Multi-criteria evaluation of the suitability of growth functions for modeling remotely sensed phenology

Ecological Modelling

Volumn 323, Issue , 2016, Pages 123-132

  Verma, Manish ^a,b   Friedl, Mark A ^a   Finzi, Adrien ^c   Phillips, Nathan ^a  

^a BOSTON UNIVERSITY   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^c USA   (United States)

Author keywords

Growth functions; Phenology; Remote sensing; Temporal dynamics

Indexed keywords

BIOLOGY; CLIMATE CHANGE; DYNAMICS; REMOTE SENSING; VEGETATION;

GROWTH FUNCTIONS; LAND-ATMOSPHERE COUPLINGS; MULTI-CRITERIA EVALUATION; NORMALIZED DIFFERENCE VEGETATION INDEX; PHENOLOGY; PHOTOSYNTHETICALLY ACTIVE RADIATION; START OF SEASONS (SOS); TEMPORAL DYNAMICS;

FORESTRY;

CLIMATE CHANGE; DECIDUOUS FOREST; ECOLOGICAL MODELING; GROWTH RESPONSE; LOGISTICS; MULTICRITERIA ANALYSIS; NDVI; PHENOLOGY; REMOTE SENSING; TEMPORAL ANALYSIS; VEGETATION INDEX;

HARVARD FOREST; HUBBARD BROOK EXPERIMENTAL FOREST; MASSACHUSETTS; NEW HAMPSHIRE; UNITED STATES;

EID: 84954271076     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2015.12.005     Document Type: Article

References (50)

