Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system...Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.展开更多
At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether la...At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content. These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488, 530, 670, 880, 1400, and 1900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%. Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the ove...Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the overlap of absorption peaks of carotenoid(C_(x+c))and chlorophyll(C_(a+b)),accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging.The objective of present study was to assess CC_(x+c)C in winter wheat(Triticum aestivum L.)with ground-and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum.In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.At the canopy level,a remarkable linear relationship(R^(2)=0.95,p<0.001)existed between C_(x+c) and Ca+b,and correlation between CC_(x+c)C and wavelengths within 400 to 1000 nm range indicated that CC_(x+c)C could be estimated using reflectance ranging from visible to near-infrared wavebands.Results of Cx+c assessment based on chlorophyll and carotenoid indices showed that red edge chlorophyll index(CI red edge)performed with the highest accuracy(R^(2)=0.77,RMSE=22.27μg/cm^(2),MAE=4.97μg/cm^(2)).Applying partial least square regression(PLSR)in CC_(x+c)C retrieval emphasized the significance of reflectance within 700 to 750 nm range in CC_(x+c)C assessment.Based on CI red edge index,use of airborne hyperspectral imagery achieved satisfactory results in mapping the spatial distribution of CC_(x+c)C.This study demonstrates that it is feasible to accurately assess CC_(x+c)C in winter wheat with red edge chlorophyll index provided that C_(x+c) correlated well with C_(a+b) at the canopy scale.it is therefore a promising method for CC_(x+c)C retrieval at regional scale from aerial hyperspectral imagery.展开更多
Fast identification of pesticide residue level in lettuce leaves plays a key role in the test of food safety.In order to identify the different concentrations pesticide residues of lettuce leaves in a fast and nondest...Fast identification of pesticide residue level in lettuce leaves plays a key role in the test of food safety.In order to identify the different concentrations pesticide residues of lettuce leaves in a fast and nondestructive way,the hyperspectra coupled with chlorophyll fluorescence spectra was used in this research.Transmission electron microscopy(TEM)was used to identify the microstructure changes of lettuce leaves under different concentrations of dimethoate residue.Besides,a method involving wavelet transform and MD-MCCV algorithm(WT-MD-MCCV)was developed for identifying the optimal wavelengths of the spectral data.The hyperspectra and chlorophyll fluorescence spectra data of 150 lettuce leaf samples at five different concentrations of pesticide residues were obtained using hyperspectral data acquisition device and Cary Eclipse Fluorescence Spectrophotometer.The combination of Savitzky-Golay(SG)algorithm and SNV algorithm(SG-SNV)preprocessing algorithms was used to preprocess the raw spectra.In addition,Principal Component Analysis(PCA),Successive Projections Algorithm(SPA)and wavelet transform coupled to MD-MCCV algorithm(WT-MD-MCCV)were applied to identify the optimal wavelengths of raw spectra including hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled with chlorophyll fluorescence spectra data.Support vector regression(SVR)was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths coupled with different spectral data.The results showed that with the increase of the concentration of dimethoate pesticide spraying,lettuce chloroplast number of osmiophilic particles increased and the starch granules decreased.Besides,the intercellular space of lettuce leaves increased gradually,with the increase of dimethoate concentration.Different concentrations of pesticide residues of lettuce in the near infrared and fluorescence spectrum have a certain difference.In addition,the related parameters of the three preferably prediction models were Rp 2=0.956 and RMSEP=0.018,Rp 2=0.937 and RMSEP=0.161,Rp 2=0.987 and RMSEP=0.005,respectively,using WT-MD-MCCV algorithm combined with hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled to chlorophyll fluorescence spectra data.WT-MD-MCCV algorithm combined with hyperspectra and chlorophyll fluorescence spectra data performed best among the nine SVR models and the hyperspectra coupled with chlorophyll fluorescence spectra can be used to identify the pesticide residue level in lettuce leaves.展开更多
