Quantification of Local Warming Trend: A Remote Sensing-Based Approach
Understanding the warming trends at local level is critical; and, the development of relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging...
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| Veröffentlicht in: | PloS one 2017-01, Vol.12 (1), p.e0169423-e0169423 |
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| creator | Rahaman, Khan Rubayet Hassan, Quazi K Chowdhury, Ehsan H |
| description | Understanding the warming trends at local level is critical; and, the development of relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961-2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming. |
| doi_str_mv | 10.1371/journal.pone.0169423 |
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Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961-2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0169423</identifier><identifier>PMID: 28072857</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Air temperature ; Alberta ; Algorithms ; Analysis ; Climate Change ; Computer and Information Sciences ; Datasets ; Earth Sciences ; Engineering and Technology ; Environmental Monitoring ; Ice ; Mitigation ; Models, Theoretical ; MODIS ; People and places ; Physical Sciences ; Remote sensing ; Remote Sensing Technology ; Research and Analysis Methods ; Seasons ; Spatial discrimination ; Spatial resolution ; Spectroradiometers ; Surface temperature ; Temperature ; Temperature data ; Temperature effects ; Trends ; Weather ; Weather stations</subject><ispartof>PloS one, 2017-01, Vol.12 (1), p.e0169423-e0169423</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Rahaman, Hassan. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961-2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming.</description><subject>Air temperature</subject><subject>Alberta</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Climate Change</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Earth Sciences</subject><subject>Engineering and Technology</subject><subject>Environmental Monitoring</subject><subject>Ice</subject><subject>Mitigation</subject><subject>Models, Theoretical</subject><subject>MODIS</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Remote sensing</subject><subject>Remote Sensing Technology</subject><subject>Research and Analysis Methods</subject><subject>Seasons</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Spectroradiometers</subject><subject>Surface 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relevant adaptation and mitigation policies at those levels are quite challenging. Here, our overall goal was to generate local warming trend map at 1 km spatial resolution by using: (i) Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composite surface temperature data; (ii) weather station-based yearly average air temperature data; and (iii) air temperature normal (i.e., 30 year average) data over the Canadian province of Alberta during the period 1961-2010. Thus, we analysed the station-based air temperature data in generating relationships between air temperature normal and yearly average air temperature in order to facilitate the selection of year-specific MODIS-based surface temperature data. These MODIS data in conjunction with weather station-based air temperature normal data were then used to model local warming trends. We observed that almost 88% areas of the province experienced warming trends (i.e., up to 1.5°C). The study concluded that remote sensing technology could be useful for delineating generic trends associated with local warming.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28072857</pmid><doi>10.1371/journal.pone.0169423</doi><tpages>e0169423</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Air temperature Alberta Algorithms Analysis Climate Change Computer and Information Sciences Datasets Earth Sciences Engineering and Technology Environmental Monitoring Ice Mitigation Models, Theoretical MODIS People and places Physical Sciences Remote sensing Remote Sensing Technology Research and Analysis Methods Seasons Spatial discrimination Spatial resolution Spectroradiometers Surface temperature Temperature Temperature data Temperature effects Trends Weather Weather stations |
| title | Quantification of Local Warming Trend: A Remote Sensing-Based Approach |
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