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TECHNICAL REPORTS

Plant and Environment Interactions

Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements

^a Joint Center for Earth Systems Technology, Univ. of Maryland, Baltimore County (UMBC), Baltimore, MD 20771, USA
^b (current address), Biospheric Sciences Branch, Code 614.4, NASA/Goddard Space Flight Center, Greenbelt, MD 20771 USA
^c Biospheric Sciences Branch, Code 614.4, NASA/Goddard Space Flight Center, Greenbelt, MD 20771 USA
^d Hydrology and Remote Sensing Lab., Agricultural Research Service, USDA, Beltsville, MD 20705 USA
^e Science Systems and Applications Inc. (SSAI), Lanham, MD 20706 USA

^* Corresponding author (pcampbel{at}pop900.gsfc.nasa.gov)

Received for publication October 14, 2005. Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral-resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400–800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content. For separation of a wide range of treatment levels, hyperspectral (5–10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters providing optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.

Abbreviations: Car, carotenoids • Chl, chlorophyll • ChlF, chlorophyll fluorescence • cps, counts per second • D, derivative • F, fluorescence • K, potassium • LS means, least square means • NDVI, normalized difference vegetation index • PRI, photochemical reflectance index • R, reflectance • REIPw, wavelength position of the red edge inflection point • RS, remote sensing • SLM, specific leaf mass • TM (1–7), spectral bands on LandsatTM • USDA, United States Department of Agriculture • UV, ultraviolet

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