Identifying the Rates and Drivers of Spatiotemporal Patterns of Land Use and Land Cover Changes in the Hurungwe District, Zimbabwe: A GIS and Remote Sensing Approach
Identifying spatiotemporal patterns of land use and land cover changes (LULCC) and their impacts on the natural environment is essential in policy decisions for effective, sustainable natural resource management solutions. This study employed supervised image classification in Google Earth Engine (GEE) cloud-based platform to assess the land cover land use changes for the past 30 years (1989-2020), as well as predict the land cover states and the risk of future forest loss in the next ten years, using TerrSet 20 software in Hurungwe district, Zimbabwe. The study findings revealed a net forest area and shrub loss of 32% and 10%, while croplands, water bodies, and bare lands have increased by about 171%, 7%, and 119% between 1989 and 2020, respectively. Croplands are the major contributor to the net change in forests, particularly tobacco farming. The predictive model estimated that by 2030 the district would lose approximately 7% of the current forest cover area, most likely converted into croplands, shrubs, and settlements. The results reinforce the importance of bridging the gap between socioeconomic activities and institutional policies to ensure proper natural resource management. Integrating institutional policy and socioeconomic goals is indispensable to ensure sustainable development.
References
[1]
Kamusoko, C., Aniya, M., Adi, B. and Manjoro, M. (2009) Rural Sustainability Under Threat in Zimbabwe—Simulation of Future Land Use/Cover Changes in the Bindura District Based on the Markov-Cellular Automata Model. Applied Geography, 29, 435-447. https://doi.org/10.1016/j.apgeog.2008.10.002
[2]
Wang, S.W., Gebru, B.M., Lamchin, M., Kayastha, R.B. and Lee, W.K. (2020) Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS. Sustainability, 12, 3925.
https://doi.org/10.3390/su12093925
[3]
Mahamba, J.A., Mulondi, G.K., Kapiri, M.M. and Sahani, W.M. (2022) Land Use and Land Cover Dynamics in the Urban Watershed of Kimemi River (Butembo/D.R.C). Journal of Geoscience and Environment Protection, 10, 204-219.
https://doi.org/10.4236/gep.2022.106013
[4]
Negassa, M.D., Mallie, D.T. and Gemeda, D.O. (2020) Forest Cover Change Detection Using Geographic Information Systems and Remote Sensing Techniques: A Spatio-Temporal Study on Komto Protected Forest Priority Area, East Wollega Zone, Ethiopia. Environmental Systems Research, 9, Article No. 1.
https://doi.org/10.1186/s40068-020-0163-z
[5]
Armenteras, D., Murcia, U., González, T.M., Barón, O.J. and Arias, J.E. (2019) Scenarios of Land Use and Land Cover Change for NW Amazonia: Impact on Forest Intactness. Global Ecology and Conservation, 17, e00567.
https://doi.org/10.1016/j.gecco.2019.e00567
[6]
Vitousek, P.M., Mooney, H.A., Lubchenco, J. and Melillo, J.M. (1997) Human Domination of Earth’s Ecosystems. Science (1979), 277, 494-499.
https://doi.org/10.1126/science.277.5325.494
[7]
Sarathi Roy, P., et al. (2022) Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change. Journal of the Indian Society of Remote Sensing, 50, 1615-1640.
[8]
Vijay, V., Pimm, S.L., Jenkins, C.N. and Smith, S.J. (2016) The Impacts of Oil Palm on Recent Deforestation and Biodiversity Loss. PLOS ONE, 11, e0159668.
https://doi.org/10.1371/journal.pone.0159668
[9]
Masson-Delmotte, V., Zhai, P., Portner, H.-O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J.B.R., Chen, Y., Zhou, X., Gomis, M.I., Lonnoy, E., Maycock, T., Tignor, M. and Waterfield, T. (eds.) (2018) Global Warming of 1.5°C. IPCC.
[10]
Itoje-Akpokiniovo and Lilian, O. (2022) Knowledge and Perception of Climate Change in Ethiopia East Local Government Area of Delta State. Contemporary Journal of Social Science and Humanities, 3, 24-30.
