Google earth engine, open-access satellite data, and machine learning in support of large-area probabilisticwetland mapping

Remote Sensing

Volumn 9, Issue 12, 2017, Pages

  Hird, Jennifer N ^a   DeLancey, Evan R ^b   McDermid, Gregory J ^a   Kariyeva, Jahan ^b  

^a UNIVERSITY OF CALGARY   (Canada)

^b Alberta Innovates Technology Futures   (Canada)

Author keywords

Boosted regression trees; Cloud computing; Digital terrain model; Machine learning; Satellite data streams; Sentinel 1; Sentinel 2; Topographic position index; Topographic wetness index; Wetland classification

Indexed keywords

APPLICATION PROGRAMS; ARTIFICIAL INTELLIGENCE; CLOUD COMPUTING; ENGINES; FORESTRY; LEARNING SYSTEMS; MAPPING; SATELLITES; WETLANDS;

BOOSTED REGRESSION TREES; DIGITAL TERRAIN MODEL; SATELLITE DATA; SENTINEL-1; SENTINEL-2; TOPOGRAPHIC POSITIONS; TOPOGRAPHIC WETNESS INDEX; WETLAND CLASSIFICATION;

TREES (MATHEMATICS);

EID: 85038215275     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9121315     Document Type: Article

References (91)

