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Land Use and Land Cover Change Prediction Using ANN-CA Model

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Tropical Forest Ecosystem Services in Improving Livelihoods For Local Communities

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Abstract

Land cover changes are characterised by the loss of natural resources, the loss of forests to urban development or the loss of agricultural areas to urban growth. This study applies the artificial neural network (ANN) method in cellular automata (CA) modelling to predict the changes, pattern and transition matrix of land use and land cover (LULC) changes for three decades with 10-year intervals: 1995–2005, 2005–2015 and 2015–2025. The maps for 1995, 2005 and 2015 obtained from Landsat 8 images are classified using the Maximum Likelihood Classification (MLC) algorithm with overall accuracies of 86%, 84% and 80%, respectively. Then, the prediction of LULC for 2025 is made after using the real map to satisfactorily validate the predicted map for 2015 derived from the map of 1995–2005, with the correctness percentage of 82.86 and the overall Kappa coefficients of 0.75. The forest cover shows a high decrement in the first 10-year period (44.87%). This trend is in line with the increasing areas of LULC for oil palm cultivation and urban development until 2025. Stable transitions can be observed from the ANN-CA model transition matrix, with values of 0.644, 0.541 and 0.787 transitions in 1995–2005, 2005–2015 and 2015–2025, respectively, for the forest area. For the urban area, the transition has been observed as 0.595, 0.736 and 0.954 in 1995–2005, 2005–2015 and 2015–2025, respectively. Meanwhile, oil palm only shows a stable transition of land-use changes in the second 20-year period, 2005–2015 with 0.606 and 2015–2025 with 0.843. The simulation of LULC changes in this study using the ANN-CA modelling can be a useful decision-making support tool to gauge the sustainability of urban area expansion, agricultural cultivation and production, as well as conservation of the remaining forest areas.

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Author information

Authors and Affiliations

  1. Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia (UPM), Serdang, Malaysia

    K. Norizah

  2. Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), Serdang, Malaysia

    K. Norizah, J. Jamhuri, M. Balqis & I. Mohd Hasmadi

  3. Department of Nature Parks and Recreation, Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), Serdang, Malaysia

    A. A. Nor Akmar

Authors
  1. K. Norizah
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  2. J. Jamhuri
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  3. M. Balqis
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  4. I. Mohd Hasmadi
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  5. A. A. Nor Akmar
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Corresponding author

Correspondence to K. Norizah .

Editor information

Editors and Affiliations

  1. School of Business and Economics, Universiti Putra Malaysia (UPM), Selangor, Malaysia

    Zaiton Samdin

  2. Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia (UPM), Selangor, Malaysia

    Norfaryanti Kamaruddin

  3. Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia (UPM), Selangor, Malaysia

    Sheriza Mohd Razali

Appendices

Appendix 1: Validation Point Distribution with Global Moran’s Index

The map for validation points distribution in the area of Selangor state that is bordered by peninsular Malaysia.

Appendix 2: (a) Digital Elevation Model (DEM), and (b) Road Network in the Study Area

The maps for Selangor state with digital evaluation model with high and low elevation bordered by peninsular Malaysia. The adjacent map is for road network with distance to road high and low bordered by peninsular Malaysia.

Appendix 3: (a) Real Map, and (b) Predicted Map for 2015

The map of Selangor state for land use and land cover in 2015. The legends marked are forest, oil palm, open area, paddy, urban, and water bodies.

Appendix 4: Validation Graph and Kappa Statistics

A graph on multiple resolution budget. A validation graph between real L U L C for 2015 and predicted L U L C for 2015. The graph has information on percentage correctness, Kappa overall, histo, and loc. The curves represent no location, no quantity inform, no location medium quantity inform, medium location medium quantity inform.

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Norizah, K., Jamhuri, J., Balqis, M., Mohd Hasmadi, I., Nor Akmar, A.A. (2023). Land Use and Land Cover Change Prediction Using ANN-CA Model. In: Samdin, Z., Kamaruddin, N., Razali, S.M. (eds) Tropical Forest Ecosystem Services in Improving Livelihoods For Local Communities. Springer, Singapore. https://doi.org/10.1007/978-981-19-3342-4_7

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