Cloud Data and Computing Services Allow Regional Environmental Assessment:A Case Study of Macquarie-Castlereagh Basin, Australia
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摘要
Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
引文
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Brown G,2013.The relationship between social values for ecosystem services and global land cover:an empirical analysis.Ecosystem Services,5:58-68.doi:10.1016/j.ecoser.2013.06.004
Chander G,Markham B L,Helder D L,2009.Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM+,and EO-1 ALI sensors.Remote Sensing of Environment,113(5):893-903.doi:10.1016/j.rse.2009.01.007
Danaher T,Collett L,2006.Development,optimization and multi-temporal application of a simple Landsat based water index.Proceeding of the 13th Australasian Remote Sensing and Photogrammetry Conference,Canberra,ACT,Australia.
Fisher A,Flood N,Danaher T,2016.Comparing Landsat water index methods for automated water classification in eastern Australia.Remote Sensing of Environment,175:167-182.doi:10.1016/j.rse.2015.12.055
Friess D A,Kudavidanage E P,Webb E L,2011.The digital globe is our oyster.Frontiers in Ecology&the Environment,9(10):542-542.doi:10.1890/11.WB.029
Gorelick N,Hancher M,Dixon M et al.,2017.Google Earth Engine:planetary-scale geospatial analysis for everyone.Remote Sensing of Environment,202:18-27.doi:10.1016/j.rse.2017.06.031
Hansen MC,Potapov PV,Moore R et al.,2013.High-resolution global maps of 21st-Century forest cover change.Science,342(6160):850.doi:10.1126/science.1244693
Guerschman J,Scarth P,Mcvicar T et al..,2015.Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation,non-photosynthetic vegetation and bare soil fractions from landsat and modis data.Remote Sensing of Environment,161:12-26.doi:10.1016/j.rse.2015.01.021
Hermosilla T,Wulder M A,White J C et al.,2015.An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites.Remote Sensing of Environment,158(158):220-234.doi:10.1016/j.rse.2014.11.005
Jay S,Jones C,Slinn P,Wood C,2007.Environmental impact assessment:retrospect and prospect.Environmental Impact Assessment Review,27(4):287-300.doi:10.1016/j.eiar.2006.12.001
Jenkins K M,Boulton A J,Ryder D S,2005.A common parched future?Research and management of Australian arid-zone floodplain wetlands.Hydrobiologia,552(1):57-73.doi:10.1007/s10750-005-1505-6
Jones J A,Grant G E,2001.Comment on‘peak flow responses to clear:cutting and roads in small and large basins,western cascades,oregon’.Water Resources Research,32(4):959-974.doi:10.1029/2000WR900277
Kong D,Zhang Q,Singh V P,2016.Seasonal vegetation response to climate change in the northern hemisphere(1982-2013).Global&Planetary Change,148:1-8.doi:10.1016/j.gloplacha.2016.10.020
Lee J S H,Wich S,Widayati A,2016.Detecting industrial oil palm plantations on landsat images with google earth engine.Remote Sensing Applications Society&Environment,4:219-224.doi:10.1016/j.rsase.2016.11.003
Lewis A,Oliver S,Lymburner L et al.,2017.The Australian Geoscience Data Cube:foundations and lessons learned.Remote Sensing of Environment,202:276-292.doi:10.1016/j.rse.2017.03.015
Lobell D B,Thau D,Seifert C et al.,2015.A scalable satellite-based crop yield mapper.Remote Sensing of Environment,164:324-333.doi:10.1016/j.rse.2015.04.021
Lymburner L,Botha E,Hestir E et al.,2016.Landsat 8:providing continuity and increased precision for measuring multi-decadal time series of total suspended matter.Remote Sensing of Environment,185:108-118.doi:10.1016/j.rse.2016.04.011
Macpherson A J,Principe P P,Shao Y,2013.Controlling for exogenous environmental variables when using data envelopment analysis for regional environmental assessments.Journal of Environmental Management,119:220-229.doi:10.1016/j.jenvman.2012.12.044
