SPATIO–TEMPORAL ANALYSIS OF LAND USE/LAND COVER DYNAMICS AND ITS IMPACT ON LAND SURFACE TEMPERATURE USING GEOSPATIAL TECHNIQUES: A CASE STUDY OF MARDAN, PAKISTAN

Abstract

Urbanization is presently a worldwide phenomenon. Pakistan, like many other South Asian nations, is experiencing rapid urbanization, with an annual growth rate of 3%. The consequences of urbanization on the climate and environment are critical for the country's natural resource management. One of the most significant aspects of land use change is the relationship between urbanization and the decrease of agriculture as a result of increased economic growth. Furthermore, analyzing dynamic changes in land use is necessary for developing a model for future land use changes. The research examines the city of Mardan's projected land use and land use development for the year 2050. Landsat pictures for the years 1990, 2000, 2010, and 2022 were used in this study. The photos were used to determine the temperature of the earth's surface, recover land use changes in land cover, and derive indices such as NDVI, NDBaI, NDBI, UI, and NDWI. Changes can be seen in built–up regions and agricultural areas, but water bodies and uncultivated places are also affected. Agriculture accounted for 51% of GDP in 1990 and will drop to 40% by 2022. From 1990 to 2022, the Built–up increased from 0.97 percent to 8.01 percent. The total accuracy of the images was between 89 and 90 percent. The LULC model has a significant impact on the projected temperature fluctuation. Additionally, a probability transition image was produced using the Markov model, demonstrating the transition forecast in the LULC model up to 2050, which shows a 35 percent decline in agricultural and a 136 sq.km rise in buildings. LST can be used to reflect the effect of a transition in the LULC model. The average maximum temperature in 1990 was 40 degrees Celsius, rising to 46 degrees Celsius in 2022,

according to seven separate yearly photos acquired by the LANDSAT thermal band. LST was analyzed using linear regression with NDVI, NDBI, NDWI, UI

and NDBaI. The study found that NDVI had a negative relationship with LST.

LST rises when plant cover decreases. While LST has a high positive connection

with NDBI, NDBaI, UI and NDWI. As a result, immediate steps must be taken to limit the rapid disappearance of urbanization in order to minimize environmental, natural resource, and biodiversity devastation by managing the evolution of soil surface temperature