Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2022, Volume and Issue: 126, P. 103117 - 103117

Published: Jan. 25, 2022

Language: Английский

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Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

Emerging Science Journal, Journal Year: 2023, Volume and Issue: 7(2), P. 428 - 444

Published: Feb. 14, 2023

Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing different classification methods, datasets, satellite images, ancillary dataset types available. However, using just low spatial resolution visible bands remotely sensed images will not provide good accuracy. Remotely thermal data contains valuable to monitor investigate CD LU/LC. So, it needs involve datasets for better outcomes. Fusion plays a big role map CD. Therefore, this study aims find out refining method estimating accurate LU/LC patterns by investigating integration effectiveness (a) adopting noise removal model, (b) resampling, (c) image fusion, combining integrating between Grim Schmidt spectral (GS) method, (d) applying Mahalanobis distances (MH), Maximum likelihood (ML) artificial neural network (ANN) classifiers on captured from Landsat-8 TIRS OLI system, these were operational imager (OLI) infrared (TIRS) sensors 2015 2020 generate about twelve LC maps. (e) The comparison was made among all classifiers' results. results reveal that ANN technique integrated combined has highest compared rest applied approaches. obtained overall 96.31% 98.40%, kappa coefficients (0.94) (0.97) years 2020, respectively. ML classifier obtains MH approach. fusion improve 5%–6% proposed than spatial-resolution alone. Doi: 10.28991/ESJ-2023-07-02-09 Full Text: PDF

Language: Английский

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Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Sustainability, Journal Year: 2023, Volume and Issue: 15(4), P. 3572 - 3572

Published: Feb. 15, 2023

Land use/land cover (LULC) changes are among the most significant human-caused global variations affecting natural environment and ecosystems. Pakistan’s LULC patterns have undergone huge since 1900s, with no clear mitigation plan. This paper aims to determine normalized difference vegetation index (NDVI) as well their causes in Southern Punjab province over four different periods (2000, 2007, 2014, 2021). Landsat-based images of 30 m × spatial resolution were used detect changes, while NDVI dynamics calculated using Modis Product MOD13Q1 (Tiles: h24 v5, v6) at a 250 m. The iterative self-organizing (ISO) cluster method (object meta-clustering minimal distance center approach) was quantify this research because its straightforward approach that requires human intervention. accuracy assessment Kappa coefficient assess efficacy results derived from changes. Our findings revealed considerable settlements, forests, barren land Punjab. Compared 2000, forest had reduced by 31.03%, settlement increased 14.52% 2021. Similarly, rapidly been converted into land. For example, 12.87% 2021 compared 2000. analysis showed forests settlements 12.87%, respectively, twenty year period area decreased 4.36% It shows 31.03% urban land, ground, farmland. formerly utilized for due expansion infrastructure commercial sector Consequently, proper monitoring is required. Furthermore, relevant agencies, governments, policymakers must focus on management development. Finally, current study provides an overall scenario how trends evolving region, which aids use planning management.

Language: Английский

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The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

Frontiers in Forests and Global Change, Journal Year: 2024, Volume and Issue: 7

Published: March 18, 2024

Introduction This study delves into the spatiotemporal dynamics of land use and cover (LULC) in a Metropolitan area over three decades (1991–2021) extends its scope to forecast future scenarios from 2031 2051. The intent is aid sustainable management urban planning by enabling precise predictions growth, leveraging integration remote sensing, GIS data, observations Landsat satellites 5, 7, 8. Methods research employed machine learning-based approach, specifically utilizing random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains Land Change Modeler (LCM), were harnessed project alterations, which facilitated development transition probability matrices among different classes. Results investigation uncovered significant shifts LULC, influenced largely socio-economic factors. Notably, vegetation decreased substantially 49.21% 25.81%, while saw an increase 31.89% 40.05%. Urban areas expanded significantly, 7.55% 25.59% total area, translating 76.31 km 2 1991 258.61 2021. Forest also 322.25 409.21 . Projections indicate further decline built-up 371.44 2051, with decrease compared 2021 levels. predictive accuracy model was confirmed overall exceeding 90% kappa coefficient around 0.88. Discussion findings underscore model’s reliability provide theoretical framework that integrates environmental conservation. results emphasize need balanced approach towards growth Islamabad metropolitan underlining essential equilibrium between conservation management. underscores importance using advanced models guiding strategies.