[11]
Chávez Michaelsen, A., et al. (2017) Effects of Drought on Deforestation Estimates from Different Classification Methodologies: Implications for REDD+ and Other Payments for Environmental Services Programs. Remote Sensing Applications: Society and Environment, 5, 36-44. https://doi.org/10.1016/j.rsase.2017.01.003
[12]
Tarazona, Y. and Miyasiro-López, M. (2020) Monitoring Tropical Forest Degradation Using Remote Sensing. Challenges and Opportunities in the Madre de Dios Region, Peru. Remote Sensing Applications: Society and Environment, 19, Article ID: 100337. https://doi.org/10.1016/j.rsase.2020.100337
[13]
Naschen, K., Diekkrüger, B., Evers, M., Hollermann, B., Steinbach, S. and Thonfeld, F. (2019) The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios. Sustainability (Switzerland), 11, 7083. https://doi.org/10.3390/su11247083
[14]
Ngwenya, K. and Marambanyika, T. (2021) Trends in Use of Remotely Sensed Data in Wetlands Assessment and Monitoring in Zimbabwe. African Journal of Ecology, 59, 676-686. https://doi.org/10.1111/aje.12858
[15]
Zvobgo, L. and Tsoka, J. (2021) Deforestation Rate and Causes in Upper Manyame Sub-Catchment, Zimbabwe: Implications on Achieving National Climate Change Mitigation Targets. Trees, Forests and People, 5, Article ID: 100090.
https://doi.org/10.1016/j.tfp.2021.100090
[16]
Marondedze, A.K. and Schütt, B. (2019) Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion. Land, 8, 155. https://doi.org/10.3390/land8100155
[17]
Ndlovu, I., Nunu, W.N., Mudonhi, N., Dube, O. and Maviza, A. (2019) Land Use-Land Cover Changes and Mopani Worm Harvest in Mangwe District in Plumtree, Zimbabwe. Environmental Systems Research, 8, 1-9.
https://doi.org/10.1186/s40068-019-0141-5
[18]
Olorunfemi, I.E., et al. (2020) GIS and Remote Sensing-Based Analysis of the Impacts of Land Use/Land Cover Change (LULCC) on the Environmental Sustainability of Ekiti State, Southwestern Nigeria. Environment, Development and Sustainability, 22, 661-692. https://doi.org/10.1007/s10668-018-0214-z
[19]
Dimobe, K., Gessner, U., Ouédraogo, K. and Thiombiano, A. (2022) Trends and Drivers of Land Use/Cover Change in W National Park in Burkina Faso. Environmental Development, 44, Article ID: 100768.
https://doi.org/10.1016/j.envdev.2022.100768
[20]
Zoungrana, B.J.B., Conrad, C., Amekudzi, L.K., Thiel, M. and Dapola Da, E. (2015) Land Use/Cover Response to Rainfall Variability: A Comparing Analysis between NDVI and EVI in the Southwest of Burkina Faso. Climate, 3, 63-77.
https://doi.org/10.3390/cli3010063
[21]
Kamusoko, C. and Aniya, M. (2007) Land Use/Cover Change and Landscape Fragmentation Analysis in the Bindura District, Zimbabwe. Land Degradation & Development, 18, 221-233. https://doi.org/10.1002/ldr.761
[22]
Fakarayi, T., Mashapa, C., Gandiwa, E. and Kativu, S. (2015) Pattern of Land-Use and Land Cover Changes in Driefontein Grassland Important Bird Area, Zimbabwe. Tropical Conservation Science, 8, 274-283.
https://doi.org/10.1177/194008291500800120
[23]
2022 Population & Housing Census—Preliminary—Zimbabwe Data Portal.
https://zimbabwe.opendataforafrica.org/anjlptc/2022-population-housing-census-preliminary
[24]
Geist, H.J. (1999) Global Assessment of Deforestation Related to Tobacco Farming. Tobacco Control, 8, 18-28. https://doi.org/10.1136/tc.8.1.18
[25]
Zikhali, P. (2008) Environment for Development Discussion Paper Series Fast Track Land Reform and Agricultural Productivity in Zimbabwe Precious Zikhali.
https://www.efdinitiative.org
[26]
Moyo, S. (2011) Three Decades of Agrarian Reform in Zimbabwe. Journal of Peasant Studies, 38, 493-531. https://doi.org/10.1080/03066150.2011.583642
[27]
Fast Track Land Reform in Zimbabwe.
https://www.hrw.org/reports/2002/zimbabwe/ZimLand0302-02.htm
[28]
Tobacco and the Environment.
