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Abstract Sea level rise affects the long-term psychological health of coastal communities. However, research on long-term and seasonal impacts on vulnerable communities’ psychological health is limited. Here, we explore the effect of sea-level rise on the psychological health of the coastal population in Satkhira and Khulna districts of southwest Bangladesh across two seasons: before monsoon (March to April) and post-monsoon months (October to November) in 2021. We leveraged the longitudinal research that involved 1144 participants. We collected data on psychological health using established scales for distress, depression, anxiety, and stress and also measured environmental factors and resource losses. Results indicate that psychological health, particularly distress, depression, anxiety, and stress, increased during the post-monsoon months in communities more vulnerable to sea-level rise. Highly vulnerable communities showed increased psychological distress post-monsoon. Environmental stressors and resource loss escalated during the post-monsoon period, especially in moderate and highly vulnerable communities. Our findings emphasize the urgent need for targeted support and resilience-building interventions in affected communities to alleviate the psychological health impacts of sea-level rise.

Sustainable development of oases enhances the capability of ecosystems to reverse land degradation in arid regions. However, it is unclear to what extent oasisation leads to ecological quality (EQ) degradation in the peripheral environment. To comprehensively evaluate the ecological effects of oasisation, this study developed a remote sensing ecological quality index to characterise spatiotemporal patterns and driving mechanisms of EQ in different oasis-type areas. Results show that the oasis area has increased by 37.4 % in the past 22 years. The EQ of the core area was stable, but EQ declined in newly developed urban areas. The effect range of the oasis on the peripheral regions EQ is 12 km, and the expansion of the oasis is improving the EQ of peripheral regions. The driving factors of EQ varied widely among different oasis areas. The vegetation index showed significant explanatory power (q-mean is 0.45) in each oasis area. Human activities and soil moisture showed the most significant explanatory power (0.76) in the core area, but weak power is observed in the changed and peripheral areas. Climate factors have the strongest influence on the changed and peripheral areas. The findings provide an in-depth understanding of the spatial heterogeneity of EQ and could be implemented in Oasis urban planning and sand prevention belt construction.

An understanding of the spatial distribution of informal settlements within a city is important for urban management decision-making and service infrastructure provision, provides useful information for planners and policymakers, and has a role in minimizing future urban environmental issues. The objective of this article is to evaluate the performance of an ontology of informal settlements mapping for Riyadh city. Satellite data include a combination of medium-resolution Landsat thematic mapper, enhanced thematic mapper plus, and operational land imager, and VHR Worldview-3 imagery. Object-based image analysis (OBIA) technique was employed to identify 30 useful indicators at defined object, settlement, environment, and temporal levels. Time-series analysis (TSA) was undertaken, and a multidimensional model was developed to define the trend of changes through 30 years. The classification process incorporated OBIA, random forest (RF), and LandTrendr techniques. The classification output included delineation of formal and informal settlement boundaries and road networks, as well as vegetated and vacant areas. The final OBIA–RF and TSA classification demonstrated an overall accuracy (OA) of 89% with the corresponding kappa value of 87%. The OBIA–RF classification developed without TSA techniques returned an OA of 87% and kappa value of 84%. The article indicated that using OBIA and RF methods, in combination with LandTrendr, can be a useful tool for planners and decision-makers to identify changes in the land cover of informal settlements within Riyadh city and beyond.

Abstract Background Excessive worry is an invisible disruptive force that has adverse health outcomes and may advance to other forms of disorder, such as anxiety or depression. Addressing worry and its influences is challenging yet crucial for informing public health policy. Methods We examined parents’ worries, influences, and variability before and during COVID-19 pandemic and across geography. Parents (n = 340) and their primary school-aged children from five Australian states completed an anonymous online survey in mid-2020. After literature review, we conceptualised the influences and performed a series of regression analyses. Results Worry levels and the variables contributing to parents’ worry varied before to during the pandemic. The proportion of parents who were "very worried all the time" increased by 14.6% in the early days of the pandemic. During the pandemic, ethnic background modified parents’ worry and parents’ history of daily distress symptoms was a significant contributor (p

