Integrating cloud-based workflows in continental-scale cropland extent classification

Remote Sensing of Environment

Volumn 219, Issue , 2018, Pages 162-179

  Massey, Richard ^a   Sankey, Temuulen T ^a   Yadav, Kamini ^b   Congalton, Russell G ^b   Tilton, James C ^c  

^a Northern Arizona University ^*  (United States)

^b UNIVERSITY OF NEW HAMPSHIRE   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Cluster computing; Google Earth Engine; Landsat; North American croplands; Object based analysis; Random Forest; RHSeg

Indexed keywords

CLUSTER COMPUTING; CROPS; DECISION TREES; ENGINES; LAND USE; REGRESSION ANALYSIS;

GOOGLE EARTHS; LANDSAT; NORTH AMERICAN; OBJECT-BASED ANALYSIS; RANDOM FORESTS; RHSEG;

CLASSIFICATION (OF INFORMATION);

AGRICULTURAL LAND; ALGORITHM; CROP PLANT; IMAGE CLASSIFICATION; IRRIGATION SYSTEM; NUMERICAL METHOD; REMOTE SENSING; SATELLITE DATA; SEGMENTATION; SPATIAL DISTRIBUTION;

CANADA; NORTH AMERICA;

EID: 85054782925     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.10.013     Document Type: Article

References (67)

