[Use of Near-Infrared Hyperspectral Images to Differentiate Architectural Coatings with Different Qualities].

Architectural coatings sold in market fall into many categories which mean different models and qualities. The research plans to differentiate different kinds of architectural coatings in quality using hyperspectral technology. Near-Infrared hyperspectral images of four kinds of architectural coatings (in a descending quality order of brand A, B, C, and D) in same color were acquired. The optimal wavelengths were selected at 1283 and 2447 nm to differentiate the four kinds of coatings through ANOVA (Analysis of Variance) method. The band ratio index of R1283/R2447 was built and the results were segmented into the corresponding coatings, and the accuracies of segmentation were compared with that from Maximum Likely Classification (MLC). The results indicated all J-M distances are more than 1.8 except between C and D; the lowest accuracy of 87.54% in segmentation and 95.63% in MLC were both from brand D, and others' accuracies all were over 90% in both ratio index and MLC. Therefore, the ratio index R1283/R2447 could be used to distinguish different kinds of architectural coatings. Also, the research could provide support for identification, quality acceptance, as well as conformity assessment of architectural coatings.

[Study on the Detection of Slight Mechanical Injuries on Apples with Hyperspectral Imaging].

Nondestructive detection is one of the hottest spots in the application of hyperspectral remote sensing. The apple is easy to produce slight mechanical injuries that affects its quality in the process of picking and transporting. The hyperspectral images of 54 "yellow banana" and "Yantai Fushi" apples with slight injuries in the visible and near-infrared (400~1 000 nm) ranges are acquired; the mean spectral curves of injury regions on apples are extracted; the endmember spectrum are extracted based on minimum noise fraction (MNF) and geometric vertex principle; and the spectral angle is calculated between spectral of injury region and endmember spectral; a model of endmember extraction spectral angle (EESA) is constructed to detect slight mechanical injuries on apples. The slight mechanical injuries on "yellow banana" and "Yantai Fushi" apples are detected by setting spectral angle threshold, and the detection accuracy is compared with MNF and principal component analysis (PCA) method. The results show that the accuracy of EESA model is the highest, and the detection accuracy rate reaches 94.44% and 90.07% respectively.

[Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat Based on Wavelet Feature].

Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350～1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer' accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user' accuracy and producer' accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image.

[Simulated Experimental Research on Using Canopy Spectra of Surface Vegetation to Detect CO2 Microseepage Spots].

With the global warming, people now pay more attention to the problem of the emission of greenhouse gas (CO2). Carbon capture and storage (CCS) technology is an effective measures to reduce CO2 emission. But there is a possible risk that the CO2 might leak from underground. However, there need to research and develop a technique to quickly monitor CO2 leaking spots above sequestration fields. The field experiment was performed in the Sutton Bonington campus of University of Nottingham (52. 8N, 1. 2W) from May to September in 2008. The experiment totally laid out 16 plots, grass (cv Long Ley) and beans (Vicia faba cv Clipper) were planted in eight plots, respectively. However, only four grass and bean plots were stressed by the CO2 leakage, and CO2 was always injected into the soil at a rate of 1 L x min(-1). The canopy spectra were measured using ASD instrument, and the grass was totally collected 6 times data and bean was totally collected 3 times data. This paper study the canopy spectral characteristics of grass and beans under the stress of CO2 microseepages through the field simulated experiment, and build the model to detect CO2 microseepage spots by using hyperspectral remote sensing. The results showed that the canopy spectral reflectance of grass and beans under the CO2 leakage stress increased in 580-680 nm with the stressed severity elevating, moreover, the spectral features of grass and beans had same rule during the whole experimental period. According to the canopy spectral features of two plants, a new index AREA(5800680 nm) was designed to detect the stressed vegetations. The index was tested through J-M distance, and the result suggested that the index was able to identify the center area and the core area grass under CO2 leakage stress, however, the index had a poor capability to discriminate the edge area grass from control. Then, the index had reliable and steady ability to identify beans under CO2 leakage stress. This result could provide theoretical basis and methods for detecting CO2 leakage spots using hyperspectral remote sensing in the future.

[Comparison and analysis of hyperspectral remote sensing identifiable models for different vegetation under waterlogging stress].

With the global climate warming, flooding disasters frequently occurred and its influence scope constantly increased in China. The objective of the present paper was to study the leaf spectral features of vegetation (maize and beetroot) under waterlogging stress and design a hyperspectral remote sensing model to monitor the flooding disasters through a field simulated experiment. The experiment was carried out in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1. 2 degrees W) from May to August in 2008, and samples were collected one time every week and spectra were measured in the laboratory. The result showed that the reflectance of the maize and beetroot decreased in the 550 and 800-1 300 nm region, and the reflectance slightly increased in the 680 nm region. This paper chose NDVI, SIPI, PRI, SRPI, GNDVI and R800 * R550/R680 to identify the vegetation under waterlogging stress, respectively. The result suggested that the SIPI and R800 * R550/R680 was sensitive for maize under waterlogging stress, and then SIPI and PRI and R800 * R550/R680 was sensitive for beetroot under waterlogging stress. In order to seek the best identifiable model, the normalized distances between means of control and stressed vegetation indices were calculated and analyzed, the result indicated that the distance of R800 * R550/R680 is more than that of indices' in the early stress stage, illustrated that the index identifiable ability for waterlogging stress is better than other indices, then the index has the strong sensitivity and stability. Therefore, the index R800 * R550/R680 could be used to quickly extract flooding disaster area by using hyperspectral remote sensing, and would provide information support for disaster relief decisions.