Contemporary understanding of the impacts of climate change on global drought characteristics (e.g., intensities, extents) is still limited and not well understood. This knowledge is critical because projected changes in climate are expected to impact on future water availability as well as influence decisions on how water resources are allocated. The main aim of this study is to improve understanding of drought characteristics (extents and duration) in the Anthropocene where rapid changes in the environment are caused by the composite influence of human activities and climate change. Multi-scale earth observation data (1980−2020) and the Coupled Model Intercomparison Project Phase 6 climate models, which incorporate the Shared Socioeconomic Pathways (2040−2070and 2070−2100) are used to assess these characteristics as well as identify climatic hotspots where changes in drought characteristics could drive groundwater hydrology. Results show that towards the end of the 21st century, global land areas under drought will significantly decrease but their durations will not. Generally, there is evidence of significant decline in the proportion of areas that will experience various drought intensities (moderate, severe and extreme drought) in the future and for each category, drought affected areas will not reach 30% on average. Moreover, some regions are potential hotspots of climate–groundwater interactions where drought events could directly impact on groundwater. This is because of the varying degree of strong correlations (positive and negative) between climate and groundwater data in some areas (e.g., Australia, Europe, Southern Africa, Asia). The relatively strong negative correlations in some of these hotspots are indicative of the presence of considerable lags, that could be caused by aridity as well as human groundwater footprints.

Measurement(s) water-based rainfall Technology Type(s) weather station

Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence characteristics. Utilizing water level observation data from stations along Bangladesh's Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and MAE were used to assess the model's performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO-LSTM model provides improved prediction accuracy and stability and improves water level forecasting accuracy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future.

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.

Urban planning within Riyadh, the capital of Saudi Arabia, has been impacted by the presence of informal settlements. An understanding of the spatial distribution of these settlements is essential in developing urban policies. This study used remotely sensed imagery to evaluate and characterize informal settlements within the city, both with and without expert knowledge of the study area (defined as expert knowledge, EK). An informal settlement ontology for four study sites within Riyadh City was developed using an analytical hierarchy process (AHP). Local knowledge was translated into a ruleset to identify and map settlement areas using spatial, spectral, textural, and geometric techniques. These were combined with an object-based image analysis (OBIA) approach. The study demonstrated that combining expert knowledge and remotely sensed data can efficiently and accurately identify informal settlements. Two classified images were produced, one with EK, and one without EK, to investigate how a detailed understanding of local conditions could affect the final image classification. Overall accuracy when using EK was 94%, with a kappa coefficient of 89%, while without EK accuracy was 68% (kappa coefficient of 61%). The final OBIA classes included formal and informal settlements, road networks, vacant blocks, shaded areas, and vegetation. This study demonstrated that local expert knowledge and OBIA helpful in urban mapping. It also indicated the value of integrating a local ontological process during digital image classification. This work provided improved techniques for mapping informal settlements in Middle Eastern cities.

Abstract Background Numerous studies have examined the association between safety and primary school-aged children’s forms of active mobility. However, variations in studies’ measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers’ knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children’s outdoor active mobility behaviour and used this analysis to propose future research directions. Method A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies’ inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. Results Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children’s neighbourhoods varied within 10 studies’ objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies’ measurement methods assisted in outlining a three-dimensional relationship between ‘what’ (determinants), ‘where’ (spatial) and ‘when’ (time) within a methodological conceptual framework. Conclusions The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children’s health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children’s active mobility and safety.

Many young adults are susceptible to obesity issues and the increased health risks associated with a lack of physical activity. Those who are prone to gaining weight include university students. An active transport system (walking and cycling), in combination with well-funded public transport, are essential components of a sustainable urban transport network, offering many benefits to the health of the individual, as well as the environment, economy, and society as a whole. The spatial association between active mobility (i.e. the physical activity of a human being for locomotion) of young adults and the environment, however, is poorly understood. This study presents a GIS-based model to determine association of various environmental (natural and built environment) factors with locational accessibility of active and public transport trips taken by university students. A GIS-based ensemble of Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP) model was established. We analysed the characteristics of locations accessed by university students in relation to eight environmental factors including slope, elevation, land use, population density, travel time, building density, intersection density, and public transport service area. The model was applied to the Grenoble metropolitan region of France, an area well-known for policies which promote active transport. The results indicated that intersection density and land use are strongly associated with active and public transport accessibility, with weights of 0.17 and 0.16, respectively. The presence of infrastructure to support active travel, and regulation to limit vehicular speed, also improved accessibility. Approximately 50% of the area of the Grenoble metropolitan region was defined as accessible and suitable (‘moderate’ to ‘very high’ degree) for active mobility. The results of this study could allow city planners to monitor the existing status of active and public transport facilities, and identify areas that require additional work to improve accessibility.

