Remote sensing of physical cycles in Lake Superior using a spatio-temporal analysis of optical water typologies

Remote Sensing of Environment

Volumn 171, Issue , 2015, Pages 149-161

  Trochta, John T ^a,b   Mouw, Colleen B ^a   Moore, Timothy S ^c  

^a MICHIGAN TECHNOLOGICAL UNIVERSITY   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c UNIVERSITY OF NEW HAMPSHIRE   (United States)

Author keywords

Classification; Inherent optical properties; Lake superior; Optical; Remote sensing

Indexed keywords

BIOGEOCHEMISTRY; BIOLOGICAL MATERIALS; CLASSIFICATION (OF INFORMATION); DYNAMICS; OPTICAL PROPERTIES; REMOTE SENSING; SATELLITE IMAGERY; STOCHASTIC SYSTEMS;

COLORED DISSOLVED ORGANIC MATTER; INHERENT OPTICAL PROPERTIES; INHERENT OPTICAL PROPERTIES (IOPS); LAKE SUPERIOR; OPTICAL; REMOTE-SENSING REFLECTANCE; SPATIOTEMPORAL ANALYSIS; TEMPORAL AND SPATIAL DISTRIBUTION;

LAKES;

BACKSCATTER; CLASSIFICATION; DISSOLVED ORGANIC MATTER; MODIS; OPTICAL PROPERTY; OUTFLOW; PARTICULATE MATTER; REMOTE SENSING; RIVER PLUME; SATELLITE IMAGERY; SPATIOTEMPORAL ANALYSIS; STOCHASTICITY; STRATIFICATION; TYPOLOGY; UPWELLING;

GREAT LAKES [NORTH AMERICA]; LAKE SUPERIOR;

EID: 84945397043     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.10.008     Document Type: Article

References (59)