[Research on the spectral feature and identification of the surface vegetation stressed by stored CO2 underground leakage].

With the global climate warming, reducing greenhouse gas emissions becomes a focused problem for the world. The carbon capture and storage (CCS) techniques could mitigate CO2 into atmosphere, but there is a risk in case that the CO2 leaks from underground. The objective of this paper is to study the chlorophyll contents (SPAD value), relative water contents (RWC) and leaf spectra changing features of beetroot under CO2 leakage stress through field experiment. The result shows that the chlorophyll contents and RWC of beetroot under CO2 leakage stress become lower than the control beetroot', and the leaf reflectance increases in the 550 nm region and decreases in the 680nm region. A new vegetation index (R550/R680) was designed for identifying beetroot under CO2 leakage stress, and the result indicates that the vegetation index R550/R680 could identify the beetroots after CO2 leakage for 7 days. The index has strong sensitivity, stability and identification for monitoring the beetroots under CO2 stress. The result of this paper has very important meaning and application values for selecting spots of CCS project, monitoring and evaluating land-surface ecology under CO2 stress and monitoring the leakage spots by using remote sensing.

[Spectral characteristics and identification research of soybean under different disease stressed].

The objective of this paper is to identify disease and its severity of soybean by using single leaf spectral data in the field. The soybean spectral were measured in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1.2 degrees W), which infected rust disease (RD) and common mosaic disease (CMD), respectively, and continuum removal method was used to process the original spectral data, and sensitive bands were selected for disease and disease severity, and vegetation index was designed for identifying RD and CMD of soybean. The result showed spectral reflectance of soybean under CMD stressed is more than that of health in the visible region. However, spectral reflectance of soybean under RD stressed will decrease in the green region and that will increase in the red region with disease severity increasing. According to the spectral changing features, a new index R500 x R550/R680 was designed for identifying the disease of soybean. In order to test the index identifying disease ability, the J-M distances were calculated among health, RD and CMD. The result indicated index R500 x R550/R680 can better identify RD and CMD, at the same time, the index has good ability for discriminating the disease severity of soybean. The research results of this paper has important theoretical value for crops disease monitoring and prevention and practical application meanings.

[Using hyperspectral remote sensing to estimate canopy chlorophyll density of wheat under yellow rust stress].

The canopy reflectance of winter wheat infected with different severity yellow rust was collected in the fields and canopy chlorophyll density (CCD) of the whole wheat was measured in the laboratory. The correlation was analyzed between hyperspectral indices and CCDs, the indices with relationship coefficients more than 0. 7 were selected to build the inversion models, and comparing the predicted results and measured results to test the models, the results showed the first derivative index (D750-D550)/(D750+D550) has higher prediction precision than other indices, while the next is first derivative index (D725-D702)/(D725+D702). Saturation analysis was performed for the above indices, the result indicated that when CCD was more than 12 microg x cm(-2), the first derivative index (D750-D550)/(D750+D550) was easiest to get to saturation level. Therefore, when CCD was less than 12 microg x cm(-2), the first derivative index (D750-D550)/(D750+D550) could be used to estimate wheat CCD and had higher prediction precision than other indices; and when CCD was more than 12 microg x cm(-2), the first derivative index (D725-D702)/(D725+D702) was not easiest to reach saturation level and could be used to estimate wheat CCD. There is a significant minus cor relation between CCD and disease index (DI), moreover, accurate estimation of CCD by using hyperspectral remote sensing not only can monitor wheat growth, but also can provide assistant information for identification of wheat disease. Therefore, this study has important meaning for prevention and reduction of disaster in agricultural field.

[Using canopy hyperspectral ratio index to retrieve relative water content of wheat under yellow rust stress].

The aim of this paper is to estimate canopy relative water contents (RWC) of winter wheat under yellow rust stress by using hyperspectral remote sensing. The canopy reflectance of winter wheat that infected different severity yellow rust was collected and the disease index (DI) of the wheat was investigated respectively in the fields, whereafter the wheat was sampled corresponding to the canopy reflectance measurements and the RWC of the whole wheat were measured in the Laboratory. The research showed that the canopy spectra reflectance gradually decreased in the near-infrared (NIR) region (900-1,300 nm) with RWC reduction, however, canopy spectra reflectance gradually increased in the short-wave-infrared (SWIR) region (1,300-2,500 nm), and there was just higher minus correlation between RWC and DI. Smoothing the canopy spectra, the ratio indices were built by using the sensitive bands for water in NIR and SWIR, and then the estimation RWC linear models were built by using ratio indices as variables, and the model inversion precision and stability were analyzed and compared for estimation RWC. The result indicated that the inversion precision and the stability of the model with ratio index R1,300/R1,200 as variable excel other models, the linear model's RMSE is 3.43, and the relative error is 4.78%. So, this study results not only can provide assistant information for diagnosing wheat disease but also can supply theories and methods for inversion vegetation RWC by using hyperspectral images in the future.

