ArticleScientific reports2025
Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Comprehensive modelling of coastal landuse and land cover (LULC) dynamics and future changes using random forest, GEE, and MLP-Markov chain integration: a geo-computational approach.Scientific reports · 2026Article
- Rethink context engineering using an attention-based architecture.Scientific reports · 2026Article
- Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033-2050).Scientific reports · 2026Article
- Climate-driven land cover change and altitudinal shifts in the Aksu region of the Tianshan mountains from 1985 to 2020.Scientific reports · 2025Article
- Impacts of tourism on LULC and LST dynamics in district Buner and Shangla, Pakistan.Scientific reports · 2025Article
- Geospatial modeling and forecasting of urban land use change using Google Earth Engine and machine learning.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.