In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km²) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.

Urban heat island (UHI) and thermal comfort conditions are among the impacts of urbanization, which have been extensively studied in most cities around the world. However, the comprehensive studies in Indonesia in the context of urbanization is still lacking. This study aimed to classify land use and land cover (LULC) and analyse urban growth and its effects on surface urban heat islands (SUHIs) and urban thermal conditions as well as contributing factors to SUHI intensity (SUHII) using remote sensing in the western part of Java Island and three focused urban areas: the Jakarta metropolitan area (JMA), the Bandung and Cimahi Municipalities (BC), and the Sukabumi Municipality (SKB). Landsat imagery from three years was used: 2000, 2009, and 2019. Three types of daytime SUHII were quantified, namely the SUHII of urban central area and two SUHIIs of urban sprawl area. In the last two decades, urban areas have grown by more than twice in JMA and SKB and nearly 1.5 times in BC. Along with the growth of the three cities, the SUHII in the urban central area has almost reached a magnitude of 6 °C in the last decade. Rates of land surface temperature change of the unchanged urban pixels have magnitudes of 0.25, 0.15, and 0.14 °C/year in JMA, SKB, and BC, respectively. The urban thermal field variance index (UTFVI) and discomfort index (DI) showed that the strongest SUHI effect was most prevalent in urban pixels and the regions were mostly in the very hot and hot categories. Anthropogenic heat flux and urban ratio have positive contributions to SUHII variation, while vegetation and water ratios are negative contributors to SUHII variation. For each city, the contributing factors have a unique magnitude that can be used to evaluate SUHII mitigation options.

The present study has been carried out to analyze the seasonal variation of the Urban Heat and Cool Island over the nine developing cities of India. The magnitude of urban heat/cool island and vegetation gradient (∆NDVI) were measured from the daytime satellite datasets. Results of this study show that during the pre-monsoon (March to May) season, the maximum magnitude of the Surface Urban Heat Island (SUHI) was experienced over Kolhapur city, whereas, in the winter, the highest intensity of SUHI was noticed over Pune city. Subsequently, outcomes also depict that the changes in ∆NDVI restrain the pre-monsoon means and the seasonal alterations in SUHI magnitude. However, during the winter (November to February) season, it is controlled by the temperature–vegetation conditions of the rural areas. For pre-monsoon and seasonal changes in SUHI, with the aid of ∆NDVI and the surface temperature of the urban area, regression equations were fitted for pre-monsoon and seasonal changes in SUHI, which explains nearly 90% of SUHI variation. Similarly, the variation of SUHI has been modeled for winter, which elucidates up to 84% of SUHI discrepancy. The study reveals that, on a seasonal scale, a decrement of 0.1 in seasonal ∆NDVI leads to an increase in the seasonal intensity of SUHI by 1.74 °C, which is quite a significant augmentation.

This study evaluates the performance of 6 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating temperature, precipitation, wind speed, and relative humidity over 29 cities in Indonesia. Modern-Era Retrospective Analysis for Research Applications (MERRA-2) was considered as reference data to assess the city-wise performance of surface air temperature, precipitation, wind speed, and relative humidity simulated by the CMIP6 GCMs during 1980–2014. Six statistical measures were computed in this process (mean annual, seasonal amplitude, mean annual bias, root mean square error, correlation coefficient, and standard deviation). For 29 cities, the mean annual values of surface air temperature, precipitation, wind speed, and relative humidity obtained from the GCMs range between 290 to 302 K, 100 cm to 450 cm, 1 to 6 m/s, and 70 to 94%, respectively. The correlation coefficient between the GCMs and the surface air temperature (precipitation) reanalysis dataset ranges from 0.3 to 0.85 (−0.14 to 0.77). The correlation coefficient for wind speed (relative humidity) varies from 0.2 to 0.6 and is positive in some cases (0.2 to 0.8). Subsequently, the relative error that combines the statistical measurement results was calculated for each city and meteorological variable. Results show that for surface air temperature and precipitation, the performance of TaiESM was outstanding over the 10 or more cities. In contrast, for wind speed and relative humidity, NOR-MM and MPI-HR were the best over 7 and 19 cities, respectively. For all the meteorological variables, the performance of AWI was found to be worst over all the cities. The outcomes of this study are essential for climate-resilience planning and GCM selection while performing downscaling experiments. It will also be useful for producing updated national climate change projections for each city in Indonesia and providing new insights into the climate system.