Publication Cover
Sustainable Environment
An international journal of environmental health and sustainability
Volume 10, 2024 - Issue 1
1,033
Views
0
CrossRef citations to date
0
Altmetric
Ecology

Impacts of farming and herding activities on land use and land cover changes in the north eastern corridor of Ghana: A comprehensive analysis

ORCID Icon, , &
Article: 2307229 | Received 03 Sep 2023, Accepted 15 Jan 2024, Published online: 29 Jan 2024

References

  • Abass, K., Adanu, S. K., & Gyasi, R. M. (2018). Urban sprawl and land use/land-cover transition probabilities in peri-urban Kumasi, Ghana. West African Journal of Applied Ecology, 26(Special Issue), 118–17.
  • Abdu, H. A. (2018). Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images. International Journal of Remote Sensing, 40(4), 1275–1300. https://doi.org/10.1080/01431161.2018.1524587
  • Addae, B., & Oppelt, N. (2019). Land-use/land-cover change analysis and urban growth modelling in the Greater Accra Metropolitan Area (GAMA), Ghana. Urban Science, 3(1), 26. https://doi.org/10.3390/urbansci3010026
  • Akubia, J. E. K., Ahmed, A., & Bruns, A. (2020). Assessing how land-cover change associated with urbanisation affects ecological sustainability in the greater Accra metropolitan area, Ghana. Land, 9(6), 182. https://doi.org/10.3390/LAND9060182
  • Alhassan, I. A. (2017). Land-use conflicts between settler farmers and nomadic Fulani Herdsmen in the Kwahu North District, Ghana. Contemporary Journal of African Studies Vol, 4(2), 127–154. https://doi.org/10.4314/contjas.v4i2.5
  • Al-Taisan, W. A. (2022). A Remote Sensing Approach for Displaying the Changes in the Vegetation Cover at Az Zakhnuniyah Island at Arabian Gulf, Saudi Arabia. Scientifica, 2022, 1–14. https://doi.org/10.1155/2022/2907921
  • Altmann, B. A., Jordan, G., & Schlecht, E. (2018). Participatory mapping as an approach to identify grazing pressure in the Altay Mountains, Mongolia. Sustainability (Switzerland), 10(6), 1–15. https://doi.org/10.3390/su10061960
  • Álvares, J. S., Costa, D. B., & Melo, R. R. S. D. (2018). Exploratory study of using unmanned aerial system imagery for construction site 3D mapping. Construction Innovation, 18(3), 301–320. https://doi.org/10.1108/CI-05-2017-0049
  • Alvino, F. C. G., Aleman, C. C., Filgueiras, R., Althoff, D., & Cunha, F. F. (2020). VEGETATION INDICES for IRRIGATED CORN MONITORING field level. To solve such a problem, remote sensing techniques have been used to therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Engenharia Agrícola, 4430(3), 322–333. https://doi.org/10.1590/1809-4430-eng.agric.v40n3p322-333/2020
  • Aly, A. A., Al-Omran, A. M., Sallam, A. S., Al-Wabel, M. I., & Al-Shayaa, M. S. (2016). Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach. Advances in Fission-Track Geochronology, 7(2), 713–725. https://doi.org/10.5194/se-7-713-2016
  • Amarappa, S., & Sathyanarayana, S. V. (2011). Data classification using Support vector Machine (SVM), a simplified approach. International Journal of Electronics and Computer Science Engineering, 3, 435–445. www.ijecse.org
  • Ampim, P. A. Y., Ogbe, M., Obeng, E., Akley, E. K., & Maccarthy, D. S. (2021). Land cover changes in ghana over the past 24 years. Sustainability, 13(9), 4951. Switzerland), 13(9. https://doi.org/10.3390/su13094951
  • Aniah, P., Bawakyillenuo, S., Codjoe, S. N. A., & Dzanku, F. M. (2023). Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana. Environmental Challenges, 10(December), 100664. https://doi.org/10.1016/j.envc.2022.100664
