An integrated quantitative and qualitative approach for landslide susceptibility mapping in West Sikkim district, Indian Himalaya

AbstractLandslides rank as the third most common natural disaster globally, and the Indian Himalaya Region is no exception, experiencing severe impacts during the rainy season. This study focuses on creating a comparative landslide susceptibility map for the West Sikkim district in India using probabilistic and heuristic approaches. The frequency ratio (FR) and information value (IV) methods are employed for the probabilistic approach, while the analytic hierarchy process (AHP) is used for the heuristic approach. Eleven factors are considered in the analysis. The resulting landslide susceptibility (LS) map demonstrates accuracies of 77% for FR, 74% for IV, and 57% for AHP methods. Preliminary qualitative risk assessment is conducted, incorporating building and population density, as population and buildings are the most vulnerable elements in the society. The LS map with the highest accuracy (from FR) serves as the landslide potential factor, combined with building and population density as the risk damage potential factors for risk zonation. The resulting risk zonation map classifies the study area into high-risk (3%), medium-risk (14%), and low-risk (83%) zones. This study primarily addresses the 3% high-risk area where landslides pose a significant threat to population and infrastructure, aiming to inform policy implementation and mitigation measures

Proximity to Neighborhood Services and Property Values in Urban Area: An Evaluation through the Hedonic Pricing Model

Neighborhood services, property attributes, and their associated amenities have positive impacts on land and property values. This impact is estimated by the hedonic pricing model, which is considered an effective method used in previous studies for such evaluations. The study uses Geographical Information Science by digitizing the point of interest in the study area for spatial modeling of data collection points and multi-linear regression as a statistical analysis of hedonic measurements. The hedonic measurements include the data of structural, locational, environmental, and community attributes of a property at a given time and space at a walkable distance from the neighborhood for measuring proximity. The results of the study are represented through the summary of the regression model, which expresses the impact of every individual variable on the entire value of the property, and the appropriateness of the results is shown by values R, R2, and adjusted R2. The result of the study concluded that property characteristics are varied from location to location, and that is why it is difficult to measure the exact market values, particularly in areas that lack urban planning and heterogeneous data. Research on such burning issues is essential for sustainable urban development

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

It is predicted that the COVID-19 lockdown decreased environmental pollutants and, hence, urban heat island. Using the hypothesis as a guide, the objective of this research is to observe the change in vegetation pattern and heat-island effect zones in Dhaka, Bangladesh, before and after COVID-19 lockdown in relation to different forms of land use and land cover. Landsat-8 images were gathered to determine the vegetation pattern and the heat island zones. The normalized difference vegetation index (NDVI) and the modified soil-adjusted vegetation index (MSAVI12) were derived for analyzing the vegetation pattern. According to the results of the NDVI, after one month of lockdown, the health of the vegetation improved. In the context of the MSAVI12, the highest MSAVI12 coverages in March of 2019, 2020, and 2021 (0.45 to 0.70) were 22.15%, 21.8%, and 20.4%, respectively. In May 2019, 2020, and 2021, dense MSAVI12 values accounted for 23.8%, 25.5%, and 18.4%, respectively. At the beginning of lockdown, the calculated LST for March 2020 was higher than March 2019 and March 2021. However, after more than a month of lockdown, the LST reduced (in May 2020). After the lockdown in May 2020, the highest UHI values ranging from 3.80 to 5.00 covered smaller land-cover regions and reduced from 22.5% to 19.13%. After the end of the lockdown period, however, industries, markets, and transportation resumed, resulting in the expansion of heat island zones. In conclusion, strong negative correlations were observed between the LST and vegetation indices. The methodology of this research has potential for scholarly and practical implications. Secondly, urban policymakers can use the methodology of this paper for the low-cost monitoring of urban heat island zones, and thus take appropriate spatial counter measures

