article · Geocarto International
Satellite imagery from Landsat missions spanning 1990, 2006, and 2022 was classified using a Random Forest algorithm to map land use and land cover changes across the Cholistan and Thal deserts in Punjab, Pakistan. The resulting classification maps achieved an overall accuracy exceeding 87 percent. An integrated Cellular Automata Markov chain approach was then developed to simulate long-term landscape transitions at 16-year time steps, projecting future changes up to 2038. The modelling indicates ongoing urban sprawl, with high-density residential land expected to increase from 8.12 to 12.26 square kilometres and low-density residential zones expanding from 18.10 to 28.45 square kilometres between 2022 and 2038. Projections also show prospective cropland expansion towards the west and north, providing quantitative evidence to guide regional ecosystem management and wildlife conservation.
Predicting how arid landscapes evolve under human pressure is vital for safeguarding fragile desert ecosystems and indigenous wildlife. By pairing historical satellite records with predictive spatial algorithms, environmental planners obtain reliable forecasts of urban sprawl. These insights enable authorities to make informed decisions that balance housing needs and agricultural development with natural resource conservation in threatened habitats.
The applied and tested modelling framework is directly relevant to regional planning authorities, conservation bodies, and environmental consultancies managing arid lands. While the abstract presents an applied academic study rather than a commercial product, the methodology can be integrated into geospatial planning services and land management decision-support tools for comparable desert regions.
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We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.
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DOI: 10.1080/10106049.2023.2210532
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