Published in , 2019
Book Name: THE WORLD OF GEOGRAPHY; Edition: First Edition; Chapter: Chapter Three; Publisher: Foundation of Practising Geographers. Authors: Mallik Akram Hossain, Sanjia Mahiuddin, Md. Hanif Bhuian
Published in , 2020
Authors: Abu Yousuf Md. Abdullah, Md. Hanif Bhuian, Grigory Kiselev, Ashraf Dewan, Quazi K. Hassan, M. Rafiuddin
Abstract
Although coastal and inland areas of Bangladesh exhibit distinct physiographic and climatic characteristics, spatiotemporal variation of extreme climatic events is poorly understood in these two areas. This study was an attempt to understand the trends in extreme climatic events in coastal and inland areas over the period 1968–2018. The missing data in daily maximum and minimum temperature, and daily rainfall datasets were imputed using the multiple imputation by chained equations technique and implementing a predictive mean matching algorithm. The imputed datasets were then tested for inhomogeneity using the penalized maximal t and modified penalized maximal F tests. A quantile matching algorithm was then applied to homogenize the datasets, which were then used for generating 13 extreme temperature and 9 extreme rainfall indices. The trends were assessed using the Trend Free Pre-whitened Mann–Kendall test and the magnitudes of the changes were determined using the Thiel–Sen slope estimator. Additionally, standardized anomalies were calculated to understand the seasonal variability of temperature and rainfall over the past five decades. Results suggested that both coastal and inland areas were warming significantly but coastal areas exhibited a higher rate of warming. Although most of the extreme rainfall indices showed statistically non-significant changes for coastal and inland stations, there is evidence of localized dryness and increased rainfall at individual stations. In particular, the drought-prone northwestern region of the country experienced decreased rainfall, which is discordant to the results of previous studies. Findings from this study highlighted important local and regional-scale changes in extreme climate events that were previously overlooked. The findings of this study can help undertake targeted climate change adaptation strategies to save population and resources.
Published in , 2021
Authors: Shahana Akther, Umme Saleh, Md. Hanif Bhuian,
Abstract
The study examines the pattern of riverbank erosion and its effects on the socioeconomic condition of the inhabitants of Islampur upazila in Jamalpur district. Riverbank erosion is a regular event in Jamalpur district, particularly in Islampur upazila because this area is more prone to erosion and many people have already lost their agricultural land, homesteads and other important structures to the river or face imminent erosion. The affected people displaced their place of origin and migrated to a new area where they faced a crisis of basic needs, occupation and security. Both qualitative and quantitative approaches were followed in this study. Data were accumulated from both primary and secondary sources. Primary data were collected from questionnaire survey and focus group discussion (FGD) and secondary data were collected from different sources of published and unpublished documents. Three satellite images (Landsat 5 TM, 7 ETM and 8 OLI TIRS) and ArcGIS 10 software were used to analyze the pattern of riverbank erosion. Satellite-based monitoring reveals that the deposition rate is higher than the erosion rate in this area during the 1997 to 2017 study period. The study also revealed that the multiple shifting of livelihood of affected people quickly goes under the poverty line and simultaneously their living status also changed. Therefore, this research is an attempt to identify the pattern of riverbank erosion and related problems in the study area and to draw attention of the authority for proper actions and measures.
Published in , 2021
Authors: Ashraf Dewan, Grigory Kiselev, Dirk Botje, Golam Iftekhar Mahmud, Md. Hanif Bhuian, Quazi K. Hassan
Abstract
There is currently a lack of knowledge regarding the spatiotemporal variation of day and night surface urban heat island intensity (SUHII) in the major cities of Bangladesh. These cities have a large population base and generally lack the resources to deal with rapid urbanisation impacts, so any increase in urban temperature has the potential to affect people both directly (due to heatwave conditions) or indirectly (due to loss of livelihood). Time series diurnal (day/night) MODIS land surface temperature (LST) data for the period 2000–2019 was used to produce baseline information about SUHI intensity, drivers and temporal trends. Five large cities were selected based on population size and historical urban expansion rates. Results indicated that annual SUHII was greater in the larger cities of Dhaka and Chittagong than in the smaller cities. SUHII observed during the day was also greater than at night. Population (in terms of city size and surface cover), lack of greenness and anthropogenic forcing were major factors affecting SUHII. Trend assessments revealed positive trends during daytime in four out of five cities, while one city recorded negative trends at night. The findings may provide new insights into impacts arising from rapid urbanisation and demographic shifts.
