The Kathmandu Valley of Nepal epitomizes the growing urbanization trend spreading across the Himalayan foothills. This metropolitan valley has experienced a significant transformation of its landscapes in the last four decades resulting in substantial land use and land cover (LULC) change; however, no major systematic analysis of the urbanization trend and LULC has been conducted on this valley since 2000. When considering the importance of using LULC change as a window to study the broader changes in socio-ecological systems of this valley, our study first detected LULC change trajectories of this valley using four Landsat images of the year 1989, 1999, 2009, and 2016, and then analyzed the detected change in the light of a set of proximate causes and factors driving those changes. A pixel-based hybrid classification (unsupervised followed by supervised) approach was employed to classify these images into five LULC categories and analyze the LULC trajectories detected from them. Our results show that urban area expanded up to 412% in last three decades and the most of this expansion occurred with the conversions of 31% agricultural land. The majority of the urban expansion happened during 1989–2009, and it is still growing along the major roads in a concentric pattern, significantly altering the cityscape of the valley. The centrality feature of Kathmandu valley and the massive surge in rural-to-urban migration are identified as the primary proximate causes of the fast expansion of built-up areas and rapid conversions of agricultural areas.

In the Third and Fourth Assessment Reports (TAR and AR4, respectively) by the Intergovernmental Panel on Climate Change (IPCC), vulnerability is conceived as a function of exposure, sensitivity, and adaptive capacity. However, in its Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) and Fifth Assessment Report (AR5), the IPCC redefined and separated exposure, and it reconceptualized vulnerability to be a function of sensitivity and capacity to cope and adapt. In this review, we found that the IPCC’s revised vulnerability concept has not been well adopted and that researchers’ preference, possible misinterpretation, possible confusion, and possible unawareness are among the possible technical and practical reasons. Among the issues that need further clarification from the IPCC is whether or not such a reconceptualization of vulnerability in the SREX/AR5 necessarily implies nullification of the TAR/AR4 vulnerability concept as far as the IPCC is concerned.

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.