Land Surface Temperature Relationship with the Land Use/Land Cover Indices Leading to Thermal Field Variation in the Turkish Republic of Northern Cyprus

Abstract

The increase in the Land Surface Temperature (LST) caused by global warming and extreme weather events is significantly increasing Urban Hot Spots (UHS) while impacting the environmental quality within urban areas. In this research, with the help of Landsat 5, 7, and 8 satellite images, the evolution of Land Use/Cover (LULC), LST, and Surface Urban Heat Island (SUHI) in the Turkish Republic of Northern Cyprus (TRNC) between the years 1985 and 2020 is examined. The main aim is to evaluate how these variables, together with the Normalized Difference Built-up Index (NDBI), Urban Index (UI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Built Index, and Proportion Vegetation (PV) have influenced the variability of the UHS and the level of thermal comfort according to the Thermal Field Variance Index (UTFVI). The results report significant average rises of the variables: LST (6.62 °C), SUHI (0.97 °C), UHS (8.61%) of the UI and NDBI indices and class 6 of UTFVI (88.53%) that are related to the variability of the LULC corroborated by statistical analysis. Our results provide valuable information on the future development of urban areas in Northern Cyprus to make them more resilient and sustainable to rising temperatures.

Microclimate Impacts of Land Cover Types in Halu Oleo University Botanical Garden and Its Surroundings

“…RH depends on several factors, including air temperature, wind movement, quantity and quality of irradiation, vegetation, and water availability (Sapariyanto et al 2016;Wang et al 2023). García et al (2023) stated that differences in the land cover area cause different microclimate characteristics between observed land cover types. This occurs because the extent of land cover will increase the intensity of retained light as a source of energy in physiological processes in the plant body so that vegetation can suppress the increase in air temperature through photosynthesis and transpiration.…”

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

“…SUHI (i,j) is the value for the pixel located at the i row and j column of the urban area, LST urban�i,j� is the LST at the pixel located at the i row and j column of the urban area, 𝐿𝐿𝑆𝑆𝑇𝑇 𝑣𝑣𝑖𝑖𝑣𝑣 is the average LST across all pixels classified as vegetation (dense vegetation, sparse vegetation) [10,27,36,46,87,91]. The urban mask is applied to the resampled LST data to extract the temperatures for urban areas, while the vegetation mask is applied to do the same for vegetation areas.…”

“…IBI, NBI specifically targets urban areas, enhancing the identification of urban and constructed spaces. UI focuses on the extent and development of urban areas, aiding in the delineation of urban areas [36,116]. Also, in many papers, it is the NDBI index that shows the closest correlation with the LST [20,21,26].…”

“…Long-term changes in land use/land cover (LULC) [17][18][19][20], stemming from the conversion of natural green spaces and arable lands into impermeable surfaces, are contributing to the formation of urban heat islands (UHI) [21][22][23][24] and the elevation of land surface temperatures (LST) [25][26][27][28][29][30][31][32][33]. Consequently, understanding the intricate dynamics between LST fluctuations and LULC modifications, through the lens of remote sensing technologies, becomes imperative for refining urban planning and management practices [34][35][36][37]. The methodological basis for studying the spatial patterns and temporal dynamics of LST and LULC transformations is the analysis of satellite data, while the source of LST data is thermal infrared range (TIR) [8][9][10][11][12][13]37].…”

“…Many studies are specifically dedicated to comparing the performance of existing methods for obtaining and improving LST data [61][62][63][64]66]. 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].…”