Mueller N,Lewis A,Roberts D et al.,2016 Water observations from space:mapping surface water from 25 years of Landsat imagery across Australia.Remote Sensing of Environment,174:341-352.doi:10.1016/j.rse.2015.11.003
Olagunju A O,Blakley J A E,2017.Towards an environmental governance agenda in regional environmental assessment:a case study of the crown managers partnership.Journal of Environmental Assessment Policy&Management,19(6):1750009.doi:10.1142/S1464333217500090
Padarian J,Minasny B,Mcbratney A B,2015.Using google's cloud-based platform for digital soil mapping.Computers&Geosciences,83:80-88.doi:10.1016/j.cageo.2015.06.023
Pekel J F,Cottam A,Gorelick N et al.,2016.High-resolution mapping of global surface water and its long-term changes.Nature,540(7633):418-422.doi:10.1038/nature20584
Rachael F T,Richard T K,Yi L et al.,2011.Landsat mapping of annual inundation(1979-2006)of the Macquarie Marshes in semi-arid Australia.International Journal of Remote Sensing,32(16):4545-4569.doi:10.1080/01431161.2010.489064
Rhemtulla J M,Mladenoff D J,Clayton M K,2007.Regional land-cover conversion in the u.s.upper midwest:magnitude of change and limited recovery(1850-1935-1993).Landscape Ecology,22(1):57-75.doi:10.1007/s10980-007-9117-3
Tang Z,Yao L,Yue G et al.,2016.Assessing nebraska playa wetland inundation status during 1985-2015 using landsat data and google earth engine.Environmental Monitoring&Assessment,188(12):654.doi:10.1007/s10661-016-5664-x
Xiang S,Shu X,Zhu X J et al.,2015.A new indices system for evaluating ecological-economic-social performances of wetland restorations and its application to Taihu Lake Basin,China.Ecological Modelling,295:216-226.doi:10.1016/j.ecolmodel.2014.10.008
Xiong J,Thenkabail P S,Gumma M K et al.,2017.Automated cropland mapping of continental Africa using Google Earth Engine cloud computing.ISPRS Journal of Photogrammetry&Remote Sensing,126:225-244.doi:10.1016/j.isprsjprs.2017.01.019
Brown G,2013.The relationship between social values for ecosystem services and global land cover:an empirical analysis.Ecosystem Services,5:58-68.doi:10.1016/j.ecoser.2013.06.004
Chander G,Markham B L,Helder D L,2009.Summary of current radiometric calibration coefficients for Landsat MSS,TM,ETM+,and EO-1 ALI sensors.Remote Sensing of Environment,113(5):893-903.doi:10.1016/j.rse.2009.01.007
Danaher T,Collett L,2006.Development,optimization and multi-temporal application of a simple Landsat based water index.Proceeding of the 13th Australasian Remote Sensing and Photogrammetry Conference,Canberra,ACT,Australia.
Fisher A,Flood N,Danaher T,2016.Comparing Landsat water index methods for automated water classification in eastern Australia.Remote Sensing of Environment,175:167-182.doi:10.1016/j.rse.2015.12.055
Friess D A,Kudavidanage E P,Webb E L,2011.The digital globe is our oyster.Frontiers in Ecology&the Environment,9(10):542-542.doi:10.1890/11.WB.029
Gorelick N,Hancher M,Dixon M et al.,2017.Google Earth Engine:planetary-scale geospatial analysis for everyone.Remote Sensing of Environment,202:18-27.doi:10.1016/j.rse.2017.06.031
Hansen MC,Potapov PV,Moore R et al.,2013.High-resolution global maps of 21st-Century forest cover change.Science,342(6160):850.doi:10.1126/science.1244693
Guerschman J,Scarth P,Mcvicar T et al..,2015.Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation,non-photosynthetic vegetation and bare soil fractions from landsat and modis data.Remote Sensing of Environment,161:12-26.doi:10.1016/j.rse.2015.01.021
Hermosilla T,Wulder M A,White J C et al.,2015.An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites.Remote Sensing of Environment,158(158):220-234.doi:10.1016/j.rse.2014.11.005