  • Arabameri, A., Chandra Pal, S., Rezaie, F., Chakrabortty, R., Saha, A., Blaschke, T., DiNapoli, M., Ghorbanzadeh, O., & Thi Ngo, P. T. (2022). Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International, 37(16), 4594–4627. https://doi.org/10.1080/10106049.2021.1892210
  • Avci, C., Budak, M., Yagmur, N., & Balcik, F. B. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8(1), 1–10. https://doi.org/10.26833/ijeg.987605
  • Awad, M. M. (2018). Forest mapping: A comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29(5), 1395–1405. https://doi.org/10.1007/s11676-017-0528-y
  • Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., Mengistu, D. T., & Teshale, E. Z. (2018). Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research, 11, 117862211775160. https://doi.org/10.1177/1178622117751603
  • Baik, H.-S., Jeong, H. S., & Abraham, D. M. (2006). Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132(1), 15–24. https://doi.org/10.1061/(asce)0733-9496(2006)132:1(15)
  • Baik, H., Seok, H., Jeong, D., & Abraham, D. M. (2006). Deterioration Models for Management of Wastewater Systems. Journal of Water Resources Planning and Management, 132(February), 15–24. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:1(15)
  • Barnetson, J., Phinn, S., & Scarth, P. (2019). Mapping woody vegetation cover across Australia’s arid rangelands: Utilising a machine-learning classification and low-cost remotely piloted aircraft system. International Journal of Applied Earth Observation and Geoinformation, 83(June), 101909. https://doi.org/10.1016/j.jag.2019.101909
  • Bessah, E., Bala, A., Agodzo, S. K., Okhimamhe, A. A., Boakye, E. A., & Ibrahim, S. U. (2019). The impact of crop farmers’ decisions on future land use, land cover changes in Kintampo North Municipality of Ghana. International Journal of Climate Change Strategies and Management, 11(1), 72–87. https://doi.org/10.1108/IJCCSM-05-2017-0114
  • Bin, K. M. (2008). IDENTIFYING LAND USE CHANGES and IT ’ S SOCIO-ECONOMIC IMPACTS ; a CASE STUDY of CHAKORIA SUNDARBAN in BANGLADESH. In Science (issue June). Linköping University.
  • Boateng, F. O., & Aduah, M. S. (2022). Mapping and predicting land use land cover dynamics in the. Ghana Journal of Technology, 6(2), 28–35.
  • Braimoh, A. K., & Vlek, P. L. G. (2004). Land-cover dynamics in an urban area of Ghana. Earth Interactions, 8(1), 1–15. https://doi.org/10.1175/1087-3562(2004)8<1:LCAITV>2.0.CO;2
  • Burley, T. M. (1961). Land use or land utilization. The Professional Geographer, 13(6), 18–20. https://doi.org/10.1111/j.0033-0124.1961.136_18.x
  • Casamitjana, M., Torres-Madroñero, M. C., Bernal-Riobo, J., & Varga, D. (2020). Applied sciences soil moisture analysis by means of multispectral images according to land use and spatial resolution on andosols in the Colombian andes. Applied Sciences, 10(16), 5540. https://doi.org/10.3390/app10165540
  • Chamling, M., & Bera, B. (2020). Spatio-temporal patterns of land Use/land cover change in the Bhutan–bengal foothill region between 1987 and 2019: Study towards geospatial applications and policy making. Earth Systems and Environment, 4(1), 117–130. https://doi.org/10.1007/s41748-020-00150-0
  • Conyers, L. B. (2016). Ground-penetrating radar mapping using multiple processing and interpretation methods. Remote Sensing, 8(7), 562. https://doi.org/10.3390/rs8070562
  • Csanyi, N., & Toth, C. K. (2007). Improvement of lidar data accuracy using lidar-specific ground targets. Photogrammetric Engineering and Remote Sensing, 73(4), 385–396. https://doi.org/10.14358/PERS.73.4.385
  • Cudjoe, S. N., Azure, T., & Assem, C. (2013). Population and Housing Ceensus Report. Ghana Statistical Service.
  • Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., & Swift, L. W. (2017). High-resolution precipitation mapping in a mountainous watershed: Ground truth for evaluating uncertainty in a national precipitation dataset. International Journal of Climatology, 37(February), 124–137. https://doi.org/10.1002/joc.4986
  • Dent, D., & Tucker, C. J. (2015). Use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales the use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales : A review of the current status, future. Issue January. https://doi.org/10.1007/978-3-319-24112-8
  • Everaerts, J. (2014). The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciendes, 37(Inter-Commission WG I/V), XXXVII(B1), 1187–1191.
  • Fahad, S., Tariq, A., Mousa, B. G., Mumtaz, F., & Aslam, M. (2022). Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Land, 11(5), 595. https://doi.org/10.3390/land11050595
  • Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772
  • Forkuo, E. K., & Frimpong, A. (2012). Analysis of forest cover change detection. International Journal of Remote Sensing Applications, 2(4), 82–92.
  • Forkuor, G. (2014). Agricultural Land Use Mapping in West Africa Using Multi-sensor Satellite Imagery Kartierung landwirtschaftlicher Landnutzung unter Verwendung multi-sensoraler Satellitendaten. Dissertation to obtain a doctorate in natural sciences, 20 June 2023. Julius Maximilian University of Würzburg. https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/deliver/index/docId/10868/file/thesis_gerald_forkuor_2014.pdf
  • Fu, B., He, X., Yao, H., Liang, Y., Deng, T., He, H., Fan, D., Lan, G., & He, W. (2022). Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images. International Journal of Applied Earth Observation and Geoinformation, 112(June), 102890. https://doi.org/10.1016/j.jag.2022.102890
  • Fu, B., Liang, Y., Lao, Z., Sun, X., Li, S., He, H., Sun, W., & Fan, D. (2023). Quantifying scattering characteristics of mangrove species from optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series. International Journal of Applied Earth Observation and Geoinformation, 122(August), 103446. https://doi.org/10.1016/j.jag.2023.103446
  • Gasirabo, A., Xi, C., Hamad, B. R, & Edovia, U. D. (2023). A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda. Land, 12(9), 1788. https://doi.org/10.3390/land12091788
  • Govender, M., Chetty, K., Naiken, V., & Bulcock, H. (2008). A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation. Water SA, 34(2), 147–154. https://doi.org/10.4314/wsa.v34i2.183634
  • Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing & Space Science, 21(1), 37–47. https://doi.org/10.1016/j.ejrs.2016.12.005
  • Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., & Kumar, V. (2016). Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine, 4(2), 8–21. https://doi.org/10.1109/MGRS.2016.2528038
  • Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173(December 2020), 24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
  • Koranteng, A., Frimpong, B. F., Adu-Poku, I., Asamoah, J. N., & Zawiła-Niedźwiecki, T. (2023). Assessment of past and future land use/land cover dynamics of the old kumasi metropolitan assembly and Atwima Nwabiagya Municipal Area, Ghana. Journal of Geoscience and Environment Protection, 11(3), 44–69. https://doi.org/10.4236/gep.2023.113004
  • Köster, E., Köster, K., Aurela, M., Laurila, T., Berninger, F., Lohila, A., & Pumpanen, J. (2013). Impact of reindeer herding on vegetation biomass and soil carbon content: A case study from Sodankylä, Finland. Boreal Environment Research, 18(SUPPL.A), 35–42.
  • Kutir, C., Agblorti, S. K. M., & Campion, B. B. (2022). Migration and estuarine land use/land cover (LULC) change along Ghana’s coast. Regional Studies in Marine Science, 54, 102488. https://doi.org/10.1016/j.rsma.2022.102488
  • Kutlug Sahin, E., & Colkesen, I. (2021). Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto International, 36(11), 1253–1275. https://doi.org/10.1080/10106049.2019.1641560
  • Kyei-Poakwah, K. (2018). Understanding Farmers and Herdsmen conflict, the case of crop farmers and Fulani herders in the Asante Akim North District. Unpublished MPhil thesis in Political science. University of Ghana.