When satellite-based SAR (Synthetic Aperture Radar) images and images acquired from the ground are registered, they offer a wealth of information such as topographic, vegetation or water surface to be extracted from the ground-level shooting images. Simultaneously, high temporal-resolution and high spatial-resolution information obtained by the ground-level shooting images can be superimposed on satellite images. However, due to the differences in imaging modality, spatial resolutions, and observation angle, it was not easy to directly extract the corresponding points between them. This paper proposes an image registration method to estimate the correspondence between SAR images and ground-level shooting images through a set of multi-altitude images taken at different heights.

The water quality in rivers around Dhaka is deteriorating very fast given extensive sources of pollutants associated with rapid urbanisation and industrialisation. This paper introduced a water quality index (WQI) utilising Landsat bands 1 to 3 (0.45 to 0.67 µm) against band 5 (1.55 to 1.75 µm). The value of the proposed index varies from – 1 to + 1, whereby around 0 (small positive and negative) indicated probable contamination, if the water depth is around 2 m or above. This approach was developed using Landsat data to study long-term water quality change in the rivers surrounding Dhaka megacity, Bangladesh. The values were converted to total suspended solids (TSS) by utilising observed in-situ data, which showed a clear seasonal influence of water pollution, suggesting it reduces to the background level with the onset of high monsoonal river flows. However, long-term analysis showed that the rivers remain polluted throughout the year, particularly since 2011. The main reason for increasing river water pollution may be linked to the contaminant load which has increased extensively in recent years. The approach developed in this study may be used in other urban rivers, particularly in Asia.

Objective To identify, summarise and evaluate evidence on the correlation between perceived and actual neighbourhood safety (personal and road danger) and diverse forms of outdoor active mobility behaviour (ie, active play, exercise, and travel) among primary-school-aged children.Design A systematic review of evidence from observational studies exploring children’s active mobility behaviour and safety.Data sources Six electronic databases were searched: Google Scholar, PubMed, Scopus, Science Direct, ProQuest and Web of Science from study inception until July 2020.Data extraction and synthesis Study selection and quality assessment were conducted independently by two reviewers. We expanded on a quality assessment tool and adopted a vote-counting technique to determine strength of evidence. The outcomes were categorised by individual, family and neighbourhood levels.Results A total of 29 studies were included, with a majority of cross-sectional design. Higher parental perceived personal safety correlated with increased children’s active mobility behaviour, but most commonly in active travel (eg, independent walking or cycling to a local destination). Increased concerns regarding road danger correlated with a decrease in each type of children’s active behaviour; active travel, play and exercise. However, these correlations were influenced by child’s sex/gender, age, car ownership, neighbourhood types, across time, and proximity to destination. Limited or inconclusive evidence was found on correlate of children’s outdoor active mobility behaviour to ‘stranger danger’, children’s perceived personal safety, race/ethnicity, socioeconomic status or measured safety.Conclusion Children are restricted by perception of safety. Encouraging children’s active travel may require future strategies to address characteristics relevant to types of the neighbourhood that promote a high sense of personal safety. Children and parents may embrace other types of active mobility behaviour if road danger is mitigated. Sex/gender and age-specific interventions and redesign of public places could lead to child-friendly cities. Future studies may benefit from adopting validated measurement methods and fill existing research gaps.

Abstract Background A spatial and temporal study of the distribution of facility-based deliveries can identify areas of low and high facility usage and help devise more targeted interventions to improve delivery outcomes. Developing countries like Bangladesh face considerable challenges in reducing the maternal mortality ratio to the targets set by the Sustainable Development Goals. Recent studies have already identified that the progress of reducing maternal mortality has stalled. Giving birth in a health facility is one way to reduce maternal mortality. Methods Facility delivery data from a demographic surveillance site was analyzed at both village and Bari (comprising several households with same paternal origins) level to understand spatial and temporal heterogeneity. Global spatial autocorrelation was detected using Moran’s I index while local spatial clusters were detected using the local Getis G i * statistics. In addition, space-time scanning using a discrete Poisson approach facilitated the identification of space-time clusters. The likelihood of delivering at a facility when located inside a cluster was calculated using log-likelihood ratios. Results The three cluster detection approaches detected significant spatial and temporal heterogeneity in the distribution of facility deliveries in the study area. The hot and cold spots indicated contiguous and relocation type diffusion and increased in number over the years. Space-time scanning revealed that when a parturient woman is located in a Bari inside the cluster, the likelihood of delivering at a health facility increases by twenty-seven times. Conclusions Spatiotemporal studies to understand delivery patterns are quite rare. However, in resource constraint countries like Bangladesh, detecting hot and cold spot areas can aid in the detection of diffusion centers, which can be targeted to expand regions with high facility deliveries. Places and periods with reduced health facility usages can be identified using various cluster detection techniques, to assess the barriers and facilitators in promoting health facility deliveries.

