Spectral CT imaging parameters and Ki-67 labeling index in lung adenocarcinoma

Objective: To explore the correlation between the spectral computed tomography(CT) imaging parameters and the Ki-67 labeling index in lung adenocarcinoma.Methods: Spectral CT imaging parameters [iodine concentrations of lesions(ICLs) in the arterial phase(ICLa)and venous phase(ICLv), normalized IC in the aorta(NICa/NICv), slope of the spectral HU curve(λHUa/λHUv)and monochromatic CT number enhancement on 40 keV and 70 keV images(CT40 keVa/v, CT70keVa/v)] in 34 lung adenocarcinomas were analyzed, and common molecular markers, including the Ki-67 labeling index, were detected with immunohistochemistry. Different Ki-67 labeling indexes were measured and grouped into four grades according to the number of positive-stained cells(grade 0, ≤1%;1%<grade 1≤10%;10%<grade 2≤30%;and grade 3, >30%). One-way analysis of variance(ANOVA) was used to compare the four different grades, and the Bonferroni method was used to correct the P value for multiple comparisons. A Spearman correlation analysis was performed to further research a quantitative correlation between the Ki-67 labeling index and spectral CT imaging parameters.Results: CT40keVa, CT40 keVv, CT70keVa and CT70keVv increased as the grade increased, and CT70keVa and CT70keVv were statistically significant(P<0.05). These four parameters and the Ki-67 labeling index showed a moderate positive correlation with lung adenocarcinoma nodules. ICL, NIC and λHU in the arterial and venous phases were not significantly different among the four grades.Conclusions: The spectral CT imaging parameters CT40keVa, CT40keVv, CT70keVa and CT70keVv gradually increased with Ki-67 expression and showed a moderate positive correlation with lung adenocarcinomas.Therefore, spectral CT imaging parameter-enhanced monochromatic CT numbers at 70 keV may indicate the extent of proliferation of lung adenocarcinomas.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Development of fragility curves by incorporating new spectral shape indicators and a weighted damage index:case study of steel braced frames in the city of Mashhad,Iran

In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon （ε） and the target Eta （η） values at different hazard levels is presented, taking into account appropriately selected SGMR＇s. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter r/is a more robust predictor of damage than searching for records with appropriate c values.

Evaluation of Spectral Scale Effects in Estimation of Vegetation Leaf Area Index Using Spectral Indices Methods

Spectral index methodology has been widely used in Leaf Area Index(LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers(SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition(VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index(NDVI), Modified Simple Ratio Indices(MSRI) and Triangle Vegetation Index(TVI), although the coefficient of determination R^2 was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Retrieval of Winter Wheat Canopy Carotenoid Content with Ground-and Airborne-Based Hyperspectral Data

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.

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。

无人机可见光遥感影像地物目标提取技术研究

无人机可见光遥感影像中地物目标边界清晰度较低,容易导致地物目标与背景之间的区分度降低,进而难以提取地物目标;为此,提出无人机可见光遥感影像地物目标提取方法;从光谱特征、纹理特征和边缘特征3个方面分析无人机可见光遥感影像特征;结合3种影像特征对无人机可见光遥感影像数据集实行增广处理;对完成增广后的数据集定义影像编码标签,以此确定地物目标增强权重,通过参量化处理地物目标光谱特征,计算光谱吸收指数,获取地物目标提取表达式,从而实现无人机可见光遥感影像地物目标提取;实验结果表明,所提方法能够保证地物目标边界的清晰度,具有较强的地物目标提取能力。

顾及信号振荡特征的慢滑移信息时空提取

针对传统滤波和固定函数拟合等方法探测慢滑移信号时易产生信息误剔除或伪信号提取等问题,提出一种基于信号振荡特征的慢滑移时空信息探测方法:利用多通道奇异谱分析分解坐标序列;然后根据慢滑移位移特征确定信号的起止时间;最后基于信号的振荡方向和振幅归一化明确空间响应方向和强度。仿真结果表明,相较于主成分分析和独立成分分析,该方法在探测慢滑移起止时间、空间响应方向和强度等方面优势明显:实际应用中,利用该方法成功探测出新西兰马纳瓦图的慢滑移事件,测站WANG和PNUI连线两侧的空间响应方向相反,强度大,地震危险性高;而独立成分分析和主成分分析探测到的空间响应可能受到共模误差影响,时间响应也无法明确慢滑移起止时间。

