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Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country's sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally. Highlights: A phenology-based algorithm was applied for delineating winter wheat. The algorithm is developed by combining Sentinel-2 images and Spectroradiometer data. Vegetative indices of wheat exhibited a strong relationship between two sensors' data. Accuracy assessment and validation of the produced maps demonstrated significant results. The method has the potential to be extended to area mapping for other crops locally and globally. [ABSTRACT FROM AUTHOR]

Urban outdoor space has a very important impact on the quality of people's outdoor activities, which has influenced people's health and moods. Its influence is the result of the combined action of various factors. Thermal and air quality environment are important factors affecting the overall comfort of the urban outdoor space. At present, there are few research on interaction with thermal and air quality environment. Therefore, a meteorological measurement and questionnaire survey have been conducted in a representative open space in a campus in Xi'an, China. The following are the research results:(1) Mean physiological equivalent temperature (MPET) is a significant factor affecting thermal sensation vote (TSV) and thermal comfort vote (TCV). PM2.5 has no significant effect on thermal comfort vote (TCV), but it is a considerable factor affecting thermal sensation vote (TSV) when 10.2°C ≤ MPET<21°C (P = 0.023 *). (2) PM2.5 is a significant factor affecting air quality vote (AQV) and breathing comfort vote (BCV).Mean physiological equivalent temperature (MPET) has no significant impact on air quality vote (AQV), but it is a considerable factor affecting breathing comfort vote (BCV) when 10.2°C ≤ MPET<21°C (P = 0.01 **). (3) Mean physiological equivalent temperature (MPET) is a significant factor affecting overall comfort vote (OCV), but PM2.5 is not. In general, When 10.2°C ≤ MPET<21°C (-0.5 < -0.37 ≤ TCV ≤ 0.12 <0.5), the interaction between thermal and PM2.5 environment is significant on thermal sensation vote (TSV) and breathing comfort vote (BCV). This study can provide experimental support for the field of multi-factor interaction, which has shown that improving the thermal environment can better breathing comfort, while reducing PM2.5 concentration can promote thermal comfort. And can also provide reference for the study of human subjective comfort in urban outdoor space in the same latitude of the world. [ABSTRACT FROM AUTHOR]

The southeastern coastline of Bangladesh where the longest natural sea beach Cox's Bazar is located has experienced more pronounced changes due to human intervention compared to the changes due to storms, cyclones and flooding. Over the past 30 years, nature-dependent livelihood and economic activities have generated employment, income and shelter to people but has also enhanced exposure level and consequent vulnerability and risks to fast growing economic activities and human settlements to projected climate-induced natural disasters. Satellite imageries clearly show the changing land-use pattern due to human intervention. On the ground, questionnaire-based, face-to-face interview method has helped in understanding the key drivers behind the changing economic activities, occupation category-wise exposure and vulnerability of the people along the coast. Fishing, salt-shrimp practice, fish drying, agriculture, tourism, and related small trading business are now the main economic activities, and human settlement expansion has changed the coastal ecosystem. The vulnerability assessment suggests that the fast emergence of salt-shrimp farm-based employment and livelihood is one of the most sensitive to natural threats. As per anthropogenic threats, the hotel and restaurant industries are polluting the most fragile coastal ecosystem. [ABSTRACT FROM AUTHOR]

Resilience of air & space defense system of systems (SoSs) is critical to national air defense security. However, the research on it is still scarce. In this study, the resilience of air & space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory. Two cases of the SoSs are considered: (a) The kill chains are relatively homogenous; (b) The kill chains are relatively heterogenous. Meanwhile, two capability assessment methods, which are based on the number of kill chains and improved self-information quantity, respectively, are proposed. The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets. Then, a resilient evaluation index consisting of risk response, survivability, and quick recovery is proposed accordingly. Finally, network models for regional air defense and anti-missile SoSs are established respectively, and the resilience measurement results are verified and analyzed under different attack and recovery strategies, and the optimization strategies are also proposed. The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air & space defense and similar SoSs. [ABSTRACT FROM AUTHOR]

This research aims to investigate the future changes in summer monsoon rainfall and temperature in Bangladesh. The study revealed that INM-CM5-0 is the best model for projecting temperature, while BCC-CSM2-MR is the best model for projecting rainfall over Bangladesh. Using data from a large ensemble of 27 models from CMIP6, the study examined the rainfall and temperature change projections of Bangladesh during the twenty first century relative to the reference period (1981–2014) under SSP2–4.5 and SSP5–8.5. Under SSP2-4.5 and SSP5-8.5, the multi-model ensemble monsoon mean rainfall over Bangladesh will fluctuate between 40 and 260 mm and 100 and 900 mm, respectively. In most parts of the country's north, northeastern, and western regions, the projected changes in spatial patterns of monsoon rainfall indicate an increase in rainfall. The projected temperature indicated that Bangladesh's northwest and west-central areas could face the most significant rise in temperatures, surpassing 3.8 °C under SSP5-8.5. [ABSTRACT FROM AUTHOR]

