Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Satellite Multi-Temporal Data and Cropping Pattern Approach for Green Gram Crop Management in the Lower Midland Zone IV and V in Kenya

Creation of a spectral signature reflectance data, which aids in the identification of the crops is important in determining size and location crop fields. Therefore, we developed a spectral signature reflectance for the vegetative stage of the green gram (Vigna. radiata L.) over 5 years (2020, 2018, 2017, 2015, and 2013) for agroecological zone IV and V in Kenya. The years chosen were those whose satellite resolution data was available for the vegetative stage of crop growth in the short rain season (October, November, December (OND)). We used Landsat 8 OLI satellite imagery in this study. Cropping pattern data for the study area were evaluated by calculating the Top of Atmosphere reflectance. Farms geo-referencing, along with field data collection, was undertaken to extract Top of Atmosphere reflectance for bands 2, 3, 4 and 7. We also carried a spectral similarity assessment on the various cropping patterns. The spectral reflectance ranged from 0.07696 - 0.09632, 0.07466 - 0.09467, 0.0704047 - 0.12188,0.19822 - 0.24387, 0.19269 - 0.26900, and 0.11354 - 0.20815 for bands 2, 3, 4, 5, 6, and 7 for green gram, respectively. The results showed a dissimilarity among the various cropping patterns. The lowest dissimilarity index was 0.027 for the maize (Zea mays L.) bean (Phaseolus vulgaris) versus the maize-pigeon pea (Cajanus cajan) crop, while the highest dissimilarity index was 0.443 for the maize bean versus the maize bean and cowpea cropping patterns. High crop dissimilarities experienced across the cropping pattern through these spectral reflectance values confirm that the green gram was potentially identifiable. The results can be used in crop type identification in agroecological lower midland zone IV and V for mung bean management. This study therefore suggests that use of reflectance data in remote sensing of agricultural ecosystems would aid in planning, management, and crop allocation to different ecozones.

Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding （HCGEC）, a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

Chlorophyll Content Retrieval of Rice Canopy with Multi-spectral Inversion Based on LS-SVR Algorithm

To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.

Multi-Spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

Evaluation of the impact of herbicides on maize was done through multi- spectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

Walsh Spectral Characteristics and the Auto-Correlation Function Characteristics of Forming Orthomorphic Permutations of Multi-Output Functions

Orthomorphic permutations have good characteristics in cryptosystems. In this paper, by using of knowledge about relation between orthomorphic permutations and multi-output functions, and conceptions of the generalized Walsh spectrum of multi-output functions and the auto-correlation function of multi-output functions to investigate the Walsh spectral characteristics and the auto-correlation function characteristics of orthormophic permutations, several results are obtained.

Predefined Exponential Basis Set for Half-Bounded Multi Domain Spectral Method

A non-orthogonal predefined exponential basis set is used to handle half-bounded domains in multi domain spectral method (MDSM). This approach works extremely well for real-valued semi-infinite differential problems. It spans simultaneously wide range of exponential decay rates with multi scaling and does not suffer from zero crossing. These two conditions are necessary for many physical problems. For comparison, the method is used to solve different problems and compared with analytical and published results. The comparison exhibits the strengths and accuracy of the presented basis set.

New Abnormal Cervical Cell Detection Method of Multi-Spectral Pap Smears

Considering the problem of traditional cervical cancer detection method that brings high false negative rate （FNR） and high false positive rate （FPR）, a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis, on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis （CCA） algorithm. Combined with the characteristic of each cell＇s area, final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.

Development of Multi-Wavelength Spectral-Correction Method

This investigation developed a new method for determining metal complex's property containing the stepwise real absorptivity(epsilon) and stability constant (K-m). The correction equation of the multi-wavelength spectral absorption was established for the simultaneous determination of various complexes to give high accuracy for trace analysis. This method was more acceptable in theory and simpler in operation than the classical methods.

