6 research outputs found
Structural method for determining deformations by geodetic measurements
Industrial equipment is a dynamic system that deforms during installation (assembly) and during operation. Under the influence of variable load and mixing of the center of gravity of the equipment and foundations on which it is installed, uneven horizontal and vertical displacements occur, therefore individual equipment elements are unevenly deformed, which can lead to poor performance or stoppage of this equipment. Timely measurement of the displacement of certain points of equipment (deformations) of precision equipment with the help of geodetic and other methods and their correct use for correcting the geometry of the equipment will contribute to improving the operational properties and increasing the period of uninterrupted operation of equipment’s, for example, precision conveyor lines for assembling cars
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Uncovering the Thermal Footprint: The Impact of Land Use and Cover Changes on Urban Land Surface Temperature Dynamics
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Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine
Peer reviewed: TrueAcknowledgements: The author Lillia Hebryn-Baidy expresses her gratitude to the British Academy and the Council for At-Risk Academics for providing support to this research study through the Researchers at Risk Research Support Grants. Sincere gratitude goes to the Department of Geography at the University of Cambridge and the Scott Polar Research Institute for their support throughout the research process. The author Vadym Belenok expresses his gratitude to the Ukrainian-Polish project “Urban greenery monitoring as an element of sustainable development principles and green deal implementation”.Publication status: PublishedRemote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects
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Supplementary data for "Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine"
Supplementary data to support the paper "Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine"
S1: Table of Landsat satellite images used in analysis of Urban Heat Island effect in Kharkiv, Ukraine
S2: Table listing calculated values of Land Surface Temperature for different land cover types in Kharkiv, Ukraine.
S3. Table of land surface temperature thresholds used to discriminate different land cover types in Kharkiv, Ukraine (April to September 1996-2023).
S4. Maps of surface urban heat island (SUHI) strength in Kharkiv, Ukraine, for July of various years between 1996 and 2022
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Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine
Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, the study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation R² = 0.879, compared to Landsat R² = 0.663. The application of supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations, manifested as a 70.3% increase in urban land, concurrently with a decrement in vegetative cover, especially 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09°C and 2.16°C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30°C and 2.24°C, with the bare land class showing the highest fluctuation 2.46°C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual data sets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as warmest classes with 39.51°C, 38.20°C and water by 35.96°C and dense vegetation 35.52°C, sparse vegetation 37.71°C as coldest, a trend consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects
Комбінований аналіз на основі машинного навчання для оцінки землекористування та земельного покриву в місті Києві (Україна)
1. P. Meyfroidt et al., “Ten facts about land systems for sustainability,” PNAS 119(7), 1–12 (2022).
2. E. Barbiroglio, “Land use puts huge pressure on Earth’s resources. Here’s what needs to change,” https://ec.europa.eu/research-and-innovation/en/horizon-magazine/land-use-putshuge-pressure-earths-resources-heres-what-needs-change (access 14 May 2022).
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4. S. Seifollahi-Aghmiuni et al., “Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: insights from Southern Europe,” J. Environ. Soc. 51, 1446–1458 (2022).
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6. S. Bontemps et al., “Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XL-7/W3, 323–328 (2015).
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9. D. Phiri et al., “Sentinel-2 data for land cover/use mapping: a review,” Remote Sens. 12(14), 2291 (2020).
10. H. T. T. Nguyen et al., “Land use/land cover mapping using multitemporal Sentinel-2 imagery and four classification methods—a case study from Dak Nong, Vietnam,” Remote Sens. 12(9), 1367 (2020).The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectraОсновною метою цього дослідження є оцінка різних моделей для подальшого вдосконалення точності класифікації землекористування та ґрунтового покриву (LULC) на Google Earth Engine з використанням алгоритмів навчання випадкового лісу (RF) і методу опорних векторів (SVM). Десять індексів, а саме нормалізований різницевий індекс рослинності, нормалізований різницевий індекс ґрунту, індекс забудованості, індекс біофізичного складу, індекс вилучення забудованої території (BAEI), міський індекс, індекс нової забудови, коефіцієнт смуги для забудованих площ, індекс голого ґрунту та нормалізований індекс забудованої площі використовувалися як вхідні параметри для алгоритмів машинного навчання для підвищення точності класифікації. Комбінований аналіз смуг Sentinel-2 і вищезгаданих індексів дозволив нам створити чотири комбінації на основі характеристик відбиття поверхні. Дослідження включає дані з квітня 2020 року по вересень 2021 року та з квітня 2022 року по червень 2022 року. Багаточасові дані Sentinel-2 із просторовою роздільною здатністю 10 м використовувалися для визначення класифікації LULC. Були отримані основні класи землекористування, такі як вода, ліси, луки, міські території та інші землі. Загалом RF-алгоритм показав вищу точність, ніж SVM. Загальна точність для RF і SVM становила 86,56% і 84,48% відповідно, а середнє значення Kappa становило 0,82 і 0,79 відповідно. Використання комбінації 2 з RF-алгоритмом і комбінації 4 з алгоритмом SVM для класифікації LULC було більш точним. Додаткове використання вегетаційних індексів дозволило підвищити точність класифікації LULC та виділити класи з подібними спектрами відбиття