Abstract
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.
Highlights
Changes in land use land cover (LULC) relevant to anthropogenic activities have significantly changed the biological and geochemical processes on earth contributing to global environmental concerns (Prakasam, 2010; Firozjaei et al, 2018)
A study on prediction of future LULC scenarios for urban growth modeling between the Multilayer Perceptron-Markov (MLP-Markov Chain (MC)) and the CA-Markov conducted in Atakum, Samsun, Turkey revealed that the MLPMC model produced results outperformed the CA-Markov regardless of LULC change simulation (Ozturk, 2015)
Based on the LULC analysis obtained for the year 2009, 2015, and 2018 in Dak Nong, the classification results showed that the overall accuracy (OA) of LULC maps achieved from 77.35to 84.55% with kappa varying from 0.75 to 0.81
Summary
Changes in land use land cover (LULC) relevant to anthropogenic activities have significantly changed the biological and geochemical processes on earth contributing to global environmental concerns (Prakasam, 2010; Firozjaei et al, 2018). Monitoring, analyzing, and understanding the conversion of LULC changes are needed to provide precise and timely information on current land use characteristics and changes for local authorities regardless of sustainable development. The application of the CA-Markov model in predicting LULC changes has conveniences due to its powerful replication, and it is used for mapping LULC changes providing good performance regardless of dynamic modeling efficiently; high productivity with data, simple analysis; and capacity to detect transitions between land use classes (Sang et al, 2011; Chan et al, 2018). A study on prediction of future LULC scenarios for urban growth modeling between the Multilayer Perceptron-Markov (MLP-MC) and the CA-Markov conducted in Atakum, Samsun, Turkey revealed that the MLPMC model produced results outperformed the CA-Markov regardless of LULC change simulation (Ozturk, 2015). The results illustrated that the MLPMarkov model yielded the most performance in monitoring and predicting the land-use dynamics
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