Countries depending on small-scale agriculture, such as Bangladesh, are susceptible to climate change and variability. Changes in the frequency and intensity of drought are a crucial aspect of this issue and the focus of this research. The goal of this work is to use SPI (standardized precipitation index) and SPEI (standardized precipitation evapotranspiration index) to investigate the differences in drought characteristics across different physiognomy types in Bangladesh and to highlight how drought characteristics change over time and spatial scales when considering different geomorphologies. This study used monthly precipitation and temperature data from 29 metrological stations for 39 years (1980–2018) for calculating SPI and SPEI values. To determine the significance of drought characteristic trends over different temporal and spatial scales, the modified Mann–Kendall trend test and multivariable linear regression (MLR) techniques were used. The results are as follows: (1) Overall, decreasing dry trend was found in Eastern hill regions, whereas an increasing drought trends were found in the in the rest of the regions in all time scaless (range is from − 0.08 decade−1 to − 0.15 decade−1 for 3-month time scale). However, except for the one-month time scale, the statistically significant trend was identified mostly in the north-central and northeast regions, indicating that drought patterns migrate from the northwest to the center region. (2) SPEI is anticipated to be better at capturing dry/wet cycles in more complex regions than SPI. (3) According to the MLR, longitude and maximum temperature can both influence precipitation. (4) Drought intensity increased gradually from the southern to the northern regions (1.26–1.56), and drought events occurred predominantly in the northwestern regions (27–30 times), indicating that drought meteorological hotspots were primarily concentrated in the Barind Tract and Tista River basin over time. Findings can be used to improve drought evaluation, hazard management, and application policymaking in Bangladesh. This has implications for agricultural catastrophe prevention and mitigation. [ABSTRACT FROM AUTHOR]

Despite being located in a semi-arid and arid part of the world, Iran enjoys a very diverse climate. Our objective is to regionalize the country into homogeneous precipitation regions and determine the monthly and annual precipitation water volume and depth in each region, required in hydro-climatological studies and applications, from simple water budget calculation to infrastructure design. We investigate the spatiotemporal diversity of precipitation over the country by analyzing the 33-year-long monthly precipitation time series (1983–2016) at 461 rain-gauge stations. We employed cluster analysis (CA) both hierarchical and non-hierarchical clustering approaches and principal component analysis (PCA) to determine the homogeneous precipitation zones at three macro-, meso-, and micro-scales (resolutions). First, the country is divided into six macro-precipitation regions (MPRs) using CA each showing a mean annual hyetograph of unique pattern and depth. The Siberian cold continental air mass enters the country from the north, the Sudan air mass from the south and southwest, the Mediterranean air mass from the west, the North Atlantic and the Black Sea cyclones from the northwest, and the Maritime air mass from the southeast create these six precipitation regions. Then, the six regions were divided into ten zones of meso-resolution through hierarchical clustering (HC) and k-means clustering. The occasional collision of the air masses causes the division of the six macro-regions into ten zones at meso-resolution. Finally, we subjected the precipitation time series of ten meso-zones to PCA, HC, and k-means clustering and established an optimal number of 24 micro-zones for the first time that reflects a comprehensive precipitation map over the country. The annual hyetograph of each zone shows a unique pattern and distribution with a varying magnitude of monthly precipitation compared to others as the result of varied physio-geographical characteristics of the country prevailing in each micro-zone. The result shows that hierarchical clustering (Ward's method-Pearson correlation) and PCA have the same classification performance and strength in meso- and micro-climatological zoning. The long-term (i.e., 33 years) mean annual hyetograph in each region and zone is also calculated, and the monthly and annual-precipitation water volume and depth in the country are estimated. The findings provide the researchers, practitioners, and decision-makers with an accurate baseline reference for future research and water resource management and will advance the understanding of precipitation dynamics in different regions of the country. [ABSTRACT FROM AUTHOR]

A substantial amount of researches have been done on the understanding and assessment of resilience from multiple perspectives, e.g., ecological, social, economic, and disaster management; however, recent international approach is trending toward more systematic and comprehensive risk assessment processes. Pivotal element of such approach is to emphasizing on promoting resilience in the face of climate change impacts. Conceptualization and identification of parameters to assess climate change resilience is one of the remaining challenges that academia is facing. Reviewing the principles of the climate change resilience highlighted in the literature, the goal of this study is to introduce a theoretical model about the climate change resilience concept to facilitate and enhance future climate change resilience-related researches. The model proposed in this study is named as the climate change resilience of place (C-CROP) model, a geo-based model which is designed to assess climate change resilience for any geographic region with an approach to the incorporation of nature-based solution (NBS). C-CROP model considers vulnerability, exposure, sensitivity to climate change on one side; another side is co-benefit, climate proofing, and disservices of proposed NBS. An operational framework of the C-CROP model is also proposed, that allows spatially explicit assessment of climate change resilience in real world by developing an indicator-based framework and comprehensive mapping using the geospatial approach. Therefore, this model includes vulnerability hotspots identification; better understanding of the pathways of resilience; and solutions (i.e., NBS) to infer the impacts and effectiveness of resilience-building interventions. [ABSTRACT FROM AUTHOR]

