Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image.
Remote Sensing Applications: Society and Environment
2023-01 | Journal article
DOI: 10.1016/j.rsase.2022.100859
CONTRIBUTORS: Taposh Mollick; Md Golam Azam; Sabrina Karim
The study aims to use UAV images to classify land use and land cover (LULC) in agricultural fields in Bangladesh. The study uses pixel-based and object-based image analysis methods, such as Maximum Likelihood (ML) and k-means clustering, to extract LULC from the UAV images. The study evaluates the accuracy of the classification using overall, user’s and producer’s accuracy, and geometric and non-geometric indices. The paper finds that object-based image analysis is 21% more accurate than pixel-based image analysis, and that both methods perform better than traditional techniques.
Assessing Spatial Vulnerability of Bangladesh to Climate Change and Extremes: A
Geographic Information System Approach
Mitigation and Adaptation Strategies for Global Change
2022-08 | Journal article
DOI: 10.1007/s11027-022-10013-w
CONTRIBUTORS: Md Golam Azam; Md Mujibor Rahman
This study aimed to justify the hypothesis that Bangladesh has spatial diversity in sectors of climate change vulnerability (CCV) by identifying the sectors of vulnerability and visualizing the spatial distribution of vulnerability through multivariate geospatial analysis in the GIS environment. For an integrated assessment of CCV, 38 indicators (socioeconomic and biophysical) have been incorporated in the IPCC framework in raster form. Sectors of CCV are the coastal vulnerability (PC1), meteorological shift vulnerability (PC2), infrastructure and demographic vulnerability (PC3), ecological vulnerability (PC4), pluvial vulnerability (PC5), and economic vulnerability (PC6) with Cronbach’s alpha 0.90, 0.81, 0.88, 0.72, 0.72, and 0.66, respectively. The present study is a new edition in climate vulnerability assessment in Bangladesh since it encompasses multivariate spatial analysis to demonstrate countrywide CCV.
Identification of Climate Change Vulnerable Zones in Bangladesh Through Multivariate Geospatial Analysis
2022 | Book chapter
DOI: 10.1007/978-981-16-6966-8_5
CONTRIBUTORS: Md. Golam Azam; Md. Mujibor Rahman
The study aims to demonstrate spatial vulnerability to climate change in Bangladesh. It uses 42 indicators, 12 from the biophysical category and 30 from the socioeconomic category, incorporated with the IPCC framework through a Geographic Information System (GIS) raster database. The overall vulnerability of the country has been indexed from exposure, sensitivity, and adaptive capacity. The coastal region, part of the hilly region, riverine areas, and the haor basin are found highly vulnerable since these regions are more exposed as well as highly sensitive to climate change effects. The current study is a new contribution from Bangladesh in climate vulnerability indexing since it incorporates multivariate geospatial analysis to quantify and visualize CV countrywide. Findings from this work will be an important foundation in taking appropriate measures to mitigation and adaptation of climate change impacts, from local level measures to policymaking stages.
Md Golam Azam 