Abstract A major challenge in flood mapping using multi‐criteria decision analysis (MCDA) is the selection of the flood risk factors and the estimation of their relative importance. A novel MCDA method through the integration of two state‐of‐the‐art MCDA methods based on catastrophe and entropy theory is proposed for mapping flood risk in the Peninsular Malaysia, an area very susceptible to flooding events, is presented. A literature review was undertaken which identified the various socioeconomic, physical and environmental factors which can influence flood vulnerability and risk. A set of variables was selected using an importance index which was developed based on a questionnaire survey. Population density, percentage of vulnerable people, household income, local economy, percentage of foreign nationals, elevation and forest cover were all deemed highly relevant in mapping flood risk and determining the zones of maximum vulnerability. Spatial integration of factors using the proposed MCDA revealed that coastal regions are highly vulnerable to floods when compared to inland locations. Flood risk maps indicate that the northeastern coastal region of Malaysia is at greatest risk of flooding. The prediction capability of the integrated method was found to be 0.93, which suggests good accuracy of the proposed method in flood risk mapping.

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images.

In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use–land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city’s thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001–2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Analyzing long-term dynamics of landscape patterns can provide important insights into the changes in landscape functions, that are necessary for optimizing resource management strategies. This study primarily aimed at quantifying landscape structural change. The Land use/land cover (LULC) layers of 1972, 1987, 2002, and 2017 were mapped from Landsat images, and projected to 2032 and 2047. Factor analysis was then employed to select independent core metrics of landscape composition and configuration to characterize the landscape. A post-classification comparison indicated that, between 1972 and 2017, natural vegetation, grassland, barren land and waterbody covers declined by 89.9%, 67.9%, 67.8 and 15.9%, respectively. On the other hand, plantation increased by 692.1% followed by human settlement (138%) and farmland (21.8%). A similar trend is likely to continue in 2032 and 2047 with a slight decline in the plantation category in 2047. Analysis of landscape metrics revealed that between 1972 and 2017, the number of patches increased. Specifically, plantation, barren land, settlement and grassland increased by 171.4%, 69.7%, 65.8% and 28.6%, respectively. In contrast, natural vegetation, farmland and waterbody declined by 53.1%, 46.3% and 33.9%, respectively. Future predictions showed a declining trend of the number of patches for all LULC types. An increasing trend in the largest patch index and patch size for farmland, plantation, and settlement categories was observed across all years, suggesting intensified human activities in the landscape. Consequently, natural habitat category has declined and become fragmented. Landscape pattern has changed considerably and become more fragmented over the last 45 years. Nevertheless, the future projections suggest a decline in fragmentation and potentially increased assemblage of patches forming simple patterns with fewer number of large size class patches. The results of this study could perhaps be applied in designing strategies for landscape management planning and resource conservation decision-making.

The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation and snowmelt could be reasons for further escalation of the problem. Therefore, estimation of hydrological processes is critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using soil and water assessment tool (SWAT). The methodology includes: (i) development of SWAT model using land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from 1978 to 1993; (iii) model validation for the time 1994–2003; (iv) bias correction of regional climate model (RCM), and (v) utilization of bias-corrected RCM for future assessment under representative concentration pathways RCP4.5 and RCP8.5 for mid (2041–2070) and late century (2071–2100). The results of the study revealed a strong correlation between simulated and observed flow with R2 and Nash–Sutcliff efficiency (NSE) equal to 0.85 each for daily flow. For validation, R2 and NSE were found to be 0.84 and 0.80, respectively. Compared to baseline period (1976–2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of the twenty-first century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP4.5 and RCP8.5, respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century, respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century, respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model could be used as effective tool for climate change valuation and for sustainable management of water resources in future.