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。

基于地面高光谱遥感的大豆产量估算模型研究

为在田间管理中对作物产量进行估测,通过两年大田试验收集了大豆生殖生长期的高光谱数据及产量数据,基于各生育期一阶微分光谱反射率计算了7个光谱指数:比值指数(Ratio index, RI)、差值指数(Difference index, DI)、归一化光谱指数(Normalized difference vegetation index, NDVI)、土壤调整光谱指数(Soil-adjusted iegetation index, SAVI)、三角光谱指数(Triangular vegetation index, TVI)、改进红边归一光谱指数(Modified normalized difference index, mNDI)和改进红边比值光谱指数(Modified simple ratio, mSR),使用相关矩阵法将光谱指数与大豆产量数据进行相关性分析并提取最佳波长组合,随后将计算结果作为与大豆产量相关的最佳光谱指数,最后将各生育期筛选出的与大豆产量相关系数最高的5个光谱指数作为模型输入变量,利用支持向量机(Support vector machine, SVM)、随机森林(Random forest, RF)和反向神经网络(Back propagation neural network, BPNN)构建大豆产量估算模型并进行验证。结果表明,各生育期(全花期(R2)、全荚期(R4)和鼓粒期(R6))计算的光谱指数与产量的相关系数均高于0.6,相关性较好,其中全荚期的光谱指数FDmSR与大豆产量的相关系数最高,达到0.717;大豆产量最优估算模型的方法是输入变量为全荚期构建的一阶微分光谱指数和RF组合的建模方法,模型验证集R2为0.85,RMSE和MRE分别为272.80 kg/hm^(2)和5.12%。本研究成果可为基于高光谱遥感技术的作物产量估测提供理论依据和应用参考。

基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价

【目的】基于无人机多源影像及产量数据评价人工合成小麦种质的抗旱性,优选高通量抗旱性鉴定指标,发掘抗旱人工合成小麦种质资源,为加快拓展小麦抗旱遗传资源、提升旱地小麦育种水平提供技术支撑和种质材料。【方法】以80份人工合成小麦种质及对照小麦品种新春37为试验材料,在田间进行小区播种,设置干旱和灌溉2种水分处理;利用无人机搭载多光谱及热红外相机采集试验材料灌浆期多源影像进行拼接处理,通过阈值分割等方法提取各试验材料的光谱指数;利用相关性分析和主成分分析鉴选抗旱相关光谱指标,结合单指标及综合评价方法鉴定人工合成小麦种质的抗旱性。【结果】基于无人机多源影像数据提取了80份人工合成小麦种质的19种光谱指数。不同光谱指数抗旱系数与小区产量抗旱指数的相关性分析结果表明,OSAVI的抗旱系数与抗旱指数的关联度最高,NDVI、CIre和NDRE的抗旱系数与抗旱指数的关联度较高。部分光谱指数的抗旱系数间相关性较高,存在冗余信息,通过主成分分析,将19个光谱指数的抗旱系数转换为3个相互独立的综合指标,3个综合指标的贡献度分别为59.6%、12.0%和9.6%。利用加权隶属函数法聚合综合指标,通过公式计算获得各人工合成小麦种质的综合抗旱性度量值。基于抗旱指数鉴定出6份强抗旱人工合成小麦种质,基于综合抗旱性度量值鉴定出5份强抗旱种质,其中,SW004和SW009在2种方法的评价结果中均被评为强抗旱种质。基于OSAVI的抗旱系数对80份人工合成小麦种质进行抗旱性分级,分级结果与基于综合抗旱性度量值的分级结果基本一致。根据OSAVI的抗旱系数鉴定出的6份强抗旱种质中,有5份在基于综合抗旱性度量值分级中也被鉴定为强抗旱种质。【结论】基于无人机多源影像提取的光谱指数NDVI、OSAVI、CIre和NDRE,以及基于光谱指数的综合抗旱性度量值均可用于辅助鉴定小麦种质抗旱性。

基于光谱指数建模的沙井子灌区土壤盐分反演

为了快速准确地获取干旱地区表层土壤盐分信息,以沙井子灌区为研究区,利用地面采集的0~10 cm和10~20 cm深度的土壤盐分数据,以及同步获取的Landsat 9 OLI遥感影像上相应点位的波段反射率值,组合两波段和三波段光谱指数,建立低植被度覆盖下盐渍化监测SDI1、SDI2、SDI3、SDI4模型,并检验4类模型对不同土层深度土壤盐分的反演精度。结果表明:(1)当土层深度为0~10 cm时,4类盐渍化监测模型对土壤盐渍化等级分类精度分别为73.56%、66.35%、43.75%和74.52%;而当土层深度为10~20 cm时,相应的分类精度分别为61.06%、62.50%、66.35%和64.42%,说明灌区内土层最佳反演深度为0~10 cm。(2)三波段光谱指数构建的SDI4模型优于双波段光谱指数构建的其余3种模型,能够有效反演沙井子灌区土壤盐渍化程度。研究结果可为灌区土壤盐渍化治理和防治提供有效的技术参考。

