WU Hantian, ZHANG Lu, ZHANG Xin. Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia[J]. Chinese Geographical Science, 2019, 20(3): 394-404. doi: 10.1007/s11769-019-1040-4
Citation: WU Hantian, ZHANG Lu, ZHANG Xin. Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia[J]. Chinese Geographical Science, 2019, 20(3): 394-404. doi: 10.1007/s11769-019-1040-4

Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia

doi: 10.1007/s11769-019-1040-4
Funds:  Under the auspices of National Key Research and Development Program of China (No. 2016YFA0600304)
More Information
  • Corresponding author: WU Hantian. E-mail:Hantian.wu@alumni.anu.edu.au
  • Received Date: 2018-01-29
  • Publish Date: 2019-06-27
  • 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.
  • [1] Alsdorf D E, Rodríguez E, Lettenmaier D P, 2007. Measuring surface water from space. Reviews of Geophysics, 45(2):1-24. doi: 10.1029/2006RG000197
    [2] Brown G, 2013. The relationship between social values for eco-system services and global land cover:an empirical analy-sis. Ecosystem Services, 5:58-68. doi:10.1016/j.ecoser.2013. 06. 004
    [3] 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 Environ-ment, 113(5):893-903. doi: 10.1016/j.rse.2009.01.007
    [4] Danaher T, Collett L, 2006. Development, optimization and mul-ti-temporal application of a simple Landsat based water index. Proceeding of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, ACT, Australia.
    [5] 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
    [6] 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
    [7] Gorelick N, Hancher M, Dixon M et al., 2017. Google Earth En-gine:planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18-27. doi:10.1016/j.rse.2017. 06.031
    [8] 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
    [9] 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
    [10] 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
    [11] Jay S, Jones C, Slinn P, Wood C, 2007. Environmental impact assessment:retrospect and prospect. Environmental Impact As-sessment Review, 27(4):287-300. doi:10.1016/j.eiar.2006. 12.001
    [12] 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
    [13] 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
    [14] 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
    [15] 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
    [16] Lewis A, Oliver S, Lymburner L et al., 2017. The Australian Ge-oscience DataCube:foundations and lessons learned. Remote Sensing of Environment, 202:276-292. doi: 10.1016/j.rse.2017.03.015
    [17] Lobell D B, Thau D, Seifert C et al., 2015. A scalable satel-lite-based crop yield mapper. Remote Sensing of Environment, 164:324-333. doi: 10.1016/j.rse.2015.04.021
    [18] 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 Envi-ronment, 185:108-118. doi: 10.1016/j.rse.2016.04.011
    [19] Macpherson A J, Principe P P, Shao Y, 2013. Controlling for ex-ogenous 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
    [20] 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
    [21] 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 En-vironmental Assessment Policy & Management, 19(6):1750009. doi: 10.1142/S1464333217500090
    [22] 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
    [23] 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
    [24] 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
    [25] 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
    [26] Tang Z, Yao L, Yue G et al., 2016. Assessing nebraska playa wet-land 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
    [27] 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
    [28] 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
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(86) PDF downloads(293) Cited by()

Proportional views
Related

Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia

doi: 10.1007/s11769-019-1040-4
Funds:  Under the auspices of National Key Research and Development Program of China (No. 2016YFA0600304)
    Corresponding author: WU Hantian. E-mail:Hantian.wu@alumni.anu.edu.au

Abstract: 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.

WU Hantian, ZHANG Lu, ZHANG Xin. Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia[J]. Chinese Geographical Science, 2019, 20(3): 394-404. doi: 10.1007/s11769-019-1040-4
Citation: WU Hantian, ZHANG Lu, ZHANG Xin. Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia[J]. Chinese Geographical Science, 2019, 20(3): 394-404. doi: 10.1007/s11769-019-1040-4
Reference (28)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return