Using hyperspectral data to detect the responses of masson pine's needle spectral reflectance to acid stress

Acid rain has been a worldwide environmental problem for decades. China is one of the most serious acid deposition polluted regions in the world. How to effectively monitor acid deposition's severity and spatial distribution has constituted a great challenge to the traditionally chemistry methodology used to monitor acid rain. Long-term acid stress will change foliar internal structure and the content of pigments (such as chlorophyll a and b). Generally, such changes of foliar attributes will result increased reflectance in the visible and near-infrared wavelength regions. In this study, field and greenhouse experiments were performed separately to illustrate the influence of both natural and simulated acid rain to the spectra reflectance and chlorophyll content of masson pine (Pinus Massoniana). As measured with a portable spectroradiometer and a portable chlorophyll meter, spectra reflectance was a more sensitive indicator than chlorophyll content to indicate the severity of acid stress. In most of our cases, the reflectance of masson pine (both natural and greenhouse) was increasing with the severity of acid stress in part or in the whole wavelength regions ranged from 400 to 800nm. Vegetation indices computed using simulated Landsat Thematic Mapper (TM) bands showed that light acid stress often caused higher indices' values, and it was suggested that multispectral image data might be used to monitor acid stress from a canopy level.

[1]  Yi Hong Wang,et al.  Effects of simulated acid rain on germination, foliar damage, chlorophyll contents and seedling growth of five hardwood species growing in China , 2000 .

[2]  N. Goel,et al.  Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .

[3]  Response of six European forest sites to decided and proposed air pollutant emission reductions , 1997 .

[4]  Jason A. Cole,et al.  Hyperspectral Remote Sensing and Its Applications , 2005 .

[5]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[6]  I. Csiszár,et al.  On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998) , 2002 .

[7]  Martin Kraft,et al.  Reflectance Measurements of Leaves for Detecting Visible and Non-visible Ozone Damage to Crops , 1996 .

[8]  A. Navabi,et al.  Can Leaf Chlorophyll Measures at Differing Growth Stages be used as an Indicator of Winter Wheat and Spring Barley Nitrogen Requirements in Eastern Canada , 2005 .

[9]  P. A. Murtha,et al.  Foliar Nutrients and Photo Interpretation of Douglas Fir Stress , 1983 .

[10]  Jan Mulder,et al.  Acid deposition and its effects in China: an overview , 1999 .

[11]  T. Sogn,et al.  Effects of N and S deposition on leaching from an acid forest soil and growth of Scots pine (Pinus sylvestris L.) after 5 years of treatment , 1998 .

[12]  Andrew D. Richardson,et al.  An evaluation of noninvasive methods to estimate foliar chlorophyll content , 2002 .

[13]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[14]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[15]  F. Maselli Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data , 2004 .

[16]  G. Carter Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[17]  Lalit Kumar,et al.  Imaging Spectrometry and Vegetation Science , 2001 .

[18]  R. Rao,et al.  Rapid Assessment of Specific Leaf Area and Leaf Nitrogen in Peanut (Arachis hypogaea L.) using a Chlorophyll Meter , 2001 .

[19]  Alicia K. Birky,et al.  NDVI and a simple model of deciduous forest seasonal dynamics , 2001 .

[20]  C. Alewell,et al.  Environmental chemistry: Is acidification still an ecological threat? , 2000, Nature.

[21]  S. Ustin,et al.  Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing , 2003 .