Search

Your search keyword '"Pang, Tianze"' showing total 15 results
Start Over Author "Pang, Tianze"
15 results on '"Pang, Tianze"'

6. Quantification of Carbon Flux Patterns in Ecosystems: A Case Study of Prince Edward Island.

Author

Basheer, Sana, Wang, Xiuquan, Van Dau, Quan, Awais, Muhammad, Kinay, Pelin, Pang, Tianze, and Mahmood, Muhammad Qasim

Abstract

Mitigating climate change by reducing heat-trapping greenhouse gas (GHG) emissions in the Earth's atmosphere is a critical global challenge. In response to this urgency, the Canadian government has set a target of achieving zero emissions by 2050. The Government of Prince Edward Island (PEI) has committed to becoming Canada's first net-zero province by 2040. Achieving this goal requires an extensive knowledge of emissions arising from ecosystem dynamics in PEI. Therefore, this study aims to quantify the carbon fluxes of these ecosystems, offering insights into their role in GHG emissions and removals. Through an extensive literature review and analysis, this research provides a detailed assessment of the potential carbon flux contributions from various ecosystems across PEI. High-resolution maps for carbon emissions, removals, and flux for the years 2010 and 2020 were developed, highlighting key findings on carbon dynamics. Additionally, a web-based tool was developed to allow decision makers and the general public to explore these carbon flux maps interactively. This work aims to inform policy decisions and enhance strategies for effective climate change mitigation in PEI. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

7. Testing Raft-Replicated Database Systems

Author

Ding, Guohao, Qian, Weining, Cai, Peng, Pang, Tianze, Zhao, Qiong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zheng, Chen, editor, and Zhan, Jianfeng, editor

Published
2019
Full Text
View/download PDF

9. Fast Log Replication in Highly Available Data Store

Author

Wang, Donghui, Cai, Peng, Qian, Weining, Zhou, Aoying, Pang, Tianze, Jiang, Jing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Lei, editor, Jensen, Christian S., editor, Shahabi, Cyrus, editor, Yang, Xiaochun, editor, and Lian, Xiang, editor

Published
2017
Full Text
View/download PDF

15. A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification

Author

Basheer, Sana, Wang, Xiuquan, Nawaz, Rana Ali, Pang, Tianze, Adekanmbi, Toyin, and Mahmood, Muhammad Qasim

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 consist of six major classes which includes 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.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results