Assessment of Spatio-Temporal Land Use/Cover Change and Its Effect on Land Surface Temperature in Lahaul and Spiti, India

Abstract

Land Use/Land Cover (LULC) changes have a significant impact on Land Surface Temperature (LST). The LST is an important parameter in various environmental and climatological studies, as it plays a crucial role in understanding the Earth’s surface–atmosphere interactions. The LULC changes can modify the surface energy balance and alter the radiation budget, leading to changes in LST. Urbanization, deforestation, and agricultural land use changes are some of the primary drivers of LULC change that have a significant impact on LST. Deforestation and agricultural land use changes result in a reduction in evapotranspiration, leading to an increase in LST. The main objective of the study is to analyze the spatio-temporal change in Land Use/Land Cover (LULC) and its effect on Land Surface Temperature (LST), as well as to establish a correlation of LST with the Normalized Difference Vegetation Index (NDVI) and Normalize Difference Snow Index (NDSI). Understanding the impact of LULC on LST is essential for developing effective land use policies that can mitigate the adverse effects of LULC change on the environment and human health.

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

“…However, T air and LST comparisons between Landsat and MODIS demonstrate a good correlation, particularly highlighting the UHI effect, with MODIS exhibiting a slightly stronger correlation with T air , possibly due to the larger dataset [12,16,36,37,65]. Studies reveal a positive correlation between LST and climatic variables like relative humidity, precipitation, and altitude, as well as solar radiation and incoming surface longwave radiation [24,38,69]. It is therefore possible to estimate global temperature trends using T air data by leveraging the LST trends obtained from regional studies in comprehending the nuances of global climate dynamics [1-6,74,79,83].…”

“…A detailed analysis of LST, derived from Landsat thermal bands for April-September, between 1984 and 2023, illustrates surface temperature trends (Table 3). Utilizing all available temporal data points (106 scenes), this study focuses on understanding the impact of local climate change, specifically regarding LST variations [14,[24][25][26][27][28][29][30][31]. The accuracy of LST analysis can vary based on the quantity of satellite images, cloud cover, and the acquisition method, especially for validations in situ [42,89].…”

“…The swift pace of urban expansion, which strains essential infrastructure, coupled with the escalation in the frequency and severity of weather events linked to global climate change, intensifies the effects of environmental hazards [12][13][14][15][16][17]. Long-term changes in land use/land cover (LULC) [18,19], stemming from the conversion of natural green spaces and arable lands into impermeable surfaces, are contributing to the formation of urban heat islands (UHI) [20][21][22][23] and the elevation of land surface temperatures (LST) [24][25][26][27][28][29][30][31]. Consequently, understanding the interactions between land surface temperature fluctuations and land use/land cover, facilitated by remote sensing technologies, becomes imperative for refining urban planning and management practices [32][33][34][35].…”

“…Many studies are specifically dedicated to comparing the performance of existing methods for obtaining and improving land surface temperature data [15,31,51,57]. 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].…”

Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia

“…Meanwhile, one indicator of LULC change that is often used is NDVI. (Husain et al, 2023). Usually, rainfall data is represented by the minimum, average, and maximum rainfall intensity recorded at the rainfall station.…”