Assessing the Impacts of Land Use Land Cover Change In Mutama Bweengwa Catchment 0f Southern Province, Zambia
Keywords:
land use land cover (LULC); change detection; Landsat images; Data; Classification
Abstract
Climate change and land use land cover have a direct impact on the alteration of hydrological cycles, making water more unpredictable and increasing the frequency and intensity of floods and droughts. However, proper planning of adaptation and mitigation options is hampered by inadequate up-to-date information on land use/Land cover in many catchments and sub-catchments of Zambia and other developing countries. In this study, we assessed the land use change in the Mutama-Bweengwa River Catchment of Southern Zambia. The objective of the study was to investigate land use land cover changes (LULCC) in the Mutama Bweengwa Catchment in the Southern Province of Zambia from 2000 to 2021. The data used for the study were satellite images of the area downloaded from the United States geological survey (USGS). Specifically, the Landsat images were from path 172/row 71 and path 172/row 72 for the period 2000, 2007, 2014 and 2021. The methods used included data identification and acquisition, image pre-processing, image processing, accuracy assessment, validation and presentation. Image pre-processing was used to correct distortions during image acquisition, the techniques used were; Image enhancement for extracting useful information, this involved carrying out band combination and brightness and contrast adjustment when conducting the mosaicking process using Erdas imagine 2014. Supervised classification based on the maximum likelihood algorithm in ERDAS Imagine was employed to generate the land use land cover classification and later exported in ArcMap 10.7.1 for map creation. The image classification was based on six different LULC classes which were; Water body, build up/settlement, forest, cultivated land-rainfed/bare land, cultivated land-irrigated, and grasslands. Preliminary results of this study have shown a decrease in the classes of water bodies and forest areas by 0.34 % and 55.5% respectively over the 20-year period. The accuracy of the resultant land use/landcover maps was evaluated with the kappa statistic and error matrix. The preliminary results have also shown an increase in the land use land cover classes categories of cultivated land-irrigated, grassland, cultivated land-rain fed/bare land and built up/settlements by 0.13 %, 46.7%, 14.6% and 8.4% respectively. In conclusion, the Supervised classification of the Landsat images indicated pronounced land cover changes over the 20-year period. Although this provides preliminary conclusions, it indicates that immediate actions should be taken to protect the sub-catchment from further loss of land cover by strengthening the regulatory framework. It is expected that further work on the project will bring out some of the factors that have contributed to this change.References
1. J M, S W, B G, KKG C. Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing: A Case Study of Kieni, Central Kenya. J Remote Sens GIS. 2020;09(01):1–5.
2. Emission GG, Paul BK, Rashid H. Land Use Change Land Use Change and Coastal Manage- ment Evaluation of land use change pre- dictions using CA-Markov model and management scenarios Land use change , climate change , and river basin management : A preliminary study in small river basin of. 2017;
3. Tewabe D, Fentahun T. Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environ Sci [Internet]. 2020;6(1). Available from: https://doi.org/10.1080/23311843.2020.1778998
4. Government of Zambia. The Water Resources Management Act. Water. 2011;(21):265–378.
5. Tena TM, Nguvulu A, Mwelwa D, Mwaanga P. Assessing Water Availability and Unmet Water Demand Using the WEAP Model in the Semi-Arid Bweengwa, Kasaka and Magoye Sub-Catchments of Southern Zambia. J Environ Prot (Irvine, Calif). 2021;12(04):280–95.
6. Salman MA, Nomaan MSS, Sayed S, Saha A, Rafiq MR. Land Use and Land Cover Change Detection By Using Remote Sensing and Gis Technology in Barishal District, Bangladesh. Earth Sci Malaysia. 2020;5(1):33–40.
7. Alam KF, Ahamed T. Assessment of Land Use Land Cover Changes for Predicting Vulnerable Agricultural Lands in River Basins of Bangladesh Using Remote Sensing and a Fuzzy Expert System. Remote Sens. 2022;14(21):5582.
8. Tahiru AA, Doke DA, Baatuuwie BN. Effect of land use and land cover changes on water quality in the Nawuni Catchment of the White Volta Basin, Northern Region, Ghana. Appl Water Sci [Internet]. 2020;10(8):1–14. Available from: https://doi.org/10.1007/s13201-020-01272-6
9. Takam Tiamgne X, Kalaba FK, Nyirenda VR. Land use and cover change dynamics in Zambia’s Solwezi copper mining district. Sci African [Internet]. 2021;14:e01007. Available from: https://doi.org/10.1016/j.sciaf.2021.e01007
10. Silva JA, Sedano F, Flanagan S, Ombe ZA, Machoco R, Meque CH, et al. Charcoal-related forest degradation dynamics in dry African woodlands: Evidence from Mozambique. Appl Geogr [Internet]. 2019;107(April):72–81. Available from: https://doi.org/10.1016/j.apgeog.2019.04.006
11. Day M, Gumbo D, Moombe KB, Wijaya A, Sunderland T. Zambia country profile: Monitoring, reporting and verification for REDD+. Occasional Paper 113. Bogor, Indonesia: CIFOR. 2014. 1–48 p.
