Abstract
Abstract. In this study, land use land cover (LULC) map of 1987, 1997 and 2007 derived from digital satellite images of Landsat - 5 TM of 1987, IRS-1C LISS III images of 1997 and IRS-P6 LISS III images of 2007, respectively, to monitor and predict future LULC scenario. Future LULC has been predicted using CA Markov Model for 2017, 2027 and 2050 by using LULC map of 1987 & 1997, 1997 & 2007 and 1987 & 2007. The period (image interval / time steps) between different predictions years (i.e., 2017, 2027 and 2050 using 1987 & 1997, 1997 & 2007 and 1987 & 2007 LULC) are 10, 20, 30, 43 and 53 years. The number of iteration was based on the time steps i.e., iterations 20 to predict LULC for 2017 (prediction from 1997 to 2017); iterations 30 for 2027 (prediction from 1997 to 2027); iterations 53 for 2050 (prediction from 1997 to 2050). The relationships (correlations) of quantity of predicted LULC found strong positive correlation for three time periods (0.981, 0.984, 0.966 for 2017, 0.981, 0.984, 0.975 for 2027, 0.977, 0.987, and 0.980 for 2050) and established that there are almost no effect in quantity of prediction results of different time steps (iterations) images and time intervals are used to predict future LULC. The predicting location of predicted LULC of 2017, 2027 & 2050 for the three cases showing positive correlation, where r are 0.728, 0.758 and 0.708 for 2017 – when relatively less time steps used; r are 0.696, 0.761 and 0.674 for 2027 – when medium time steps used; r are 0.599, 0.721, 0.574 for 2050 – when more time steps used. The analysis established that although there are nearly no effect on quantitative prediction results but have small impact of iterations (time steps) and time intervals on spatial distribution of predicted LULC results.
Highlights
CA (Cellular Automata) Markov is a combined of CA and Markov chain land use land cover (LULC) prediction procedure that adds an element of spatial contiguity as well as knowledge of the likely spatial distribution of transition to Markov change analysis
The number of iteration was based on the time steps i.e., iterations 20 to predict LULC for 2017; iterations 30 for 2027; iterations 53 for 2050
Transition probabilities matrix between LULC of 1987 & LULC of 2007 and Transition suitability image collection for all LULC classes are used as an input to CA Markov model
Summary
CA (Cellular Automata) Markov is a combined of CA and Markov chain land use land cover (LULC) prediction procedure that adds an element of spatial contiguity as well as knowledge of the likely spatial distribution of transition to Markov change analysis. Within CA Markov model, the transition areas file from a Markov Chain analysis of two prior LULC maps establishes the quantity of expected land use and land cover change from each existing category to each other category in the time period. The transition areas file from a Markov chain analysis of two prior LULC maps establishes the quantity of expected land use land cover change (LULCC) from each existing category to each other category in the time period, and number of iterations chosen establishes the number of time steps that will be used in the simulation. The spatio-temporal CA Markov model of landscape change using multi-temporal satellite imagery has been used which enabled us to predict spatial pattern of future land use/land cover for the study area – Kamrup Metropolitan district of Assam state in India (Figure 1).
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