Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

International Journal of Applied Earth Observation and Geoinformation

Volumn 33, Issue 1, 2014, Pages 312-320

  Croft, H ^a   Chen, J M ^a   Zhang, Y ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b DELTA STATE UNIVERSITY   (United States)

Author keywords

Canopy structure; GPP; Leaf physiology; Phenology; Remote sensing

Indexed keywords

BIOGEOCHEMISTRY; CANOPY; CARBON DIOXIDE; CHLOROPHYLL; DECIDUOUS FOREST; LEAF AREA INDEX; PHOTOSYNTHESIS; REMOTE SENSING; SPATIAL RESOLUTION; SPATIAL VARIATION; TEMPORAL VARIATION; VEGETATION INDEX;

EID: 84904735126     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.06.005     Document Type: Article

References (60)

1. Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64, 234-253.
2. Blackburn, G.A., 2007. Hyperspectral remote sensing of plant pigments. J. Exp. Bot.58, 855-867.
3. Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K., Thomsen, A.,2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ.81, 179-193.
4. Canisius, F., Fernandes, R., Chen, J., 2010. Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use. Remote Sens. Environ. 114, 950-960.
5. Caspersen, J.P., Saprunoff, M., 2005. Seedling recruitment in a northern temperate forest: the relative importance of supply and establishment limitation. Can. J.Forest Res. 35, 978-989.
6. Carter, G.A., Knapp, A.K., 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88,677-684.
7. Chen, J.M., Black, T.A., 1992. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421-429.
8. Chen, J.M., Cihlar, J., 1995. Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index. Appl. Opt. 34, 6211-6222.
9. Chen, J.M., Plummer, P.S., Rich, M., Gower, S.T., Norman, J.M., 1997. Leaf area index measurements. J. Geophys. Res. 102, 29-429.
10. Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., 2004. A simplemethod for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens. Environ. 91, 332-344.
11. Churkina, G., Schimel, D., Braswell, B.H., Xiao, X., 2005. Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biol. 11,1777-1787.
12. Croft, H., Chen, J.M., Zhang, Y., Simic, A., 2013. Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data. Remote Sens. Environ. 133, 128-140.
13. Croft, H., Chen, J.M., Zhang, Y., 2014a. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol. Complex 17, 119-130.
14. Croft, H., Chen, J.M., Noland, T., 2014b. Stand age effects on Boreal forest physiology using a long time-series of satellite data. For. Ecol. Manage. 328, 202-208.
15. Dash, J., Curran, P.J., 2004. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25, 5403-5413.
16. Demarez, V., Gastellu-Etchegorry, J.P., Mougin, E., Marty, G., Proisy, C., Dufrêne,E., Dantec, V.L., 1999. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model. Int. J. Remote Sens. 20, 879-894.
17. Eklundh, L., Johansson, T., Solberg, S., 2009. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens. Environ. 113, 1566-1573.
18. Eklundh, L., Jönsson, P., 2010. Timesat 3.0 Software Manual.
19. Fisher, J.I., Mustard, J.F., Vadeboncoeur, M.A., 2006. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 100,265-279.
20. Gamon, J.A., Coburn, C., Flanagan, L.B., Huemmrich, K.F., Kiddle, C., Sanchez-Azofeifa,G.A., Thayer, D.R., Vescovo, L., Gianelle, D., Sims, D.A., 2010. SpecNet revisited: bridging flux and remote sensing communities. Can. J. Remote Sens. 36, 376-390.
21. Gitelson, A., Merzlyak, M.N., 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J.Photochem. Photobiol. B: Biol. 22, 247-252.
22. 2 flux in maize. Geophys. Res. Lett. 30, 29-31.
23. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Leavitt, B., Ciganda, V., Burba, G.G., Suyker, A.E., 2006. Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res. D: Atmos. 111,D08S11.
24. Gradowski, T., Thomas, S.C., 2006. Phosphorus limitation of sugar maple growth in central Ontario. For. Ecol. Manage. 226, 104-109.
25. Hird, J.N., McDermid, G.J., 2009. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ. 113, 248-258.
26. Houborg, R., Boegh, E., 2008. Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data.Remote Sens. Environ. 112, 186-202.
27. Jenkins, J.P., Braswell, B.H., Frolking, S.E., Aber, J.D., 2002. Detecting and predicting spatial and interannual patterns of temperate forest springtime phenology inthe eastern US. Geophys. Res. Lett. 29, 2201.
28. Jonsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40, 1824-1832.
29. Jönsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40, 1824-1832.
30. Jönsson, P., Eklundh, L., 2004. TIMESAT - a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833-845.