  • Macarringue, L. S., Bolfe, É. L., Roberto, P., & Pereira, M. (2022). Developments in land use and land cover classification techniques in remote sensing : A review. https://doi.org/10.4236/jgis.2022.141001
  • Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645–657. https://doi.org/10.1109/TGRS.2016.2612821
  • Mashala, M. J., Dube, T., Mudereri, B. T., Ayisi, K. K., & Ramudzuli, M. R. (2023). A systematic review on advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi-arid tropical environments. Remote Sensing, 15(16), 3926. https://doi.org/10.3390/rs15163926
  • Masood, M. U., Haider, S., Rashid, M., Aldlemy, M. S., Pande, C. B., Đurin, B., Homod, R. Z., Alshehri, F., & Elkhrachy, I. (2023). Quantifying the impacts of climate and land cover changes on the hydrological regime of a complex dam catchment area. Sustainability, 15(21), 15223. https://doi.org/10.3390/su152115223
  • McIver, D. K., & Friedl, M. A. (2001). Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1959–1968. https://doi.org/10.1109/36.951086
  • Mekasha, A., Gerard, B., Tesfaye, K., Nigatu, L., & Duncan, A. J. (2014). Inter-connection between land use/land cover change and herders’/farmers’ livestock feed resource management strategies: A case study from three Ethiopian eco-environments. Agriculture, Ecosystems and Environment, 188, 150–162. https://doi.org/10.1016/j.agee.2014.02.022
  • Mengistu, T., Teketay, D., Hulten, H., & Yemshaw, Y. (2005). The role of enclosures in the recovery of woody vegetation in degraded dryland hillsides of central and northern Ethiopia. Journal of Arid Environments, 60(2), 259–281. https://doi.org/10.1016/j.jaridenv.2004.03.014
  • Mihi, A., Tarai, N., & Chenchouni, H. (2019). Can palm date plantations and oasification be used as a proxy to fight sustainably against desertification and sand encroachment in hot drylands? Ecological Indicators, 105, 365–375. https://doi.org/10.1016/j.ecolind.2017.11.027
  • Miyazaki, H., Iwao, K., & Shibasaki, R. (2011). Development of a new ground truth database for global urban area mapping from a gazetteer. Remote Sensing, 3(6), 1177–1187. https://doi.org/10.3390/rs3061177
  • Nébié, E. K. I., West, C. T., & Crane, T. A. (2020). “Where’s the map?”: Integrating ethnography with maps to understand the complementarity between pastoral mobility and border formation. Journal of Political Ecology, 27(1), 795–818. https://doi.org/10.2458/v27i1.23152
  • Olorunfemi, I. E., Fasinmirin, J. T, Olufayo, A. A., & Fasinmirin, A. A. (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(0123456789), 661–692. https://doi.org/10.1007/s10668-018-0214-z
  • Onyango, O. D. (2015). Application of hyper- temporal ndvi data in grassland mapping and biomass estimation in the masai mara ecosystem, kenya Application of hyper- temporal ndvi data in grassland mapping and biomass estimation in the masai mara ecosystem.
  • Pande, C. B., Kadam, S. A., Jayaraman, R., Gorantiwar, S., & Shinde, M. (2022). Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods. Journal of the Saudi Society of Agricultural Sciences, 21(1), 21–28. https://doi.org/10.1016/j.jssas.2021.06.016
  • Pande, C. B., Moharir, K. N., Kumar Singh, S., Varade, A. M., Elbeltagi, A., Khadri, S. F. R., & Choudhari, P. (2021). Estimation of crop and forest biomass resources in a semi-arid region using satellite data and GIS. Journal of the Saudi Society of Agricultural Sciences, 20(5), 302–311. https://doi.org/10.1016/j.jssas.2021.03.002
  • Rahman, M. H., Okubo, A., Sugiyama, S., & Mayland, H. F. (2008). Physical, chemical and microbiological properties of an andisol as related to land use and tillage practice. Soil and Tillage Research, 101(1–2), 10–19. https://doi.org/10.1016/j.still.2008.05.006
  • Rapinel, S., Hubert-Moya, L., & Clémentb’, B. (2015). Combined use of lidar data and multispectral earth observation imagery for wetland habitat mapping. International Journal of Applied Earth Observation and Geoinformation, 37, 56–64. https://doi.org/10.1016/j.jag.2014.09.002
  • Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., & Roberts, D. (2008). Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, 112(5), 2272–2283. https://doi.org/10.1016/j.rse.2007.10.004
  • Rossiter, D. G. (2004). Technical note: Statistical methods for accuracy assesment of classified thematic maps. GeoInformation Science, 1–46. http://www.itc.nl/personal/rossiter/teach/R/R_ac.pdf
  • Shetty S. (2019). Analysis of machine learning classifiers for LULC classification on Google earth engine. Master’s thesis Geo-information Science and Earth Observation. University of Twente. https://essay.utwente.nl/83543/1/shetty.pdf
  • Sousa, V., Salami, G., Isabelle, M., Silva, E. A., Jorge, J., Junior, M., & Alba, E. (2020). Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology & Landscapes, 4(2), 159–169. https://doi.org/10.1080/24749508.2019.1608409
  • Talukdar, S., Singha, P., Mahato, S., Shahfahad Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071135
  • Tang, W., Hu, J., Zhang, H., Wu, P., & He, H. (2015). Kappa coefficient: A popular measure of rater agreement. Shanghai Archives of Psychiatry, 27(1), 62–67. https://doi.org/10.11919/j.issn.1002-0829.215010
  • Tolera A. (2012). Feed resources availability and quality vs. Animal productivity. In A. Tolera,A. Yami, &D. Alemu (Eds.), Livestock Feed Resources in Ethiopia; Challenges, Opportunities and the Need for Transformation (pp. 37–46). Ethiopian Animal Feeds Industry Association.
  • Ural, S., Hussain, E., & Shan, J. (2011). Building population mapping with aerial imagery and GIS data. International Journal of Applied Earth Observation and Geoinformation, 13(6), 841–852. https://doi.org/10.1016/j.jag.2011.06.004
  • Verpoorter, C., Kutser, T., & Tranvik, L. (2012). Automated mapping of water bodies using landsat multispectral data. Limnology and Oceanography: Methods, 10(DECEMBER), 1037–1050. https://doi.org/10.4319/lom.2012.10.1037
  • Viana, C. M., & Rocha, J. (2020). Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method. Sustainability, 12(10), 4332. Switzerland), 12(10. https://doi.org/10.3390/su12104332
  • Wangyel, S., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan ’ s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2(January2021). https://doi.org/10.1016/j.envc.2020.100017
  • Wiesner, C., Vliet Van, V., Butt, E., Pavensta, H., Sto, M., Linder, S., & Kremerskothen, J. (2012). A meta-analysis of global urban land expansion. PloS One, 7(4), 1–10. https://doi.org/10.1371/Citation
  • Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1–17. https://doi.org/10.1155/2017/1353691
  • Yenibehit, N., Amikuzuno, J., & Abubakari, A. (2022). Factors influencing changing boundaries and/or routes of Fulani herders in the north eastern corridors of Ghana: A count model approach. Asian Journal of Agricultural Extension, Economics & Sociology, 40(11), 414–429. https://doi.org/10.9734/AJAEES/2022/v40i111727
  • Yiran, G. A. B., Kusimi, J. M., & Kufogbe, S. K. (2012). A synthesis of remote sensing and local knowledge approaches in land degradation assessment in the Bawku East District, Ghana international journal of applied earth observation and geoinformation a synthesis of remote sensing and local knowledge approach. International Journal of Applied Earth Observations & Geoinformation, 14(1), 204–213. https://doi.org/10.1016/j.jag.2011.09.016
  • Yu, Z., Di, L., Yang, R., Tang, J., Lin, L., Zhang, C., Rahman, M. S., Zhao, H., Gaigalas, J., Yu, E. G., & Sun, Z. (2019). Selection of landsat 8 OLI band combinations for land use and land cover classification. 2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019, July. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820595
  • Zakaria, H., Abujaja, A. M., & Adam, H. (2014). Socioeconomic analysis of smallholder yam production in north eastern corridors of Northern Ghana: Does type of planting material used makes any difference. International Journal of Agriculture Innovations and Research, 3(4), 1043–1051.