Variability In Spectral Characteristics Research Articles

Detection of moisture content of polyester fabric based on hyperspectral imaging and BP neural network

To validate the feasibility and improve the accuracy of water content detection in polyester fabrics using hyperspectral imaging, 150 sets of hyperspectral images of polyester fabrics with varying thicknesses and moisture contents were obtained, and the characteristics of the spectral curves and impact of moisture content were elucidated. In addition, the area and full width at half maximum of the characteristic peaks around 1363 and 1890 nm were determined as spectral characteristic variables. Furthermore, the models of polyester fabric moisture content detection were developed using backpropagation neural networks, and their accuracy was evaluated using correlation coefficient and mean squared error. It was observed that the change in the moisture content of polyester fabrics not only affected the reflectance of the overall spectral curve of polyester fabrics but also altered the position and overall shape of the characteristic peaks. As the moisture content increased, the proportion of pure water spectra in the mixed spectra of water-containing polyester fabrics also increased, leading to a change in the overall shape of the characteristic peaks of polyester fabrics. Because of the overlap between the near-infrared absorption bands of pure water and the polyester fabric around 1363 and 1890 nm, the area and full width at half maximum of the characteristic peaks were considered to be more representative than the reflection for modeling. The established backpropagation neural network–based moisture content quantitative detection model has shown extremely high detection accuracy, with the correlation coefficient for the test set being higher than 0.999 and the root mean square error being lower than 0.3 %, indicating that the detection error of moisture content was only about 0.3 wt%.

Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.

UAV hyperspectral combined with LiDAR to estimate chlorophyll content at the stand and individual tree scales.

Chlorophyll is an important indicator of vegetation health status, accurate estimation of which is important for evaluating forest carbon sink. In this study, we estimated the chlorophyll content of coniferous forests, broad-leaved forests and mixed forest stands at stand and individual tree level by unmanned air vehicle (UAV) hyperspectral data combined with light detection and ranging (LiDAR) point clouds, which improved the non-destructive estimation accuracy of forest chlorophyll. We further comprehensively analyzed the spatial distribution of chlorophyll content at different scales. A total of 36 spectral characteristic variables related to chlorophyll content were screened by correlation analysis based on the fusion of UAV hyperspectral data and LiDAR point clouds combining with the empirical data from ground plots. We constructed multiple models for chlorophyll estimation by using statistical model, including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest and hybrid data-driven PROSPECT mechanism model. The optimal model was selected to estimate the chlorophyll content. The horizontal and vertical distribution of chlorophyll content at the stand level and individual tree level were analyzed. The results showed that the random forest model was superior to the models constructed by multiple stepwise regression, BP neural network and BP neural network optimized by firefly algorithm for chlorophyll estimation with R2 and RMSE of 0.59-0.64 and 3.79-5.83 μg·cm-2, respectively. The accuracy of the mechanism model was the highest, with R2 and RMSE of 0.97 and 3.40 μg·cm-2. The chlorophyll contents differed across stand types, with that in broad-leaved forest (25.25-31.60 μg·cm-2) being higher than mixed forest (13.52-23.93 μg·cm-2) and coniferous forest (6.40-13.71 μg·cm-2). There were significant differences in chlorophyll contents the in vertical direction among different stands. For individual tree of different species, the chlorophyll content inside the canopy was lower than that outside the canopy in the horizontal direction. In the vertical direction, there was no difference in chlorophyll content among different layers of Pinus sylvestris var. mongolica canopy. However, significant differences were observed among the upper, middle, and lower layers of Juglans mandshurica canopy. Using the fusion of hyperspectral image and LiDAR point cloud data, the mechanism model driven by hybrid data could effectively improve the accuracy and stability of chlorophyll content estimation at different scales.

Hyperspectral Inversion Model of Relative Heavy Metal Content in Pennisetum sinese Roxb via EEMD-db3 Algorithm

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed.

Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China

ABSTRACT The spectral characteristics of sunlit and shaded leaves are critical to improving the utilization of remote sensing methodology to quantify forest physiology. However, spectral characteristics within the tree canopies, especially normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI), are poorly understood. Our study used an imaging observation platform to obtain hyperspectral imagery of a Manchurian Ash canopy on Changbai Mountain. A non-imaging spectrometer was employed for an assisted analysis. The study results of the corresponding spectrum obtained at two observation spatial scales were significantly different between sunlit and shaded leaves. For imaging spectral observations, there were significant differences in NDVI and PRI between sunlit and shaded leaves (P < 0.001). PRI near the petiole was significantly lower than in other parts of leaves (P = 0.049). Non-imaging spectral observations of the reflectance of sunlit and shaded leaves were different only in the visible light region. The PRI of the shaded leaves were higher than that of sunlit leaves, which was consistent with the imaging spectral observations. The complexity of light environment within the canopy, especially the differences in incident irradiance, contributed to the range of leaf attribute measurements, which resulted in the variability of spectral characteristics.

Fusion of THz-TDS and NIRS Based Detection of Moisture Content for Cattle Feed

As an essential index to evaluate feed quality, feed moisture content which is too high or too low will impose an adverse impact on feed nutritional value. Therefore, the quantitative analysis of feed moisture content is significant. In this paper, the detection of feed moisture content based on terahertz (THz) and near-infrared (NIR) spectroscopy and data fusion technology of THz and NIR (THz-NIR) was investigated. First, feed samples with different water content (29.46%–49.46%) were prepared, and THz (50–3000 μm) and NIR (900–1700 nm) spectral data of samples was collected and preprocessed, and the feed samples were divided into correction set and verification set by 2:1. Second, the spectral data was fused through the head-to-tail splicing, and the feed moisture content prediction model was established combined with partial least squares regression (PLSR). Third, competitive adaptive reweighting sampling (CARS) was applied to extract spectral characteristic variables for feature layer fusion, and the feed moisture content prediction model in feature level was constructed combined with PLSR. Finally, the evaluation parameters validation set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were employed to evaluate the prediction effect of the model. The results indicated that THz, NIR spectra, and data fusion technology could quickly and effectively predict feed moisture content. Among them, the characteristic layer spectral data fusion model achieved the optimal prediction effect while Rp, RMSEP, and RPD reached 0.9933, 0.0069, and 8.7386 respectively. In conclusion, compared with the prediction model established by single THz and NIR spectrum, THz-NIR spectrum data fusion could more accurately predict feed moisture content and provide certain theoretical and technical support for inspirations and methods for quantitative analysis of feed moisture content of livestock and poultry.

Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics

The milk containing only A2 β-casein (called A2 milk) is globally popular because of its unique health benefits. Traditionally, genetic testing (such as gene sequencing) is used to identify the cows with A2 β-casein gene that can only produce A2 milk, which is a time-consuming and costly method. The objective of this study was to directly identify A1 and A2 milk from a large quantity of milk using mid-infrared (MIR) spectroscopy and chemometrics without genotyping cows. Before establishing the predictive model, we firstly genotyped the A1 β-casein and A2 β-casein of cows from blood as reference values. Further, the MIR spectra of the milk collected from these cows were obtained using a dairy product analyzer. The MIR spectroscopy data and the reference values were used as the independent and dependent variables, respectively, to establish a category classification model for A1 and A2 milk. Seven preprocessing methods were combined with two feature extraction algorithms to establish the model. Subsequently, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were developed. The average accuracy of the test set of the two models were 94.9% and 94.4%, respectively, while the PLS-DA model exhibited better effect, and the accuracy of training set and test set reached 96.6% and 96.0%, respectively. We used a set of independent samples for the external validation of the PLS-DA model, and the prediction accuracy was 95.2%. Overall, the proposed prediction models based on MIR spectroscopy can be used for low-cost, rapid, and large-scale classification of A1 and A2 milk, which may be extremely beneficial in milk production industries. • A1 and A2 milk identification was feasible using MIR spectroscopy. • UVE and CARS algorithm can used to extract spectral characteristic variables. • PLS-DA model exhibited better effect than SVM model.

Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm.

