Advanced spaceborn thermal emission and reflection radiometer (ASTER) has fine spectral bands in short-wave infrared (SWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum. The purpose behind the study is to explore the potential of ASTER for lithological and minerals detection; in comparison with Landsat-ETM+ Khaira Murat range (KMR) of Gali Jagir area, district Attock was selected as a test site; enriched with industrial minerals of the Eocene age. Maximum likelihood classification was applied on Landsat-ETM+ and ASTER images. Maximum likelihood classification on ASTER satellite image exhibits better discrimination among various lithologies as compared to Landsat-ETM+. Classified image of ASTER showed a correlation coefficient of 0.6 with the geological survey of Pakistan’s map while a classified image of Landsat-ETM+ exhibited a correlation of only 0.43. Landsat-ETM+ and ASTER satellite images were further investigated for minerals detection. Landsat-ETM+ band ratio detected clay. ASTER SWIR band ratios detected various clay and carbonate minerals. X’PertPRO diffractometer and differential thermal analysis of field samples verified the detected minerology. The results suggest that ASTER can be successfully used for lithological and minerals mapping of less-examined areas.

City green infrastructure (CGI) makes cities more resilient and sustainable, as required by the United Nations’ (UN) Sustainable Development Goal 11–Sustainable Cities and Communities. Based on the CGI policies of Beijing, land use/land cover (LULC) changes of two Asian capitals, Beijing, China and Islamabad, Pakistan, are simulated. LULC maps for 2010 and 2015 are developed by applying object-based image analysis (OBIA) to Landsat imagery. Dynamics of land system (DLS) model was used to simulate the LULC changes for 2020 and 2025 under three scenarios: (1) business-as-usual (BAU); (2) urban green space work plan (UGWP); and (3) landscape and greening policies (LGP). Results reveal that DLS is efficient than other simulation models. The BAU scenario predicts an overall expansion in Beijing’s greenery, while Islamabad will encounter a decline by 7.3 km² per year. Under the UGWP scenario, urban green spaces and other vegetation area of Beijing will expand by 7.6 km², while, for Islamabad, vegetation degradation rate will slow down to 6.9 km² per year. The LGP scenario envisage a massive increase of 23.5 km² per year in green resources of Beijing and Islamabad’s green land loss rate will further slowdown to 6.1 km² per year. It is inferred from the results that vegetation degradation in Islamabad need to lessen by implementing LGP policy after basic amendments according to the local conditions and available resources.

Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical indices have been used to assess the capability of products in the detection of rain/no-rain. Hourly rain rates are assessed by employing the most commonly used statistical measures, such as correlation coefficients (CC), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). Further, a diurnal analysis is performed to authenticate TMPA’s performance in specific hours of the day. In general, the results show the good capability of both TMPA products in the detection of rain/no-rain events in all seasons except winter. Specifically, 3B42V7 performed better than 3B42RT. Moreover, both products detect a high number of rainy days falsely in light rain ranges. Regarding rainfall measurements, TMPA products exhibit an overall underestimation. Seasonally, 3B42V7 underestimates rainfall in monsoon and post-monsoon, and overestimates in winter and pre-monsoon. 3B42RT, on the other hand, underestimates rainfall in all seasons. A greater MBE and RMSE are found with both TMPA rain measurements in monsoon and post-monsoon seasons. Overall, a weak correlation and high MBE between the TMPA (3B42RT, 3B42V7) and reference gauge hourly rain rates are found at a three-hourly time scale (CC = 0.41, 0.38, MBE = −0.92, −0.70). The correlation is significant at decadal (CC = 0.79, 0.77) and monthly (CC = 0.91, 0.90) timescales. Furthermore, diurnal rainfall analysis indicates low credibility of 3B42RT to detect flash flooding. Within the parameters of this study, we conclude that the TMPA products are not the best choice at a three-hourly time scale in hilly/semi-hilly areas of Pakistan. However, both products can be used at daily, yet more reliably above daily, time scales, with 3B42V7 preferable due to its consistency.