One of the essential anthropogenic influences on urban climate is land-use/land-cover (LULC) change due to urbanization, which has a direct impact on land surface temperature (LST). However, LULC changes affect LST, and further, urban heat island (UHI) still needs to be investigated. In this study, we estimated changes in LULC from 1993 to 2018, its warming (positive) and cooling (negative) effect, and their contribution to relative LST (RLST) in the city of Islamabad using satellite remote-sensing data. The LULC was classified using a random forest (RF) classifier, and LST was retrieved by a standardized radiative transfer equation (RTE). Our results reveal that the impervious surfaces has increased by 11.9% on the cost of declining barren land, forest land, grass/agriculture land, and water bodies in the last 26 years. LULC conversion contributed warming effects such as forest land, water bodies, and grass/agriculture land transformed into impervious surfaces, inducing a warming contribution of 1.52 °C. In contrast, the replacement of barren land and impervious surfaces by forest land and water bodies may have a cooling contribution of −0.85 °C to RLST. Furthermore, based on the standardized scale (10%) of LULC changes, the conversion of forest land into impervious surfaces contributed 1% compared to back conversion by −0.2%. The positive contribution to UHI due to the transformation of a natural surface to the human-made surface was found higher than the negative (cooler) contribution due to continued anthropogenic activities. The information will be useful for urban managers and decision makers in land-use planning to control the soaring surface temperature for a comfortable living environment and sustainable cities.

As public service facilities, urban parks offer many benefits for daily life and social activities for residents. However, the accessibility of public parks to urban residents is often unevenly distributed in spaces that cannot be utilized fully. Here, we used the urban parks in Beijing, China as a case study and examined the relationship between urban park accessibility and population distribution at different administrative levels. Gini coefficient and Lorenz curve were used to evaluate the social equity of urban park accessibility, and the location quotient was used to identify the spatial difference between urban parks and resident population. The results of our study show that the urban park accessibility varies at district and subdistrict levels and that places with more urban parks usually have higher accessibility. Very importantly, the spatial equity is different from the social equity, a mismatch exists between the spatial distribution of urban parks and population, particularly for the elderly residents. These results generate valuable insights, as, in China and many developing countries, current urban public green space planning only uses the ratio of public green space to urban construction land and the per capita public green area.

Land-use/land cover (LULC) changes have an impact on land surface temperature (LST) at the local, regional, and global scales. To simulate the LULC and LST changes of the environmentally important area of northern Pakistan, this research focused on spatio-temporal LULC and associated LST changes since 1987 and made predictions to 2047. We classified LULC from Landsat TM and ETM data, using the maximum probability supervised categorization approach. LST was retrieved using the Radiative Transfer Equation (RTE) methodology. Furthermore, we simulated LULC using the integrated approaches of Cellular Automata (CA) and Weighted Evidence (WE) and used a regression model to predict LST. The built-up areas and vegetation have increased by 2.1% and 11% due to a decline in the barren land by −8.5% during the last 30 years. The LULC is expected to increase, particularly the built-up and vegetation classes by 2.74% and 13.66%, respectively, and the barren land would decline by −4.2% by 2047. Consequently, the higher LST classes (i.e., 27 °C to <30 °C and ≥30 °C) soared up by about 25.18% and 34.26%, respectively, during the study period, which would further expand to 30.19% and 14.97% by 2047. The lower LST class (i.e., 12 °C to <21 °C) indicated a downtrend of about −41.29% and would further decrease to −3.13% in the next 30 years. The study findings are useful for planning and management, especially for climatologists, land-use planners, and researchers in sustainable land use with rapid urbanization.

The urban thermal environment is closely related to landscape patterns and land surface characteristics. Several studies have investigated the relationship between land surface characteristics and land surface temperature (LST). To explore the effects of the urban landscape on urban thermal environments, multiple land-use/land-cover (LULC) remote sensing-based indices have emerged. However, the function of the indices in better explaining LST in the heterogeneous urban landscape has not been fully addressed. This study aims to investigate the effect of remote-sensing-based LULC indices on LST, and to quantify the impact magnitude of green spaces on LST in the city built-up blocks. We used a random forest classifier algorithm to map LULC from the Gaofen 2 (GF-2) satellite and retrieved LST from Landsat-8 ETM data through the split-window algorithm. The pixel values of the LULC types and indices were extracted using the line transect approach. The multicollinearity effect was excluded before regression analysis. The vegetation index was found to have a strong negative relationship with LST, but a positive relationship with built-up indices was found in univariate analysis. The preferred indices, such as normalized difference impervious index (NDISI), dry built-up index (DBI), and bare soil index (BSI), predicted the LST (R² = 0.41) in the multivariate analysis. The stepwise regression analysis adequately explained the LST (R² = 0.44) due to the combined effect of the indices. The study results indicated that the LULC indices can be used to explain the LST of LULC types and provides useful information for urban managers and planners for the design of smart green cities.

Alteration in Land Use/Cover (LULC) considered a major challenge over the recent decades, as it plays an important role in diminishing biodiversity, altering the macro and microclimate. Therefore, the current study was designed to examine the past 30 years (1987–2017) changes in LULC and Land Surface Temperature (LST) and also simulated for next 30 years (2047). The LULC maps were developed based on maximum probability classification while the LST was retrieved from Landsat thermal bands and Radiative Transfer Equation (RTE) method for the respective years. Different approaches were used, such as Weighted Evidence (WE), Cellular Automata (CA) and regression prediction model for the year 2047. Resultantly, the LULC classification showed increasing trend in built-up and bare soil classes (13 km² and 89 km²), and the decreasing trend in vegetation class (−144 km²) in the study area. In the next 30 years, the built-up and bare soil classes would further rise with same speed (25 km² and 36.53 km²), and the vegetation class would further decline (−147 km²) until 2047. Similarly for LST, the temperature range for higher classes (27 -< 30 °C) increased by about 140 km² during 1987–2017, which would further enlarge (409 km²) until 2047. The lower LST range (15 °C to <21 °C) showed a decreasing trend (−54.94 km²) and would further decline to (−20 km²) until 2047 if it remained at the same speed. Prospective findings will be helpful for land use planners, climatologists and other scientists in reducing the increasing LST associated with LULC changes.