1. Artigas F.J., Yang J.S. Hyperspectral remote sensing of marsh species and plant vigour gradient in the New Jersey Meadowlands. International Journal of Remote Sensing 2005, 26(23):5209-5220.
2. Bailey S.W., Franz B.A., Werdell P.J. Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. Optics Express 2010, 18(7):7521-7527.
3. Balasko B., Abonyi J., Feil B. Fuzzy clustering and data analysis toolbox: For use with Matlab, Department of Process Engineering University of Veszprem, Hungary (Available at:). http://www.fmt.vein.hu/softcomp.
4. Barbiero R.P., Tuchman M.L. Results from the U.S. EPA's biological open water surveillance program of the Laurentian great lakes: I. Introduction and Phytoplankton Results. Journal of Great Lakes Research 2001, 27(2):134-154.
5. Barbiero R.P., Tuchman M.L. The deep chlorophyll maximum in lake superior. Journal of Great Lakes Research 2004, 30(S1):256-268.
6. Barbiero R.P., Lesht B.M., Warren G.J. Evidence for bottom-up control of recent shifts in the pelagic food web of Lake Huron. Journal of Great Lakes Research 2011, 37(1):78-85.
7. Barni M., Cappellini V., Mecocci A. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1996, 4(3):393-396.
8. Bennington V., McKinley G.A., Kimura N., Wu C.H. General circulation of Lake Superior: Mean, variability, and trends from 1979 to 2006. Journal of Geophysical Research 2010, 115.
9. Bensaid A.M., Hall L.O., Bezdek J.C., Clarke L.P., Silbiger M.L., Arrington J.A., Murtagh R.F. Validity-guided (Re) clustering with applications to image segmentation. IEEE Transactions on Fuzzy Systems 1996, 4:112-123.
10. Bezdek J.C., Ehrlich R., Full W. FCM: Fuzzy C-means algorithm. Computers and Geosciences 1984, 10(2-3):191-203.
11. Brown C.W., Yoder J.A. Coccolithophorid blooms in the global ocean. Journal of Geophysical Research, Oceans 1994, 99(C4):7467-7482.
12. Budd J., Warrington D. Satellite-based sediment and chlorophyll a estimates for Lake Superior. Journal of Great Lakes Research 2004, 30(Suppl. 1):459-466.
13. Bunn A.G., Korpela M., Biondi F., Campelo F., Mérian P., Qeadan F., Zang C. dplR: Dendrochronology program library in R. R package version, 1(4) 2012.
14. Bunnell D.B., Barbiero R.P., Ludsin S.A., Madenjian C.P., Warren G.J., Dolan D.M., Weidel B.C. Changing ecosystem dynamics in the Laurentian Great Lakes: bottom-up and top-down regulation. Bioscience 2014, 64(1):26-39.
15. Craig S.E., Jones C.T., Li W.K.W., Lazin G., Horne E., Caverhill C., Cullen J.J. Deriving optical metrics of coastal phytoplankton biomass from ocean colour. Remote Sensing of Environment 2012, 119:72-73.
16. D'Alimonte D., Melin F., Zibordi G., Berthon J.-F. Use of the novelty detection technique to identify the range of applicability of empirical ocean color algorithms. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(12):2833-2843.
17. Dave R.N. Validating fuzzy partition obtained through c-shells clustering. Pattern Recognition Letters 1996, 17:613-623.
18. Devred E., Sathyendranath S., Platt T. Decadal changes in ecological provinces of the Northwest Atlantic Ocean revealed by satellite observations. Geophysical Research Letters 2009, 36.
19. Dunn J.C. Well separated clusters and optimal fuzzy partitions. Journal Cybernetics 1974, 4:95-104.
20. Effler S.W., Gelda R., Perkins M., Peng F., Hairston N.G., Kearns C.M. Patterns and modeling of the long-term optics record of Onondaga Lake, New York. Fundamental and Applied Limnology/Archiv für Hydrobiologie 2008, 172(3):217-237.
21. Effler S.W., Perkins M., Peng F., Strait C., Weidemann A.D., Auer M.T. Light-absorbing components in Lake Superior. Journal of Great Lakes Research 2010, 36:656-665.
22. Gons H.J., Auer M.T. Some notes on water color in Keweenaw Bay (Lake Superior). Journal of Great Lakes Research 2004, 30:481-489.
23. Hardman-Mountford N.J., Hirata T., Richardson K.A., Aiken J. An objective methodology for the classification of ecological pattern into biomes and provinces for the pelagic ocean. Remote Sensing of Environment 2008, 112:3341-3352.
24. Hubbs C.L., Lagler K.F. Fishes of the Great Lakes region 2004, University of Michigan Press, rev. revised ed. G.R. Smith (Ed.).
25. Hüttich C., Herold M., Wegmann M., Cord A., Strohbach B., Schmullius C., Dech S. Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring. Remote Sensing of Environment 2011, 115(10):2445-2459.
26. Iglesias-Rodríguez M.D., Brown C.W., Doney S.C., Kleypas J., Kolber D., Kolber Z., Falkowski P.G. Representing key phytoplankton functional groups in ocean carbon cycle models: Coccolithophorids. Global Biogeochemical Cycles 2002, 16(4):47/1-47/20.
27. Jansen L.J.M., Di Gregorio A. Parametric land cover and land-use classifications as tools for environmental change detection. Agriculture, Ecosystems & Environment 2002, 91(1-3):89-100. (ISSN 0167-8809).
