Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification

Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.

Simulated Reflectance of Apple Trees in Canopy Level Based on the PROSAIL Model and HJ-1A-HSI Data

Using the PROSAIL radiation transfer model and HJ-1A-HSI data to simulate the canopy reflectivity of apple trees, this study lays the foundation for the inversion of canopy parameters. Taking Qixia City of Yantai City, Shandong Province as the research area, the apple tree was taken as the research object, and the hyperspectral reflectance, LAI and sample GPS of apple canopy were measured in the field. The parameters required for the PROSAIL model were obtained by experimental methods. The model simulates the reflectivity;the HSI image data is preprocessed, and the canopy reflectivity is extracted by GPS coordinates. The PROSAIL model and the HSI image simulated reflectance were fitted to the measured apple canopy reflectivity. The decisive factor (R2) of the simulated reflectance and the measured reflectance of the PROSAIL model was 0.9944, and the relative error (RE%)was 0.1845. The HSI data simulated reflectance and measured reflectance. The coefficient of determination is 0.9714 and the relative error is 0.6202. Both have achieved good fitting effects and can be used for inversion studies of apple canopy parameters.

Phytoremediation Potential of Three Species of Macrophytes for Nitrate in Contaminated Water

Three species of aquatic plants (Scirpus validus, Phragmites australis and Acorus calamus) were used as experimental materials to study their capacity to purify contaminated water and their effects on water pH and dissolved oxygen (DO). The water was contaminated with different concentrations of nitrate (5 mg/L, 15 mg/L and 25 mg/L). The results indicated that the concentration of nitrate, species of aquatic plant and their interaction significantly impacted denitrification (P = 0.00). Under the same concentrations, the three species of aquatic plants provided varying degrees of purification. Acorus calamus provided effective purification under all three concentrations of nitrate wastewater, with removal percentages of 87.73%, 83.80% and 86.72% for nitrate concentrations of 5 mg/L, 15 mg/L and 25 mg/L, respectively. In terms of the purification ability by unit fresh weight, Acorus calamus exhibited the worst purification capacity, whereas the capacities of Scirpus validus and Phragmites australis were higher. The purification capacity of Scirpus validus for the three concentrations was as follows: 0.08 mg/(L·g FW), 0.29 mg/(L·g FW), and 0.51 mg/(L·g FW). The capacity of Phragmites australis was 0.07 mg/(L·g FW), 0.25 mg/(L·g FW), and 0.53 mg/(L·g FW). The capacity of Acorus calamus was 0.04 mg/(L·g FW), 0.12 mg/(L·g FW), and 0.21 mg/(L·g FW). Under increased concentrations of nitrate, the three species of aquatic plants exhibited various degrees of increased purification capacity. Under the different concentrations of nitrate, the three species exhibited the same trends with respect to water pH and DO, increasing first and then falling. The pH remained at approximately 7.5, and the DO fell to 4.0 mg/L. A comprehensive analysis reveals that Acorus calamus provides excellent nitrate purification, although by unit fresh weight, both Scirpus validus and Phragmites australis provide superior purification capacity.

Estimating Total Nitrogen Content in Brown Soil of Orchard Based on Hyperspectrum

The best hyperspectral estimation model of soil total nitrogen (TN) was established, which provided the basis for rapid and accurate estimation of soil total nitrogen content, scientific and rational fertilization and soil informatization management. A total of 92 brown soil samples were collected from the orchard of Qixia County, Yantai City, Shandong Province. After drying and grinding, the hyperspectrum of the soil was measured in the laboratory using ASD FieldSpec3. The TN contents of brown soil were measured by Kjeldahl method. The sensitive wavelengths were selected by multiple linear stepwise regression method. The hyperspectral estimation model of TN was established by Random Forest (RF) and Support Vector Machines (SVM). The models were validated by independent samples. The best estimation model was obtained. The sensitive wavelengths were 956 nm, 995 nm, 1020 nm, 1410 nm, 1659 nm and 2020 nm. The coefficients of determination (R2) of the two estimation models were 0.8011 and 0.8283, the root mean square errors (RMSE) were 0.022 and 0.025, and relative errors (RE) were 0.1422 and 0.1639, respectively. Random Forest model and Support Vector Machines model are feasible in estimating TN contents, but the Support Vector Machines model is better.

Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index

The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.

Visualization of Chlorophyll Content Distribution in Apple Leaves Based on Hyperspectral Imaging Technology

We took distribution visualization of chlorophyll content in apple leaves to estimate the nutrient content and growth levels of apple leaves. 130 mature and non-destructive apple leaves were collected, and imaging spectroscopy data were collected by SOC710VP hyperspectral imager. The chlorophyll content of the leaves was determined on the spectral information of the leaves. After pre-processing, we took linear wavelength stepwise regression method to choose the sensitive wavelength of chlorophyll content. And then we established partial least squares, principal component analysis and stepwise regression model. Finally, the chlorophyll content distribution visualization was realized. The results showed that the sensitive wavelengths of the chlorophyll content were 712.50 nm, 509.95 nm, 561.22 nm, 840.62 nm, 696.67 nm and 987.91 nm. The R2, RMSE, RE of the optical chlorophyll content estimation model, and the principal component analysis regression model, were 0.800, 0.319 and 26.4%. The chlorophyll content of each pixel on the hyperspectral image of apple leaves was calculated by the best estimation model and we completed the visualization distribution of chlorophyll content, which provided a technical support for the rapid detection of nutrient distribution.