[29]
Waluye, J. (1994) Environmental Impact of Tobacco Growing in Tabora/Urambo, Tanzania. Tobacco Control, 3, 252-254. https://doi.org/10.1136/tc.3.3.252
[30]
Phillips, A. (1995) Bellagio Statement on Tobacco and Sustainable Development. Canadian Medical Association Journal, 153, 1109-1110.
[31]
Muller, M. (1978) Tobacco and the Third World—Tomorrow’s Epidemic? A War on Want Investigation into the Production, Promotion, and Use of Tobacco in the Developing Countries. War on Want, London.
[32]
Ruckert, A., et al. (2022) The Political Economy of Tobacco Production and Control in Zimbabwe.
[33]
Lawrence, M., Andrew Tapiwa, K., Lovemore, M. and Michael, M. (2020) Smallholder Tobacco Farmers and Forest Conservation in Mutasa District, Zimbabwe. Ecology and Evolutionary Biology, 5, 6. https://doi.org/10.11648/j.eeb.20200501.12
[34]
Jew, E.K.K., Dougill, A.J. and Sallu, S.M. (2017) Tobacco Cultivation as a Driver of Land Use Change and Degradation in the Miombo Woodlands of South-West Tanzania. Land Degradation & Development, 28, 2636-2645.
https://doi.org/10.1002/ldr.2827
[35]
Kamuti, T. (2018) The Critical Nexus and Implications of Smallholder Tobacco Production as a Livelihood Strategy to Forest Landscapes in Zimbabwe.
https://doi.org/10.20944/preprints201804.0114.v1
[36]
Magige Mwita James, B. (2018) Impacts of Tobacco Farming on Forest Cover in Bukira West/Bukira East Location, Migori County, Kenya. A Project Report Submitted in Partial Fulfillment of the Requirements of the Award for the Bachelor’s Degree in Environmental Planning and Management Department of Environmental Planning and Management Kenyatta University.
[37]
Kamusoko, C. and Chikati, E. (2017) Harare Metropolitan Area. In: Murayama, Y., et al., Eds., Urban Development in Asia and Africa: Geospatial Analysis of Metropolises, Springer, Berlin, 347-370. https://doi.org/10.1007/978-981-10-3241-7_17
[38]
Matsa, M., Mupepi, O. and Musasa, T. (2021) Spatio-Temporal Analysis of Urban Area Expansion in Zimbabwe between 1990 and 2020: The Case of Gweru City. Environmental Challenges, 4, Article ID: 100141.
https://doi.org/10.1016/j.envc.2021.100141
[39]
Liu, C., Li, W., Zhu, G., et al. (2020) Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture. Remote Sensing (Basel), 12, 3139. https://doi.org/10.3390/rs12193139
[40]
Allan, A., Soltani, A., Abdi, M.H. and Zarei, M. (2022) Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review. Land (Basel), 11, 1222. https://doi.org/10.3390/land11081222
[41]
Manatsa, D., Darlington Mushore, T. and Wuta, M. (2020) Report on Revised Agroecological Zones of Zimbabwe (In Press). IMPALA View Project Geospatial Capabilities for Revision of Zimbabwe’s Agroecological Zones View Project.
https://www.researchgate.net/publication/347966377
[42]
Fertilizer Use by Crop in Zimbabwe (2022).
https://www.fao.org/3/a0395e/a0395e06.htm
[43]
Dube, L. (2016) Factors Influencing Smallholder Crop Diversification: A Case Study of Manicaland and Masvingo Provinces in Zimbabwe. International Journal of Regional Development, 3, 1-25. https://doi.org/10.5296/ijrd.v3i2.9194
Teluguntla, P., et al. (2018) A 30-m Landsat-Derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 325-340. https://doi.org/10.1016/j.isprsjprs.2018.07.017
[46]
Amani, M., et al. (2019) A Generalized Supervised Classification Scheme to Produce Provincial Wetland Inventory Maps: An Application of Google Earth Engine for Big Geo Data Processing. Big Earth Data, 3, 378-394.
https://doi.org/10.1080/20964471.2019.1690404
[47]
Yang, L., Driscol, J., Sarigai, S., et al. (2022) Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sensing, 14, 3253.
