Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.

Transport planning and public health have been intertwined historically. The health impact of public transport services, such as social exclusion, is a widely discussed research topic. Social exclusion is a paramount concern for older adults’ health in the wake of emerging global challenges. However, there remains a significant research gap on how psychosocial barriers faced by older adults in using public transport services influence the social exclusion behavior. The present research provides empirical evidence and shows the impact of certain psychosocial barriers of public transportation on older adults’ social exclusion. A total of 243 Pakistani older adults (aged 60–89 years old) voluntarily participated in this cross-sectional study. The participants provided self-reports on their psychosocial barriers (including perceived norms, attitude, personal ability, habits, neighborhood social constraints, and intention) and the corresponding social exclusion. Partial Least Square Structural Equation Modeling (PLS-SEM) was utilized for the data analysis. The structural path model supported the significant associations between psychosocial barriers and social exclusion. Except for perceived descriptive norms, all other psychosocial barriers predicted older adults’ social exclusion. The research portrays the significance of the psychosocial factors to examine social exclusion and offers practical implications for urban and transport planners. The concerned policymakers can use the research findings to develop age-sensitive, socially sustainable, and healthy cities.