1. Woodcock, C.; Allen, R.; Anderson, M.; Belward, A.; Bindschadler, R.; Cohen, W.; Gao, F.; Goward, S.N.; Helder, D.; Helmer, E.; et al. Free access to landsat imagery. Science 2008, 320, 1011-1012
2. Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25-36
3. Google. A Planetary-Scale Platform for Earth Science Data & Analysis. Available online: https://earthengine. google.com/ (accessed on 29 May 2017)
4. National Aeronautics and Space AdministrationWelcome to the NASA Earth Exchange (NEX). Available online: https://nex.nasa.gov/nex/ (accessed on 12 September 2016)
5. Amazon Web Services Inc. Earth on AWS: Build Planetary-Scale Applications in the Cloud with Open Geospatial Data. Available online: https://aws.amazon.com/earth/ (accessed on 28 November 2017)
6. Chandrashekar, S. Announcing Real-Time Geospatial Analytics in Azure Stream Analytics. Available online: https://azure.microsoft.com/en-us/blog/announcing-real-time-geospatial-analytics-in-azurestream- analytics/ (accessed on 12 September 2017)
7. Yang, C.; Yu, M.; Hu, F.; Jiang, Y.; Li, Y. Utilizing Cloud Computing to address big geospatial data challenges. Comput. Environ. Urban Syst. 2017, 61, 120-128
8. Warren, M.S.; Brumby, S.P.; Skillman, S.W.; Kelton, T.;Wohlberg, B.; Mathis, M.; Chartrand, R.; Keisler, R.; Johnson, M. Seeing the Earth in the Cloud: Processing one petabyte of satellite imagery in one day. In Proceedings of the 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 13-15 October 2015
9. Hansen, M.C.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.V.; Goetz, S.J.J.; Loveland, T.R.R.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850-854
10. Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418-422
11. Yamazaki, D.; Trigg, M.A. The dynamics of Earth's surface water. Nature 2016, 540, 348-349
12. DeLancey, E.R.; Kariyeva, J.; Cranston, J.; Brisco, B. Monitoring hydro temporal variability in Alberta, Canada with multi-temporal Sentinel-1 SAR data. Can. J. Remote Sens. 2017, in press
13. Moody, D.I.; Warren, M.S.; Skillman, S.W.; Chartrand, R.; Brumby, S.P.; Keisler, R.; Kelton, T.; Mathis, M. Building a living Atlas of the earth in the cloud. In Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 6-9 November 2016; pp. 1273-1277
14. Goldblatt, R.; You, W.; Hanson, G.; Khandelwal, A.K. Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in google earth engine. Remote Sens. 2016, 8, 634
15. Zhou, L.; Chen, N.; Chen, Z.; Xing, C. ROSCC: An efficient remote sensing observation-sharing method based on cloud computing for soil moisture mapping in precision agriculture. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 5588-5598
16. Huntington, J.L.; Hegewisch, K.C.; Daudert, B.; Morton, C.G.; Abatzoglou, J.T.; McEvoy, D.J.; Erickson, T. Climate Engine: Cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding. Bull. Am. Meteorol. Soc. 2017
17. Waske, B.; Fauvel, M.; Benediktsson, J.A.; Chanussot, J. Machine learning techniques in remote sensing data analysis. In Kernel Methods for Remote Sensing Data Analysis; Camps-Valls, G., Bruzzone, L., Eds.; John Wiley & Sons: Chichester, UK, 2009; pp. 3-24
18. Richards, J.A. Analysis of remotely sensed data: The formative decades and the future. IEEE Trans. Geosci. Remote Sens. 2005, 43, 422-432
19. Lary, D.J.; Alavi, A.H.; Gandomi, A.H.;Walker, A.L. Machine learning in geosciences and remote sensing. Geosci. Front. 2016, 7, 3-10
20. Bey, A.; Sánchez-Paus Díaz, A.; Maniatis, D.; Marchi, G.; Mollicone, D.; Ricci, S.; Bastin, J.-F.; Moore, R.; Federici, S.; Rezende, M.; et al. Collect Earth: Land use and land cover assessment through augmented visual interpretation. Remote Sens. 2016, 8, 807
21. Azzari, G.; Lobell, D.B. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sens. Environ. 2017
22. Ozesmi, S.L.; Bauer, M.E. Satellite remote sensing of wetlands. Wetl. Ecol. Manag. 2002, 10, 381-402
23. Corcoran, J.; Knight, J.; Brisco, B.; Kaya, S.; Cull, A.; Murnaghan, K. The integration of optical, topographic, and radar data for wetland mapping in northern Minnesota. Can. J. Remote Sens. 2011, 37, 564-582
24. Gabrielsen, C.G.; Murphy, M.A.; Evans, J.S. Using a multiscale, probabilistic approach to identify spatial-temporal wetland gradients. Remote Sens. Environ. 2016, 184, 522-538
25. Maxa, M.; Bolstad, P. Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands 2009, 29, 248-260
26. Alberta Environment and Sustainable Resource Development. Alberta Wetland Policy; Alberta Environment and Sustainable Resource Development: Edmonton, AB, Canada, 2013
27. Alberta Environment and Parks. Alberta Merged Wetland Inventory. Available online: https://geodiscover. alberta.ca/geoportal/catalog/main/home.page (accessed on 29 May 2017)
28. Kloiber, S.M.; Macleod, R.D.; Smith, A.J.; Knight, J.F.; Huberty, B.J. A semi-automated, multi-source data fusion update of a wetland inventory for East-Central Minnesota, USA. Wetlands 2015, 35, 335-348
29. Moore, I.D.; Grayson, R.B.; Ladson, A.R. Digital terrain modeling: A review of hydrological geomorphological and biological applications. Hydrol. Process. 1991, 5, 3-30