1. Azzari, G., Lobell, D.B., Landsat-based classification in the cloud. Remote Sens. Environ. 202 (2017), 64–74.
2. Beaulieu, J.M., Goldberg, M., Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 150–163.
3. Blaschke, T., Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2010), 2–16.
4. Boryan, C.G., Yang, Zhengwei, Implementation of a new automatic stratification method using geospatial cropland data layers in NASS area frame construction. 2014 IEEE Geosci. Remote Sens. Symp, 2014, 2110–2113.
5. Boryan, C., Yang, Z., Mueller, R., Craig, M., Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int. 26 (2011), 341–358.
6. Breiman, L., Random forests. Mach. Learning, 45, 2001, 5–32.
7. Chen, J., Liao, A., Chen, J., et al. 30-meter Global Land cover data product - GLobeLand30. Geomatics World 24 (2017), 1–8.
8. Congalton, Russell G., A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (1991), 35–46.
9. Congalton, R.G., Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd edition, 2009, CRC/Taylor & Francis, Boca Raton, FL (183 pp.).
10. Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore, B., Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 185 (2015), 142–154.
11. Erickson, T., Multi-source geospatial data analysis with Google Earth Engine. American Geophysical Union, Fall Meeting 2014, 2014 (abstract #IN53E-05).
12. Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., et al. The shuttle radar topography mission. Rev. Geophys., 45, 2007.
13. Fischer, G., van Velthuizen, H., Nachtergaele, F.O., Global Agroecological Zones Assessment: Methodology and Results (IIASA Interim Report IR-00-064). 2000.
14. Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., Jarvis, I., AAFC annual crop inventory: status and challenges. The 2nd International Conference on Agro-Geoinformatics, Fairfax, VA, 2013, 270–274.
15. Fisette, T., Davidson, A., Daneshfar, B., Rollin, P., Campbell, L., Annual space-based crop inventory for Canada: 2009–2014. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, 2014, 5095–5098.
16. Food and Agriculture Organization of the United Nations, Food Outlook: Biannual report on global food markets. 1560-8182, October 2016.
17. Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Duerauer, M., Albrecht, F., Schill, C., et al. Mapping global cropland and field size. Glob. Chang. Biol. 21 (2015), 1980–1992.
18. Gao, B., NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58 (1996), 257–266.
19. Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., Random forests for land cover classification. Pattern Recogn. Lett. 27 (2006), 294–300.
20. Gitelson, A., Kaufman, Y.J., Stark, R., Rundquist, D., Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80 (2002), 76–87.
21. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 (2017), 18–27.
22. Han, W., Yang, Z., Di, L., Mueller, R., CropScape: a Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Comput. Electron. Agric. 84 (2012), 111–123.
23. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., et al. High-resolution global maps of 21st-century forest cover change. Science 342 (2013), 850–853.
24. Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A., Schmullius, C., Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ. 112 (2008), 2538–2556.
25. Homer, C., Huang, C., Yang, L., Wylie, B., Michael, C., Development of a 2001 National Land Cover Database for the United States. Photogramm. Eng. Remote. Sens. 70 (2004), 829–840.
26. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, N., Wickham, J., Completion of the 2001 National Land Cover Database for the counterminous United States. Photogramm. Eng. Remote. Sens., 73(4), 2007, 337.
27. Homer, C.H., Fry, J.A., Barnes, C.A., The National Land Cover Database. U. S. Geol. Surv. Fact Sheet 3020. 4, 2012, 1–4.
28. Housman, I., Tanpipat, V., Biswas, T., Clark, A., Stephen, P., Maus, P., Megown, K., Monitoring Forest Change in Southeast Asia: Case Studies for USAID Lowering Emissions in Asia's Forests. RSAC-10108-RPT1. 16, 2015, U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center, Salt Lake City, UT.
29. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (2002), 195–213.
30. Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., et al. Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens. 7 (2015), 12356–12379.
31. Instituto Nacional de Estadística y Geografía (INEGI), Digital Map of Mexico. Agricultural Atlas of Mexico: Agricultural Census 2007. http://gaia.inegi.org.mx/mdm5/viewer.html, 2007. (Accessed 1 July 2017)
32. Johnson, D.M., Mueller, R., The 2009 cropland data layer. Photogramm. Eng. Remote. Sens., 2010, 1202–1205.
33. Key, C.H., Benson, N., The Normalized Burn Ratio (NBR): a Landsat TM radiometric measure of burn severity. www.nrmsc.usgs.gov/research/ndbr.htm, 1999.
34. Lawrence, R.L., Wood, S.D., Sheley, R.L., Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100 (2006), 356–362.
35. Liaw, A., Package ‘randomForest’: Breiman and Cutler's Random Forests for Classification and Regression. https://cran.r-project.org/web/packages/randomForest/randomForest.pdf, 2015. (Accessed 1 July 2017)
36. Maiersperger, T.K., Scaramuzza, P.L., Leigh, L., Shrestha, S., Gallo, K.P., Jenkerson, C.B., Dwyer, J.L., Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sens. Environ. 136 (2013), 1–13.
37. Masek, J., Vermote, E., Saleous, N., Wolfe, R., Hall, F., Huemmrich, K., Gao, F., Kutler, J., Lim, T., A Landsat surface reflectance dataset for North America, 1990–2000 RID F-3944–2010. IEEE Geosci. Remote Sens. Lett. 3 (2006), 68–72.
38. Massey, R., Sankey, T.T., Yadav, K., Congalton, R.G., Tilton, J.C., Thenkabail, P.S., NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2010 North America 30 m V001 [Data set]. 2017, NASA EOSDIS Land Processes DAAC, 10.5067/MEaSUREs/GFSAD/GFSAD30NACE.001.
