Cloud-Resilient Forest Monitoring for Sustainable Urban Land Management: L-Band SAR Assessment of Charcoal-Driven Peri-Urban Woodland Change in Lusaka, Zambia

Authors

DOI:

https://doi.org/10.25034/ijcua.2026.v10n1-13

Keywords:

Urban hinterland, Peri-urban protected area, Synthetic Aperture Radar, Miombo Woodland, Charcoal economy, MRV, Lusaka, Sustainable urban land use

Abstract

Rapid urban growth in sub-Saharan Africa is intensifying pressure on forest areas surrounding major cities. In Lusaka, with a metropolitan population of approximately 3.32 million growing at 4.5% annually, peri-urban miombo woodlands supply charcoal and farmland, yet these areas are hardest to monitor during the five-month rainy season when cloud cover exceeds 80% and optical satellite imagery fails. This study examines whether L-band Synthetic Aperture Radar can provide year-round woodland monitoring and whether such a workflow is financially practical for a resource-limited African city. A 17-year ALOS/PALSAR time series (2007–2024) for Lusaka National Park was analysed using QGIS and Python, combining multi-temporal statistics, a GEDI-calibrated Random Forest biomass model (R² = 0.76), and a disturbance indicator. The approach achieved 85.4% classification accuracy and a 31% cost reduction over conventional ground monitoring, demonstrating an operationally feasible, open-source SAR monitoring framework for African urban governance contexts.

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References

Aidaoui, A., Dechaicha, A., Alkama, D., Menai, I., & Salah Salah, H. 2024. Mapping tomorrow’s cities: GeoAI strategies for sustainable urban planning and land-use optimisation. Journal of Contemporary Urban Affairs, 8(1), 158–176. https://doi.org/10.25034/ijcua.2024.v8n1-9

Amitrano, D., Di Martino, G., Guida, R., Iervolino, P., Iodice, A., Papa, M. N., Riccio, D., & Ruello, G. (2021). Earth environmental monitoring using multi-temporal synthetic aperture radar: A critical review of selected applications. Remote Sensing, 13(4), 604. https://doi.org/10.3390/rs13040604

Bullock, E. L., Woodcock, C. E., & Olofsson, P. (2020a). Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote Sensing of Environment, 238, 110968. https://doi.org/10.1016/j.rse.2018.11.011

Bullock, E. L., Woodcock, C. E., Souza, C., & Olofsson, P. (2020b). Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biology, 26(5), 2956–2969. https://doi.org/10.1111/gcb.15029

Carreiras, J. M. B., Melo, J. B., & Vasconcelos, M. J. (2013). Estimating the above-ground biomass in miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sensing, 5(4), 1524–1548. https://doi.org/10.3390/rs5041524

Cobbinah, P. B., Asibey, M. O., Opoku-Gyamfi, M., & Peprah, C. (2020). Urban planning and climate change in Ghana. Journal of Urban Management, 9(2), 261–271. https://doi.org/10.1016/j.jum.2020.02.002

Demol, M., Aguilar-Amuchastegui, N., Bernotaite, G., Disney, M., Duncanson, L., & et al. (2024). Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought. Communications Earth & Environment, 5(1), 366. https://doi.org/10.1038/s43247-024-01448-x

Dubayah, R. O., Armston, J., Healey, S. P., Yang, Z., Patterson, P. L., Saarela, S., Stahl, G., Duncanson, L., & Kellner, J. R. (2022). GEDI L4B Gridded Aboveground Biomass Density, Version 2. ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2017

Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., & et al. (2022). Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270, 112845. https://doi.org/10.1016/j.rse.2021.112845

Fang, X., Li, J., & Ma, Q. (2023). Integrating green infrastructure, ecosystem services and nature-based solutions for urban sustainability: A comprehensive literature review. Sustainable Cities and Society, 98, 104843. https://doi.org/10.1016/j.scs.2023.104843

Flores-Anderson, A. I., Herndon, K. E., Thapa, R. B., & Cherrington, E. (Eds.). (2019). The SAR handbook: Comprehensive methodologies for forest monitoring and biomass estimation. NASA. https://doi.org/10.25966/nr2c-s697

Güneralp, B., Lwasa, S., Masundire, H., Parnell, S., & Seto, K. C. (2018). Urbanization in Africa: challenges and opportunities for conservation. Environmental Research Letters, 13(1), 015002. https://doi.org/10.1088/1748-9326/aa94fe

He, W., Li, X., Zhou, Y., Liu, X., Gong, P., Hu, T., Yin, P., Huang, J., Yang, J., Miao, S., Wang, X., & Wu, T. (2023). Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model. Cities, 133, 104146. https://doi.org/10.1016/j.cities.2022.104146

Hethcoat, M. G., Carreiras, J. M. B., Edwards, D. P., Bryant, R. G., & Quegan, S. (2021). Detecting tropical selective logging using C-band SAR data may require a time-series approach. Remote Sensing of Environment, 259, 112411. https://doi.org/10.1016/j.rse.2021.112411

Ingram, V., Schure, J., Awono, A., Ferrer Velasco, R., & Tieguhong, J. C. (2020). Can de facto governance influence deforestation drivers in the Zambian Miombo? Forest Policy and Economics, 118, 102231. https://doi.org/10.1016/j.forpol.2020.102231

Liang, D., Yang, X., Hu, X., Sun, F., Liu, J., & Chen, K. (2024). Monitoring spatiotemporal changes in land use/land cover and its impacts on ecosystem services in southern Zambia. Environmental Research Communications, 6(4), 045004. https://doi.org/10.1088/2515-7620/ad37f3