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

Abstract Background Visceral leishmaniasis is a considerable public health burden on the Indian subcontinent. The disease is highly endemic in the north-central part of Bangladesh, affecting the poorest and most marginalized communities. Despite the fact that visceral leishmaniasis (VL) results in mortality, severe morbidity, and socioeconomic stress in the region, the spatiotemporal dynamics of the disease have largely remained unexplored, especially in Bangladesh. Methods Monthly VL cases between 2010 and 2014, obtained from subdistrict hospitals, were studied in this work. Both global and local spatial autocorrelation techniques were used to identify spatial heterogeneity of the disease. In addition, a spatial scan test was used to identify statistically significant space-time clusters in endemic locations of Bangladesh. Results Global and local spatial autocorrelation indicated that the distribution of VL was spatially autocorrelated, exhibiting both contiguous and relocation-type of diffusion; however, the former was the main type of VL spread in the study area. The spatial scan test revealed that the disease had ten times higher incidence rate within the clusters than in non-cluster zones. Both tests identified clusters in the same geographic areas, despite the differences in their algorithm and cluster detection approach. Conclusion The cluster maps, generated in this work, can be used by public health officials to prioritize areas for intervention. Additionally, initiatives to control VL can be handled more efficiently when areas of high risk of the disease are known. Because global environmental change is expected to shift the current distribution of vectors to new locations, the results of this work can help to identify potentially exposed populations so that adaptation strategies can be formulated.

Abstract Background Visceral leishmaniasis (VL) is a parasitic infection (also called kala-azar in South Asia) caused by Leishmania donovani that is a considerable threat to public health in the Indian subcontinent, including densely populated Bangladesh. The disease seriously affects the poorest subset of the population in the subcontinent. Despite the fact that the incidence of VL results in significant morbidity and mortality, its environmental determinants are relatively poorly understood, especially in Bangladesh. In this study, we have extracted a number of environmental variables obtained from a range of sources, along with human VL cases collected through several field visits, to model the distribution of disease which may then be used as a surrogate for determining the distribution of Phlebotomus argentipes vector, in hyperendemic and endemic areas of Mymensingh and Gazipur districts in Bangladesh. The analysis was carried out within an ecological niche model (ENM) framework using a maxent to explore the ecological requirements of the disease. Results The results suggest that VL in the study area can be predicted by precipitation during the warmest quarter of the year, land surface temperature (LST), and normalized difference water index (NDWI). As P. argentipes is the single proven vector of L. donovani in the study area, its distribution could reasonably be determined by the same environmental variables. The analysis further showed that the majority of VL cases were located in mauzas where the estimated probability of the disease occurrence was high. This may reflect the potential distribution of the disease and consequently P. argentipes in the study area. Conclusions The results of this study are expected to have important implications, particularly in vector control strategies and management of risk associated with this disease. Public health officials can use the results to prioritize their visits in specific areas. Further, the findings can be used as a baseline to model how the distribution of the disease caused by P. argentipes might change in the event of climatic and environmental changes that resulted from increased anthropogenic activities in Bangladesh and elsewhere.

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies.

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

The impact of the COVID-19 pandemic on the physical health of children is limited. However, the changes in life behaviour imposed to contain its spread may have prolonged lifelong impact, particularly on vulnerable child populations. These potential harms can be mitigated, but require identification of those most at risk. This paper aims to analyze the spatial disparity of the impact COVID-19 outbreak placed on parents and their primary school-age children’s (grades 4–6) levels of worry. We conducted a national online survey sampling of parents and their children across five Australian states. Participants were recruited using social media for western Australia and a research company for the remaining states. Postal code, socio-demographics, exposures to COVID-19 cases, amount and frequency of following the news before and during the pandemic outbreak were captured. Univariate, bivariate and multivariate spatial statistical analyses were performed. Parents’ level of worry related to COVID-19 is significantly associated with parents’ amount and frequency of following COVID-19 news. It is critical to convey urgent information for public health awareness during health crises such as COVID-19. However, limiting the amount of exposure time towards unneeded media news may reduce population anxiety.

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.

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.