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

Identification of zinc pollution in rice plants based on two characteristic variables

Traditional chemical methods used to measure the zinc content in rice plants are time-consuming, laborious, requires reagents, and have a limited monitoring range, while the Raman spectroscopy method has the advantage of being fast, non-destructive, and requires no reagents. Unfortunately, the identification accuracy of the Raman partial least squares (PLS) model based on principal components is only 53.33%. To boost this, a One-Way ANOVA method was used to extract the characteristic variables in the Raman spectra. Based on these Raman variables, a model for identifying zinc stressed samples was established. The identification accuracy was improved to 70% but still fell short of the measurement requirements. To further enhance these results, the Raman spectrum was decomposed into components based on the Hilbert Vibration Decomposition (HVD) method. Using characteristic variables of the Raman spectrum and its HVD components to establish a PLS model, the identification accuracy of the test set is raised to 90.25%. These results are a significant improvement from those obtained using a model solely based on the Raman spectral characteristic variables, revealing that HVD components provide highly effective identification information. A Raman modeling method based on the characteristic variables of the HVD component is an innovative way for improving the accuracy of Raman detection, especially for the measurement of trace substances.

Quantitative analysis of coal quality by mutual information-particle swarm optimization (MI-PSO) hybrid variable selection method coupled with spectral fusion strategy of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectroscopy (FTIR)

Coal quality analysis can provide basic work for the design and operation of related equipment and technological processes, which mainly including ash content and volatile matter. Variable selection plays an important role in the model and properly variable selection methods which can promote the predictive performance of the model and reduce the modeling time. In this article, a hybrid variable selection method mutual information-particle swarm optimization (MI-PSO) was used to realize precise screening of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectrometer (FTIR) spectral characteristic variables. Firstly, to obtain the fusion spectrum matrix, the pre-processed LIBS and FTIR spectrum matrix were directly connected end to end. Then the MI was used to eliminate redundant variables in the spectral data, and the PSO was used to further filter the retained variables to find a set of variables with higher prediction accuracy. Finally, a mutual information-particle swarm optimization-kernel extreme learning machine (MI-PSO-KELM) model is constructed based on the selected variables that the ash content and the volatile matter variables are 6499, 5093 respectively. The performance of MI-PSO-KELM is better than the KELM model based on LIBS and FTIR and the KELM model based on LIBS and FTIR primary spectral fusion, and can more accurately predict the ash content and volatile matter of coal quality analysis. The results for the determination of ash content, the optimal cross-validation (root mean square error of cross validation (RMSECV) =1.6170, determination coefficient of cross validation (RCV2) =0.9684) and prediction set the root mean square error of prediction (RMSEP) =0.9687, determination coefficient of prediction (RP2 = 0.9821) results are obtained by the MI-PSO based on primary spectral fusion model. For the determination of the volatile matter, the best results of cross-validation (RMSECV = 1.2886, RCV2=0.9868) and prediction set (RMSEP = 1.3218, RP2=0.9789) are obtained by the MI-PSO based on primary spectral fusion model. It shows that the spectral fusion technology can help improve the performance of LIBS spectral quantitative analysis, and a spectrum fusion method based on LIBS and FTIR is established to realize the rapid determination of ash content and volatile matter of coal.

Amino acid-sensitive reagents with coumarin moiety for latent prints examination

New ninhydrin derivatives were designed for the development of latent fingerprints on paper surfaces. The target compounds combine the selectivity of an amino acid-sensitive reagent with the variability of spectral characteristics of a fluorescent probe. These ninhydrin analogues, 2,2-dihydroxy-5-(2-oxo-2H-chromen-4-yloxy)-2H-indane-1,3-diones, prepared by multistep synthesis show enhanced fluorogenic properties compared with the commonly used ninhydrin. These types of derivatives can be potentially used for development of the latent prints on the dark surfaces, because their ninhydrin reaction products provide fluorescent prints under the UV light. The prints developed using these compounds are stable, visible and do not vanish from paper for a relatively long time (5 months).

Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by Raman spectroscopy coupled with chemometric methods

The quality of Pseudostellaria heterophylla depends on the growing area of the plants greatly. Compared with near infrared spectroscopy employed to discriminate the geographic regions before, the Raman signal is more obvious. So far, discrimination of P. heterophylla from different regions by Raman spectroscopy has not yet been realized. Hence, Raman spectroscopy coupled with chemometric methods to rapidly and effectively discriminate P. heterophylla from different regions was studied. Original spectra of P. heterophylla in wavenumber range of 4000–100 cm−1 were acquired. Then, a steady and exact model, partial least squares discriminant analysis (PLS-DA), was constructed. And competitive adaptive reweighted sampling (CARS) was further used to extract effective wavelength spectral characteristic variables. Results show that CARS-PLS-DA model is an appropriate model to discriminate the P. heterophylla, with determination coefficient of calibration (R2C), root mean square error of cross validation (RMSECV), determination coefficient of prediction (R2P), and root mean squared error of prediction (RMSEP) are 0.9468, 0.1979, 0.9665, and 0.1120, respectively. These results demonstrated that the built method is useful and effective to discriminate P. heterophylla from different regions, which provide a new idea in the application of rapid field test.

Rapid Pseudomonas Species Identification from Chicken by Integrating Colorimetric Sensors with Near-Infrared Spectroscopy

Pseudomonas spp. are the dominant spoilage bacteria which can cause chicken spoilage. Some traditional detection methods are often unsuitable for their rapid real-time detection. Thus, in this paper, a fusion strategy based on colorimetric sensors and near-infrared spectroscopy was applied to rapidly identify Pseudomonas spp. in chicken. First, four different species of Pseudomonas—Pseudomonas gessardii, Pseudomonas psychrophila, Pseudomonas fragi, and Pseudomonas fluorescens—were isolated from putrid chicken, and then, the odor and spectral information of the Pseudomonas species and their mixture were obtained by colorimetric sensors and near-infrared spectroscopy, respectively. Thirty-six odor characteristic variables and 33 spectral characteristic variables were extracted from each technique and used for data fusion based on principal component analysis (PCA). Back-propagation artificial neural network (BP-ANN) was used to build identification model for the discrimination of the different Pseudomonas species. The results showed that the discrimination capability of the model based on data fusion was superior to that based on the two techniques independently, and eventually BP-ANN achieved 100% classification rate by cross-validation and 98.75% classification rate in predication set. This work indicates that the combination of colorimetric sensors and near-infrared spectroscopy is promising for the rapid identification of Pseudomonas species in chicken extract, and hence may be applied towards quality monitoring.

Online Discriminant Model of Blood Spot Eggs Based on Spectroscopy

AbstractTo explore the best method for online detection of blood spot eggs, fiber‐optic spectrometer was used to collect the transmission spectra of eggs. Three different kinds of wavebands were extracted using competitive adaptive reweighed sampling (CARS), interval partial least squares (IPLS) and successive projections algorithm (SPA), respectively. The discrimination results of the models established with partial least square discriminant analysis (PLSDA) and support vector machine (SVM) showed that the CARS‐screened wavebands had the best modeling results. Through stepwise Bayes discriminant analysis (SBDA) based on the spectral characteristic variables of CARS, five wavelength variables (509, 511, 526, 571 and 599 nm) were determined to be the characteristic variables for the detection of blood spot eggs finally. The detection accuracy of blood spot eggs and normal eggs by Bayes discriminant model using these five variables was 95%. These results show that stepwise Bayes discriminant model can effectively simplify the characteristic wavebands and improve the prediction accuracy, indicating that it can be applied to the real‐time online detection of blood spot eggs.Practical ApplicationsThis study explore three different kinds of spectral characteristic variables were extracted using CARS, IPLS and SPA, respectively, only five characteristic variables are required in the stepwise Bayes discriminant model. This model can avoid the influence of excessive spectral variables on the accuracy of the model, so it is feasible to apply this model to real‐time online detection of blood spot eggs.

