Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use–land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city’s north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.

Thermal remote sensing is currently an emerging technique for monitoring active volcanoes around the world. The study area, the Aso volcano, is currently the most active and has erupted almost every year since 2012. For the first time, Landsat 8 TIRS thermal data were used in this study area to evaluate and monitor the recent thermal status of this volcano, situated in Southwest Japan, from 2013 to 2016 using four sets of images. The total heat discharged rate (HDR), radiative heat flux (RHF), land surface temperature (LST), and land cover (LC) were evaluated, and the relationship between them was determined, to understand the thermal status of the study area. We used the NDVI (normalized difference vegetation index) for land cover, the NDVI-threshold method for emissivity, the split-window algorithm for LST, and the Stefan–Boltzmann equation for radiative heat flux estimation in this study. The total heat discharge rate was computed using a relationship coefficient of RHF and HDR here. The highest HDR was obtained in 2013, at about 4715 MW, and was the lowest in 2016, at about 3819 MW. The total heat loss showed a declining trend, overall, from 2013 to 2016. The highest pixel RHF was in 2013 and the lowest was in 2014; after that, it increased gradually until 2016, coinciding with the LST of this study area. LC showed that, with decreasing heat loss, the vegetated coverage increased and bare land or mixed land decreased, and vice versa. From the spatial distribution of RHF, we saw that, within the Nakadake craters of the Aso volcano, Crater 1 was the most active part of this volcano throughout the study period, and Crater 3 was the most active after 2014. We inferred that the applied methods using the continuous Landsat 8 TIRS data showed an effective and efficient method of monitoring the thermal status of this active volcano.

The Hatchobaru–Otake (HO) geothermal field is proximal to the Kuju volcano on Kyushu, Japan. There are currently three geothermal power plants operating within this geothermal field. Herein, we explore the thermal status of the HO geothermal area using ASTER thermal infrared data to monitor heat losses from 2009 to 2017. We assessed the solar effects and seasonal variation on heat losses based on day- and night-time Landsat thermal infrared images, and compared three conventional methods of land surface temperature (LST) measurements. The normalized difference vegetation index threshold method of emissivity, the split window algorithm for LST, and the Stefan–Boltzmann equation for radiative heat flux (RHF) were used to determine the heat loss within the study area. The radiative heat loss (RHL) was 0.36 MW, 38.61 MW, and 29.14 MW in 2009, 2013, and 2017, respectively, from the HO geothermal field. The highest anomaly in RHF was recorded in 2013, while the lowest was in 2009. The RHLs were higher from Otake than from the Hatchobaru thermal area in the year of 2013 (~31%) and 2017 (~78%). The seasonal variation in the RHLs based on all three LST estimation methods had a similar pattern, with the highest RHL (about 383–451 MW) in spring and the lowest (about 10–222 MW) in autumn for the daytime images from the HO geothermal field. In the nighttime images, the highest RHL was about 35–67 MW in autumn and the lowest was about 1–3 MW in spring, based on the three LST methods for RHFs. The highest RHL was about 35–42 MW in spring (day) and 3–7 MW in autumn (night) from the Hatchobaru thermal area, analyzed separately. Similarly, the highest RHL was about 22–25 MW in spring (day) and 4–5 MW in winter (night) from the Otake thermal area. The seasonal variation was greatly influenced by the regional ambient temperature. We also observed that clouds had a huge effect, with the highest values for both LST and RHF recorded below clouds on an autumn day. Overall, we obtained higher LSTs at nighttime and lower LSTs during the day from the improved mono-window algorithm than the split window algorithms for all of the seasons. The heat losses were also higher for the improved mono-window algorithm than the split window algorithms, based on the LST nighttime thermal infrared data. Considering the error level of the LST methods and Landsat 8 band 11, this study recommends the IWM method for LST using the Landsat 8 band 10 data. This study also suggests that both the nighttime ASTER and Landsat 8 thermal infrared data could be effective for monitoring the thermal status of the HO geothermal area, given that data is available for the entire period.

Bangladesh is a low-lying riverine country with the mighty Ganges–Brahmaputra–Meghna (GBM) major river system including their abundant tributaries and distributaries. Land erosion–accretion is a very common phenomenon in this riverine country. This process extensively erodes huge productive landmasses at the river confluence zones every year. The main objective of this study was to understand the confluence morpho-dynamics and identify the vulnerable areas near the Padma–Meghna Confluence (PMC) and Ganges–Jamuna confluence (GJC) due to confluence shifting and erosion–accretion phenomenon of those rivers. The present study utilized multi-temporal Landsat satellite images from 1972 to 2019 approximately ten years of interval. Results showed that the PMC indicated frequent variation in migration trend towards NW from 1972 to 1980, SE from 1980 to 2010, and then reversed towards NW direction from 2010 to 2019. On the other hand, the GJC confluence point moved NW direction (2.37 km) from the year 1972 to 1980, but from 1980 to 2019, the confluence shifted towards the SE direction. Due to the migration dynamics, huge changes happened in width and sand bars area of both confluences. In PMC, confluence width increased remarkably indicating erosive flow during 1972–1980, then progressively shortened up to 2019, indicating accretion. In contrast, GJC shows a significant accretional trend over the 47 years. The sand bar area of the PMC increased about 147.09 km² throughout the study period. But, GJC shows an opposite scenario where the total sand bar area decreased about 51.02 km² in the same period. From the vulnerability study of erosion–accretion scenarios, it is predicted that Paturia Ferry Ghat area, Aricha Ferry Ghat area, Arua, Baruria, Dashkin Saljana, Bhadiakola, Masundia, Khanganj and Nyakandi areas near GJC and Chandpur sadar, Srimandi, Sakhua, Bilaspur and char Atra near PMC are highly vulnerable zones. The outputs of the study will enable policy makers to take necessary measures to reduce the erosional severity on both confluence zones and could also provide a basis for proper land management.

A two-dimensional bay and river coupled numerical model in Cartesian coordinates was developed to find the impact of the river on the simulated water levels associated with a storm along the coast of Bangladesh. The shallow water models developed for both the bay and river were discretized by the finite difference method with forward in time and central in space. The boundaries for the coast and islands were approximated through proper stair steps representation and solved by a conditionally stable semi-implicit manner on a staggered Arakawa C-grid. A one-way nested scheme technique was used in the bay model to include coastal complexities as well as to save computational costs. A stable tidal condition was made by forcing the sea levels with the most energetic tidal constituent, M₂, along with the southern open boundary of the bay model omitting wind stress. The developed model was then applied to foresee the sea-surface elevation associated with the catastrophic cyclone of 1991 and cyclone MORA. A comparative study of the water levels associated with a storm was made through model simulations with and without the inclusion of the river system. We found that the surge height in the bay-river junction area decreased by 20% and the surge height reduced by about 3–8% outside the junction area from this study. The obtained results were found to have a satisfactory similarity with some of the observed data.