30. Gómez-Plaza, A.; Alvarez-Rogel, J.; Albaladejo, J.; Castillo, V.M. Spatial patterns and temporal stability of soil moisture across a range of scales in a semi-arid environment. Hydrol. Process. 2000, 14, 1261-1277
31. Hogg, A.R.; Todd, K.W. Automated discrimination of upland and wetland using terrain derivatives. Can. J. Remote Sens. 2007, 33, S68-S83
32. Devito, K.; Creed, I.; Gan, T.; Mendoza, C.; Petrone, R.; Silins, U.; Smerdon, B. A framework for broad-scale classification of hydrologic response units on the Boreal Plain: Is topography the last thing to consider? Hydrol. Process. 2005, 19, 1705-1714
33. Sass, G.Z.; Creed, I.F. Characterizing hydrodynamics on boreal landscapes using archived synthetic aperture radar imagery. Hydrol. Process. 2008, 22, 1687-1690
34. Natural Regions Committee. Natural Regions and Subregions of Alberta; Natural Regions Committee: Edmonton, AB, Canada, 2006
35. Bourgeau-Chavez, L.; Endres, S.; Powell, R.; Battaglia, M.; Benscoter, B.; Turetsky, M.; Kasischke, E.; Banda, E. Mapping boreal peatland ecosystem types from a fusion of multi-temporal radar and optical satellite imagery. Can. J. For. Res. 2017, 559, 545-559
36. Alberta Environment and Sustainable Resource Development. Alberta Wetland Classification System;Water Policy Branch, Policy and Planning Division: Edmonton, AB, Canada, 2015
37. Smith, D.W.; Prepas, E.E.; Putz, G.; Burke, J.M.; Meyer, W.L.; Whitson, I. The Forest Watershed and Riparian Disturbance study: A multi-discipline initiative to evaluate and manage watershed disturbance on the Boreal Plain of Canada. J. Environ. Eng. Sci. 2003, 2, S1-S13
38. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2016
39. Weiss, A. Topographic position and landforms analysis. In Proceedings of the Poster Presentation, ESRI User Conference, San Diego, CA, USA, 9-13 July 2001; Volume 200
40. Alexander, C.; Deak, B.; Heilmeier, H. Micro-topography driven vegetation patterns in open mosaic landscapes. Ecol. Indic. 2016, 60, 906-920
41. De Reu, J.; Bourgeois, J.; Bats, M.; Zwertvaegher, A.; Gelorini, V.; De Smedt, P.; Chu, W.; Antrop, M.; De Maeyer, P.; Finke, P.; et al. Application of the topographic position index to heterogeneous landscapes. Geomorphology 2013, 186, 39-49
42. Gallant, J.C.; Wilson, J.P. Primary topographic attributes. In Terrain Analysis: Principles and Applications; Wilson, J.P., Gallant, J.C., Eds.;Wiley: New York, NY, USA, 2000; pp. 51-85
43. Laamrani, A.; Valeria, O.; Bergeron, Y.; Fenton, N.; Cheng, L.Z. Distinguishing and mapping permanent and reversible paludified landscapes in Canadian black spruce forests. Geoderma 2015, 237, 88-97
44. Lang, M.; McCarty, G.; Oesterling, R.; Yeo, I.Y. Topographic metrics for improved mapping of forested wetlands. Wetlands 2013, 33, 141-155
45. Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 1979, 24, 43-69
46. Google. Sentinel-2: MultiSpectral Instrument (MSI), Level-1C. Available online: https://explorer.earthengine. google.com/#detail/COPERNICUS%2FS2 (accessed on 29 May 2017)
47. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS; Paper-A20; National Aeronautics and Space Administration (NASA):Washington, DC, USA, 1974; pp. 309-317
48. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425-1432
49. Fensholt, R.; Rasmussen, K.; Nielsen, T.T.; Mbow, C. Evaluation of earth observation based long term vegetation trends-Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sens. Environ. 2009, 113, 1886-1898
50. Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetl. Ecol. Manag. 2010, 18, 281-296
51. Wu, Q.; Lane, C.; Liu, H. An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sens. 2014, 6, 11444-11467
52. Tang, Z.; Li, Y.; Gu, Y.; Jiang, W.; Xue, Y.; Hu, Q.; LaGrange, T.; Bishop, A.; Drahota, J.; Li, R. Assessing Nebraska playa wetland inundation status during 1985-2015 using Landsat data and Google Earth Engine. Environ. Monit. Assess. 2016, 188, 654
53. Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. Water bodies' mapping from Sentinel-2 imagery with Modified Normalized DifferenceWater Index at 10-m spatial resolution produced by sharpening the swir band. Remote Sens. 2016, 8, 354
54. European Space Agency. The SENTINEL-1 Toolbox. Available online: https://sentinel.esa.int/web/sentinel/ toolboxes/sentinel-1 (accessed on 29May 2017)
55. Ulaby, F.T.; Moore, R.K.; Fung, A.K. Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications; Artech House, Inc.: Dedham, MA, USA, 1986
56. Patel, P.; Srivastava, H.S.; Panigrahy, S.; Parihar, J.S. Comparative evaluation of the sensitivity of multi-polarized multi-frequency SAR backscatter to plant density. Int. J. Remote Sens. 2006, 27, 293-305
57. Kornelsen, K.C.; Coulibaly, P. Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. J. Hydrol. 2013, 476, 460-489
58. Mattia, F.; Le Toan, T.; Souyris, J.-C.; De Carolis, C.; Floury, N.; Posa, F.; Pasquariello, N.G. The effect of surface roughness on multifrequency polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 1997, 35, 954-966
59. Gherboudj, I.; Magagi, R.; Berg, A.A.; Toth, B. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sens. Environ. 2011, 115, 33-43