高光谱遥感广泛采用无人机作为飞行平台进行数据采集,具有灵活高效的优点。受无人机性能以及环境天气的影响,采集过程中传感器难以保持固定的拍摄姿态,导致数据产生一定的错位扭曲和变形,虽然无人机载定位系统和惯性测量装置为高光谱相...高光谱遥感广泛采用无人机作为飞行平台进行数据采集,具有灵活高效的优点。受无人机性能以及环境天气的影响,采集过程中传感器难以保持固定的拍摄姿态,导致数据产生一定的错位扭曲和变形,虽然无人机载定位系统和惯性测量装置为高光谱相机提供了实时位置和姿态,但受定位测姿精度的限制往往需要采集大量地面控制点进行辅助几何校正,耗费大量时间和人力。为了高效省时消除高光谱数据在采集过程中产生的畸变,基于共线方程原理,设计了一种无人机载推扫式高光谱相机数据采集系统,集成高精度惯性测量系统,并同步采集测区激光雷达(light detection and ranging,LiDAR)点云数据,利用激光点云中蕴含的高精度地形信息进行高光谱数据几何校正,并研究不同密度点云数据对于几何校正结果的影响。实验表明,使用LiDAR点云相对于使用平均高程几何校正结果精度提升了67%,利用激光雷达和高光谱相机同步采集对于提高高光谱数据精度有着显著效果。展开更多
【目的】为了快速、准确的检测烤烟叶片生理生化指标,获取烟田施肥和烤烟生长信息。【方法】通过不同施氮、钾量试验,采用ASD Field spec FR 2500光谱仪,分析不同处理烤烟叶片光谱特性,同时运用相关分析、逐步回归分析等统计方法,研究...【目的】为了快速、准确的检测烤烟叶片生理生化指标,获取烟田施肥和烤烟生长信息。【方法】通过不同施氮、钾量试验,采用ASD Field spec FR 2500光谱仪,分析不同处理烤烟叶片光谱特性,同时运用相关分析、逐步回归分析等统计方法,研究了不同营养水平下烤烟叶片高光谱遥感特征参数与烟叶生物化学成分(总氮、叶绿素、类胡萝卜素)和生理指标(鲜叶重、干叶重、含水率)之间的关系。【结果】不同氮、钾处理间叶片光谱曲线差异较大、规律明显。叶片多种高光谱参数与10种生理生化指标之间的相关性较强,通过逐步回归方程筛选出了叶片10种生理生化指标的高光谱特征变量,其中与7种生化成分关系最密切的光谱特征变量均是红边面积与蓝边面积比值SDr/SDb,建立的回归方程的的复确定系数和回归系数相伴概率显著性都达到0.05水平,同时通过检测样本的检验,估测效果较好。【结论】因此,可利用这些变量建立的估测模型对烤烟叶片中多种生理生化指标进行快速测定,同时可根据叶片高光谱特性初步监测烟田氮钾施用量和烤烟生长状况。展开更多
基金Supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates(201310434025)the Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province(BS2013NY004)+2 种基金the Innovation Fund Designated for Post-Doctor of Shandong Province(201302023)the Big Agricultural Data Project of Shandong Agricultural University(75005)the Innovation Fund for Youths of Shandong Agricultural University(23813)~~
文摘Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.
基金Project supported by the German Federal Ministry for Research and Education, Germany (No. AZ39742)the National Natural Science Foundation of China (No. 40571066).
文摘At different times over the past 30 years in Zhejiang Province, China, the coastal tidelands have been successively enclosed and reclaimed for agricultural land use. The purpose of this work was to evaluate whether laboratory hyperspectral data might be used to estimate the physicochemical characteristics of these reclaimed saline soils. A coastal region of Shangyu City (Zhejiang Province), which was grouped into four subzones according to reclamation history, was used as the study area, and soil samples were collected in each subzone. Physicochemical analyses showed that the soils were characterized by high electrical conductivity and sand content with low organic matter; the longer the saline lands had been reclaimed, the lower were the electrical conductivity and sand content and the higher the organic matter content. These changing trends of soil chemical and physical properties were found in laboratory reflectance spectra of soil samples and their first-order derivative curves. Stepwise discriminant analysis (SDA) identified six salient spectral bands at 488, 530, 670, 880, 1400, and 1900 nm. Using derived discriminant functions for saline lands with different historical years of reclamation, classification revealed an overall accuracy from a self-test of 86.6% and from cross-validation of 89.3%. Therefore, as opposed to time-consuming field investigations, this study suggested that remotely sensed hyperspectral data could serve as a promising measure to assess the reclamation levels of coastal saline lands.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
基金supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang(Project No.GK229909299001-302)the National Natural Science Foundation of China(Project No.41901268)+1 种基金the Natural Science Foundation of Zhejiang Province(Project No.LQ19D010009)the Provincial Education Department General Scientific Research Items(Project No.Y202249845).
文摘Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the overlap of absorption peaks of carotenoid(C_(x+c))and chlorophyll(C_(a+b)),accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging.The objective of present study was to assess CC_(x+c)C in winter wheat(Triticum aestivum L.)with ground-and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum.In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.At the canopy level,a remarkable linear relationship(R^(2)=0.95,p<0.001)existed between C_(x+c) and Ca+b,and correlation between CC_(x+c)C and wavelengths within 400 to 1000 nm range indicated that CC_(x+c)C could be estimated using reflectance ranging from visible to near-infrared wavebands.Results of Cx+c assessment based on chlorophyll and carotenoid indices showed that red edge chlorophyll index(CI red edge)performed with the highest accuracy(R^(2)=0.77,RMSE=22.27μg/cm^(2),MAE=4.97μg/cm^(2)).Applying partial least square regression(PLSR)in CC_(x+c)C retrieval emphasized the significance of reflectance within 700 to 750 nm range in CC_(x+c)C assessment.Based on CI red edge index,use of airborne hyperspectral imagery achieved satisfactory results in mapping the spatial distribution of CC_(x+c)C.This study demonstrates that it is feasible to accurately assess CC_(x+c)C in winter wheat with red edge chlorophyll index provided that C_(x+c) correlated well with C_(a+b) at the canopy scale.it is therefore a promising method for CC_(x+c)C retrieval at regional scale from aerial hyperspectral imagery.
