Abstract
Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.
Highlights
Land use and land cover (LULC) change analysis provides useful information for studying the urban land changes for growing cities of the world [1,2,3]
We considered the 2016 normalized difference vegetation index (NDVI) image that was arbitrarily divided into 80 classes
The overall accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. This accuracy was better as compared to the accuracies of NDVI-based output maps produced in other studies; it was 81.74%, 83.91%, and 83.91% for different band combinations in a study accomplished by [30]; it was 87%, 91%, and 88% for different years in a study completed by [31]; and it was 86.15%, and 89.31% for different sub-regions in a study performed by [34]
Summary
Land use and land cover (LULC) change analysis provides useful information for studying the urban land changes for growing cities of the world [1,2,3]. Urban growth leads to land development, industrialization, and urbanization, but creates problems related to population, traffic, and environmental degradation [4]. These challenging factors have effects on past, current, and future urban land in terms of urbanization and economic activities. It is important to use historical satellite data using remote sensing techniques and an authentic model for future forecasting of LULC [5,6]. Digital data of satellite images can accurately compute LULC classes. It aids in maintaining spatial infrastructure, which is profoundly required to monitor urban expansion and land use transitions. A Markov model was widely utilized [16,17]
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