Prediction Model of Nitrogen Content in Apple Leaves based on Ground Imaging Spectroscopy

A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves.SOC710VP hyperspectral imager was used to obtain the imaging spectral information of apple leaves,and the average spectral curve of interest region was extracted.The study is to analyze the characteristics of imaging spectral curves of apple leaves with different nitrogen content.On the basis of the SG smoothing and first derivative pretreatment of the spectral curve,the maximum sensitive band with nitrogen content is screened and the spectral parameters are constructed.Three modeling methods of BP,SVM and RF were used to establish the prediction model of nitrogen content in apple leaves.The results showed that in the visible range,the nitrogen content of apple leaves was negatively correlated with the reflectance of the spectral curve,and was most obvious in the green range.The R2 of BP,SVM and RF of apple leaf nitrogen content prediction model were 0.7283,0.8128,0.9086,RMSE were 0.9359,0.7365,0.5368,the R2 of test model were 0.6260,0.7294,0.6512,RMSE were 0.9460,0.7350,0.9024.Comparing the prediction results of the three models,the optimal prediction model is SVM model,which can well predict the nitrogen content of apple leaves.

Inversion of Canopy Nitrogen Content in Apple Orchard Based on GF-1 Satellite Image

The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.

Nitrogen Estimation Model of Apple Leaves Based on Imaging Spectroscopy

Imaging spectrometer was used to measure the spectral data of apple leaves.The spectral reflectance of apple leaves was extracted.The nitrogen content of apple leaves was correlated with the spectral reflectance after SG smoothing first-order differential treatment.The sensitive wavelengths were selected and nitrogen content prediction models were founded.The results showed that the spectral of apple leaves with different concentration gradients were obvious.The higher nitrogen content was,the lower spectral reflectance was.Established estimation models by using the selected SG smooth first-order differential spectral sensitive wavelengths SG-FDR403,SG-FDR469,SG-FDR525,SG-FDR566,SG-FDR650,SG-FDR696,SG-FDR781,SG-FDR851,SG-FDR933.The determined coefficient(R^2)of the partial least squares model was 0.5202.The root mean square error(RMSE)of that was 2.19 and the relative error(RE)of that was 5.89%.The R^2 of the support vector machine(SVM)model was 0.724.The RMSE of that was 1.94,and the RE of that was 5.13%.It is indicated that the SVM model can estimate the nitrogen content of apple leaves effectively.

Monitoring Soil Nitrate Nitrogen Based on Hyperspectral Data in the Apple Orchards

This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835;the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.

Accumulation of 1-deoxynojirimycin in silkworm,Bombyx mori L.

1-deoxynojirimycin (1-DNJ) contents in the silkworm,Bombyx mori,at different developmental stages and tissues were investigated by using reverse-phase high-performance liquid chromatography. The 1-DNJ contents of silkworm larvae change significantly with their developmental stages. The male larvae showed higher accumulation efficiency of 1-DNJ than the females and also a significant variation was observed among the silkworm strains. The present results show that tissue distribution of 1-DNJ was significantly higher in blood,digestive juice,and alimentary canal,but no 1-DNJ was observed in the silkgland. Moreover,1-DNJ was not found in silkworms fed with artificial diet that does not contain mulberry leaf powder. This proves that silkworms obtain 1-DNJ from mulberry leaves; they could not synthesize 1-DNJ by themselves. The accumulation and excretion of 1-DNJ change periodically during the larval stage. There was no 1-DNJ in the newly-hatched larvae and 1-DNJ was mainly accumulated during the early and middle stages of every instar,while excreted at later stages of larval development. Further,it is possible to extract 1-DNJ from the larval feces and it is optimal to develop the 1-DNJ related products for diabetic auxiliary therapy.

PagGRF12a interacts with PagGIF1b to regulate secondary xylem development through modulating PagXND1a expression in Populus alba×P.glandulosa

Growth-regulating factors(GRFs)are important regulators of plant development and growth,but their possible roles in xylem development in woody plants remain unclear.Here,we report that Populus alba×Papulus glandulosa PagGRF12a negatively regulates xylem development in poplar.PagGRF12a is expressed in vascular tissues.Compared to non-transgenic control plants,transgenic poplar plants overexpressing PagGRF12a exhibited reduced xylem width and plants with repressed expression of PagGRF12a exhibited increased xylem width.Xylem NAC domain 1(XND1)encodes a NAC domain transcription factor that regulates xylem development and transcriptional analyses revealed that PagXND1a is highly upregulated in PagGRF12a-overexpressing plants and downregulated in PagGRF12a-suppressed plants,indicating that PagGRF12a may regulate xylem development through PagXND1a.Transient transcriptional assays and chromatin immunoprecipitation-polymerase chain reaction assays confirmed that PagGRF12a directly upregulates PagXND1a.In addition,PagGRF12a interacts with the GRF-Interacting Factor(GIF)PagGIF1b,and this interaction enhances the effects of PagGRF12a on PagXND1a.Our results indicate that PagGRF12a inhibits xylem development by upregulating the expression of PagXND1a.