Spectral Correction Method of Multi-Channels Near-Infrared Spectrometer and Applications

Near-infrared (NIR) spectrometer based on semiconductor lasers can combine light source and splitter into one, which is an important direction for development of miniature instruments. In order to avoid random interference caused by inconsistency between light sources, the novel evaluation indicators for global stability of multi-channels spectral system were proposed based on the correlation between dynamic deviation spectra of any two channels. The NIR analysis of moisture for corn powder samples based on the partial least squares combined with Savitzky-Golay (SG) smoothing was taken as an example, and a spectral correction method for enhancing prediction performance of multi-channels spectral system was further provided using above evaluation indicators. The experiment results showed that the global stability evaluation indicators significantly increased after SG smoothing correction. Meanwhile, the root-mean-square errors of prediction for corn moisture reduced from 0.373 to 0.283 (%), and the correlation coefficient between predicted and actual values was improved from 0.702 to 0.855. The above results indicated that by improving global stability indicators, the prediction ability of multi-channels spectral system can be improved. The proposed method provided a valuable reference for designing multi-channels diminutive spectrometer with high prediction performance, which had significance for large-scale application of NIR technology.

Solving a Nonlinear Multi-Order Fractional Differential Equation Using Legendre Pseudo-Spectral Method

In this paper, we apply the Legendre spectral-collocation method to obtain approximate solutions of nonlinear multi-order fractional differential equations (M-FDEs). The fractional derivative is described in the Caputo sense. The study is conducted through illustrative example to demonstrate the validity and applicability of the presented method. The results reveal that the proposed method is very effective and simple. Moreover, only a small number of shifted Legendre polynomials are needed to obtain a satisfactory result.

融合无人机光谱信息与纹理特征的大豆土壤含水率估测模型研究

及时获取大田作物根区土壤含水率(Soil moisture content,SMC)对于实现精准灌溉至关重要。本研究采用无人机多光谱技术,通过连续2年(2021—2022年)田间试验,采集了大豆开花期不同土壤深度的SMC数据以及相应的无人机多光谱图像,建立了与作物参数具有较强相关性的植被指数及冠层纹理特征。通过分析植被指数和纹理特征与各深度土层SMC的相关性,分别筛选出与各深度土层SMC相关系数达显著相关(P<0.05)的参数作为模型的输入变量(组合1:植被指数;组合2:纹理特征;组合3:植被指数结合纹理特征),分别利用支持向量机(Support vector machine,SVM)、梯度提升模型(Extreme gradient boosting,XGBoost)和梯度提升决策树(Gradient boosting decision tree,GDBT)对各深度土层SMC进行建模。结果表明,与20~40 cm和40~60 cm土层深度相比,植被指数和纹理特征在0~20 cm土层深度中与SMC表现出更高的相关性。XGBoost模型为SMC估算的最佳建模方法,特别是对于0~20 cm土层深度。该深度估计模型验证集决定系数为0.881,均方根误差为0.7%,平均相对误差为3.758%。本研究结果为大豆根区SMC无人机多光谱监测提供了基础,为水分胁迫条件下作物生长的快速评估提供了参考。

联合运用多光谱和激光雷达技术构建的林分生物量估算模型

以广东省广州市从化石门国家森林公园为研究区域,选择4种不同林分类型(针阔混交林、阔叶林、针叶林、竹林),各林分类型选择3个20 m×20 m地块作为样方;结合激光雷达、多光谱图像、实测数据,构建多元非线性反演模型和多元线性回归模型,估算森林地上生物量,并选择最佳模型进行精度评价。结果表明:(1)依据多源数据建立的4种不同林分类型的多元非线性地上生物量反演模型的精度最高,针阔混交林样地地上生物量预测值为42.79 t·hm^(-2)、阔叶林样地地上生物量预测值为60.46 t·hm^(-2)、针叶林样地地上生物量预测值为32.99 t·hm^(-2)、竹林样地地上生物量预测值为1.92 t·hm^(-2)。(2)研究区中4种不同林分类型的多元非线性地上生物量反演模型的拟合精度,由大到小依次为竹林(决定系数为0.919)、阔叶林(决定系数为0.813)、针叶林(决定系数为0.786)、针阔混交林(决定系数为0.713),均符合精度要求。采用多光谱和激光雷达数据结合的方式,能够较精准地提取林分地上生物量信息,可准确估算针阔混交林、阔叶林、针叶林、竹林的地上生物量。