Jay S,Jones C,Slinn P,Wood C,2007.Environmental impact assessment:retrospect and prospect.Environmental Impact Assessment Review,27(4):287-300.doi:10.1016/j.eiar.2006.12.001
Jenkins K M,Boulton A J,Ryder D S,2005.A common parched future?Research and management of Australian arid-zone floodplain wetlands.Hydrobiologia,552(1):57-73.doi:10.1007/s10750-005-1505-6
Jones J A,Grant G E,2001.Comment on‘peak flow responses to clear:cutting and roads in small and large basins,western cascades,oregon’.Water Resources Research,32(4):959-974.doi:10.1029/2000WR900277
Kong D,Zhang Q,Singh V P,2016.Seasonal vegetation response to climate change in the northern hemisphere(1982-2013).Global&Planetary Change,148:1-8.doi:10.1016/j.gloplacha.2016.10.020
Lee J S H,Wich S,Widayati A,2016.Detecting industrial oil palm plantations on landsat images with google earth engine.Remote Sensing Applications Society&Environment,4:219-224.doi:10.1016/j.rsase.2016.11.003
Lewis A,Oliver S,Lymburner L et al.,2017.The Australian Geoscience Data Cube:foundations and lessons learned.Remote Sensing of Environment,202:276-292.doi:10.1016/j.rse.2017.03.015
Lobell D B,Thau D,Seifert C et al.,2015.A scalable satellite-based crop yield mapper.Remote Sensing of Environment,164:324-333.doi:10.1016/j.rse.2015.04.021
Lymburner L,Botha E,Hestir E et al.,2016.Landsat 8:providing continuity and increased precision for measuring multi-decadal time series of total suspended matter.Remote Sensing of Environment,185:108-118.doi:10.1016/j.rse.2016.04.011
Macpherson A J,Principe P P,Shao Y,2013.Controlling for exogenous environmental variables when using data envelopment analysis for regional environmental assessments.Journal of Environmental Management,119:220-229.doi:10.1016/j.jenvman.2012.12.044
Mueller N,Lewis A,Roberts D et al.,2016 Water observations from space:mapping surface water from 25 years of Landsat imagery across Australia.Remote Sensing of Environment,174:341-352.doi:10.1016/j.rse.2015.11.003
Olagunju A O,Blakley J A E,2017.Towards an environmental governance agenda in regional environmental assessment:a case study of the crown managers partnership.Journal of Environmental Assessment Policy&Management,19(6):1750009.doi:10.1142/S1464333217500090
Padarian J,Minasny B,Mcbratney A B,2015.Using google's cloud-based platform for digital soil mapping.Computers&Geosciences,83:80-88.doi:10.1016/j.cageo.2015.06.023
Pekel J F,Cottam A,Gorelick N et al.,2016.High-resolution mapping of global surface water and its long-term changes.Nature,540(7633):418-422.doi:10.1038/nature20584
Rachael F T,Richard T K,Yi L et al.,2011.Landsat mapping of annual inundation(1979-2006)of the Macquarie Marshes in semi-arid Australia.International Journal of Remote Sensing,32(16):4545-4569.doi:10.1080/01431161.2010.489064
Rhemtulla J M,Mladenoff D J,Clayton M K,2007.Regional land-cover conversion in the u.s.upper midwest:magnitude of change and limited recovery(1850-1935-1993).Landscape Ecology,22(1):57-75.doi:10.1007/s10980-007-9117-3
Tang Z,Yao L,Yue G et al.,2016.Assessing nebraska playa wetland inundation status during 1985-2015 using landsat data and google earth engine.Environmental Monitoring&Assessment,188(12):654.doi:10.1007/s10661-016-5664-x
Xiang S,Shu X,Zhu X J et al.,2015.A new indices system for evaluating ecological-economic-social performances of wetland restorations and its application to Taihu Lake Basin,China.Ecological Modelling,295:216-226.doi:10.1016/j.ecolmodel.2014.10.008
Xiong J,Thenkabail P S,Gumma M K et al.,2017.Automated cropland mapping of continental Africa using Google Earth Engine cloud computing.ISPRS Journal of Photogrammetry&Remote Sensing,126:225-244.doi:10.1016/j.isprsjprs.2017.01.019