Published in , 2022
Authors: Ashraf Dewan, Shamsuddin Shahid, Md. Hanif Bhuian, Shaikh M. Jobayed Hossain, Mohamed Salem Nashwan, Eun-Sung Chung, Quazi K. Hassan & Md Asaduzzaman
Abstract
A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901–2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product’s ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of −2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R2) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901–2018 was in the range of −4.5 to 4.3%, NRMSE between 9 and 19% and R2 in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.
Published in , 2022
Author
Abstract
The goal of this study is to critically review the existing UHI research and depict the actual scenario of UHI intensity (UHII) in the major cities of Bangladesh. It also provided in-depth knowledge about the materials and methods as well as the existing lacuna of UHI research. A total of 12 studies were discovered that used Landsat data while only three studies found MODIS products (day and night) to examine the UHI intensity (UHII). There are two different methods observed to calculate the UHII while the mono and the split-window algorithm are separately used for LST extraction. However, the summary of the literature provided that most of the major cities of Bangladesh are experiencing the highest rate of UHI intensity particularly Dhaka and Chattogram with 5 ˚C and 3 ˚C from the core city while a study showed the contradicted result as the lowest (1.46 ˚C) for Dhaka and highest (10 ˚C) for Mymensingh. This study also detected the major UHI influencing factors and significant research gaps that play the greatest role to raised UHII. This study will support by providing comprehensive knowledge about the cavities in existing UHI research which aids to evaluate the future UHI study in Bangladesh.
Published in , 2023
Author: M. Hanif Bhuian, Hatef Dastour, Quazi K. Hassan
Published in , 2024
Authors: Hanif Bhuian, Hatef Dastour, Mohammad Razu Ahmed, Quazi K Hassan
Abstract
Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.
| Renewable Energy | GIS & Field-based |
Published:
The studies assessed the environmental and economic impacts of solar PV systems in off-grid areas where electricity was not available.
| Cadastral Mapping | GIS-based |
Published:
The project focused on cadastral mapping using GIS tools across all unions, districts, and divisions in Bangladesh to convert land data into a digital format.
| Urban Warming in Dhaka megacity | GIScience, RS, & Field-based |
Published:
The project focused on assessing the urban heat island (UHI) effects and their impact on the outdoor environment. The project involved collecting and analyzing meteorological data related to UHI in the Dhaka megacity, Bangladesh.
| Forest Fires/Wildfires | GIScience & RS |
Published:
The project supervised by Dr. Quazi Hassan (Professor, Geomatics Engineering, University of Calgary), focused on monitoring and managing active forest fires, including automatic fire clustering, perimeter delineation, and the generation of timely fire progression models using FIRMS ptovided satellite-based active fire data such as NRT/URT)/RT data in Canada, particularly in Alberta and the Northwest Territories.
| Arsenic Contamination Mapping | GIS-based |
Published:
The project focused on monitoring and managing arsenic contamination in Bangladesh, including mapping (GIS-based) contamination from the union to district level. It was funded by the government and supported by international NGOs.
| Change Detection (Land Cover & Climate) | GIScience & RS |
Published:
The project, titled ‘Investigation of Climate Scenario with Their Land Cover Condition of Two Major Cities in Bangladesh: A Spatio-Temporal Study,’ was conducted under the Department of Geography and Environment at Jagannath University, Bangladesh. It examines the decadal spatio-temporal climate and morphological scenarios in Dhaka and Rangpur, utilizing hybrid classification for land use and land cover analysis. The study reveals temperature increases, declining rainfall trends, and land cover changes, offering critical insights for urbanization, sustainable development, and climate change adaptation policies.
| Micro-climate | GIScience & RS |
Published:
The undergraduate dissertation project, conducted by the Department of Geography and Environment at Jagannath University, Bangladesh, assessed microclimatic variations in four areas of Dhaka city using primary and secondary data. The study explored temperature, humidity, and wind speed differences, utilizing digital weather tools, ArcGIS, and Google Earth Pro.
Video Tutorial-based, Online, Geo. Academy, 2017
I am the founder and lead instructor of Geo. Academy, a free online learning platform dedicated to providing education in geography, GIS, remote sensing, statistics, and research-related tools and techniques. Our mission is to equip learners with the skills and knowledge needed to excel in geography and scientific research.
Undergraduate Lab, University of Calgary, Geomatics Engineering, 2023
Course: ENGO 435 - Remote Sensing