UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
The Federal University of Technology at Akure (FUTA) in Nigeria is experiencing ongoing development that is leading to the replacement of agricultural and forestry land cover types. This study aimed to assess and predict changes in land use/land cover (LULC) types and their impact on crop characteristics in 17 plots of FUTA from 1991 to 2031. Crop characteristics were evaluated using the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), vegetation condition index (VCI), watershed delineation, and spectral characteristics using Landsat imageries. The land change modeler in TerraSet software was used to predict future LULC scenarios. Results showed an increase in built-up areas (15%) and bare land areas (14%), but a reduction of 19% in light vegetation cover from 1991 to 2021. The predicted LULC map illustrated a decrease in light vegetation area (11%) and an increase in built-up area (19%) from 1991 to 2031. NDVI values denoting vegetation health and coverage extent, and NDWI & NDMI values indicating water stress in vegetation and soil showed that palm tree (Plot 1) had the highest average indices values (0.31, 0.34, and 0.06, respectively), while mixed cropping of cassava, cashew, and potatoes (Plot 6) had the lowest average indices values (0.23, 0.28, and ?0.029 respectively). This indicates that Plot 1 (palm tree) had better vegetation health with higher green canopy coverage and lower water stress in vegetation and soil compared to Plot 6 (cassava, cashew, and mixed potato vegetation). Drought analysis (VCI) showed that light drought areas became severe drought areas during 20012021 in Plots 4 and 6. The reduction of green cover and growing built-up areas accelerated the increase in drought severity. This study advocates for sustainable land use management to manage water stress and drought at the field level. 2023 The Authors |
References |
Adugna, T., Xu, W., Fan, J., 2022. Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Rem. Sens. 14 (3), 574. Al Kafy, A., Dey, N.N., Al Rakib, A., Rahaman, Z.A., Nasher, N.M.R., Bhatt, A., 2021. Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm. Environ. Challeng. 4 (June), 100190 https://doi.org/10.1016/j.envc.2021.100190. Al Kafy, A.-, et al., 2023a. Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms. Sci. Total Environ. 867, 161394 https://doi.org/10.1016/j.scitotenv.2023.161394. Al Kafy, A., et al., 2023b. Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms. Sci. Total Environ. 867 (August 2022), 161394 https://doi.org/10.1016/j.scitotenv.2023.161394. AlDousari, A.E., et al., 2022a. Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustain. Cities Soc. 86 (August), 104107 https://doi.org/10.1016/j.scs.2022.104107. AlDousari, A.E., et al., 2022b. Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustain. Cities Soc. 86, 104107 https://doi.org/10.1016/j.scs.2022.104107. AlDousari, A.E., Al Kafy, A.-, Saha, M., Fattah, Md A., Bakshi, A., Rahaman, Z.A., 2023. Summertime microscale assessment and prediction of urban thermal comfort zone using remote-sensing techniques for Kuwait. Earth Syst. Environ. https://doi.org/10.1007/s41748-023-00340-6. Alharbi, R.S., et al., 2022. Assessment of drought vulnerability through an integrated approach using AHP and Geoinformatics in the Kangsabati river basin. J. King Saud Univ. Sci. 34 (8), 102332. Ali, Md Y., et al., 2023. Environmental impact assessment of tobacco farming in northern Bangladesh. Heliyon, e14505. https://doi.org/10.1016/j.heliyon.2023.e14505. Aznar-Sanchez, J.A., Piquer-Rodriguez, M., Velasco-Munoz, J.F., Manzano-Agugliaro, F., 2019. Worldwide research trends on sustainable land use in agriculture. Land Use Pol. 87, 104069. Balogun, I.A., Ishola, K.A., 2017. Projection of future changes in landuse/landcover using cellular automata/markov model over Akure city, Nigeria. J. Remote Sens. Technol. 5 (1), 22–31. Barman, D., Saha, R., Bhowmick, T., Bagui, A., Dutta, G., 2022. Role of GIS, remote sensing and agro advisory in conservation agriculture. In: Conservation Agriculture and Climate Change. CRC Press, pp. 233–248. Debnath, J., et al., 2022. Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management. Environ. Sci. Pollut. Control Ser. 1–24. Delgado-Moreno, D., Gao, Y., 2021. Forest degradation estimation through trend analysis of annual time series NDVI, NDMI and NDFI (2010–2020) using landsat images. Adv. Geospat. Data Sci.: Selected Papers from the International Conference on Geospatial Information Sciences 149–159, 2022. Fabijanczyk, ´ P., Zawadzki, J., 2022. Spatial correlations of NDVI and MSAVI2 indices of green and forested areas of urban agglomeration, case study Warsaw, Poland. Remote Sens. Appl. 26, 100721. Fahad, S., et al., 2017. Crop production under drought and heat stress: plant responses and management options. Front. Plant Sci. 1147. Grizzetti, B., Lanzanova, D., Liquete, C., Reynaud, A., Cardoso, A.C., 2016. Assessing water ecosystem services for water resource management. Environ. Sci. Pol. 61, 194–203. Guha, S., Govil, H., Diwan, P., 2020. Monitoring LST-NDVI relationship using premonsoon landsat datasets. Adv. Meteorol. (1) https://doi.org/10.1155/2020/4539684. Hanad´e Houmma, I., el Mansouri, L., Gadal, S., Garba, M., Hadria, R., 2022. Modelling agricultural drought: a review of latest advances in big data technologies. Geomatics, Nat. Hazards Risk 13 (1), 2737–2776. Hossain, M.S., Arshad, M., Qian, L., Zhao, M., Mehmood, Y., K¨achele, H., 2019. Economic impact of climate change on crop farming in Bangladesh: an application of Ricardian method. Ecol. Econ. 164, 106354. Hussain, S., et al., 2020. Study of land cover/land use changes using RS and GIS: a case study of Multan district, Pakistan. Environ. Monit. Assess. 192, 1–15. Hussain, S., et al., 2022a. Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere 13 (10). https://doi.org/10.3390/atmos13101609. Hussain, S., et al., 2022b. Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Land 11 (5). https://doi.org/10.3390/land11050595. Hussain, S., et al., 2022c. Assessment of Land Use/land Cover Changes and its Effect on Land Surface Temperature Using Remote Sensing Techniques in Southern Punjab. Environmental Science and Pollution Research, Pakistan, pp. 1–17. Kafy, A.-A., et al., 2021a. Assessment and prediction of seasonal land surface temperature change using multi-temporal Landsat images and their impacts on agricultural yields in Rajshahi, Bangladesh. Environ. Challeng. 4, 100147. Kafy, A.-A., Faisal, A.-A., Raikwar, V., al Rakib, A., Kona, M.A., Ferdousi, J., 2021b. Geospatial approach for developing an integrated water resource management plan in Rajshahi, Bangladesh. Environ. Challeng. 4 (March), 100139 https://doi.org/10.1016/j.envc.2021.100139. Kura, A.L., Beyene, D.L., 2020. Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia. Remote Sens. Appl. 18, 100321. Li, T., Johansen, K., McCabe, M.F., 2022. A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data. ISPRS J. Photogrammetry Remote Sens. 186, 83–101. Lienhard, P., et al., 2020. Opportunities and constraints for adoption of maize-legume mixed cropping systems in Laos. Int. J. Agric. Sustain. 18 (5), 427–443. Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A.M., Erkbol, H., Fritschi, F.B., 2020. Crop monitoring using satellite/UAV data fusion and machine learning. Rem. Sens. 12 (9), 1357. Martellozzo, F., Amato, F., Murgante, B., Clarke, K.C., 2018. Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Appl. Geogr. 91, 156–167. Merenlender, A.M., Huntsinger, L., Guthey, G., Fairfax, S.K., 2004. Land trusts and conservation easements: who is conserving what for whom? Conserv. Biol. 18 (1), 65–76. Mihi, A., 2022. Dynamic simulation of future date palm plantation (Phoenix dactylifera L.) growth using CA–Markov model and FAO-LCCS data in Algerian dryland oases desert. Model Earth Syst. Environ. 8 (3), 3215–3230. Ming, D., Zhou, T., Wang, M., Tan, T., 2016. Land cover classification using random forest with genetic algorithm-based parameter optimization. J. Appl. Remote Sens. 10 (3), 035021. Morshed, S.R., Fattah, M.A., Haque, M.N., Morshed, S.Y., 2022. Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh. Phys. Chem. Earth, Parts A/B/C 126, 103021. |
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