31. Jönsson, A.M., Eklundh, L., Hellström, M., Bärring, L., Jönsson, P., 2010. Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology. Remote Sens. Environ. 114, 2719-2730.
32. Kodani, E., Awaya, Y., Tanaka, K., Matsumura, N., 2002. Seasonal patterns of canopys tructure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy. For. Ecol. Manage. 167, 233-249.
33. Liang, L., Schwartz, M.D., 2009. Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landscape Ecol. 24 (4), 465-472.
34. Maccioni, A., Agati, G., Mazzinghi, P., 2001. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J.Photochem. Photobiol. B: Biol. 61, 52-61.
35. Morin, X., Lechowicz, M.J., Augspurger, C., O'Keefe, J., Viner, D., Chuine, I., 2009. Leaf phenology in 22 North American tree species during the 21st century. GlobalChange Biol. 15, 961-975.
36. Morisette, J.T., Richardson, A.D., Knapp, A.K., Fisher, J.I., Graham, E.A., Abatzoglou, J.,Wilson, B.E., Breshears, D.D., Henebry, G.M., Hanes, J.M., Liang, L., 2009. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253-260.
37. Nagai, S., Maeda, T., Gamo, M., Muraoka, H., Suzuki, R., Nasahara, K.N., 2011. Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol. Divers. 4, 79-89.
38. Olofsson, P., Lagergren, F., Lindroth, A., Lindström, J., Klemedtsson, L., Kutsch, W.,Eklundh, L., 2008. Towards operational remote sensing of forest carbon balance across Northern Europe. Biogeosciences 5, 817-832.
39. Olsson, L., Eklundh, L., Ardö, J., 2005. A recent greening of the Sahel - trends, patterns and potential causes. J. Arid Environ. 63, 556-566.
40. Peng, Y., Gitelson, A.A., Keydan, G., Rundquist, D.C., Moses, W., 2011. Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content. Remote Sens. Environ. 115, 978-989.
41. Peng, Y., Gitelson, A.A., Sakamoto, T., 2013. Remote estimation of gross primary productivity in crops using MODIS 250 m data. Remote Sens. Environ. 128, 186-196.
42. Pfeifer, M., Disney, M., Quaife, T., Marchant, R., 2012. Terrestrial ecosystems from space: a review of earth observation products for macroecology applications.Global Ecol. Biogeogr. 21, 603-624.
43. Potter, C.S., 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem. Cycles 7, 811-841.
44. Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W., O'Keefe, J., 2006. Phenology of a northern hardwood forest canopy. Global Change Biol. 12, 1174-1188.
45. Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P., Ollinger, S.V., 2009a.Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19, 1417-1428.
46. Richardson, A.D., Hollinger, D.Y., Dail, D.B., Lee, J.T., Munger, J.W., O'Keefe, J., 2009b.Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321-331.
47. Rowe, J.S., 1972. Forest regions of Canada. Information Canada.
48. Ruimy, A., Dedieu, G., Saugier, B., 1996. TURC: a diagnostic model of continental gross primary productivity and net primary productivity. Global Biogeochem.Cycles 10, 269-285.
49. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004.A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547-560.
50. Sims, D.A., Gamon, J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81, 337-354.
51. Soudani, K., Hmimina, G., Delpierre, N., Pontailler, J.Y., Aubinet, M., Bonal, D., Caquet,B., de Grandcourt, A., Burban, B., Flechard, C., 2012. Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens. Environ. 123, 234-245.
52. Vogelmann, J.E., Rock, B.N., Moss, D.M., 1993. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 14, 1563-1575.
53. Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J., 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices.Agric. For. Meteorol. 149, 1015-1021.
54. 2eddy flux observations. In: Noormets, A.(Ed.), Phenology of ecosystem processes. Springer, New York, NY, pp. 247-270.
55. Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., Mohammed, G.H., Sampson, P.H., 2001.Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sens. 39, 1491-1507.
56. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C.,Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sens.Environ. 84, 471-475.
57. Zhang, Q., Xiao, X., Braswell, B., Linder, E., Baret, F., Moore Iii, B., 2005. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 99,357-371.
58. Zhang, Q., Xiao, X., Braswell, B., Linder, E., Ollinger, S., Smith, M.L., Jenkins, J.P., Baret,F., Richardson, A.D., Moore, B., 2006. Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data. Remote Sens. Environ. 105, 189-203.
59. Zhang, Y., Chen, J.M., Thomas, S.C., 2007. Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data. Can. J. Remote Sens. 33, 406-415.
60. chl) improve predictions of light-use efficiency and ecosystem photosyn-thesis for a boreal aspen forest? Remote Sens. Environ. 113, 880-888.