Spectral Characteristics of Common Reed Beds: Studies on Spatial and Temporal Variability

Reed beds are the second largest producer of biomass in Olkiluoto Island. Quantitative information on the extent and amount of reed stands is an integral part of the biosphere assessment related to long-term safety analysis of nuclear fuel repository site currently under construction. The major challenge in reed bed mapping is discrimination between reed and other green vegetation. Spectral field measurements were used to study the temporal and spatial variability of spectral characteristics of reed beds. Feasibility of discriminating reed beds from other vegetation based on hyperspectral measurements was studied as well. Results indicate that there is large temporal variation of reed bed spectra and the optimal time for data acquisition differs for old and new reed bed types. Comparing spectral characteristics of the reed bed and meadow classes in a local neighborhood indicated that the classes have high within-class spectral variability and similar mean spectra, however, 10 out of 11 targets had lower angle to the mean spectrum of the corresponding class than that of the other class when Spectral Angle Mapper (SAM) was used. Comparing the spectral characteristics of reed beds at four test sites within the Olkiluoto Island indicated that while some of the sites had similar spectra, the difference between others was remarkable. This is partly explained by different density and height of dead and live reed stems at the four sites.

Nondestructive detection of infertile hatching eggs based on spectral and imaging information

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

Variability in spectral characteristics of trampled high-mountain grasslands

Abstract The goal of the paper is a presentation of field remote sensing methods for the analysis of the trampled plants of a highly protected mountain meadow ecosystem (M&amp;B UNESCO Reserve and one of the most important Polish National Parks). The research area covers a core part of the Western Tatras - the Gąsienicowa Valley and Kasprowy Wierch summit, which are among the most visited destinations of the Polish Tatras. The research method is based on field hyperspectral measurements, using the ASD FieldSpec 3 spectrometer, on the dominant plant species of alpine swards. Sampling sites were located on trampled areas (next to trails) and reference plots, with the same species, but located more than 10 m from the trail (where the probability of trampling was very low, but the same composition of analysed plants). In each case, homogenous plots with a domination of one plant species were investigated. Based on the hyperspectral measurements, spectral characteristics as well as vegetation indices were analysed with the ANOVA statistical test. This indicated a varied resistance to trampling of the studied plant species. The analysis of vegetation indices enabled the selection of those groups which are the most useful for research into mountain vegetation condition: the broadband greenness group; the narrowband greenness group, measuring chlorophyll content and cell structure; and the canopy water content group. The results of the analyses show that vegetation of the High Tatras is characterised by optimal ranges of remote sensing indices. Only plants located nearest to the trails were in a worse condition (chlorophyll and water content was lower for the reference targets). These differences are statistically significant, but the measured values indicate a good condition of vegetation along trampled trails, within the range of optimum plant characteristics.

基于光谱特征变量的湿地典型植物生态类型识别方法——以北京野鸭湖湿地为例

Certain spectral characteristic variables have a direct impact to the accuracy and efficiency of identifying the wetland vegetation.The Wild Duck Lake,a typical freshwater wetland of North China,was selected as the study area of this research.The canopy hyperspectral reflectances of typical wetland vegetation were measured by Field-Spec 3 high-spectrum radiometer.In this research,a total of seven vegetation covers were measured.The reflectance spectrum of submerged vegetation species was special because of the influence of water body,suspended solid in water,etc.The reflectance spectra of the other six vegetation covers were similar.But different vegetation cover had different feature,such as plant morphology,water content and chlorophyll content so that they had their own spectral reflectance characteristics.Thus,based on the canopy spectral reflectance,first-derivative and continuum removal were applied to analyze and contrast spectral features of different vegetation types.The spectral characteristic variables were selected for identifying plant ecological.The research established eight distinct spectral characteristic variables(red edge position WP_r,red edge amplitude Dr,green peak position WP_g,green peak amplitude Rg,absorption depth around 510 nm and 675 nm,absorption area around 510 nm and 675 nm) that have importance for mapping vegetation cover types in the Wild Duck Lake wetland.In addition,the absorption features of seven vegetation types were larger differences than first-derivative spectral features.Excepting WP_r and WP_g,the average of Rg and Dr of submerged plant were minimum,the average of Rg of hygrophilous plant was maximum(0.164) and the average of Dr of cultivated plant was maximum(0.012).The DEP-675 and AREA-675 around 675 nm of seven vegetation types were higher than the DEP-510 and AREA-510 around 510 nm.Absorption depth and absorption area of other six vegetation types had shown fall after rise along with the water environment gradient change except cultivated plant.Then we made use of single factor analysis of variance(One-way ANOVA) to verify the discrimination of the spectral characteristic variables.When the confidence level was less than or equal to 0.01,the selected spectral characteristic variables could distinguish seven plant ecological types better with a minimum discrimination of 13 and a maximum of 18.Moreover,the discrimination of absorption characteristic parameters was better than first-derivative parameters.Finally the nonlinear back propagation artificial neural network(BP-ANN) and fisher linear discriminant analysis(FLDA) were applied to identify the wetland vegetation types and the selected spectral characteristic variables were also included in.According to accuracy test,the results of overall accuracy of two methods were 85.5% and 87.98%,respectively.The single factor analysis of variance(One-way ANOVA) and the classification accuracy of different classifiers indicated that the selected eight spectral characteristic variables had great applicability and reliability.The results of this paper would not only provide a scientific base for hyperspectral remote sensing image processing and wetland vegetation mapping in the Wild Duck Lake,but also supply reference for identifying and classification of freshwater wetland vegetation applying remote sensing technology.In future research,we should increase sample numbers and refine the research objectives.Applying the results of this research to hyperspectral image interpretations,we can fully explore the potential and advantage of hyperspectral remote sensing technology.

