A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

References

• Almeer, M. H. 2012. “Cloud Hadoop Map Reduce for Remote Sensing Image Analysis.” Journal of Emerging Trends in Computing and Information Sciences 3 (4): 637–644.
   Google Scholar
• Amidon, T. E., C. D. Wood, A. M. Shupe, Y. Wang, M. Graves, and S. Liu. 2008. “Biorefinery: Conversion of Woody Biomass to Chemicals, Energy and Materials.” Journal of Biobased Materials and Bioenergy 2: 100–120. doi: 10.1166/jbmb.2008.302
   Web of Science ®Google Scholar
• Bartuska, A. 2006. Why Biomass is Important: The Role of the USDA Forest Service in Managing and Using Biomass for Energy and Other Uses. Speech Given at 25x25 Summit II, Washington, DC, USA. Accessed 17 July 2018. http://www.fs.fed.us/research/pdf/biomass_importance.pdf.
   Google Scholar
• Borthakur, D. 2008. “HDFS Architecture Guide.” Hadoop Apache Project, 53.
   Google Scholar
• Chen, Q., R. E. McRoberts, C. Wang, and P. J. Radtke. 2016. “Forest Aboveground Biomass Mapping and Estimation across Multiple Spatial Scales Using Model-based Inference.” Remote Sensing of Environment 184: 350–360. doi: 10.1016/j.rse.2016.07.023
   Web of Science ®Google Scholar
• Chodorow, K. 2013. MongoDB: The Definitive Guide: Powerful and Scalable Data Storage, 3–27. Sebastopol: O’Reilly Media.
   Google Scholar
• D’Almeida, C., C. J. Vörösmarty, G. C. Hurtt, J. A. Marengo, S. L. Dingman, and B. D. Keim. 2007. “The Effects of Deforestation on the Hydrological Cycle in Amazonia: A Review on Scale and Resolution.” International Journal of Climatology 27: 633–647. doi: 10.1002/joc.1475
   Web of Science ®Google Scholar
• Dong, J., X. Xiao, M. A. Menarguez, Geli Zhang, Yuanwei Qin, David Thau, Chandrashekhar Biradar, and Berrien Moore. 2016. “Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-based Algorithm and Google Earth Engine.” Remote Sensing of Environment 185: 142–154. doi: 10.1016/j.rse.2016.02.016
   PubMed Web of Science ®Google Scholar
• Edmonds, D. A., E. A. Hajek, N. Downton, and A. B. Bryk. 2016. “Avulsion Flow-path Selection on Rivers in Foreland Basins.” Geology 44: 695–698. doi: 10.1130/G38082.1
   Web of Science ®Google Scholar
• Foody, G. M., D. S. Boyd, and M. E. J. Cutler. 2003. “Predictive Relations of Tropical Forest Biomass from Landsat TM Data and their Transferability between Regions.” Remote Sensing of Environment 85: 463–474. doi: 10.1016/S0034-4257(03)00039-7
   Web of Science ®Google Scholar
• Gardner, J. 2009. “The Web Server Gateway Interface (Wsgi).” In The Definitive Guide to Pylons, 369–388. New York: Apress.
   Google Scholar
• Garrett, J. J. 2005. “Ajax: A New Approach to Web Applications.”
   Google Scholar
• George, L. 2011. HBase: The Definitive Guide: Random Access to Your Planet-size Data, 1–29. Sebastopol: O’Reilly Media.
   Google Scholar
• Goldblatt, R., W. You, G. Hanson, and A. K. Khandelwal. 2016. “Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-based Image Classification in Google Earth Engine.” Remote Sensing 8: 634. doi: 10.3390/rs8080634
   Web of Science ®Google Scholar
• Gonzalez, H., A. Y. Halevy, C. S. Jensen, Anno Langen, Jayant Madhavan, Rebecca Shapley, Warren Shen, and Jonathan Goldberg-Kidon. 2010. “Google Fusion Tables: Web-centered Data Management and Collaboration.” Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ACM, 1061–1066.
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 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.
   Web of Science ®Google Scholar
• Halili, E. H. 2008. “Load/Performance Testing of Website.” In Apache JMeter: A Practical Beginner's Guide to Automated Testing and Performance Measurement for Your Websites, 51–74. Olton: Packt.
   Google Scholar
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-resolution Global Maps of 21st-century Forest Cover Change.” Science 342: 850–853. doi: 10.1126/science.1244693
   PubMed Web of Science ®Google Scholar
• Houghton, R. A. 2005. “Aboveground Forest Biomass and the Global Carbon Balance.” Global Change Biology 11 (6): 945–958. doi:10.1111/j.1365-2486.2005.00955.x.
