Analysis of Decadal Land Use Changes and Its Impacts on Urban Heat Island (UHI) Using Remote Sensing-Based Approach: A Smart City Perspective

Sahoo, Sudhansu S.; Majumder, Atin; Swain, Sarat Chandra; Gareema,; Al‐Ansari, Nadhir

doi:10.3390/su141911892

Cited by 25 publications

(14 citation statements)

References 60 publications

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“…The GPS sensor network has continuous data procurement, connectivity, and consistency issues. However, satellite remote sensing offers a cost-effective technique that obtains long-term data for monitoring dynamic changes without requiring significant manpower 20 , 21 . Therefore, it acts as an efficient tool for continuously monitoring land deformation processes 22 .…”

Section: Introductionmentioning

confidence: 99%

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Awasthi,

Jain,

Sahoo

et al. 2024

Sci Rep

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The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022–2023 using a PSInSAR-based approach, varies from − 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas − ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016–2017, 2018–2019, and 2020–2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022–2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022–2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022–2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to − 13.13 mm/year (2022–2023). Towards the south-east, the town experienced rapid land subsidence, − 13.13 mm/year to − 5 mm/year (2022–2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

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Section: Introductionmentioning

confidence: 99%

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Awasthi,

Jain,

Sahoo

et al. 2024

Sci Rep

Self Cite

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“…Remote sensing-derived spectral indices such as the NDVI and NDBI serve as valuable indicators of vegetation cover and built-up areas, respectively [23,24]. Furthermore, land use classification allows for the identification and categorization of different land use categories within urban areas, thereby aiding in the assessment of urban morphology and contributing to urban planning efforts [25,26]. These remote sensing products provide a solid foundation for comprehensive urban environmental monitoring.…”

Section: Introductionmentioning

confidence: 99%

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Wu,

Zhang,

et al. 2023

Sustainability

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Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.

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“…Remote sensing technology has been widely employed for spatial and temporal monitoring of LULC (Sahoo et al 2022 ) and tea health conditions during the last few decades (1990–2022) (Prokop 2018 ; Phan et al 2020 ; Moni et al 2022 ). Spectral indices such as normalized difference vegetation index (NDVI), leaf area index (LAI), Simple Ratio (SR), Triangular Vegetation Index (TVI), Leaf Area Index (LAI), Tasseled Cap Transformation (TCT), and Normalized Difference Vegetation Index (NDWI) were evaluated as a method for detecting and identifying tea farms along with the tea patch texture and tone changes (Zhu et al 2019 ; Rao et al 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

2023

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Background Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km 2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.

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Analysis of Decadal Land Use Changes and Its Impacts on Urban Heat Island (UHI) Using Remote Sensing-Based Approach: A Smart City Perspective

Cited by 25 publications

References 60 publications

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

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