders ; Pastoralists ; Local knowledge ; Empowerment / South Africa / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H050968)
(1.35 MB)
Participatory Geographic Information Systems (PGIS) is an empowering tool for the enhancement of local communities’ participation in the planning and management of natural resources. The inadequate involvement of local stakeholders in rangeland planning and management has been of great concern. Discussions on the role of PGIS and collaborative management approaches in promoting local involvement in rangeland management has not been clearly understood due to the scarcity of literature. This paper assessed how local participation in rangeland management can be enhanced using a combined collaborative management framework and PGIS approach. The objective was achieved through a focus group discussion, local ecological knowledge mapping and key informant interviews. The combined PGIS and collaborative management approach enabled the empowerment of local actors through knowledge enhancement, encouraged the practice of rangeland governance and the transfer of responsibility to local actors. This study provides a conceptual contribution toward the improvement of local actors’ participation in rangeland management.
2 Cho, M. A.; Mutanga, O.; Mabhaudhi, Tafadzwanashe. 2023. Understanding local actors’ perspective of threats to the sustainable management of communal rangeland and the role of Participatory GIS (PGIS): the case of Vulindlela, South Africa. South African Geographical Journal, 105(4):516-533. [doi: https://doi.org/10.1080/03736245.2023.2190153]
Sustainable land management ; Rangelands ; Common lands ; Local knowledge ; Participatory rural appraisal ; Geographical information systems ; Land governance ; Mapping ; Techniques ; Grazing lands ; Land productivity ; Ecological factors ; Socioeconomic aspects ; Pastoralists ; Communities ; Livelihoods ; Inclusion ; Assessment / South Africa / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051819)
(2.79 MB)
Rangelands in arid and semi-arid regions serve as grazing land for domesticated animals and therefore offer livelihood opportunities for most pastoral communities. Thus, the exposure of most rangelands in arid and semi-arid regions to threats that are associated with natural, social, economic, and political processes affects their capacity to provide socioeconomic and environmental support to the immediate and global communities. In spite of the effects of rangeland transformations on both the natural and human environment, the assessment of threats affecting rangeland productivity has often been approached from a conventional scientific perspective. Most existing literature is focused on the assessment of threats to the biophysical environment. As such the social dimension of rangeland threats is not well understood. This research employed participatory rural appraisal (PRA) and PGIS techniques to assess rangeland threats and management actions from a local perspective. The result revealed that local actors prioritize threats to their social and economic needs over threats to the biophysical environment and their preference is informed by the frequency and magnitude of the threats. The outcome of the research demonstrates the need to promote rangeland governance through interdisciplinary and inclusive participation in research and development.
3 Masenyama, A.; Mutanga, O.; Dube, T.; Sibanda, M.; Odebiri, O.; Mabhaudhi, T. 2023. Inter-seasonal estimation of grass water content indicators using multisource remotely sensed data metrics and the cloud-computing Google Earth Engine platform. Applied Sciences, 13(5):3117. (Special issue: Remote Sensing Applications in Agricultural, Earth and Environmental Sciences) [doi: https://doi.org/10.3390/app13053117]
Grasslands ; Plant water relations ; Estimation ; Remote sensing ; Datasets ; Leaf area index ; Vegetation index ; Climatic factors ; Indicators ; Satellite observation ; Forecasting ; Spatial distribution ; Models / South Africa / KwaZulu-Natal / Vulindlela
(Location: IWMI HQ Call no: e-copy only Record No: H051820)
https://www.mdpi.com/2076-3417/13/5/3117/pdf?version=1677581546
https://vlibrary.iwmi.org/pdf/H051820.pdf
https://vlibrary.iwmi.org/pdf/H051820.pdf
(4.12 MB) (4.12 MB)
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m-2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m-2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R 2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m-2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m-2 and R 2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m-2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m-2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
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