Detecting drought status and LAI of two Quercus species canopies using derivative spectra

Abstract Derivative spectrum analysis has the advantage of reducing additive constants and minimizing soil background effects and is a potential method for exploiting hyperspectral data in vegetated areas. The shift of red-edge position (REP) of vegetation reflectance spectra, which is a focus point of derivative spectrum analysis, has been studied by many researchers as an indicator of environmental stresses and LAI. However, it is less satisfactory at canopy level than at leaf level. A field experiment was conducted, using 3-year-old potted Quercus glauca and Q. serrata , to examine the utility of derivative spectrum analysis for detecting drought status and LAI at canopy level and to find the optimal bands that can independently detect those variables. Five levels of drought status (including a control) and three levels of LAI were set. Measurements were made of canopy reflectance spectra at approximately 3 nm intervals, xylem water potentials, leaf water contents and LAI. Two measures for representing high or low drought status were chosen. One was water-cessation duration in hours (WD) and the other was leaf water content (LWC). They corresponded to a gradual change and an abrupt change, respectively, during drought development. The results showed that REP and REP-relevant indices were not very successful for independent detection of WD, LWC or LAI at canopy level. The best single bands for detecting WD, LWC and LAI were 611.4 nm in the first derivative ( r =0.807), 519.6 nm in the first derivative ( r =0.916) and 676.0 nm in the second derivative ( r =0.828), respectively. The initial part of the red-edge peak was a better indicator than the top (i.e. REP or the first derivative at REP) for the independent detection of LAI. A simulation to test lower spectral resolution proved that the wavelengths at 10 nm intervals that approximated the desirable bands at 3 nm intervals retained similar correlation coefficients for the three variables.

[1]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[2]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[3]  Y. Shan.  Effects of Simulated Acid Rain on Pinus densiflora: Inhibition of Net Photosynthesis by the Pheophytization of Chlorophyll , 1998 .

[4]  William P. Kustas,et al.  Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .

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

[6]  E. Fukusaki,et al.  Removal of Magnesium by Mg-dechelatase Is a Major Step in the Chlorophyll-Degrading Pathway in Ginkgo biloba in the Process of Autumnal Tints , 2000, Zeitschrift für Naturforschung C - A Journal of Biosciences.

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

[8]  F. Boochs,et al.  Shape of the red edge as vitality indicator for plants , 1990 .

[9]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[10]  John R. Miller,et al.  Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model , 1990 .

[11]  C. Bacour,et al.  Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .

[12]  Sheng-Huei Chang,et al.  Airborne biogeophysical mapping of hidden mineral deposits , 1983 .

[13]  S. Ollinger,et al.  DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN , 2002 .

[14]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[15]  A. Karnieli,et al.  Effects of air pollution on cell membrane integrity, spectral reflectance and metal and sulfur concentrations in lichens , 1997 .

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

[17]  W. R. Windham,et al.  Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.

[18]  J. Lagouarde,et al.  The assessment of regional crop water conditions from meteorological satellite thermal infrared data , 1991 .

[19]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[20]  P. Curran,et al.  A new technique for interpolating the reflectance red edge position , 1998 .

[21]  G. Bonham-Carter.  Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data , 1988 .

[22]  H. Lichtenthaler.  CHLOROPHYLL AND CAROTENOIDS: PIGMENTS OF PHOTOSYNTHETIC BIOMEMBRANES , 1987 .

[23]  D. Vidal-Madjar,et al.  Estimation of stomatal resistance and canopy evaporation during the HAPEX-MOBILHY experiment , 1992 .

[24]  F. M. Danson,et al.  RED-EDGE RESPONSE TO FOREST LEAF-AREA INDEX (VOL 16, PG 183, 1995) , 1995 .

[25]  A. K. Skidmore,et al.  Derivation of the red edge index using the MERIS standard band setting , 2002 .

[26]  B. Rock,et al.  Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline , 1988 .

[27]  F. M. Danson,et al.  High-spectral resolution data for determining leaf water content , 1992 .

[28]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[29]  J. Barber,et al.  Monitoring grassland dryness and fire potential in australia with NOAA/AVHRR data , 1988 .

[30]  D. Riaño,et al.  Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment , 2002 .

[31]  A. Kuusk.  A fast, invertible canopy reflectance model , 1995 .

[32]  A. Perrier,et al.  Irrigation monitoring by following the water balance from NOAA-AVHRR thermal infrared data , 1990 .

[33]  Frédéric Baret,et al.  Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands , 1992 .

[34]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.