Geographic information systems (GIS) can be effectively utilized to carry out spatio-temporal analysis of spatial accessibility to primary healthcare services. Spatial accessibility to primary healthcare services is commonly measured using floating catchment area models which are generally defined with three variables; namely, an attractiveness component of the service centre, travel time or distance between the locations of the service centre and the population, and population demand for healthcare services. The nearest-neighbour modified two-step floating catchment area (NN-M2SFCA) model is proposed for computing spatial accessibility indices for the entire country. Accessibility values from 2010 to 2013 for Bhutan were analysed both spatially and temporally by producing accessibility ranking maps, plotting Lorenz curves, and conducting spatial clustering analysis. The spatial accessibility indices of the 205 sub-districts show great disparities in healthcare accessibility in the country. The mean- and median-based classification results indicate that, in 2013, 24 percent of Bhutan’s population have poor access to primary healthcare services, 66 percent of the population have medium-level access, and 10 percent have good access.

Background: Influenza seasonality in the tropics is poorly understood and not as well documented as in temperate regions. In addition, low-income populations are considered highly vulnerable to such acute respiratory disease, owing to limited resources and overcrowding. Nonetheless, little is known about their actual disease burden for lack of data. We therefore investigated associations between tropical influenza incidence and weather variability among children under five in a poor urban area of Dhaka, Bangladesh. Design: Acute respiratory illness data were obtained from a population-based respiratory and febrile illness surveillance dataset of Kamalapur, a low-income urban area in southeast Dhaka. Analyzed data were from January 2005 through December 2008. Nasopharyngeal wash specimens were collected from every fifth eligible surveillance participant during clinic visits to identify influenza virus infection with viral culture and reverse transcriptase–polymerase chain reaction. Time series analysis was conducted to determine associations between the number of influenza cases per week and weather factors. Zero-inflated Poisson and generalized linear Poisson models were used in the analysis for influenza A and B, respectively. Results: Influenza A had associations with minimum temperature, relative humidity (RH), sunlight duration, and rainfall, whereas only RH was associated with influenza B. Although associations of the other weather factors varied between the two subtypes, RH shared a similar positive association when humidity was approximately 50–70%. Conclusions: Our findings of a positive RH association is consistent with prior studies, and may suggest the viral response in the tropics. The characteristics of settlement areas, population demographics, and typical overcrowding of urban poverty may also contribute to different impacts of rainfall from higher economic population. Further investigations of associations between tropical influenza and weather variability for urban low-income populations are required for better understanding.

Changes in rainfall and land use/land cover (LULC) can influence river discharge from a catchment in many ways. Homogenized river discharge data from three stations and average rainfall records, interpolated from 13 stations, were examined for long-term trends and decadal variations (1970−2017) in the headwater, upper and middle catchments of the Bagmati River. LULC changes over five decades were quantified using multitemporal Landsat images. Mann−Kendall tests on annual time series showed a significant decrease in river discharge (0.61% per year) from the entire Bagmati catchment, although the decrease in rainfall was statistically insignificant. However, declines in river discharge and rainfall were both significant in upper catchment. Decadal departures from long-term means support these trend results. Over tenfold growth in urban area and a decrease in agricultural land were observed in the upper catchment, while forest cover slightly increased in the entire catchment between 1975 and 2015. Correlation analysis showed a strong association between surface runoff, estimated using the curve number method, observed river discharge and rainfall in the upper catchment, while the relationship was weaker in the headwater catchment. These results were also supported by multiple regression analysis, suggesting that human activities together with climate change have contributed to river discharge changes in the Bagmati catchment.

Open educational resources (OER) have become increasingly popular in recent times. Here, the aim was to synthesise the lessons learned through the development of OER materials for a university-level course called “environmental modelling„. Consequently, the topics of discussion included: (i) how to choose an appropriate creative commons license; (ii) ways of incorporating materials from other sources, such as publicly available sources, other open access materials, and an author’s own published materials if not published under a creative commons license; (iii) the impact of the developed OER in the field of environmental modelling; and (iv) the challenges in developing OER material. Upon developing the materials, we observed the following: (i) students enrolled in the course did not purchase textbooks; (ii) our OER materials ranked as one of the most accessed (i.e., number 7) materials according to the usage data that summed the number of file downloads and item views from PRISM (i.e., the hosting platform maintained by the University of Calgary); (iii) the students learned relatively better as per the data acquired by the University of Calgary’s universal student ratings of instruction (USRI) instrument; and (iv) other universities expressed interest in adopting the materials.

Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990–2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user’s accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh’s coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government’s afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape.

Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions.