The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIV-3/W1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-3/W1-2020, 99–105, 2020
https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-99-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-3/W1-2020, 99–105, 2020
https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-99-2020

  18 Nov 2020

18 Nov 2020

LAND USE/LAND COVER CHANGE PREDICTION USING MULTI-TEMPORAL SATELLITE IMAGERY AND MULTI-LAYER PERCEPTRON MARKOV MODEL

H. T. T. Nguyen1, T. A. Pham2, M. T. Doan1, and P. T. X. Tran1 H. T. T. Nguyen et al.
  • 1Dept. of Forest Resource and Environment management, Tay Nguyen University, Vietnam
  • 2Department of Agriculture and Rural Development Dak Nong, Vietnam

Keywords: Land use/ land cover change, Landsat satellite images, Markov–Chain (MC), Multi-Layer Perceptron Neutral Network (MLP-NN), prediction

Abstract. This paper aims to predict the trend of land use land cover (LULC) changes in Dak Nong province over time. Data from Landsat images captured in 2009, 2015, and 2018 was employed to analyze and predict the spatial distributions of LULC categories. The Random Forest (RF) was adopted to classify the images into ten different LULC classes. Besides, integration of Multi-Layer Perceptron Markov Neural Network (MLP-NN) with Markov Chain (MC) was applied to predict the future LULC changes in the region based on the change detection over the previous years. For all classified images, overall accuracy (OA) ranged from 77.35% to 84.55% with kappa (K) coefficient index ranging from 0.75 to 0.8. The results revealed that the annual population growth together with social-economic development was regarded as major drives for land conversion in the area. The predicted map showed a significant decrease trend inthe forest classes by 2025, accounting for 23 thousand ha. However, residential areas, rubber, and agricultural land classes are predicted to rise to 460 ha, 3,000 ha, and 20,000 ha, respectively. The simulated model and calibrated area data may be a vital contribution to sustainable development efforts of the local based on the dynamics of LULC and future LULC change scenarios. Overall, ascertaining the complex interface related to changes in land use and its major drivers over time provides useful information predict to explore the future trend of LULC changes, establish alternative land-use schemes and serve as guidelines for urban planning policymakers.