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This article illustrates the critical findings of an empirical investigation of resilience, vulnerability, and livelihood nexus in one of the worst cyclone-affected sub-districts “Mongla” in Bangladesh. Results obtained from the survey conducted in 2018 and 2019 explore the co-existence of climate change vulnerability and resilience at the rural household level. Additionally, the study identifies the role of assets (e.g., land, cash, and livestock) in order to enhance the resilience of poor inhabitants. Quantitative data have been collected using structured and semi-structured interviews. The outcome of the study demonstrates that the relationships between vulnerability and resilience are very complex and exist in the study area. An exciting outcome has revealed that in some places, more vulnerable people exhibit higher resilience capacity and vice versa. Furthermore, this research emphasizes that local livelihood systems may be improved if appropriate policies are considered by local government organizations in collaboration with multiple stakeholders. Consequently, the local citizens have to play their critical role to assist government policies in order to enhance resilience at the community level. Moreover, local residents can have a better understanding of their livelihood issues in the face of climate change.

River bathymetry maps are the prerequisite for any river engineering and management projects.Traditionally, river bathymetry mapping is carried out using an echo sounder instrument, and this practiceinvolves both time and costs. The process faces challenges with inaccessible water regions, thus, adaptingremote sensing technology seems to be an alternate solution. This study aimed to determine Karnaphuli riverbathymetry using the Stumpf model based on the ratio transform algorithm for the selected years. Then,eroded and deposited areas within the Karnaphuli river were identified. Riverbank line Landsat images for theselected years were analyzed and 21 observation points were identified for further investigations. Thus, thisstudy envisages as a basis to develop river bathymetry maps using geospatial techniques once ground-truthingis available (4) (PDF) Mapping river bathymetry using Stumpf model. Available from: https://www.researchgate.net/publication/348436868_Mapping_river_bathymetry_using_Stumpf_model [accessed Sep 11 2023].

This chapter explores the influence of different socioeconomic factors on adaptation choices of rural communities, and it also explains how households’ adaptation choices differentiate within two rural communities. Although the geographical vulnerability is apparent in both communities, the choice of adaptation strategies by the relatively highly vulnerable community is different rather than being similar. To understand the factors that shape rural vulnerable communities’ adaptation choices and the underneath difference between them, this chapter explains the results of an empirical investigation on two geographical vulnerable communities in Southwest Bangladesh. We adopted a quantitative research approach in this research. Specific methods utilized for the data collection process included a face-to-face questionnaire survey of 98 households deploying the stratified random sampling technique. We have completed the data analysis process by following two major steps: first, using the chi-square test and odds ratio, we have identified the difference in adaptation strategies among the geographical vulnerable communities, and then we have measured the extent of their differences. It highlighted that the households who live in the downstream or highly vulnerable condition have higher adaptive responses in most of the adaptation categories than the households in the upstream or less vulnerable community where the vulnerable households’ cognitive understanding of geographical vulnerability might have an influence. Further, we have used the binomial logistic regression model to explain the differentiated nature of adaptation choices of the households by using various predictors. Results show that the extent of adaptation choices depends primarily on elements of the socioeconomic asset profile of the households. Households with more socioeconomic assets have more adaptation choices, regardless of their geographical vulnerability. Several factors have an influence on vulnerable households’ adaptation choices including dependent members, social safety nets, inherited property, vocational training, NGO’s memberships, etc. This chapter concludes with the implications of these results for supporting adaptations of vulnerable and disadvantaged populations and mainstreaming pro-poor climate strategies into the development planning process.

This Working Paper compiles the experiences and lessons learnt from specific adaptation pilot interventions under the Himalayan Adaptation, Water and Resilience (HI-AWARE) Research on Glacier and Snowpack Dependent River Basins for Improving Livelihoods. The climate change adaptation solutions tested in HI-AWARE pilots have shown huge potential in the areas of flood resilient housing and sanitation, water management, climate-smart agriculture, and heat stress management. The lessons learnt can help inform and improve the design and management of interventions in these areas.

Elucidating the impact of Land Surface Temperature (LST) is an important aspect of urban studies. The impact of urbanization on LST has been widely studied to monitor the Urban Heat Island (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) Panaji and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat data were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur city, while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development

Land cover (LC) is an essential variable for environmental monitoring in many application domains. The detection of changes in LC can support the understanding of environmental dynamics. However, LC legends present a high degree of inconsistencies that significantly reduce their usability. This study investigates the effectiveness of ISO standard 19144-2, better known as Land Cover Meta-Language (LCML), to improve the standardization and harmonization of different LC taxonomies and maps. LCML vocabulary and syntactic rules facilitate the integration of natural resources information. LC classes are represented by a sequence of “Basic Elements” and attributes defined as “Properties” and “Characteristics.” Such elements are formalized in a Unified Modeling Language class diagram. This study presents first, a method to evaluate and score the “similarity” of different LCML legends, second, an application of the similarity assessment criteria to an area located in Bangladesh for translating its specific LCML legend into a different taxonomy, i.e., the System of Environmental Economic Accounting, and third, a Python implementation to be incorporated in new or already existing tools. The results obtained show that when class similarity assessment is carried out by Basic Elements only, the process performs well for simple classes. When classes are characterized by similar basic elements (e.g., biotic elements) structure, the introduction of class properties is needed to disambiguate complex situations. The findings indicate that the proposed methodology can exploit LCML land feature semantic representation. Moreover, it can be used for translating LCML classes into different taxonomies, for facilitating class comparison and change detection.