   Web of Science ®Google Scholar
• Houghton, R. A., K. T. Lawrence, J. L. Hackler, and S. Brown. 2001. “The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates.” Global Change Biology 7: 731–746. doi: 10.1046/j.1365-2486.2001.00426.x
   Web of Science ®Google Scholar
• Keahey, K., R. Figueiredo, J. Fortes, T. Freeman, and M. Tsugawa. 2008. “Science Clouds: Early Experiences in Cloud Computing for Scientific Applications.” Cloud Computing and Applications 2008: 825–830.
   Google Scholar
• Kumar, R., K. Jain, H. Maharwal, N. Jain, and A. Dadhich. 2014. “Apache Cloudstack: Open Source Infrastructure as a Service Cloud Computing Platform.” Proceedings of the International Journal of Advancement in Engineering Technology, Management and Applied Science 1 (2): 111–116.
   Google Scholar
• Lamourine, M. 2014. “Openstack.” Login:: The Magazine of USENIX & SAGE 39: 17–20.
   Google Scholar
• Lee, S., W. Ni-Meister, W. Yang, and Q. Chen. 2011. “Physically Based Vertical Vegetation Structure Retrieval from ICESat Data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA.” Remote Sensing of Environment 115 (11): 2776–2785. doi:10.1016/j.rse.2010.08.026.
   Web of Science ®Google Scholar
• Li, W., H. Shao, S. Wang, X. Zhou, and S. Wu. 2016. “A2CI: A Cloud-based, Service-oriented Geospatial Cyberinfrastructure to Support Atmospheric Research.” In Cloud Computing in Ocean and Atmospheric Sciences, edited by T. C. Vance, N. Merati, C. Yang, and M. Yuan, 137–161. London: Elsevier.
   Google Scholar
• Li, Z., M. E. Hodgson, and W. Li. 2018. “A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data.” International Journal of Digital Earth, 11 (1):26-47. doi:10.1080/17538947.2016.1269842.
   Web of Science ®Google Scholar
• Liu, Y., X. Wei, X. Guo, D. Niu, J. Zhang, X. Gong, and Y. Jiang. 2012. “The Long-term Effects of Reforestation on Soil Microbial Biomass Carbon in Sub-tropic Severe Red Soil Degradation Areas.” Forest Ecology and Management 285: 77–84. doi: 10.1016/j.foreco.2012.08.019
   Web of Science ®Google Scholar
• Lobell, D. B., D. Thau, C. Seifert, E. Engle, and B. Little. 2015. “A Scalable Satellite-based Crop Yield Mapper.” Remote Sensing of Environment 164:324–333. doi:10.1016/j.rse.2015.04.021.
   Google Scholar
• Lu, D., Q. Chen, G. Wang, L. Liu, G. Li, and E. Moran. 2016. “A Survey of Remote Sensing-based Aboveground Biomass Estimation Methods in Forest Ecosystems.” International Journal of Digital Earth 9 (1): 63–105. doi: 10.1080/17538947.2014.990526
   Web of Science ®Google Scholar
• Ma, Y., H. Wu, L. Wang, Bormin Huang, Rajiv Ranjan, Albert Zomaya, and Wei Jie. 2015. “Remote Sensing Big Data Computing: Challenges and Opportunities.” Future Generation Computer Systems 51: 47–60. doi: 10.1016/j.future.2014.10.029
   Web of Science ®Google Scholar
• Mell, P., and T. Grance. 2011. “The NIST Definition of Cloud Computing.”
   Google Scholar
• Moore, R., and M. Hansen. 2011. “Google Earth Engine: A New Cloud-computing Platform for Global-scale Earth Observation Data and Analysis.” In AGU Fall Meeting Abstracts, 02.
   Google Scholar
• Okoro, S. U., U. Schickhoff, J. Bohner, and U. A. Schneider. 2016. “A Novel Approach in Monitoring Land-cover Change in the Tropics: Oil Palm Cultivation in the Niger Delta, Nigeria.” Erde 147: 40–52.
   Web of Science ®Google Scholar
• Padarian, J., B. Minasny, and A. B. McBratney. 2015. “Using Google's Cloud-based Platform for Digital Soil Mapping.” Computers & Geosciences 83:80–88. doi:10.1016/j.cageo.2015.06.023.