Macave, O. A., Ribeiro, N. S., Ribeiro, A. I., Chauque, A., Bandeira, R., Branquinho, C., & Washington-Allen, R. (2022). Modelling aboveground biomass of miombo woodlands in Niassa Special Reserve, northern Mozambique. Forests, 13(2), 311. https://doi.org/10.3390/f13020311

Mauya, E. W., Mugasha, W. A., Njana, M. A., Malimbwi, R., & Katani, J. Z. (2025). Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia. International Journal of Applied Earth Observation and Geoinformation, 136, 104307. https://doi.org/10.1016/j.jag.2025.104307

Mitchard, E. T. A., Saatchi, S. S., Lewis, S. L., Feldpausch, T. R., Woodhouse, I. H., Sonké, B., Rowland, C., & Meir, P. (2011). Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest–savanna boundary region of central Africa using multi-temporal L-band radar backscatter. Remote Sensing of Environment, 115(11), 2861–2873. https://doi.org/10.1016/j.rse.2011.02.022

Mitchard, E. T. A., Saatchi, S. S., White, L. J. T., Abernethy, K. A., Jeffery, K. J., Lewis, S. L., Collins, M., Lefsky, M. A., Leal, M. E., Woodhouse, I. H., & Meir, P. (2012). Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: Overcoming problems of high biomass and persistent cloud. Biogeosciences, 9(1), 179–191. https://doi.org/10.5194/bg-9-179-2012

Olesk, A., Praks, J., Antropov, O., Zalite, K., Arumäe, T., & Voormansik, K. (2021). Assessing the utility of Sentinel-1 coherence time series for temperate and tropical forest mapping. Remote Sensing, 13(23), 4814. https://doi.org/10.3390/rs13234814

Özelkan, E., & Eren, E. (2025). Assessing urban sprawl and agricultural land loss: A 40-year remote sensing study in Çanakkale. Journal of Contemporary Urban Affairs, 9(2), 402–425. https://doi.org/10.25034/ijcua.2025.v9n2-5

Özer, B., & Yalçıner Ercoşkun, Ö. (2024). Assessing the impact of urbanization on flood risk by RS and GIS: A case study on Istanbul-Esenyurt. Journal of Contemporary Urban Affairs, 8(1), 57–78. https://doi.org/10.25034/ijcua.2024.v8n1-4

Pelletier, J., Hamalambo, B., Trainor, A. M., & Barrett, C. B. (2021). How land tenure and labor relations mediate charcoal’s environmental footprint in Zambia: Implications for sustainable energy transitions. World Development, 146, 105600. https://doi.org/10.1016/j.worlddev.2021.105600

Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.-E., Odongo-Braun, C., Vollrath, A., Weisse, M. J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., & Herold, M. (2021). Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters, 16(2), 024005. https://doi.org/10.1088/1748-9326/abd0a8

Ribeiro, N. S., Timberlake, J., Matimele, H., Alves Banze, J., Machipane, A., & Ribeiro, A. I. (2021). Carbon stocks in miombo woodlands: Evidence from over 50 years. Forests, 12(7), 862. https://doi.org/10.3390/f12070862

Ryan, C. M., Pritchard, R., McNicol, I., Owen, M., Fisher, J. A., & Lehmann, C. (2016). Ecosystem services from southern African woodlands and their future under global change. Philosophical Transactions of the Royal Society B, 371(1703), 20150312. https://doi.org/10.1098/rstb.2015.0312

Sedano, F., Mizu-Siampale, A., Duncanson, L., & Liang, M. (2022). Influence of charcoal production on forest degradation in Zambia: A remote sensing perspective. Remote Sensing, 14(14), 3352. https://doi.org/10.3390/rs14143352

Shendryk, Y. (2022). Fusing GEDI with Earth observation data for large area aboveground biomass mapping. International Journal of Applied Earth Observation and Geoinformation, 115, 103108. https://doi.org/10.1016/j.jag.2022.103108

Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., & Lucas, R. (2014). New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sensing of Environment, 155, 13–31. https://doi.org/10.1016/j.rse.2014.04.014

Simwanda, M., & Murayama, Y. (2020). Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach. Land Use Policy, 92, 104441. https://doi.org/10.1016/j.landusepol.2019.104441

Simwanda, M., Murayama, Y., Phiri, D., Nyirenda, V. R., & Ranagalage, M. (2021). Simulating scenarios of future intra-urban land-use expansion based on the neural network–Markov model: A case study of Lusaka, Zambia. Remote Sensing, 13(5), 942. https://doi.org/10.3390/rs13050942

Sumbo, D. K., Anane, G. K., & Inkoom, D. K. B. (2023). “Peri-urbanisation and loss of arable land”: Indigenes’ farmland access challenges and adaptation strategies in Kumasi and Wa, Ghana. Land Use Policy, 126, 106534. https://doi.org/10.1016/j.landusepol.2022.106534

Watanabe, M., Koyama, C. N., Hayashi, M., Nagatani, I., Tadono, T., & Shimada, M. (2021). Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions. Remote Sensing of Environment, 265, 112643. https://doi.org/10.1016/j.rse.2021.112643

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Published

2026-06-25

How to Cite

Nyongolo, M., Lubinda, F. ., Nyimbili, P. H., Nguvulu, A. ., Kilundo, A. ., & Mwanaumo, E. . (2026). Cloud-Resilient Forest Monitoring for Sustainable Urban Land Management: L-Band SAR Assessment of Charcoal-Driven Peri-Urban Woodland Change in Lusaka, Zambia. Journal of Contemporary Urban Affairs, 10(1), 282-295. https://doi.org/10.25034/ijcua.2026.v10n1-13

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