Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region

Abstract

Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

“…Following the approach of Ullah et al (2019), the first step involved a preliminary interpretation of the Landsat imageries aided by Anderson's (1971) Level I Classification Scheme and the use of Google Earth imagery for training sites development. This interpretation categorised the study area land cover into four classes, such as bare land, built-up area, vegetation and water body (Table 1).…”

“…Traditionally, the NDVI is calculated using a combined operation between the Red band and Near-Infrared (NIR) band (Qiu et al 2018, Ferrelli et al 2018, Ullah et al 2019, Guha et al 2020 as follows:…”

“…The brightness temperature was subsequently converted to LST using Eq. (7) (Hamoodi et al 2019, Ullah et al 2019:…”

Predicting hydrologic responses to climate changes in highly glacierized and mountainous region Upper Indus Basin

“…The rapid population growth and the accompanying land-use alterations further intensified the problems induced by climate change. The surface temperature also increases due to land-use changes [ 11 ]. Therefore, preparation for such extreme events requires investigation and further research in hydrological modelling and climatic change studies.…”

Estimation of Land Surface Heat Fluxes Based on Landsat 7 ETM+ Data and Field Measurements over the Northern Tibetan Plateau

“…Land surface heat fluxes are essential to land-atmosphere interactions in the atmospheric boundary layer. The variability of land surface heat fluxes may originate from land use land cover changes and natural variations, which could serve as an indicator of land cover change over both the artificial and natural underlying surface [2]. Land surface heat fluxes can directly influence the land surface temperature, which is an important driver of local warming [3,4].…”