   Web of Science ®Google Scholar
• Patel, N. N., E. Angiuli, P. Gamba, A. Gaughan, G. Lisini, F. R. Stevens, A. J. Tatem, and G. Trianni. 2015. “Multitemporal Settlement and Population Mapping from Landsat Using Google Earth Engine.” International Journal of Applied Earth Observation and Geoinformation 35: 199–208. doi: 10.1016/j.jag.2014.09.005
   Web of Science ®Google Scholar
• Ritter, N., M. Ruth, B. B. Grissom, G. Galang, J. Haller, G. Stephenson, S. Covington, T. Nagy, J. Moyers, and J. Stickley. 2000. “GeoTIFF Format Specification GeoTIFF Revision 1.0.” SPOT Image Corp.
   Google Scholar
• Romero, D. M., W. Galuba, S. Asur, and B. A. Huberman. 2011. “Influence and Passivity in Social Media.” In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 18–33. Berlin Heidelberg: Springer.
   Google Scholar
• Ronacher, A. 2008. “Jinja2 Documentation.” Welcome to Jinja2—Jinja2 Documentation (2.8-dev).
   Google Scholar
• Sabharwal, N., and R. Shankar. 2013. Apache CloudStack Cloud Computing, 17–40. Olton: Packt.
   Google Scholar
• Soulard, C. E., C. M. Albano, M. L. Villarreal, and J. J. Walker. 2016. “Continuous 1985–2012 Landsat Monitoring to Assess Fire Effects on Meadows in Yosemite National Park, California.” Remote Sensing 8: 371. doi: 10.3390/rs8050371
   Web of Science ®Google Scholar
• Tan, X., L. Di, M. Deng, J. Fu, G. Shao, M. Gao, Z. Sun, X. Ye, Z. Sha, and B. Jin. 2015. “Building an Elastic Parallel OGC Web Processing Service on a Cloud-based Cluster: A Case Study of Remote Sensing Data Processing Service.” Sustainability 7 (10): 14245–14258. doi:10.3390/su71014245.
   Web of Science ®Google Scholar
• Verschuyl, J., S. Riffell, D. Miller, and T. B. Wigley. 2011. “Biodiversity Response to Intensive Biomass Production from Forest Thinning in North American Forests – A Meta-Analysis.” Forest Ecology and Management 261: 221–232. doi: 10.1016/j.foreco.2010.10.010
   Web of Science ®Google Scholar
• Wuhib, F., R. Stadler, and H. Lindgren. 2012. “Dynamic Resource Allocation with Management Objectives—Implementation for an OpenStack Cloud.” Network and Service Management 610: 309–315.
   Google Scholar
• Yalew, S. G., A. van Griensven, and P. van der Zaag. 2016. “AgriSuit: A Web-based GIS-MCDA Framework for Agricultural Land Suitability Assessment.” Computers and Electronics in Agriculture 128: 1–8. doi: 10.1016/j.compag.2016.08.008
   Web of Science ®Google Scholar
• Yang, S., W. B. Ligon, and E. C. Quarles. 2011. “Scalable Distributed Directory Implementation on Orange File System.” Proc. IEEE Intl. Wrkshp. Storage Network Architecture and Parallel I/Os (SNAPI).
   Google Scholar
• Yang, C., R. Raskin, M. Goodchild, and M. Gahegan. 2010. “Geospatial Cyberinfrastructure: Past, Present and Future.” Computers, Environment and Urban Systems 34 (4): 264–277. doi: 10.1016/j.compenvurbsys.2010.04.001
   Web of Science ®Google Scholar
• Zhang, G., Q. Huang, A-X. Zhu, and J. H. Keel. 2016. “Enabling Point Pattern Analysis on Spatial Big Data Using Cloud Computing: Optimizing and Accelerating Ripley’s K function.” International Journal of Geographical Information Science 30 (11): 2230–2252. doi:10.1080/13658816.2016.1170836.
   Web of Science ®Google Scholar
• Zhao, T., V. March, S. Dong, and S. See. 2010. “Evaluation of a Performance Model of Lustre File System.” In ChinaGrid Conference (ChinaGrid), 2010 Fifth Annual (pp. 191–196). IEEE, July.
   Google Scholar
• Zolkos, S. G., S. J. Goetz, and R. Dubayah. 2013. “A Meta-analysis of Terrestrial Aboveground Biomass Estimation Using Lidar Remote Sensing.” Remote Sensing of Environment 128: 289–298. doi: 10.1016/j.rse.2012.10.017
   Web of Science ®Google Scholar