基金National Natural Science Funds Projects(31471413)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Six Talent Peaks Project in Jiangsu Province(ZBZZ-019)+1 种基金Natural Science Foundation of Jiangsu Province of China(BK20141165)the Key Laboratory of Modern Agricultural Equipment and Technology of Jiangsu University(NZ201306).
文摘Fast identification of pesticide residue level in lettuce leaves plays a key role in the test of food safety.In order to identify the different concentrations pesticide residues of lettuce leaves in a fast and nondestructive way,the hyperspectra coupled with chlorophyll fluorescence spectra was used in this research.Transmission electron microscopy(TEM)was used to identify the microstructure changes of lettuce leaves under different concentrations of dimethoate residue.Besides,a method involving wavelet transform and MD-MCCV algorithm(WT-MD-MCCV)was developed for identifying the optimal wavelengths of the spectral data.The hyperspectra and chlorophyll fluorescence spectra data of 150 lettuce leaf samples at five different concentrations of pesticide residues were obtained using hyperspectral data acquisition device and Cary Eclipse Fluorescence Spectrophotometer.The combination of Savitzky-Golay(SG)algorithm and SNV algorithm(SG-SNV)preprocessing algorithms was used to preprocess the raw spectra.In addition,Principal Component Analysis(PCA),Successive Projections Algorithm(SPA)and wavelet transform coupled to MD-MCCV algorithm(WT-MD-MCCV)were applied to identify the optimal wavelengths of raw spectra including hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled with chlorophyll fluorescence spectra data.Support vector regression(SVR)was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths coupled with different spectral data.The results showed that with the increase of the concentration of dimethoate pesticide spraying,lettuce chloroplast number of osmiophilic particles increased and the starch granules decreased.Besides,the intercellular space of lettuce leaves increased gradually,with the increase of dimethoate concentration.Different concentrations of pesticide residues of lettuce in the near infrared and fluorescence spectrum have a certain difference.In addition,the related parameters of the three preferably prediction models were Rp 2=0.956 and RMSEP=0.018,Rp 2=0.937 and RMSEP=0.161,Rp 2=0.987 and RMSEP=0.005,respectively,using WT-MD-MCCV algorithm combined with hyperspectra data,chlorophyll fluorescence spectra data and hyperspectra coupled to chlorophyll fluorescence spectra data.WT-MD-MCCV algorithm combined with hyperspectra and chlorophyll fluorescence spectra data performed best among the nine SVR models and the hyperspectra coupled with chlorophyll fluorescence spectra can be used to identify the pesticide residue level in lettuce leaves.
文摘高光谱遥感广泛采用无人机作为飞行平台进行数据采集,具有灵活高效的优点。受无人机性能以及环境天气的影响,采集过程中传感器难以保持固定的拍摄姿态,导致数据产生一定的错位扭曲和变形,虽然无人机载定位系统和惯性测量装置为高光谱相机提供了实时位置和姿态,但受定位测姿精度的限制往往需要采集大量地面控制点进行辅助几何校正,耗费大量时间和人力。为了高效省时消除高光谱数据在采集过程中产生的畸变,基于共线方程原理,设计了一种无人机载推扫式高光谱相机数据采集系统,集成高精度惯性测量系统,并同步采集测区激光雷达(light detection and ranging,LiDAR)点云数据,利用激光点云中蕴含的高精度地形信息进行高光谱数据几何校正,并研究不同密度点云数据对于几何校正结果的影响。实验表明,使用LiDAR点云相对于使用平均高程几何校正结果精度提升了67%,利用激光雷达和高光谱相机同步采集对于提高高光谱数据精度有着显著效果。
文摘【目的】为了快速、准确的检测烤烟叶片生理生化指标,获取烟田施肥和烤烟生长信息。【方法】通过不同施氮、钾量试验,采用ASD Field spec FR 2500光谱仪,分析不同处理烤烟叶片光谱特性,同时运用相关分析、逐步回归分析等统计方法,研究了不同营养水平下烤烟叶片高光谱遥感特征参数与烟叶生物化学成分(总氮、叶绿素、类胡萝卜素)和生理指标(鲜叶重、干叶重、含水率)之间的关系。【结果】不同氮、钾处理间叶片光谱曲线差异较大、规律明显。叶片多种高光谱参数与10种生理生化指标之间的相关性较强,通过逐步回归方程筛选出了叶片10种生理生化指标的高光谱特征变量,其中与7种生化成分关系最密切的光谱特征变量均是红边面积与蓝边面积比值SDr/SDb,建立的回归方程的的复确定系数和回归系数相伴概率显著性都达到0.05水平,同时通过检测样本的检验,估测效果较好。【结论】因此,可利用这些变量建立的估测模型对烤烟叶片中多种生理生化指标进行快速测定,同时可根据叶片高光谱特性初步监测烟田氮钾施用量和烤烟生长状况。