12. Wang L, Mondela CL, Kuuluvainen J. Striking a Balance between Livelihood and Forest Conservation in a Forest Farm Facility in Choma, Zambia. Forests. 2022;13(10).
13. Sang CC, Olago DO, Nyumba TO, Marchant R, Thorn JPR. Assessing the Underlying Drivers of Change over Two Decades of Land Use and Land Cover Dynamics along the Standard Gauge Railway Corridor, Kenya. Sustain. 2022;14(10).
14. Nedd R, Light K, Owens M, James N, Johnson E, Anandhi A. Knowledge Gaps on a Global Landscape. Land. 2021;10(2020):1–30.
15. Pfaff A, Amacher GS, Sills EO, Coren MJ, Streck C, Lawlor K. Deforestation and Forest Degradation: Concerns, Causes, Policies, and Their Impacts [Internet]. 1st ed. Vols. 2–3, Encyclopedia of Energy, Natural Resource, and Environmental Economics. Elsevier Inc.; 2013. 144–149 p. Available from: http://dx.doi.org/10.1016/B978-0-12-375067-9.00052-8
2. Emission GG, Paul BK, Rashid H. Land Use Change Land Use Change and Coastal Manage- ment Evaluation of land use change pre- dictions using CA-Markov model and management scenarios Land use change , climate change , and river basin management : A preliminary study in small river basin of. 2017;
3. Tewabe D, Fentahun T. Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environ Sci [Internet]. 2020;6(1). Available from: https://doi.org/10.1080/23311843.2020.1778998
4. Government of Zambia. The Water Resources Management Act. Water. 2011;(21):265–378.
5. Tena TM, Nguvulu A, Mwelwa D, Mwaanga P. Assessing Water Availability and Unmet Water Demand Using the WEAP Model in the Semi-Arid Bweengwa, Kasaka and Magoye Sub-Catchments of Southern Zambia. J Environ Prot (Irvine, Calif). 2021;12(04):280–95.
6. Salman MA, Nomaan MSS, Sayed S, Saha A, Rafiq MR. Land Use and Land Cover Change Detection By Using Remote Sensing and Gis Technology in Barishal District, Bangladesh. Earth Sci Malaysia. 2020;5(1):33–40.
7. Alam KF, Ahamed T. Assessment of Land Use Land Cover Changes for Predicting Vulnerable Agricultural Lands in River Basins of Bangladesh Using Remote Sensing and a Fuzzy Expert System. Remote Sens. 2022;14(21):5582.
8. Tahiru AA, Doke DA, Baatuuwie BN. Effect of land use and land cover changes on water quality in the Nawuni Catchment of the White Volta Basin, Northern Region, Ghana. Appl Water Sci [Internet]. 2020;10(8):1–14. Available from: https://doi.org/10.1007/s13201-020-01272-6
9. Takam Tiamgne X, Kalaba FK, Nyirenda VR. Land use and cover change dynamics in Zambia’s Solwezi copper mining district. Sci African [Internet]. 2021;14:e01007. Available from: https://doi.org/10.1016/j.sciaf.2021.e01007
10. Silva JA, Sedano F, Flanagan S, Ombe ZA, Machoco R, Meque CH, et al. Charcoal-related forest degradation dynamics in dry African woodlands: Evidence from Mozambique. Appl Geogr [Internet]. 2019;107(April):72–81. Available from: https://doi.org/10.1016/j.apgeog.2019.04.006
11. Day M, Gumbo D, Moombe KB, Wijaya A, Sunderland T. Zambia country profile: Monitoring, reporting and verification for REDD+. Occasional Paper 113. Bogor, Indonesia: CIFOR. 2014. 1–48 p.
12. Wang L, Mondela CL, Kuuluvainen J. Striking a Balance between Livelihood and Forest Conservation in a Forest Farm Facility in Choma, Zambia. Forests. 2022;13(10).
13. Sang CC, Olago DO, Nyumba TO, Marchant R, Thorn JPR. Assessing the Underlying Drivers of Change over Two Decades of Land Use and Land Cover Dynamics along the Standard Gauge Railway Corridor, Kenya. Sustain. 2022;14(10).
14. Nedd R, Light K, Owens M, James N, Johnson E, Anandhi A. Knowledge Gaps on a Global Landscape. Land. 2021;10(2020):1–30.
15. Pfaff A, Amacher GS, Sills EO, Coren MJ, Streck C, Lawlor K. Deforestation and Forest Degradation: Concerns, Causes, Policies, and Their Impacts [Internet]. 1st ed. Vols. 2–3, Encyclopedia of Energy, Natural Resource, and Environmental Economics. Elsevier Inc.; 2013. 144–149 p. Available from: http://dx.doi.org/10.1016/B978-0-12-375067-9.00052-8
Published
2023-03-02
How to Cite
1.
Lungomesha S, Chabala L. Assessing the Impacts of Land Use Land Cover Change In Mutama Bweengwa Catchment 0f Southern Province, Zambia. Journal of Agricultural and Biomedical Sciences [Internet]. 2Mar.2023 [cited 21Nov.2024];6(2). Available from: https://law.unza.zm/index.php/JABS/article/view/933
Section
Agriculture Sciences
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