39. McFeeters, S., The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17 (1996), 1425–1432.
40. Melrose, J., Perroy, R., Cares, S., Statewide Agricultural Land use Baseline 2015. 2015, Hawai'i Department of Agriculture http://hdoa.hawaii.gov/wp-content/uploads/2016/02/StateAgLandUseBaseline2015.pdf. (Accessed 15 February 2017)
41. Monfreda, C., Ramankutty, N., Foley, J.A., Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles, 22, 2008, GB1022.
42. Na, X., Zhang, S., Li, X., Yu, H., Liu, C., Improved land cover mapping using Random Forests combined with Landsat Thematic Mapper Imagery and ancillary geographic data. Photogramm. Eng. Remote. Sens. 76:7 (2010), 833–840.
43. Nemani, R., Votava, P., Michaelis, A., Melton, F., Milesi, C., Collaborative supercomputing for global change science. EOS Trans. Am. Geophys. Union 92:13 (2011), 109–110.
44. Padarian, J., Minasny, B., Mcbratney, A., Using Google's cloud-based platform for digital soil mapping. Comput. Geosci. 83 (2015), 80–83.
45. Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J., Trianni, G., Multitemporal settlement and population mapping from landsatusing google earth engine. Int. J. Appl. Earth Obs. Geoinf. 35 (2015), 199–208.
46. Pekel, J., Cottam, A., Gorelick, N., Belward, A.S., High-resolution mapping of global surface water and its long-term changes. Nature 540 (2016), 418–422.
47. Pelletier, C., Valero, S., Inglada, J., Champion, N., Dedieu, G., Remote Sensing of Environment Assessing the robustness of Random Forests to map land cover with high-resolution satellite image time series over large areas. Remote Sens. Environ. 187 (2016), 156–168.
48. Portmann, F.T., Siebert, S., Döll, P., MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24 (2010), 1–24.
49. Ramankutty, N., Evan, A.T., Monfreda, C., Foley, J.A., Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22 (2008), 1–19.
50. Rodriguez-Galiano, V.F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.M., Jeganathan, C., Remote Sensing of Environment Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 121 (2012), 93–107.
51. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symp, 1973, NASA, Washington, 309–317.
52. Sacks, W.J., Deryng, D., Foley, J.A., Ramankutty, N., Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19 (2010), 607–620.
53. Schneider, P., Roberts, D.A., Kyriakidis, P.C., A VARI-based relative greenness from MODIS data for computing the Fire Potential Index. Remote Sens. Environ. 112 (2008), 1151–1167.
54. Servicio de Información Agroalimentaria y Pesquera (SIAP), National Coverage of Agricultural Borders Series II. https://datos.gob.mx/busca/dataset/frontera-agricola-serie-ii, 2017 (Mexico). (Accessed 1 May 2017)
55. Simonetti, D., Simonetti, E., Szantoi, Z., Lupi, A., Eva, H.D., First results from the phenology-based synthesis classifier using Landsat 8 imagery. IEEE Geosci. Remote Sens. Lett. 12 (2015), 1–5.
56. Story, M., Congalton, R., Accuracy assessment: a user's perspective. Photogramm. Eng. Remote. Sens. 52 (1986), 397–399.
57. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Sankey, T.R., Massey, R., Phalke, A., Yadav, K., Global food security support analysis data at nominal 1 km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: current achievements and future possibilities. Chapter 6. Thenkabail, P.S., (eds.) Remote Sensing Handbook. Volume II: Land Resources: Monitoring, Modeling, and Mapping, 2015, Taylor and Francis, New York, USA, 131–159.
58. Thenkabail, P.S., Global croplands and their importance for water and food security in the twenty-first century: towards an ever green revolution that combines a second green revolution with a blue revolution. Remote Sens. 2 (2010), 2305–2312.
59. Thenkabail, P.S., Biradar, C.M., Noojipady, P., Dheeravath, V., Li, Y., Velpuri, M., Gumma, M., Gangalakunta, O.R.P., Turral, H., Cai, X., Vithanage, J., Schull, M.a., Dutta, R., Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 30 (2009), 3679–3733.
60. Thenkabail, P.S., Hanjra, M.A., Dheeravath, V., Gumma, M., Global croplands and their water use remote sensing and non-remote sensing perspectives. Weng, Q., (eds.) Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, 2011, Taylor and Francis, Boca Raton, Florida, 383–419.
61. Tilton, J.C., RHSeg User's Manual: Including HSWO, HSeg, HSegExtract, HSegReader, HSegViewer and HSegLearn, Version 1.64. 2016 (Mar. 22, 2016 Available on request: James.C.Tilton@nasa.gov).
62. Tilton, J., Pasolli, E., Incorporating edge information into best merge region growing segmentation. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Quebec, Canada, 2014, 4891–4894, 10.1109/IGARS.2014.6947591 (July 13–18, 2014).
63. Tilton, J., Tarabalka, Y., Montesano, P., Gofman, E., Best Merge Region-growing Segmentation With Integrated Nonadjacent Region Object Aggregation. 50, 2012, 4454–4467.
64. Vieira, M.A., Formaggio, A.R., Rennó C.D., Atzberger, C., Aguiar, D.A., Mello, M.P., Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas. Remote Sens. Environ. 123 (2012), 553–562.
65. Wulder, M.A., White, J.C., Cranny, M., Hall, R.J., Luther, J.E., Beaudoin, A., Goodenough, D.G., Dechka, J.A., Monitoring Canada's forests. Part 1: completion of the EOSD land cover project. Can. J. Remote. Sens. 34 (2008), 549–562.
66. Yan, L., Roy, D.P., Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction. Remote Sens. Environ. 158 (2015), 478–491, 10.1016/j.rse.2014.11.024.
67. Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., Gong, P., FROM-GC: 30 m global cropland extent derived through multisource data integration. Int. J. Digital Earth 6 (2013), 521–533.