Mapping, intensities and future prediction of Land use/Land cover dynamics using google earth engine and CA- artificial neural network model

Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role plays by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge on LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988–2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). The results exhibited drastic LULC dynamics driven mainly by cropland and settlement expansions, which increased by 13.92% and 319.61%, respectively, between 1988 and 2018. In contrast, forest and grassland declined by 56.47% and 56.23%, respectively. Moreover, the study shows that the gains in cropland coverage in Gedaref state over the studied period was at the expense of grassland and forest acreage, whereas the gains in settlements partially targeted cropland. Future LULC predictions showed a slight increase in cropland area from 89.59% to 90.43% and a considerable decrease in forest area (0.47% to 0.41%) between 2018 and 2048. Our findings provide reliable information on LULC patterns in Gedaref region that could be used for designing land use and environmental conservation frameworks for monitoring crop produce and grassland condition. In addition, the result could help in managing other natural resources, and mitigating landscape fragmentation and degradation

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Additional Info

Field Value
Name of principal investigator Elfatih Abdel-Rahman
Email of principle investigator Elfatih Abdel-Rahman
  • Collaborators: Maysoon A. A. Osman
  • Collaborators: Elfatih Abdel-Rahman
  • Collaborators: Joshua Orungo Onono
  • Collaborators: Lydia A. Olaka
  • Collaborators: Muna M. Elhag
  • Collaborators: Marian Adan
  • Collaborators: Henri E. Z. Tonnang
Donor/funding agency icipe core funds - RUFORUM
Start date of project 2019-11-01
End date of project 2022-12-31
Region North Africa
  • Country: Sudan
Administrative area(s) Gedaref state
Name of contact person Elfatih Abdel-Rahman
Email of contact person
Date uploaded 2023-05-18
Maintainer Kennedy Senagi
Email of maintainer
Citation narrative Osman, M. A. ; Abdel-Rahman, E. M. ; Onono, J. O. ; Elhag, M. M.; Olaka, L. A. ; Adan, M.; Tonnang, H.E.Z. Mapping, intensities and future prediction of Land use/Land cover dynamics using google earth engine and CA- artificial neural network model.
Is this third party data? No - This is not third party data, and I consent to archive it
Acknowledgement statement The authors gratefully acknowledge icipe, RUFORUM, Ministry of Higher Education and Scientific Research, Sudan, Mawazo Institute, Kenya and Abdelhautalab Azrag for the support provided.