1. Ahl D., Gower S., Burrows S., Shabanov N. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 2006, 104:88-95.
2. 2 uptake enhancement induced by warmer springs. Proc. Natl. Acad. Sci. U. S. A. 2005, 102:10823.
3. Atkinson P.M., Jeganathan C., Dash J., Atzberger C. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 2012, 123:400-417.
4. Baldocchi D. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 2008, 56:1-26.
5. 2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales. Ecol. Model. 2001, 142:155-184.
6. Beck P.S.A., Atzberger C., Hogda K.A., Johansen B., Skidmore A.K. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 2006, 100:321-334.
7. Buchwald P. A general bilinear model to describe growth or decline time profiles. Math. Biosci. 2007, 205:108-136.
8. Buermann W., Lintner B.R., Koven C.D., Angert A., Pinzon J.E., Tucker C.J., Fung I.Y. The changing carbon cycle at Mauna Loa Observatory. Proc. Natl. Acad. Sci. U. S. A. 2007, 104:4249-4254.
9. 2 exchange of mixed forest in Belgium over 5 years. Agric. For. Meteorol. 2003, 119:209-227.
10. Chuine I., Yiou P., Viovy N., Seguin B., Daux V. Historical phenology: grape ripening as a past climate indicator. Nature 2004, 432:289-290.
11. de Beurs K.M., Henebry G.M. Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens. Environ. 2004, 89:497-509.
12. Fensholt R., Sandholt I., Rasmussen M.S. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens. Environ. 2004, 91:490-507.
13. Fisher J.I., Mustard J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens. Environ. 2007, 109:261-273.
14. Fisher J.I., Mustard J.F., Vadeboncoeur M.A. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 2006, 100:265-279.
15. Friedl M.A., Sulla-Menashe D., Tan B., Schneider A., Ramankutty N., Sibley A., Huang X.M. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114:168-182.
16. Gompertz B. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philos. Trans. R. Soc. Lond. 1825, 513-583.
17. Hermance J.F., Jacob R.W., Bradley B.A., Mustard J.F. Extracting phenological signals from multiyear AVHRR NDVI time series: framework for applying high-order annual splines with roughness damping. IEEE Trans. Geosci. Remote Sens. 2007, 45:3264-3276.
18. Hird J.N., McDermid G.J. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ. 2009, 113:248-258.
19. Hufkens K., Friedl M., Sonnentag O., Braswell B.H., Milliman T., Richardson A.D. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 2012, 117:307-321.
20. Jenkins J.P., Richardson A.D., Braswell B.H., Ollinger S.V., Hollinger D.Y., Smith M.L. Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agric. For. Meteorol. 2007, 143:64-79.
21. Jönsson P., Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 2002, 40:1824-1832.
22. Karlsen S., Tolvanen A., Kubin E., Poikolainen J., Høgda K., Johansen B., Danks F., Aspholm P., Wielgolaski F., Makarova O. MODIS-NDVI-based mapping of the length of the growing season in northern Fennoscandia. Int. J. Appl. Earth Obs. Geoinf. 2008, 10:253-266.
23. Kathuroju N., White M.A., Symanzik J., Schwartz M.D., Powell J.A., Nemani R.R. On the use of the advanced very high resolution radiometer for development of prognostic land surface phenology models. Ecol. Model. 2007, 201:144-156.
24. Katsanevakis S. Modelling fish growth: model selection, multi-model inference and model selection uncertainty. Fish. Res. 2006, 81:229-235.
25. Kumar M., Monteith J.L. Remote sensing of plant growth. Plants Daylight Spectr. 1982, 133-144.
26. Liang L., Schwartz M. Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landsc. Ecol. 2009, 24:465.
27. Menzel A. Trends in phenological phases in Europe between 1951 and 1996. Int. J. Biometeorol. 2000, 44:76-81.
28. Moody A., Johnson D.M. Land-surface phenologies from AVHRR using the discrete Fourier transform. Remote Sens. Environ. 2001, 75:305-323.
29. Morris A.K., Silk W.K. Use of a flexible logistic function to describe axial growth of plants. Bull. Math. Biol. 1992, 54:1069-1081.
30. Moulin S., Kergoat L., Viovy N., Dedieu G. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Clim. 1997, 10:1154-1170.
31. Myneni R.B., Williams D.L. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 1994, 49:200-211.
32. Myneni R.B., Maggion S., Iaquinto J., Privette J.L., Gobron N., Pinty B., Kimes D.S., Verstraete M.M., Williams D.L. Optical remote-sensing of vegetation - modeling, caveats, and algorithms. Remote Sens. Environ. 1995, 51:169-188.
33. O'Keefe J. Phenology of woody species at Harvard Forest since 1990. Harvard Forest Data Archive: HF003 2000.
34. Olsson L., Eklundh L. Fourier-series for analysis of temporal sequences of satellite sensor imagery. Int. J. Remote Sens. 1994, 15:3735-3741.
35. Parmesan C., Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003, 421:37.
36. Prince S.D., Goward S.N. Global primary production: a remote sensing approach. J. Biogeogr. 1995, 22:815-835.
37. Reed B.C., Brown J.F., Vanderzee D., Loveland T.R., Merchant J.W., Ohlen D.O. Measuring phenological variability from satellite imagery. J. Veg. Sci. 1994, 5:703-714.
38. Richards F.J. A flexible growth function for empirical use. J. Exp. Bot. 1959, 10:290-300.
39. Richardson A.D., O'Keefe J. Phenological differences between understory and overstory: a case study using the long-term Harvard Forest records. Phenology of Ecosystem Processes 2009, 87-117. Springer Science + Business, New York. A. Noormets (Ed.).
40. Richardson A.D., Bailey A.S., Denny E.G., Martin C.W., O'Keefe J. Phenology of a northern hardwood forest canopy. Glob. Change Biol. 2006, 12:1174-1188.
41. Richardson A.D., Keenan T.F., Migliavacca M., Ryu Y., Sonnentag O., Toomey M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 2013, 169:156-173.
42. Schaaf C.B., Gao F., Strahler A.H., Lucht W., Li X., Tsang T., Strugnell N.C., Zhang X., Jin Y., Muller J.P. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 2002, 83:135-148.
43. Schwartz M.D., Hanes J.M. Intercomparing multiple measures of the onset of spring in eastern North America. Int. J. Climatol. 2010, 30:1614-1626.
44. Sellers P.J., Hall F.G. Fife in 1992 - results, scientific gains, and future-research directions. J. Geophys. Res. Atmos. 1992, 97:19091-19109.
45. Soudani K., le Maire G., Dufrene E., Francois C., Delpierre N., Ulrich E., Cecchini S. Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens. Environ. 2008, 112:2643-2655.
46. Wang Q., Tenhunen J., Dinh N.Q., Reichstein M., Vesala T., Keronen P. Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sens. Environ. 2004, 93:225-237.
47. White M.A., Thornton P.E., Running S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycl. 1997, 11:217-234.
48. White M.A., de Beurs K.M., Didan K., Inouye D.W., Richardson A.D., Jensen O.P., O'Keefe J., Zhang G., Nemani R.R., Van Leeuwen W.J.D., Brown J.F., De Wit A., Schaepman M., Lin X., Dettinger M., Bailey A.S., Kimball J., Schwartz M.D., Baldocchi D.D., Lee J.T., Lauenroth W.K. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Glob. Change Biol. 2009, 15:2335-2359.
49. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., Reed B.C., Huete A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84:471-475.
50. Zhang X., Friedl M., Schaaf C. Global vegetation phenology from moderate resolution imaging spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. 2006, 111:G04017.