28. Krishnapuram R., Keller J.M. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1993, 1(2):98-110.
29. Li H., Budd J., Green S. Evaluation and regional optimization of bio-optical algorithms for central Lake Superior. Journal of Great Lakes Research 2004, 30(Suppl. 1):443-458.
30. Lomb N.R. Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science 1976, 39(2):447-462.
31. Matheson D.H., Munawar M. Lake Superior Basin and its development. Journal of Great Lakes Research 1978, 4:249-263.
32. Melin F., Vantrepotte V. How optically diverse is the coastal ocean?. Remote Sensing of Environment 2015, 160:235-251.
33. Minor E., Stephens B. Dissolved organic matter characteristics within the Lake Superior watershed. Organic Geochemistry 2008, 39(11):1489-1501. 10.1016/j.orggeochem.2008.08.001.
34. Moore T.S., Campbell J.W., Dowell M.D. A class-based approach to characterizing and mapping the uncertainty of the MODIS chlorophyll product. Remote Sensing of Environment 2009, 113(11):2424-2430.
35. Moore T.S., Campbell J.W., Feng H. A fuzzy logic classification scheme for selecting and blending satellite ocean colour algorithms. IEEE Transactions on Geoscience and Remote Sensing 2001, 39:1764-1776.
36. Moore T.S., Campbell J.W., Feng H. Characterizing the uncertainties in spectral remote sensing reflectance for SeaWiFS and MODIS-aqua based on global in situ matchup data sets. Remote Sensing of Environment 2015, 159:14-27.
37. Moore T.S., Dowell M.D., Bradt S., Verdu A.R. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sensing of Environment 2014, 143:97-111.
38. Mouw C.B., Chen H., McKinley G.A., Effler S., O'Donnell D., Perkins M.G., Strait C. Evaluation and optimization of bio-optical inversion algorithms for remote sensing of Lake Superior's optical properties. Journal of Geophysical Research, Oceans 2013, 118:1696-1714.
39. Mouw C.B., Greb S., Aurin D., DiGiacomo P.M., Lee Z., Twardowski M., Craig S. Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote Sensing of Environment 2015, 160:15-30.
40. Munawar M., Munawar I.F. Phytoplankton of Lake Superior. Journal of Great Lakes Research 1973, 4:415-442.
41. Oliver M.J., Irwin A.J. Objective global ocean biogeographic provinces. Geophysical Research Letters 2008, 35(15).
42. Palmer S.C.J., Hunter P.D., Lankester T., Hubbard S., Spyrakos E., Tyler A.N., et al. Validation of Envisat MERIS algorithm for chlorophyll retrieval in a large, turbid and optically-complex shallow lake. Remote Sensing of Environment 2015, 157(C):158-169.
43. Peng F., Effler S., O'Donnell D., Weidemann A., Auer M. Characterizations of minerogenic particles in support of modeling light scattering in Lake Superior through a two-component approach. Limnology and Oceanography 2009, 54(4):1369-1381.
44. Perkins M., Effler S.W., Peng F., O'Donnell D.M., Strait C. Light-absorbing components in the Great Lakes. Journal of Great Lakes Research 2013, 39:123-136.
45. R Core Team R: A language and environment for statistical computing 2014, R Foundation for Statistical Computing, Vienna, Austria, (URL http://www.R-project.org/).
46. Scargle J.D. Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal 1982, 263:835-853.
47. Schertzer W.M., Elder F.C., Jerome J. Water transparency of Lake Superior in 1973. Journal of Great Lakes Research 1978, 4(3-4):350-358.
48. Schulz M., Mudelsee M. REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series. Computers & Geosciences 2002, 28(3):421-426.
49. Schwab D.J., Sellers D.L. Computerized bathymetry and shorelines of the Great Lakes. NOAA Data Report ERL GLERL-16 1996, (http://www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-016/dr-016.html).
50. Sterner R. C:N:P stoichiometry in Lake Superior: freshwater sea as end member. Inland Waters 2011, 1(1):29-46.
51. Sterner R.W. In situ-measured primary production in Lake Superior. Journal of Great Lakes Research 2010, 36(1):139-149.
52. Szeto M., Werdell P.J., Moore T.S., Campbell J.W. Are the world's oceans optically different?. Journal of Geophysical Research 2011, 116:C00H04.
53. Titze D.J., Austin J.A. Winter thermal structure of Lake Superior. Limnology and Oceanography 2014, 59(4):1336-1348.
54. Tomaselli V., Dimopoulos P., Marangi C., Kallimanis A.S., Adamo M., Tarantino C., Blonda P. Translating land cover/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment. Landscape Ecology 2013, 28(5):905-930.
55. Vantrepotte V., Loisel H., Dessailly D., Meriaux X. Optical classification of contrasted coastal waters. Remote Sensing of Environment 2012, 123:306-323.
56. Weller R.R. Chemistry of Lake Superior. Journal of Great Lakes Research 1978, 4:370-385.
57. Wu K., Yang M. A cluster validity index for fuzzy clustering. Pattern Recognition Letters 2005, 26:1275-1291.
58. Xie X.L., Beni G.A. A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13(4):841-846.
59. Zadeh L. Fuzzy sets. Information and Control 1965, 8:338-353.