[Using the distance between hyperspectral red edge position and yellow edge position to identify wheat yellow rust disease].

The objective of the present paper is to identify healthy wheat and disease wheat by using hyeprspectral remote sensing as soon as possible. The canopy spectral reflectance of winter wheat infected by different severity yellow rust was measured and the disease indices (DI) were investigated in the field respectively. Smoothing the canopy spectra and calculating the first derivative values, the two methods were used to calculate the red edge position (REP) and yellow edge position (YEP) of the first derivative values: (a) maximum of the first derivative value; (b) Cho and Skidmore method. The result showed that REP gradually shifted to short-wave band, and the YEP gradually shifted to long-wave band with disease severity increasing, however, REP-YEP quickly became smaller. Analyzing and comparing the prediction precision of REP, YEP and REP-YEP for DI, the result indicated that the model REP-YEP as variable has the best estimation precision for DI than REP and YEP, the model estimation error is 6.22, and relative error is 14.3%, and it could identify healthy and disease wheat 12 days before the disease symptom apparently appeared. Therefore, this study not only can provide theory and technology for large areas monitoring of wheat disease by using hyperspectral remote sensing in the future, but also has the important meaning and practical application value for implementing precision agriculture.

[Comparison of principal component analysis with VI-empirical approach for estimating severity of yellow rust of winter wheat].

The canopy reflectance of winter wheat infected by yellow rust with different severity was measured through artificial inoculation, and the disease index (DI) of the wheat corresponding to the spectra acquired in the field was obtained. Principal component analysis (PCA) was used to compute the first 5 principal components (PCs) of canopy spectra in the 350-1 350 nm range and the first 3 PCs of first-order derivative in blue edge (490-530 nm), yellow edge (550-582 nm) and red edge (630-673 nm), respectively. Step-wise regression was used to build models, the results of those models are compared with that of VI-empirical models, and the result shows that the model based on PCs of first-order derivative is particularly accurate compared to others, with the RMSE of 7.65 and relative error of 15.59%. Comparison was made between the estimated DI and the measured DI, indicating that the model based on SDr'/SDg' is suitable to monitoring early disease and the model based on PCs of first-order derivative is suitable to monitoring the more severe disease of yellow rust of winter wheat. The conclusion has great practical and application value to acquiring and evaluating wheat disease severity using hyperspectral remote sensing, and has an important meaning for increasing yields of crops and ensuring security of food supplies.

[Study on hyperspectra estimation of pigment contents in canopy leaves of winter wheat under disease stress].

The canopy reflectance of winter wheat infected with stripe rust was measured in the field through artificial inoculation, and the pigment contents of the wheat leaves were determined indoor. The correlation between pigment contents and canopy hyperspectra data and the first derivative data of the disease wheat were analyzed respectively. Using linear and non-linear regression methods, and choosing a part of samples, the estimation models about pigment contents of disease wheat were built. Through the test of the other part samples, the result shows that the model containing the normalized value of the sum of first derivative within green edge (SD(g)) and the sum of first derivative within red edge (SD(r)) is the best one. The model was used to estimate the contents of chlorophyll a and chlorophyll b and carotenoid of the disease wheat, and the relative errors were 17.0%, 16.3% and 12.4%, respectively. This study shows that canopy hyperspectra data can be used to estimate the pigment contents of crops leaves and the estimation precision is high. This conclusion has great practice and application value to monitor the grow-ing way of and disease influence on crops by using hyperspectral remote sensing.

[Using hyperspectral derivative index to monitor winter wheat disease].

The canopy reflectance of winter wheat that infected different severity stripe rust was measured through artificial inoculation, the disease index (DI) of the wheat corresponding to the spectra was acquired in the field, and the parameters of biochemistry and biophysics were measured indoors. The 1st derivatives were analyzed. The results show that the 1st derivative values increase at the green edge (500-560 nm), while decrease at the red edge (680-760 nm) with DI increasing. The ratio of the sum of derivatives within the red edge (SDr') to the sum of derivatives within the green edge (SDg') has a higher negative linear correlation with DI, with a coefficient of determination r2=0.9210 (n=28), and that can be use to identify the healthy and disease crops 12 days before symptoms appearing. Therefore, the derivative vegetation index SDr/SDg can be used to monitor crops disease information. The conclusion is significant and may find application in acquiring crops disease information using hyperspectral remote sensing, and has a important meaning for increasing yields of crops and ensuring security of food supplies.