The following paper about the assessment of urban wastewater flooding risks for coastal communities has been recently accepted for publication by Journal of Hydrology.
Aziz, F., X. Wang, and M. Q. Mahmood. Urban Wastewater Flooding Risk Assessment for Coastal Communities: Compound Impacts of Climate Change and Population Growth. Journal of Hydrology, accepted on September 20, 2024.
More details will come soon once the paper is published.
Title: A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification
Journal: Geomatica
DOI: https://doi.org/10.1016/j.geomat.2024.100023
Abstract: Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth’s features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consists of six major classes, which include Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery.
The following paper about a comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification has been recently accepted for publication by Geomatica.
Basheer, S. and X. Wang. A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification. Geomatica, accepted on August 28, 2024.
More details will come soon once the paper is published.
The following paper about the prediction of long-term photovoltaic power generation has been recently accepted for publication by Renewable Energy.
Liu, Z., J. Guo, X. Wang, Y. Wang, W. Li, X. Wang, Y. Fan, and W. Wang. Prediction of long-term photovoltaic power generation in the context of climate change. Renewable Energy, accepted on August 28, 2024.
More details will come soon once the paper is published.
Title: Quantitative assessment of The Group of Seven’s collaboration in sustainable development goals
Journal: Nature Communications
DOI: https://doi.org/10.1038/s41467-024-51663-5
Abstract: Strengthening international collaboration is essential to achieving the United Nations’ SDGs. The Group of Seven (G7) is recognized for acting and enhancing cooperation to achieve the SDGs. However, the current understanding of G7’s cooperation is rather subjective without quantitative measurements. Here we show a comprehensive and quantitative analysis of G7’s cooperation with regards to the economic and environmental SDGs over the period of 2000-2020. The results suggest that G7 countries have all contributed positively to economic indicators thanks to their closely binding relationship. By contrast, significant discrepancies and uncooperative performances in environmental indicators have been revealed. Particularly, Canada and Germany have shown considerable negative synergy contributions to environmental indicators, which might offset the positive contributions brought by France and Italy and lead to an overall negative synergy. Our results highlight the need for further collaboration among G7 to tackle emerging environmental issues, such as climate change and shrinking biodiversity.