Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale

Abstract

Due to expanding populations and thriving economies, studies into the built environment’s thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3 °C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST’s effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30 °C and 29.91 °C, respectively, with a mean LST of 25.1 °C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31 °C and 25.35 °C, respectively, with a mean LST of 21.33 °C. There was a 3.8 °C drop in the LST of this grid. The minimum and maximum NDVI were also − 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Spatial Approach to Assess Multidimensional Vulnerability to Urban Flooding: A Proposal for Indicators

“…To ensure that the urban scale is not a constraint, a grid-level analysis is proposed, which is a geographic information system (GIS) technique used to analyze data within a grid or raster format. It divides the study area into smaller grids or cells, where each cell represents a speci c area or unit of analysis, which allows the visualization and analysis of data at a higher resolution and provides a more detailed understanding of spatial patterns and relationships (Kikon et al, 2023). In addition, it is considered that most cities depend on urban planning at least have cadastral data.…”

“…The xed grid index is a powerful tool for organizing and querying spatial data based on their intersections or overlaps with the cells in the grid (Jiang et al, 2018). This allows us to analyze the data at different scales (Kikon et al, 2023).…”

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

“…The main statistical approaches and methods commonly used in the study of LST and especially LULC interdependence are supervised and unsupervised techniques [9,20,25,[67][68][69][70]; Mann-Kendall statistics [13,71]; principal component analysis end ordinary least squares [72,73]; cellular-automata [21,33,48,74] and most widely used linear and multiple linear regression analyses [9,11,15,28,[33][34][35][36][37][38][39]49,57,76]. Particular attention is merited by studies focused on establishing linear and nonlinear dependencies between LST, UHI effects, and various vegetation indices [31,67,[77][78][79][80][81][82][83].…”

“…The reduction of urban greenery directly correlates with the expansion of bare land and built-up surfaces, further exacerbating urban temperatures [67,71]. Research into the spatial distribution of LST across various urban and rural settings reveals that populated areas exhibit the highest LST across all seasonal phases, with agricultural lands, vegetation, and water bodies following in descending order of LST intensity [19,24,26,78,81]. This seasonal variability underscores a pronounced dependency on LULC changes, especially during warmer months, with topography and albedo gaining prominence in colder seasons [11,44,93].…”

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

“…The main statistical approaches and methods commonly used in the study of land surface temperature, especially land use/land cover interdependence, are supervised and unsupervised techniques [9,19,24,[54][55][56][57][58]; Mann-Kendall statistics [13,24,59,60]; cellular-automata [20,31]; and, the most commonly used, linear and multiple linear regression analyses [9,16,27,[31][32][33][34][35][36][37]61,62]. Particular attention is merited by studies focused on establishing dependencies between land surface temperature, UHI effects, and various vegetation indices [29,54,[63][64][65][66][67][68][69].…”