https://doi.org/10.3390/rs14143253
[48]
Google Earth Engine. https://earthengine.google.com
[49]
Haralick, R.M. and Shanmugam, K. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610-621.
https://doi.org/10.1109/TSMC.1973.4309314
[50]
Gandhi, G.M., Parthiban, S., Thummalu, N. and Christy, A. (2015) Ndvi: Vegetation Change Detection Using Remote Sensing and Gis—A Case Study of Vellore District. Procedia Computer Science, 57, 1199-1210.
https://doi.org/10.1016/j.procs.2015.07.415
[51]
Pettorelli, N. (2013) The Normalized Difference Vegetation Index. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001
[52]
Khan, M.S.A. and Rahman, Md.I. (2021) Forest Land Analysis Using Normalized Difference Vegetation Index (NDVI): A Case Study of Bangladesh. International Journal of Computer Applications, 183, 15-19.
https://doi.org/10.5120/ijca2021921855
[53]
Luigi Crisigiovanni, E., Figueiredo Filho, A., Alex Pesck, V. and Aparecido de Lima, V. (2020) Potential of Machine Learning and WorldView-2 Images for Recognizing Endangered and Invasive Species in the Atlantic Rainforest. Annals of Forest Science, 78, Article No. 54. https://doi.org/10.1007/s13595-021-01070-3
[54]
Alencar, A., et al. (2020) Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sensing (Basel), 12, 924. https://doi.org/10.3390/rs12060924
[55]
Breiman, L. (2001) Random Forests.
[56]
Congalton, R.G. (1991) A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, 35-46.
https://doi.org/10.1016/0034-4257(91)90048-B
[57]
Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. (1976) A Land Use and Land Cover Classification System for Use with Remote Sensor Data.
https://doi.org/10.3133/pp964
[58]
Kotr, J.W. and Higgins, C.C. (2001) Information Technology, Learning, and Performance.
[59]
Hamad, R., Balzter, H. and Kolo, K. (2018) Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios. Sustainability, 10, 3421. https://doi.org/10.3390/su10103421
[60]
Floreano, I.X. and de Moraes, L.A.F. (2021) Land Use/Land Cover (LULC) Analysis (2009-2019) with Google Earth Engine and 2030 Prediction Using Markov-CA in the Rondonia State, Brazil. Environmental Monitoring and Assessment, 193, 239.
https://doi.org/10.1007/s10661-021-09016-y
[61]
Sibanda, M., et al. (2022) Correction: Sibanda et al. Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South (Drones 2021, 5, 84). Drones, 6, 131. https://doi.org/10.3390/drones6050131
[62]
Sibanda, M., Dube, T., Mubango, T. and Shoko, C. (2016) The Utility of Earth Observation Technologies in Understanding Impacts of Land Reform in the Eastern Region of Zimbabwe. Journal of Land Use Science, 11, 384-400.
https://doi.org/10.1080/1747423X.2015.1130756
[63]
Soropa, G., et al. (2021) Spatial Variability and Mapping of Soil Fertility Status in a High-Potential Smallholder Farming Area under Sub-Humid Conditions in Zimbabwe. SN Applied Sciences, 3, Article No. 396.
https://doi.org/10.1007/s42452-021-04367-0
[64]
Useya, J., Chen, S. and Murefu, M. (2019) Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory. IEEE Access, 7, 53603-53620.
https://doi.org/10.1109/ACCESS.2019.2912807
[65]
Flamenco-Sandoval, A., Martínez Ramos, M. and Masera, O.R. (2007) Assessing Implications of Land-Use and Land-Cover Change Dynamics for Conservation of a Highly Diverse Tropical Rain Forest. Biological Conservation, 138, 131-145.
https://doi.org/10.1016/j.biocon.2007.04.022
[66]
Chipika, J.T. and Kowero, G. (2000) Deforestation of Woodlands in Communal Areas of Zimbabwe: Is It Due to Agricultural Policies? Agriculture, Ecosystems & Environment, 79, 175-185. https://doi.org/10.1016/S0167-8809(99)00156-5
[67]
Jombo, S., Adam, E. and Odindi, J. (2017) Quantification of Landscape Transformation Due to the Fast Track Land Reform Programme (FTLRP) in Zimbabwe Using Remotely Sensed Data. Land Use Policy, 68, 287-294.
https://doi.org/10.1016/j.landusepol.2017.07.023