Relationship between floristic similarity and vegetated land surface phenology: Implications for the synoptic monitoring of species diversity at broad geographic regions

Assessing species composition and its changes through time across broad geographic regions is time consuming and a difficult endeavor. The synoptic view provided by imaging remote sensors offers an alternative. But while many studies have developed procedures for assessing biodiversity using multi- and hyper-spectral imagery, they may only provide snapshots at particular months/seasons due to the seasonal variability of spectral characteristics induced by vegetated land surface phenologies. Thus, procedures for remotely assessing biodiversity patterns may not fully represent the biodiversity on the ground if vegetated land surface phenologies are not considered. Using Mantel tests, ordinarily least square regression models and spatial autoregressive models, we assessed the relationship between floristic diversity and vegetated land surface phenologies, as captured by time series of vegetation indices derived from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The relationship was calibrated with data from temperate montane forests of the Qinling Mountains region, Shaanxi Province, China. Our results show that floristically similar areas also exhibit a comparable similarity in phenological characteristics. However, phenological similarity obtained using the Visible Atmospherically Resistant Index (VARI), a spectral vegetation index found to be not only sensitive to changes in chlorophyll content but also linearly related with the relative content of foliar anthocyanins, exhibited the strongest relationship with floristic similarity. Therefore, analysis of the temporal dynamics of pigments through the use of satellite-derived metrics, such as VARI, may be used for evaluating the spatial patterns and temporal dynamics of species composition across broad geographic regions.

Spectral Detection of Emergence in Corn and Cotton

Multispectral reflectance of emerging cotton (Gossypium hirsutum) and corn (Zeamays) seedlings was measured during the 2000 and 2001 growing seasons. Reflectance in blue, green, red, and near infrared (NIR) wave lengths was used to detect seedling emergence, to monitor leaf area growth, and to measure the effect of bare soil reflectance on scene (bare soil and seedlings) reflectance. Cotton and corn seedlings were detected 1 day after initial emergence (1 DAE) in 2000 by the red band. The red band detected seedlings in 2001 at 9 and 8 DAE in early and late planted corn, respectively, and on 0 DAE for cotton. The normalized difference vegetative index (NDVI) detected seedlings at 1 DAE or 2 DAE in both years. Seedling ground cover in 2000 on the initial detection date in the target areas averaged 1.3% and 0.9%, respectively, for cotton and corn; comparable values in 2001 for cotton, early planted corn, and late planted corn, were 1.4%, 0.4%, and 0.8%, respectively. The red wave band was the most sensitive single band for detecting the presence of seedlings, but NDVI was the most sensitive spectral indicator, which was apparently due to the red band since the NIR band did not always detect seedlings. Seedling leaf area was linearly correlated with NDVI values beginning at 1 or 2 DAE. Bare soil was the major component of the scene during stand establishment and dominated single band reflectance and NDVI values. A dry soil surface that was smoothed and sealed by rain usually caused single band reflectance to increase. The high variability in spectral characteristics of bare soil restricted the interpretation of the spectral data to concluding whether or not seedlings were emerging, but without estimating numbers and seedling size.