Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan

Geoscience Letters, Journal Year: 2023, Volume and Issue: 10(1)

Published: July 26, 2023

Abstract At the global and regional scales, green vegetation cover has ability to affect climate land surface fluxes. Climate is an important factor which plays role in cover. This research aimed study changes relation of different indices with temperature using multi-temporal satellite data Sahiwal region, Pakistan. Supervised classification method (maximum likelihood algorithm) was used achieve based on ground-truthing. Our denoted that during last 24 years, almost 24,773.1 ha (2.43%) area been converted roads built-up areas. The increased coverage from 43,255.54 (4.24%) 1998 2022 area. Average (LST) values were calculated at 16.6 °C 35.15 for winter summer season, respectively. In average RVI, DVI, TVI, EVI, NDVI SAVI noted as 0.19, 0.21, 0.26, 0.28, 0.30 0.25 For LST relation, statistical linear regression analysis indicated kappa coefficient R 2 = 0.79 0.75 0.78 0.81 0.83 0.80 related LST. remote sensing (RS) technology can be monitor over time, providing valuable information sustainable use management. Even though findings provide significant references reasoned optimal resources through policy implications.

Language: Английский

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Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 23, 2024

Abstract This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables CHIRPS ERA5 datasets, our approach leverages Google Earth Engine (GEE) efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, cross wavelet transform analyses. The findings highlight significant spatial temporal NDVI, an annual increase averaging 0.00197 per year (p < 0.0001). positive is coupled precipitation by 0.4801 mm/year = 0.0016). In contrast, analysis recorded a slight decrease temperature (− 0.01011 °C/year, p 0.05) reduction solar radiation 0.27526 W/m 2 /year, 0.05). Notably, cross-wavelet underscored coherence NDVI factors, revealing periods of synchronized fluctuations distinct lagged relationships. particularly highlighted as primary driver growth, illustrating its crucial impact across various Pakistani regions. Moreover, revealed seasonal patterns, indicating that health most responsive during monsoon season, correlating strongly peaks precipitation. Our investigation has Pakistan's complex association which varies different Through analysis, we have identified phase critical influence drivers on patterns. These insights are developing regional adaptation strategies informing sustainable agricultural environmental management practices face ongoing changes.

Language: Английский

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Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 25, 2025

This study addresses the significant issue of rapid land use and cover (LULC) changes in Lahore District, which is critical for supporting ecological management sustainable land-use planning. Understanding these crucial mitigating adverse environmental impacts promoting development. The main goal to evaluate historical LULC from 1994 2024 forecast future trends 2034 2044 utilizing CA-Markov hybrid model combined with GIS methodologies. Landsat images various sensors (TM, OLI) were employed supervised classification, attaining high accuracy (> 90%). Historical analyzed, revealing transformations Lahore. build-up area expanded by 359.8 km², indicating urbanization, while vegetation decreased 198.7 km² barren lands 158.5 km². Water bodies remained relatively stable during this period. Future projected using (CA-MHM), achieved a prediction kappa coefficient 0.92. research indicated urban growth at expense land. forecasts suggest ongoing underscoring necessity techniques. framework planners, providing insights that combine development conservation. results highlight incorporating predictive models into policy promote preservation quickly changing areas such as

Language: Английский

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Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

Journal of Forestry Research, Journal Year: 2024, Volume and Issue: 35(1)

Published: April 27, 2024

Language: Английский

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Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques

Cogent Food & Agriculture, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 16, 2024

Agricultural Land Suitability Analysis plays a pivotal role in sustainable land use planning, aiding decision-makers identifying areas most conducive to agriculture. This study employs systematic approach integrating Analytical Hierarchy Process and Multi-Criteria Decision techniques assess prioritize the suitability of agricultural Southern Punjab (Multan region). The methodology involves defining clear objectives, relevant criteria sub-criteria, establishing hierarchical structure conducting pairwise comparisons determine relative importance each factor. Our outcomes indicated that almost 43% area was highly suitable for agriculture, 27% moderately suitable, 16% marginally 8% less 6% not agriculture area. All lands had silty clay or type soil, which sandy loam soil Multan region. output is comprehensive map identifies Sensitivity analysis validation are incorporated enhance robustness reliability results. provides valuable tool planners policymakers make informed decisions regarding allocation, contributing practices resource management.

Language: Английский

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Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242

Published: June 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Language: Английский

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Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method

Urban Climate, Journal Year: 2023, Volume and Issue: 53, P. 101766 - 101766

Published: Nov. 25, 2023

Language: Английский

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Monitoring Agricultural Land Loss by Analyzing Changes in Land Use and Land Cover

Emerging Science Journal, Journal Year: 2024, Volume and Issue: 8(2), P. 687 - 699

Published: April 1, 2024

The agricultural sector's output holds paramount significance for the global population, serving as an indispensable resource survival and consumption. Consequently, alterations in landscapes bear substantial implications world's food supply. objectives of this research are to investigate depletion land, with a specific focus on Samut Songkhram Province—an agriculturally prominent region Thailand renowned supplying seafood fruits Bangkok. By employing advanced remote sensing change detection methods incorporating indices like NDVI, NDWI, NDBI, study meticulously analyzed land-use changes. outcomes were rigorously scrutinized through supervised classification, validated by on-site inspections, corroborated data from pertinent agencies. Findings revealed that had sustained its prominence constituting around 70% province's total area over past two decades. However, expanse has undergone persistent transformation during last 20 years. Notably, most surge was observed conversion land urban developed areas, particularly zones Amphawa District, followed Mueang Bang Khonthi districts. This investigation illuminates consistent downward trend vital source sustenance Thailand's population community. Doi: 10.28991/ESJ-2024-08-02-020 Full Text: PDF

Language: Английский

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Evaluation of Land Use Land Cover Changes in Response to Land Surface Temperature With Satellite Indices and Remote Sensing Data

Rangeland Ecology & Management, Journal Year: 2024, Volume and Issue: 96, P. 183 - 196

Published: Aug. 2, 2024

Language: Английский

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The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Sustainability, Journal Year: 2023, Volume and Issue: 16(1), P. 274 - 274

Published: Dec. 28, 2023

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance scientific discovery research. This collaboration accelerates research efforts, aiding hypothesis generation, experiment design, large dataset interpretation, providing insights beyond traditional methods. Mapping land cover patterns at global, regional, local scales is crucial for monitoring the dynamic world, given significant impact distribution on climate environment. remote sensing an efficient tool across vast spatial extents. change through satellite images critical influencing ecological balance, mitigation, urban development guidance. paper conducts a comprehensive review LCCD using images, encompassing exhaustive examination data types contemporary methods, with specific focus advanced AI technology applications. Furthermore, study delves into challenges potential solutions field LCCD, overview state art, offering future practical applications this domain.

Language: Английский

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