Climate change is affecting the life and livelihood of people all over the world, where Bangladesh is one of the most susceptible countries. Anthropogenic climate change is one of the consequences of an increase in the emission of greenhouse gases. Green building practice may be helpful in reducing the emissions. Focusing on the importance of green building and climate change, this study was conducted in Bangladesh to understand the potentiality of green building practice as an adaptation to climate change. To fulfill the objective, 21 key informant interviews and four in-depth interviews were conducted with experts from various government and non-government sectors in Bangladesh. From the findings, it has been revealed that design and construction efficiency, and reduction of energy use are the highest ranked indicators of green building, furthermore, environment-friendly design and construction, and long-term resource efficiency aspects should be included in the design and construction practices of Bangladesh. The consensus is that, buildings should be designed to be more energy-efficient. Besides, the efficient use of roof gardens, climate change mitigation, and long-term resiliencies are identified as the most highlighted sub-indicators from the economic and social perspectives of green building benefits in Bangladesh. Although several organizations are working on green buildings in Bangladesh, a majority of the experts (57%) said the evaluating tools are not in place yet. Besides, public perceptions toward a new change and a lack of regulatory authorities are marked as major obstacles for implementing green building practices. Enforcing fair rules and regulations may aid in overcoming these challenges. [ABSTRACT FROM AUTHOR]

Understanding the historical and future spatio-temporal changes in climate extremes and their potential risk to rice production is crucial for achieving food security in Bangladesh. This paper presents results from a study on trend analysis for 13 climate metrics that significantly influence rice production. The analysis was conducted using the non-parametric Mann-Kendall test and the Theil-Sen slope estimator methods. The study included data from all available weather stations in Bangladesh and the assessment was done for both the wet (May to October) and dry (November to April) seasons, which cover the growing seasons of the country's three types of rice: Aus, Aman and Boro. Results show significant decreasing trends for wet season rainfall (>12 mm/season/year in some stations) in the central and north regions. In addition, dry season rainfall is decreasing significantly in many areas, whilst dry season dry spells are increasing throughout Bangladesh. Decrease in rainfall in some of these areas are of concern because of its impacts on rainfed Aus rice and in the sowing/planting of rainfed Aman rice and irrigated dry season Boro rice. The maximum temperatures in the wet season are increasing throughout the country at 0.5°C every ten years, significantly at most of the climate stations. The analysis shows that the number of days with temperature >36°C has significantly increased in 18 stations over the last three decades, which implies a serious risk to Aman rice yield. The current maximum temperatures (both in the wet and dry seasons) are higher than the optimum temperature ranges for rice production, and this will have likely adverse effects on yield in the face of climate change with increasing temperatures. The results herein have practical implications for planning appropriate adaptation policies to ensure food security in the country. [ABSTRACT FROM AUTHOR]

Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan's ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction. [ABSTRACT FROM AUTHOR]

Global mean temperature is continuously rising and causing changes in the extreme climatic events. Following these changes, climate extremes—the rare events that reside in the tail of the distribution of essential climate variables—are expected to be further intensified, more frequent, and prolonged. Changes in extremes would vary spatially from region to region and thus need regional assessment for future adaptation planning. This study assesses the climate extremes at 1.5 °C, 2 °C, and 4 °C of global warming over Bangladesh which is one of the most vulnerable countries to climate change. Future changes in climate extremes are assessed using a subset of extreme temperature and precipitation indices devised by Expert Team on Climate Change Detection and Indices (ETCCDI). Projections from high-resolution regional climate model ensembles are used to derive extreme climate indices. Our analysis shows overall upward changes in warm indices and downward changes in cold indices at higher specific warming levels. We found a much higher increase in extreme rainfall compared with the annual total rainfall. Increasing variability of rainfall indices is found at higher specific warming levels. Our analysis also suggests a higher increase of temperature during the winter and post-monsoon seasons, as well as an increase in the 1-day and 5-day maximum rainfall during pre- and post-monsoon seasons. A significant regional difference is found in almost all the rainfall indices. The forecasted increase of extreme rainfall and consecutive dry days (CDD) over the northeast region indicates a possibility of an increase of flash floods in the future. Moreover, the increase in the extreme rainfall over the southeast region will increase the chances of landslides. [ABSTRACT FROM AUTHOR]