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Cycle 7 (2018 Deadline)
Application of GIS and geospatial analysis in understanding charcoal production, supply, and demand in selected sites of Lusaka, Central, Copperbelt, and North Western provinces of Zambia
PI: Stephen Syampungani (syampungani@cbu.ac.zm; ssyampungani@yahoo.com), Copperbelt University
U.S. Partner: Andrew Hudak, USDA Forest Service
Dates: November 2018 - October 2022
Project Overview
Charcoal consumption is expected to increase in Zambia in the coming decades, and as a result, charcoal must be integrated into development, energy, land use, and food security strategies. This project undertook a detailed value chain analysis of charcoal with the aim of identifying how to reduce the impact of charcoal production, supply, and demand. The project generated spatial distribution information of above-ground woody biomass suitable for charcoal making, data that are currently lacking but are highly useful for designing improved forest management practices in the country.
This project also sought to evaluate the performance of various charcoal production methods, including traditional methods, with a view to improving them. The researchers hoped to contribute to appropriate government policies required to introduce improved charcoal-production technologies at scale, as well as promote rural development through greening of the charcoal value chain and reduction of demands for charcoal through improved stoves to reduce pressure on Zambia’s forests and woodlands.
Final Summary of Project Activities
The PI Dr. Syampungani and his team identified and classified tree species in the Miombo woodlands using multispectral drone imagery, combined with a field inventory to obtain training and verification data for a classification algorithm. They also used drone-collected Lidar data in estimating forest structure attributes in the Miombo and undertook field work to collect ground-truth data in 16 plots of a 20 m radius in a site in Mwekera forest. The researchers also enhanced the diffusion of improved charcoal kilns, collecting data from four study sites in Copperbelt and North Western provinces using the software KoBoToolkit on their mobile phones. Key informational interviews and a tree inventory using the Point Centered Quarter Method were also completed.
As a result, the researchers generated useful data for forest management throughout the Miombo ecoregion, which covers nine countries in Southern Africa, and provided improved methods of charcoal production through simple technologies affordable to an ordinary charcoal producer.
The team published several papers related to their PEER project. In addition to the research outputs, the project also supported one Master’s and two Ph.D. students. Dr. Syampungani also received a new ongoing grant from the Oliver R. Tambo Africa Research Chairs Initiative Chair of Environment and Development worth US$250,000 to $300,000 annually over the next five years, renewable twice based on performance.
Publications
Jean Moussa Kouroumaa, Concillia Monde, Darius Phiri, and Stephen Syampungani. 2023. Community-centred approach for assessing social sustainability in mining regions: a case study of Chingola district, Zambia. Sustainable Development 31(4): 3102-3127. https://doi.org/10.1002/sd.2572
Hastings Shamaoma, Paxie W. Chirwa, Jules C. Zekeng, Abel Ramoelo, Andrew T. Hudak, Ferdinand Handavu, and Stephen Syampungani. 2023. Use of multi-date and multi-spectral UAS imagery to classify dominant tree species in the wet Miombo woodlands of Zambia. Sensors 2023, 23, 2241. https://doi.org/10.3390/s23042241
Hastings Shamaoma, Paxie W. Chirwa, Abel Ramoelo, Andrew T. Hudak, and Stephen Syampungani. 2022. The application of UASs in forest management and monitoring: challenges and opportunities for use in the Miombo woodland. Forests 2022, 13, 1812. https://doi.org/10.3390/f13111812
Jean Moussa Kouroumaa, Darius Phiri, Andrew T. Hudak, and Stephen Syampungani. 2022. Land use/cover spatiotemporal dynamics, and implications on environmental and bioclimatic factors in Chingola district, Zambia. Geomatics, Natural Hazards, and Risk 13(1): 1898-1942. https://doi.org/10.1080/19475705.2022.2097132
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