Prioritizing sub-watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin

Abstract The hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform a quantitative evaluation of the watershed’s geometry to determine the impact of such processes on its hydrology. Geographic information systems (GIS) and remote sensing (RS) techniques have become increasingly significant because they enable decision-makers and strategists to make accurate and efficient decisions. To prioritize sub-watersheds within the Wyra watershed, this research employs two methods: morphometric analysis and hypsometric analysis. The watershed was divided into eleven sub-watersheds (SWs). The prioritization of sub-watersheds in the Wyra watershed involved assessing several morphometric parameters, such as relief, linear, and areal features, for each sub-watershed. Furthermore, the importance of the sub-watersheds was determined by computing hypsometric integral (HI) values using the elevation–relief ratio method. The final prioritization of sub-watersheds based on morphometric analysis was determined through the integration of principal component analysis (PCA) and weighted sum approach (WSA). SW2 and SW9 have had higher priorities using morphometric analysis, whereas SW6, SW7, and SW10 have obtained higher priorities using hypsometric analysis. SW4 is the most common SW that shares the same priority. The most vulnerable sub-watersheds are those with the highest priority, and therefore, programmes for soil and water conservation should pay more attention to them. The conclusions of the study may prove useful to various stakeholders involved in initiatives related to watershed development and management

Different Forms of Solar Energy Progress: The Fast-Growing Eco-Friendly Energy Source in Bangladesh for a Sustainable Future

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society

Earthquake preparedness in an urban area: the case of Dhaka city, Bangladesh

Abstract This study aims to assess people’s preparedness for a potential earthquake in Dhaka, the capital of Bangladesh. We have employed a model with six dimensions of holistic individual preparedness. A self-reported online survey included 677 total participants. The multiple linear regression model and the Spearman rank correlation were used as needed. The majority of the participants (> 65%) did not have experience with any earthquake preparedness program, despite the fact that 92% of the population surveyed claimed to have experienced an earthquake in their region. More than 50% of those who experienced earthquakes acquired knowledge. 30% of people do not have access to immediate financial support in the event of a crisis. It was estimated that almost 50% of the population did not have earthquake insurance. Females lack the adaptability of males. A person’s level of earthquake preparedness was significantly associated with their level of education, household head occupation and monthly income, type of residential unit, and experience of earthquake preparedness program. Therefore, these factors should be considered while figuring out how to better prepare for earthquakes. A combination of holistic earthquake preparedness programs and effective education is generally required for competent holistic earthquake preparedness

Households’ vulnerability assessment: empirical evidence from cyclone-prone area of Bangladesh

Abstract Despite Bangladesh being vulnerable to cyclones, there is a dearth of research on cyclone vulnerability assessment. Assessing a household's vulnerability is considered a crucial step in avoiding the adverse effects of catastrophe risks. This research was conducted in the cyclone-prone district of Barguna, Bangladesh. This study's purpose is to evaluate this region's vulnerability. A questionnaire survey was conducted using a convenience sample technique. A door-to-door survey of 388 households in two Unions of Patharghata Upazila, Barguna district, was conducted. Forty-three indicators were selected to assess cyclone vulnerability. The results were quantified using an index-based methodology with a standardized scoring method. Where applicable, descriptive statistics have been obtained. In terms of vulnerability indicators, we also utilized the chi-square test to compare Kalmegha and Patharghata Union. When appropriate, the non-parametric Mann–Whitney U test was employed to evaluate the relationship between the Vulnerability Index Score (VIS) and the union. According to the results, the environmental vulnerability (0.53 ± 0.17) and the composite vulnerability index (0.50 ± 0.08) were significantly greater in Kalmegha Union than in Patharghata Union. They faced inequity in government assistance (71%) and humanitarian aid (45%) from national and international organizations. However, 83% of them underwent evacuation practices. 39% were satisfied with the WASH conditions at the cyclone shelter, whereas around half were dissatisfied with the status of the medical facilities. Most of them (96%) rely only on surface water for drinking. National and international organizations should have a comprehensive plan for disaster risk reduction that encompasses all individuals, regardless of race, geography, or ethnicity

Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach

A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

Abstract Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as “very high susceptible” and “high susceptible,” respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area