基于Sentinel-2多光谱遥感影像的小浪底水质反演

多光谱遥感技术可根据遥感波段信息反演水质参数,降低监测成本,提高监测速度和质量,为大范围水环境监测提供了一种新的方法。通过分析小浪底水库的Sentinel-2多光谱影像以及采样点实测水质数据,建立了最佳光谱波段的水质参数反演模型,对小浪底水库的化学需氧量(COD)、总磷(TP)、总氮(TN)和氨氮(NH_3-N)进行了遥感反演,验证了反演模型的精确度和稳定性,并反演了各水质参数的空间分布规律。结果表明:在4种水质参数反演模型中,COD模型精确度和稳定性最高,其次是TP、TN,最低的是NH_3-N,水库出水口和部分边缘COD质量浓度较高,水库中心TN、TP和NH_3-N质量浓度高于边缘处。

航拍多光谱田间秸秆覆盖量反演模型的建立与优化

保护性耕作是农业耕地可持续性发展的重要方法,已被世界多地采用,秸秆覆盖量实现从“有无”到“多少”的进一步判定,是秸秆还田检测的重要指标。通过无人机搭载多光谱相机航拍研究区内春秋两季遥感数据,并同步测定玉米秸秆覆盖量。首先,通过遥感数据提取光谱反射率并构建光谱指数,采用相关系数法筛选出对秸秆覆盖量敏感的波段变量和光谱变量,作为模型输入变量;然后,采用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、BP神经网络(Back Propagation Neural Network,BPNN)和极限学习机(Extreme Learning Machine,ELM)4种机器学习算法,建立玉米秸秆覆盖量的反演模型,比较不同时期和不同研究区域的模型精度;最后,为解决预测性能受其模型参数影响较大问题,引入遗传算法(Genetic Algorithm,GA)和粒子群算法(Particle Swarm Optimization,PSO),并提出遗传-粒子群混合算法(Genetic-Particle Swarm Optimization,GA-PSO),利用它们的互补性提高模型的性能,完成区域内秸秆覆盖量的估算。实验结果表明,基于GA-PSO优化的RF算法玉米秸秆覆盖量反演模型取得了最佳的反演效果,其中R^(2)达到了0.74。同时,对比分析不同数据的反演结果,均较为真实地反映了区域内秸秆覆盖量,估测准确率达到91.36%,说明可以通过优化模型实现结果估算。研究为保护性耕作秸秆还田量检测提供科学参考,亦为其他作物秸秆覆盖量估测提供了可靠的模型反演方法。

术前能谱CT多参数成像对晚期肺癌患者淋巴结转移的预测价值

目的探讨晚期肺癌患者术前采用能谱CT多参数成像检查对淋巴结转移的预测价值。方法回顾性收集2023年1—6月在医院经手术病理检查诊断为晚期肺癌的60例患者的临床资料,所有患者术前均接受能谱CT多参数成像检查,以手术病理学检查结果为金标准,绘制受试者工作特征(ROC)曲线分析能谱CT多参数成像参数预测晚期肺癌患者淋巴结转移的价值。结果经手术病理检查确诊,60例晚期肺癌患者中淋巴结转移患者19例,非淋巴结转移患者41例;淋巴结转移患者淋巴结能谱曲线斜率(λ_(HU))、淋巴结λ_(HU)/原发病灶λ_(HU)、淋巴结标准化碘密度(NIC)、淋巴结NIC/原发病灶NIC、淋巴结Neff-Z/原发病灶Neff-Z参数低于非淋巴结转移患者,原发病灶λ_(HU)、原发病灶NIC参数高于非淋巴结转移患者(P<0.05);绘制ROC曲线结果显示,淋巴结λ_(HU)/原发病灶λ_(HU)、淋巴结NIC/原发病灶NIC、淋巴结Neff-Z/原发病灶Neff-Z参数评估晚期肺癌患者淋巴结转移的曲线下面积均>0.7,具有一定预测价值。结论术前应用能谱CT多参数成像有助于诊断晚期肺癌患者是否存在淋巴结转移,且晚期肺癌患者术前采用能谱CT多参数成像检查对淋巴结转移具有一定的预测价值。

