Diurnal Radiance and Shadow Fluntuations in a Cold Desert Shrub Plant C Community

Abstract Remote sensing data can be acquired during different times of the year and different times of the day using satellite or aircraft systems. Radiance values of pixels vary with ground target, latitude, time of year, and the time of day for both aerial and ground-measured data. An understanding of the resulting fluctuations in spectra provides information necessary to improve the interpretation of airborne and satellite data. Aerial and field measured spectra v vary for a typical desert shrub plant community with low vegetation cover (27%) and considerable shade differences and bare ground. Low shadow percentages were found from 1000 to 1650 h Pacific Daylight Time (PDT) in July and between 1100 and 1500 PDT in September. Both ground-measured and 5 × 5 m aerial pixels had low radiance values (corrected for a 45° sun-angle) at 0800 PDT and higher radiance values throughout the day until solar zenith. September data had a greater range of values compared to July data. MSS7 / MSS5 and MSS6 / MSS5 ratiobased indices were higher in July than September, indicating a higher photosynthetically active biomass in the shrub component in July. Vegetation index measurements varied throughout the day and from season to season and were influenced by solar altitude and the amount of bare soil, plant biomass, and shadow present.

[1]  C. J. Tucker,et al.  Soil spectra contributions to grass canopy spectral reflectance , 1977 .

[2]  Ray D. Jackson,et al.  Diurnal Patterns of Wheat Spectral Reflectances , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[3]  R. Jackson,et al.  Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands , 1987 .

[4]  Philip N. Slater,et al.  Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres , 1983 .

[5]  W. Boyd,et al.  Soil background effects on the spectral response of a three-component rangeland scene , 1986 .

[6]  C. Tucker.  Asymptotic nature of grass canopy spectral reflectance. , 1977, Applied optics.

[7]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[8]  R. Walraven.  Calculating the position of the sun , 1978 .

[9]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[10]  R. Jackson.  Spectral indices in N-Space , 1983 .

[11]  L. Biehl,et al.  Sun-View Angle Effects on Reflectance Factors of Corn Canopies , 1985 .

[12]  J. Colwell.  Vegetation canopy reflectance , 1974 .

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

[14]  R. Jackson,et al.  Soil spectral effects on 4-space vegetation discrimination , 1984 .

[15]  Ronald J. P. Lyon,et al.  Influence of rock-soil spectral variation on the assessment of green biomass , 1985 .

[16]  W. Freiberger.  International Dictionary of Applied Mathematics. , 1962 .

[17]  D S Kimes,et al.  Spectral radiance errors in remote sensing ground studies due to nearby objects. , 1983, Applied optics.

[18]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[19]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[20]  K. Ranson,et al.  Scene Shadow Effects on Multispectral Response , 1987, IEEE Transactions on Geoscience and Remote Sensing.

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

[22]  James H. Torrie,et al.  Principles and procedures of statistics: a biometrical approach (2nd ed) , 1980 .