Seasonal patterns of canopy structure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy

Abstract The reflectance of a deciduous forest varies from spring to autumn owing to phenological or seasonal changes in the biophysical or biochemical attributes of the canopy. During the growing season in a broad-leaved deciduous stand of Japanese beech ( Fagus crenata ), we measured the continuous reflectance factor in the visible to near infrared spectrum (380–900 nm) from a tower and measured biophysical and biochemical attributes of the canopy (leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), and chlorophyll content. We analyzed the seasonal variations in the reflectance factor with respect to the seasonal variations in the biophysical or biochemical attributes of the canopy. We simulated the four bands of the moderate resolution imaging spectrometer (MODIS), band 3 (459–479 nm, blue), band 4 (545–565 nm, green), band 1 (620–670 nm, red), band 2 (841–876 nm, near infrared or NIR) and calculated the normalized difference vegetation index (NDVI) by averaging the continuous reflectance factor (380–900 nm) over the spectral range of each band. During the growing season, canopy chlorophyll content had the strongest linear correlation with the red band, LAI had a strong linear correlation with the NIR band, and LAI and fAPAR had the strongest linear correlations with the NDVI. The NDVI had strong linear correlations with the biophysical or biochemical attributes of the entire canopy during the growing season. We analyzed the correlations between narrow bands (380–900 nm, 5 nm band width) and the canopy attributes and found that, during the growing season, LAI had the highest linear correlation with the wavelengths between 750 and 900 nm (NIR), canopy chlorophyll content had the highest linear correlation with the wavelengths between 600 and 640 nm (red), and fAPAR had the highest linear correlation with the wavelengths between 660 and 680 nm. The red edge (the position of maximum slope in the 680–750 nm region) was linearly correlated with LAI and canopy chlorophyll content. HSI conversion from three visible bands allowed us to detect the turning point of canopy attributes in time series analysis. The canopy attributes changed quickly in the seasons of flushing and yellow coloring. To detect the processes and the turning points of the canopy phenology in a deciduous forest, it was necessary to measure reflectance at least once a week, especially in the flushing season.

[1]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[2]  M. Numata,et al.  Studies in conservation of natural terrestrial ecosystems in Japan , 1975 .

[3]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[4]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[5]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

[6]  Ramakrishna R. Nemani,et al.  Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation , 1989 .

[7]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[8]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.

[9]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  K. Kikuzawa.  Leaf survival of woody plants in deciduous broad-leaved forests. 1. Tall trees , 1983 .

[11]  J. E. Sanger,et al.  Quantitative Investigations of Leaf Pigments From Their Inception in Buds Through Autumn Coloration to Decomposition in Falling Leaves , 1971 .

[12]  J. Dungan,et al.  The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration , 1991 .

[13]  T. Koike,et al.  Dimethylsulfoxide method for the extraction of chlorophylls a and b from the leaves of wheat, field bean, dwarf bamboo, and oak , 1996 .

[14]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[15]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[16]  V. V. Yegorov,et al.  Spectral characteristics of vegetation cover: factors of variability , 1992 .

[17]  S. Wofsy,et al.  Scaling gross ecosystem production at Harvard Forest with remote sensing: a comparison of estimates from a constrained quantum‐use efficiency model and eddy correlation , 1995 .

[18]  U. Peterson.  Seasonal reflectance factor dynamics in boreal forest clear-cut communities , 1992 .

[19]  M. Bauer,et al.  Spectral estimators of absorbed photosynthetically active radiation in corn canopies. , 1985 .

[20]  S. Running,et al.  Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates , 1993 .

[21]  George Alan Blackburn,et al.  Seasonal variations in the spectral reflectance of deciduous tree canopies , 1995 .

[22]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[23]  M. Shibayama,et al.  Detecting phenophases of subarctic shrub canopies by using automated reflectance measurements , 1999 .

[24]  Tsuyoshi Akiyama,et al.  A Spectroradiometer for Field Use : VI. Radiometric estimation for chlorophyll index of rice canopy , 1986 .

[25]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.