Language: Английский

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Synergy of Remote Sensing and Geospatial Technologies to Advance Sustainable Development Goals for Future Coastal Urbanization and Environmental Challenges in a Riverine Megacity

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(1), P. 30 - 30

Published: Jan. 14, 2025

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or impacts independently, there remains a critical gap understanding the integrated of land use/land cover (LULC) changes on both ecosystem vulnerability sustainable development achievements. This study addresses this through an innovative integration multitemporal Landsat imagery (5, 7, 8), SRTM-DEM, historical use maps, population data using MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling monitor LULC over three decades (1990–2020) project future for 2025, 2030, 2035, supporting Sustainable Development Goals (SDGs) Karachi, southern Pakistan, one world’s most populous megacities. The framework integrates analysis SDG metrics, achieving overall accuracy greater than 97%, user producer accuracies above 77% Kappa coefficient approaching 1, demonstrating high level agreement. Results revealed significant expansion from 13.4% 23.7% total area between 1990 2020, concurrent reductions vegetation cover, water bodies, wetlands. Erosion along riverbank has caused Malir River’s decrease 17.19 5.07 km2 by highlighting key factor contributing flooding during monsoon season. Flood risk projections indicate that urbanized areas will be affected, 66.65% potentially inundated 2035. study’s contribution lies quantifying achievements, showing varied progress: 26% 9 (Industry, Innovation, Infrastructure), 18% 11 (Sustainable Cities Communities), 13% 13 (Climate Action), 16% 8 (Decent Work Economic Growth). However, declining bodies pose 15 (Life Land) 6 (Clean Water Sanitation), 11%, respectively. approach provides valuable insights planners, offering novel adaptive planning strategies advancing practices similar stressed megacity regions.

Language: Английский

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The Impact of Land Use/Land Cover (LULC) Changes on Land Surface Temperature in Sivas City Center and Its Surroundings and Assessment of Urban Heat Island

Asia-Pacific Journal of Atmospheric Sciences, Journal Year: 2019, Volume and Issue: 55(4), P. 669 - 684

Published: March 22, 2019

Language: Английский

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Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives

Computers and Electronics in Agriculture, Journal Year: 2018, Volume and Issue: 155, P. 103 - 120

Published: Oct. 13, 2018

Language: Английский

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Land use/land cover change assessment of Halda watershed using remote sensing and GIS

The Egyptian Journal of Remote Sensing and Space Science, Journal Year: 2018, Volume and Issue: 23(1), P. 63 - 75

Published: Dec. 13, 2018

Being the only tidal river where major Indian carps spawn naturally, Halda is a unique heritage of Bangladesh and surroundings this undergo chronological changes because rapid urbanization, anthropogenic socioeconomic activities. In study, an attempt has been made to analyze Land use/land cover change (LULCC) Watershed over last 40 years using multispectral satellite data obtained from Landsat 2 MSS for April 15, 1978; 5 TM February 26, 1999 8 OLI/TIRS May 2, 2017. The watershed classified into five land cover/use classes viz. Agriculture, Bare soil, Settlements, Vegetation Water Body. Resultant LULC overlay maps indicate significant shift (35.1%) class (85.47%) soil Settlements. This study envisages facilitate policy makers, planners other associated development workers adopt best suitable land-use management option Watershed.

Language: Английский

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Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process

ISPRS International Journal of Geo-Information, Journal Year: 2020, Volume and Issue: 9(2), P. 134 - 134

Published: Feb. 24, 2020

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City its environs (DCEN) to study future scenario years 2025, 2030, 2040 based on 2018 simulation results from 2007 LULC maps. This evaluates spatial temporal variations of LULCC, including potential landscape risk (FPLR) area 2008 great (8.0 Mw) earthquake south-west China. The Cellular automata–Markov chain (CA-Markov) model multicriteria analytical hierarchy process (MC-AHP) approach have been using integration remote sensing GIS techniques. analysis shows along with FPLR pattern. Based LULCC scenarios, provided suggestions for development close proximity fault lines strong magnitude earthquakes. Our suggest a better safe planning Belt Road Corridor (BRC) China control Silk-Road Disaster, which will also be useful urban planners sustainable manner.

Language: Английский

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Trend shifts in satellite-derived vegetation growth in Central Eurasia, 1982–2013

The Science of The Total Environment, Journal Year: 2016, Volume and Issue: 579, P. 1658 - 1674

Published: Dec. 2, 2016

Language: Английский

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