Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Published: March 19, 2024

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), use cover (LULC) changes, identification urban heat island (UHI) (SUHI) phenomena. This research focuses on nexus between LULC alterations variations in LST air (Tair), with a specific emphasis intensified SUHI effect Kharkiv, Ukraine. Employing an integrated approach, study analyzes time-series data from Landsat MODIS satellites, alongside Tair records, utilizing machine learning techniques linear regression analysis. Key findings indicate statistically significant upward trend during summer months 1984 to 2023, notable positive correlation across both datasets. exhibit stronger R² = 0.879, compared 0.663. The application supervised classification through Random Forest algorithms vegetation indices reveals alterations, manifested as 70.3% increase land, concurrently decrement vegetative cover, especially 15.5% reduction dense 62.9% decrease sparse vegetation. Change detection analysis elucidates 24.6% conversion underscoring pronounced trajectory towards urbanization. Temporal seasonal different classes were analyzed using kernel density estimation (KDE) boxplot Urban areas had smallest average fluctuations, at 2.09°C 2.16°C, respectively, but recorded most extreme values. Water exhibited slightly larger fluctuations 2.30°C 2.24°C, bare class showing highest fluctuation 2.46°C, fewer extremes. Quantitative Kolmogorov-Smirnov tests various substantiated normality distributions p > 0.05 monthly annual sets. Conversely, Shapiro-Wilk test validated normal distribution hypothesis exclusively data, indicating deviations data. Thresholded classifies lands warmest 39.51°C, 38.20°C water by 35.96°C 35.52°C, 37.71°C coldest, consistent annually monthly. effects demonstrates UHI intensity, statistical trends growth values over time. comprehensive underscores role remote understanding addressing urbanization local climates, emphasizing need sustainable planning green infrastructure mitigate effects.

Language: Английский

Remote sensing inversion of soil organic matter in cropland combining topographic factors with spectral parameters

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: June 20, 2024

Remote sensing has become an effective way for regional soil organic matter (SOM) quantitative analysis. Topographic factors affect SOM content and distribution, also influence the accuracy of remote inversion. In large region with complex topographic conditions, characteristic in different regions are unknown, effect combining spectral parameters on improving inversion remains to be further studied. Three typical Shandong Province China, namely Western plain (WPR), Central southern mountain (CSMR), Eastern hilly (EHR), were selected. factors, Elevation, Slope, Aspect Relief Amplitude, introduced. Respectively, each identified. The models built separately by integrating factors. results revealed that as SOM, none was WPR, E, RA, S CSMR, E RA EHR. combination improved, calibration R 2 increased 0.075–0.102, RMSE (Root mean square error) decreased 0.162–0.171 g/kg, validation 0.067–0.095, 0.236–0.238 RPD (Relative prediction deviation) 0.129–0.169. most significant improvement observed CSMR 0.725, 0.713 1.852, followed This study not only contributes advancement theory but offers more precise data support development green, low-carbon, precision agriculture.

Language: Английский