Research On The N Content And The Spectral Characteristic Of Garden Plants Leaves Exposed To Nitrogen Dioxide Pollution

With the serious environmental problems, China has faced unprecedented pressures on environmental governance, while the highly emission of the NO2is the important root leading to a host of environmental problems in China. In this paper, the injured symptoms, the leaves nitrogen content and the spectral reflectance of leaves of13plant species are observed by the static artificial fumigation experiments. The levels of resistance to NO2and absorbility of13plant species are evaluated, and the retrieval models of leaves nitrogen content are established based on hyperspeetral remote sensing.To sum up, the results can provide a reference for the landscaping tree selection in contaminated areas and also for the real-time, nondestructive testing of plant leaf nitrogen content. And the major research results are as follows:(1) In this experiment, we can find that in different time intervals the plants showed different injured symptoms, but these symptoms are first showed in the leaves. Injured mainly in between the veins of the leaves victims, their injured symptoms was first with russet spots mainly and as fumigation time extension, some of the plants leaves brown spots edges gradually spread, Brown appears on the leaves of some plants of the plaque.The injured symptoms are mainly appeared russet spots between the veins of the leaves, and with longer duration of fume gas, part of the plant leaves’ yellow-brown spots edge will gradually spread, and some of the plant leaves will appear brown patches.(2) All of the plants’ resistance strength varies greatly, reflects the different sensitivity of NO2stress. And their resistance strength can be divided into four levels: high-resistant species are Nerium indicum and Robinia pseudoacacia; middle-resistant species are Rhododendron simsii, Ophiopogon japonicas and Broussonetia papyrifera; middle-sensitive species are Photinia serratifolia, Cornus alba, Reineckea carnea, Celtis sinensis and Acer rubrum; while the high-sensitive plant species are Ligustrum lucidum, Cinnamomum camphora and Koelreuteria paniculata.(3) We can see from the results of this experiment, the relative purification ability of these13plants is different. Using cluster analysis method the plants’ purification ability can be divided into four levels:low-purification plant species are Ligustrum lucidum, Acer rubrum and Cornus alba; middle low-purification plant species are Nerium indicum, Celtis sinensis, Photinia serratifolia and Ophiopogon japonicus; middle high-purification plant species are Cinnamomum camphora, Reineckea carnea, Rhododendron simsii, Robinia pseudoacacia and Broussonetia papyrifera; while high-purification plant species is Koelreuteria paniculata.(4) Suitability evaluation of13species of landscape plants are as followings: more suitable plants species are Rhododendron simsii, Robinia pseudoacacia and Broussonetia papyrifera. While the suitable plants species are Nerium indicum and Ophiopogon japonicas. And the less suitable plants species are Reineckea carnea, Cinnamomum camphora and Koelreuteria paniculata. But the not suitable plants species are Ligustrum lucidum, Acer rubrum, Celtis sinensis Photinia serratifolia and Cornus alba.(5) In all of the single-variable fitting equations, the coefficient of correlation between leaf nitrogen content and differential spectral is much higher than the correlation coefficient between leaf nitrogen content and original spectrum, inverse spectrum, logarithmic spectrum and the parameter of the hyperspeetral characteristics. Therefore, in order to better reflect the spectral characteristics of leaf nitrogen content, we should first use differential spectral transformation method to deal with spectral data.(6) By comparing the multiple linear fitting equation of differential spectrum as the independent variable is superior to other forms in single plant. While the best multiple linear regression models of arbor plants and shrub and herbaceous plants are the original spectrum and logarithmic spectrum with independent variables.(7) Through the comparison with the optimal single variable regression model and the optimal multiple linear regression model, we can found that the final estimate model of each plant’s leaf nitrogen content is the multiple linear regression. We can draw a conclusion that by the combination of multiple wavelengths to assess plant leaf nitrogen content is more effective than use only one independent variable. All kinds of plant leaf nitrogen content of the best estimating models are as shown below: Broussonetia papyrifera: y=9.399-15816.059*p"583-6620.931*p"456-604.738*p"870-826.415*p"403;Celtis sinensis: y=33.092-18353.589*p"573+2515.012*p"707-6726.558*p"635；Robinia pseudoacacia: y=29.467+5267.717*p"627-5602.89*p"687+2980.222*p"685+1825.581*p"517；Ligustrum lucidum: y=16.869+14054.170*p"520-291.167*p"852；Cinnamomum camphora: y=25.536+4758.658*p’663-1366.454*p’832；Koelreuteria paniculata: y=21.095+663.168*p’729+1552.237*p’831-974.348*p’800；Acer rubrum: y=30.394-5523.751*p"541+9041.992*p"793+13368.538*p"534；Arbor plants: y=30.504+1076.270*p437-5602.922*p706-767.279*p680+5633.500*p705+66.398*p756；Rhododendron simsii: y=16.678-5826.036*p’419-359.768*p’729+2646.423*p’404-1386.630p’651；Nerium indicum: y=27.623+2491.556*p"717-4807.945*p"615；Photinia serratifolia: y=21.084+2258.184*p"870+5358.266*p"690+850.728*p"843；Cornus alba: y=17.859+11873.488*p"524-2943.608*p"791+7622.635*p"556+3036.098*p"843+488.521*p"878；Reineckea carnea: y=15.55-10727.94*p"643-21982.501*p"483-2322.693*p"416+225.942*p"712；Ophiopogon japonicus: y=24.017+14337.906*p"501-12011.753*p"559+5127.170*p"638；Shrub and herbaceous plants: y=24.669-84.664*log10(p711)+35.786*log10(p461)+83.885*log10(p717)-32.606*log10(p569).