基于无人机多光谱的棉花多生育期叶面积指数反演

【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数(NDVI)、绿度归一化差植被指数(GNDVI)、归一化差红边指数(NDRE)、叶片叶绿素指数(LCI)、优化的土壤调节植被指数(OSAVI)5种多光谱指数和修正红绿植被指数(MGRVI)、红绿植被指数(GRVI)、绿叶指数(GLA)、超红指数(EXR)、大气阻抗植被指数(VARI)5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合,结合打孔法获取地面LAI实测数据,使用机器学习算法中偏最小二乘(PLSR)、岭回归(RR)、随机森林(RF)、支持向量机(SVM)、神经网络(BP)构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势,现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧(P<0.05);选择的指数在各时期彼此间均呈显著相关(P<0.05),总体而言,多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势,选择的指数在各时期均与棉花LAI相关性显著(P<0.05),多光谱指数相关系数介于0.35—0.85,颜色指数相关系数介于0.49—0.71,相关系数绝对值较大的指数多为多光谱指数,颜色指数与棉花LAI的相关系数绝对值较小;估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数,且各指数模型预测性能随着生育期的变化呈现一定规律性,NDVI是预测棉花LAI的最优指数。从模型结果上看,RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高,最优模型验证集R2为0.809,MAE为0.288,NRMSE为0.120。多生育期最优模型验证集R2为0.386,MAE为0.700,NRMSE为0.198。【结论】棉花内外侧LAI在现蕾期、初花期、结铃期存在显著差异。在各生育期中,RF和BP模型是预测棉花LAI较优模型。NDVI在各指数中表现最好,是预测棉花LAI的最优指数。多生育期模型效果较单生育期明显下降,最优指数为GNDVI,最优模型为BP。本研究中预测棉花LAI的最优窗口期是初花期。研究结果可为无人机遥感监测棉花LAI提供理论依据和技术支持。

基于采样点光谱信息窗口尺度优化的土壤含水率无人机多光谱遥感反演

针对空间异质性导致的土壤含水率反演误差较大的问题,分别以玉米灌浆期和小麦苗期的土壤含水率反演为例,利用无人机多光谱遥感技术获取喷灌和畦灌灌溉方式下的正射影像。将34组光谱特征变量按照滑动窗口法提取不同空间尺度的光谱信息平均值,通过极端梯度提升(Extreme gradient boosting, XGBoost)、支持向量机回归(Support vector machine regression, SVR)以及偏最小二乘回归(Partial least squares regression, PLSR)3种机器学习模型确定采样点光谱信息最优窗口尺度;然后,采用皮尔逊相关系数特征变量筛选法(Pearson correlation coefficient feature variable screening method, R)结合XGBoost和SVR模型对提取的34组光谱特征变量进行筛选,选取与土壤含水率敏感的特征变量;最后,估算土壤含水率。结果表明:喷灌方式下所选择的采样点最优光谱信息窗口尺度比畦灌小,其最优窗口尺度范围分别为11×11~21×21和15×15~29×29;采用皮尔逊相关系数特征变量筛选方法结合机器学习模型可有效提高土壤含水率反演精度;5种机器学习模型(R_XGBoost、R_SVR、XGBoost、SVR、PLSR)中R_XGBoost模型估算土壤含水率精度最优,在喷灌和畦灌方式下玉米灌浆期R_XGBoost模型的测试集决定系数R2分别为0.80、0.83,均方根误差(Root mean square error, RMSE)分别为1.27%和0.98%,小麦苗期R2分别为0.76、0.79,RMSE分别为1.68%和0.85%;土壤含水率反演模型在畦灌条件下的精度优于喷灌条件下。该研究可为基于无人机多光谱影像分析的信息挖掘和土壤水分监测提供参考。