60. Becker, F.; Choudhury, B.J. Relative sensitivity of normalized difference vegetation Index (NDVI) and microwave polarization difference Index (MPDI) for vegetation and desertification monitoring. Remote Sens. Environ. 1988, 24, 297-311
61. Chauhan, S.; Srivastava, H.S. Comparative evaluation of the sensitivity of multi-polarised sar and optical data for various land cover. Int. J. Adv. Remote Sens. Gis Geogr. 2016, 4, 1-14
62. European Space Agency. SENTINEL-1 Observation Scenario. Available online: https://sentinel.esa.int/ web/sentinel/missions/sentinel-1/observation-scenario (accessed on 21 November 2017)
63. Pamaploni, P.; Marcelloni, G.; Paloscia, S.; Sigismondi, S. The potential of C- and L- band SAR in assessing vegetation biomass: The Ers-1 and JERS-1 experiments. In Proceedings of the 3rd ERS Symposium on Space at the Service of Our Environment, Florence, Italy, 14-21 March 1997; p. 1729
64. Baghdadi, N.; Bernier, M.; Gauthier, R.; Neeson, I. Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sens. 2001, 22, 71-88
65. Pope, K.O.; Rejmankova, E.; Paris, J.F.; Woodruff, R. Detecting seasonal cycle of the Yucatan Peninsula with SIR-C polarmetric radar imagery. Remote Sens. Environ. 1997, 59, 157-166
66. Alberta Vegetation Inventory Interpretation Standards; Resource Information Management Branch, Alberta Sustainable Resource Development: Edmonton, AB, Canada, 2005
67. Ducks Unlimited Canada. Enhanced Wetland Classification Inferred Products User Guide; Version 1.0; Ducks Unlimited Canada: Stonewall, MB, Canada, 2011
68. De'ath, G. Boosted regression trees for ecological modeling and prediction. Ecology 2007, 88, 243-251
69. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802-813
70. Buston, P.M.; Elith, J. Determinants of reproductive success in dominant pairs of clownfish: A boosted regression tree analysis. J. Anim. Ecol. 2011, 80, 528-538
71. Parisien, M.A.; Parks, S.A.; Krawchuk, M.A.; Flannigan, M.D.; Bowman, L.M.; Moritz, M.A. Scale-dependent controls on the area burned in the boreal forest of Canada, 1980-2005. Ecol. Appl. 2011, 21, 789-805
72. Parisien, M.A.; Parks, S.A.; Krawchuk, M.A.; Little, J.M.; Flannigan, M.D.; Gowman, L.M.; Moritz, M.A. An analysis of controls on fire activity in boreal Canada: Comparing models built with different temporal resolutions. Ecol. Appl. 2014, 24, 1341-1356
73. R Development Core Team R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016
74. Ridgeway, G. GBM: Generalized Boosted Regression Models. 2017. Available online: https://cran.r-project. org/web/packages/gbm/gbm.pdf (accessed on 8 December 2017)
75. Swets, J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285-1293
76. Zweig, M.H.; Campbell, G. Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin. Chem. 1993, 39, 561-577
77. Freeman, E.A.; Moisen, G.G. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Model. 2008, 217, 48-58
78. Guisan, A.; Zimmermann, N.E. Predictive habitat distribution models in ecology. Ecol. Model. 2000, 135, 147-186
79. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43, 1223-1232
80. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46
81. Murphy, P.N.C.; Ogilvie, J.; Connor, K.; Arp, P.A. Mapping wetlands: A comparison of two different approaches for New Brunswick, Canada. Wetlands 2007, 27, 846-854
82. Ågren, A.M.; Lidberg, W.; Strömgren, M.; Ogilvie, J.; Arp, P.A. Evaluating digital terrain indices for soil wetness mapping-a Swedish case study. Hydrol. Earth Syst. Sci. 2014, 18, 3623-3634
83. Hogg, A.R.; Holland, J. An evaluation of DEMs derived from LiDAR and photogrammetry for wetland mapping. For. Chron. 2008, 84, 840-849
84. Riley, J.W.; Calhoun, D.L.; Barichivich, W.J.;Walls, S.C. Identifying small depressional wetlands and using a topographic position index to infer hydroperiod regimes for pond-breeding amphibians. Wetlands 2017, 37, 325-338
85. Kasischke, E.S.;Melack, J.M.; Craig Dobson, M. The use of imaging radars for ecological applications-A review. Remote Sens. Environ. 1997, 59, 141-156
86. Government of Canada. Historical Climate Data. Available online: http://climate.weather.gc.ca/index_e. html (accessed on 27 November 2017)
87. Alberta Agriculture and Forestry. Current and Historical AlbertaWeather Station Data Viewer. Available online: https://agriculture.alberta.ca/acis/alberta-weather-data-viewer.jsp (accessed on 27 November 2017)
88. Alberta Biodiversity Monitoring Institute. 3 × 7-km Photoplot Land Cover Data. Available online: http://abmi.ca/home/data-analytics/da-top/da-product-overview/GIS-Human-Footprint-Land- Cover-Data/Photoplot-Land-Cover-Dataset.html (accessed on 2 October 2017)
89. Alberta Biodiversity Monitoring Institute. 3 × 7-km Sample-Based Human Footprint Data. Available online: http://abmi.ca/home/data-analytics/da-top/da-product-overview/GIS-Human-Footprint-Land- Cover-Data/Human-Footprint-Sample-Based-Inventory.html (accessed on 2 October 2017)
90. European Space Agency. The Sentinel-2 Toolbox. Available online: https://sentinel.esa.int/web/sentinel/ toolboxes/sentinel-2 (accessed on 27 November 2017)
91. Alberta Environment and Parks. Available online: http://aep.alberta.ca/forms-maps-services/maps/ resource-data-product-catalogue/biophysical.aspx (accessed on 8 December 2017)