基于波谱响应特征的雄安新区农田土壤重金属含量反演

分析并监测雄安新区农田土壤污染状况,对于保障粮食安全、建设绿色雄安具有重要意义。该研究以雄安新区为研究区,基于多源遥感数据珠海一号(Zhuhai-1)OHS数据、哨兵二号(Sentinel-2)L2A级数据的波谱响应特征及实地测得的农田土壤重金属含量数据,在对土壤重金属含量单因子与多因子污染评价的基础上,筛选出3种超标的农田重金属元素铅(Pb)、铜(Cu)、锌(Zn),采用偏最小二乘回归方法(partial least squares regression,PLSR)建立农田土壤重金属含量反演模型。利用Zhuhai-1提取土壤样本点的原始光谱反射率以及4种变换后的光谱反射率,Sentinel-2提取7种对重金属胁迫敏感的植被指数,将其与3种土壤重金属含量作相关性分析,筛选出敏感波段与植被指数,即波谱响应特征,构建土壤重金属含量反演模型。结果表明,3种模型整体反演精度较为优良,Pb含量反演模型决定系数(determination coefficient,R^(2))为0.490,均方根误差(root mean squared error,RMSE)为4.66 mg/kg,平均绝对值误差(mean absolute error,MAE)为1.92 mg/kg;Cu含量反演模型R^(2)为0.491,RMSE为16.85 mg/kg,MAE为3.69 mg/kg;Zn含量反演模型R^(2)为0.664,RMSE为20.63 mg/kg,MAE为9.36 mg/kg。将该模型应用于雄安新区农田区域,得到大部分农田土壤中Pb含量均未超过风险筛选标准,在研究区西南部、西部部分区域Cu含量超过土壤污染风险筛选值,同时在研究区西部、西南部Zn污染较严重,雄安新区东南部部分农田有Zn零星分布,其他区域Cu和Zn含量未超过国家土壤污染风险管控值。因此,利用多源遥感数据波谱响应特征反演土壤重金属Pb、Cu和Zn含量,能够快速准确地实现对雄安新区土壤重金属污染情况的调查,同时为大面积土壤重金属含量监测提供解决方案。

基于无人机多光谱植被指数的生菜全氮含量预测

我国露地蔬菜种植规模庞大,生产方式高度集约化,但过量施肥等导致的水氮利用效率低下的问题较为严重。为实现露地蔬菜规模化种植中精准施肥、高效生产的目标,以露地生菜为研究对象,设无氮(N0)、低氮(N1)、高氮(N2)三个处理,通过无人机搭载多光谱相机,建立3种多光谱植被指数(NDVI、RVI和SAVI)与生菜叶绿素、生物量、吸氮量、全氮含量数据集,并构建单生育期和多生育期氮素诊断模型。结果表明:(1)在莲座期和结球期,生菜各处理NDVI、RVI和SAVI值表现出随施氮量的增加而增大,但在收获期,N1处理达到最大值。(2)在生菜结球期,NDVI与生菜的产量、吸氮量、叶绿素均存在显著相关性,其中生菜全氮含量与叶绿素在p≤0.01水平下显著相关,相关系数(R)为0.51;综合生菜多生育期,NDVI值与生菜的产量、叶绿素、吸氮量和全氮含量均在p≤0.001水平下达到极显著相关,相关系数分别为0.85、0.82、0.81和0.71。(3)通过相应数据集拟合出指数、线性、对数和幂函数4种模型关系,建立生菜多生育期植株全氮最佳预测模型:全氮=16.52ln(NDVI)+73.514;应用生菜全氮估层模型反演基地生产田块,其平均相对误差为3.22%、RMSE=0.5566、NRMSE=0.0108,说明模型估算效果均较好,通过无人机多光谱遥感对蔬菜氮素诊断具有一定的可行性。