基于GF-5卫星的西藏珠勒—芒拉地区矿物蚀变信息提取及找矿前景分析

【研究目的】近年来,遥感在地质调查和矿产勘查领域取得了广泛的应用,基于多光谱遥感数据的蚀变矿物填图为地质找矿工作提供了重要技术支撑,然而基于国产高光谱遥感数据在此领域的研究却为数不多。高分五号(GF-5)较小的波谱间隔提供了相比于多光谱更为丰富的目标地物波谱信息,为矿物的精细识别提供了良好的数据源。本文主要基于GF-5开展西藏革吉南地区的矿物蚀变信息提取,同时结合Landsat-8、ASTER多光谱数据提取结果叠加对比,综合野外调查验证,进一步深化遥感在地质矿产资源调查领域的应用。【研究方法】基于多光谱数据建立了不同类别蚀变矿物的光谱指数模型,在GF-5数据蚀变信息提取方面,摒弃了传统的光谱角匹配等方法,提出了基于决策树分类辅助混合调谐匹配滤波技术进行矿化蚀变信息的提取方法,最后综合区域构造、蚀变信息提取结果等要素,圈定成矿有利区,并开展野外调查验证。【研究结果】基于Landsat-8、ASTER两种多光谱数据对铁染、羟基类(Mg-OH、Al-OH)、碳酸盐类矿物信息进行了增强与提取;基于GF-5数据识别出了方解石、钠云母、普通白云母、多硅白云母、明矾石、高岭石、地开石、绿帘石8种蚀变矿物。【结论】结合不同数据源的提取与叠加结果,证实了本文提出的矿化蚀变信息提取方法的可行性。根据野外验证情况综合揭示了该地区发育高硫型浅成低温热液蚀变矿物组合,具有斑岩-浅成低温热液矿床的成矿潜力。本文认为高光谱与多光谱数据相结合有助于后续蚀变分带的分析与更精确的成矿预测,从而更好地服务于矿产勘查工程等领域。

应用光谱指数和机器学习反演紫丁香叶片的叶绿素面密度

为无损监测植物叶片叶绿素面密度,及时反映植物的生长状态,以内蒙古农业大学东校区紫丁香(Syringa oblata Lindl.)为研究对象,获取了160枚叶片反射率光谱(350~2500 nm)及其对应的叶绿素面密度数据,在一维和二维光谱指数的基础上引入三维光谱指数(I_(TBI)),基于原始光谱(R)、一阶微分光谱(R_(FD))和二阶微分光谱(R_(SD))构建了全波段不同维度光谱指数,经皮尔逊相关系数法(PCC)筛选出最优光谱指数,构建了海洋捕食者算法优化孪生支持向量机融合模型(MPA-TSVR),并与孪生支持向量回归机(TSVR)、偏最小二乘回归法(PLSR)、反向传播神经网络(BPNN)和支持向量回归机(SVR)模型对比分析。结果表明:不同维度下最优光谱指数与叶绿素面密度间的最大相关系数分别是I_(TBI3)(R_(SD714),R_(SD745),R_(SD700))为0.9015、I_(SRI)(R_(FD704),R_(FD738))为0.8911和R_(FD744)为0.8740。不同预处理下最优光谱指数建模精度由高到低顺序为:R_(SD)、R_(FD)、R,R_(SD)-MPA-TSVR反演效果最佳,测试集决定系数(R^(2))为0.9060,均方根误差(R_(MSE))为3.8827;不同维度下最优光谱指数建模精度由高到低顺序为:三维光谱指数、二维光谱指数、一维光谱指数,I_(TBI3)-MPA-TSVR反演效果最佳,测试集R^(2)为0.9110,R_(MSE)为3.7763,相比于I_(TBI3)-TSVR、I_(TBI3)-PLSR、I_(TBI3)-BPNN和I_(TBI3)-SVR模型更稳定。因此,I_(TBI3)-MPA-TSVR模型可为无损监测紫丁香的生长状态提供参考。

草甸草原植物beta多样性高光谱遥感估算方法

目前,全球生物多样性的状态非常堪忧,因此,利用光谱技术估算生物多样性成为了生态学家和遥感学家共同关注的热点。目前关于植物alpha多样性的研究很多,而关于beta多样性的研究较少,仍存在一些问题值得去探索。为了探究利用遥感技术估算植物beta多样性的最佳光谱指数以及最佳影像空间分辨率,以草甸草原为研究区,基于无人机高光谱遥感影像,从光谱距离、光谱角度、生物多样性概念三个方面计算了6种beta多样性估算指数(4种为我们构建的新指数,2种为已有的指数),并采用Mantel tests和相关系数筛选最佳的光谱指数。然后将筛选出来的指数应用于不同空间分辨率的影像,以期得到最佳观测尺度。另外,为了提高指数的估算能力,对比了一阶导数变换和Savitzky-Golay滤波两种光谱变换方法,以及相关系数法、连续投影法、竞争性自适应重加权法三种特征波段选择方法。结果表明,不论是采用亚尺度观测(像元大小<样方大小)还是等尺度观测(像元大小=样方大小),最佳的光谱指数均为光谱距离指数,且光谱距离指数在不同影像空间分辨率下均表现良好。在草原地区,当影像空间分辨率约为0.25 m时,该指数可以取得最佳的估算结果。经一阶导数变换并用相关系数法提取特征波段后构建的光谱距离指数与beta多样性拥有最强的相关性,今后可利用该指数构建估算模型或者直接表征beta多样性。该研究对于科学的选取光谱指数和影像空间分辨率去估算植物beta多样性具有一定的指导意义。

最优光谱特征变量反演颗粒物污染生菜的生理信息

叶类蔬菜的品质和产量与其净光合速率息息相关,秋冬季节受颗粒物污染的影响,温室大棚内叶菜的光合作用受到制约,对生理信息的准确预测不利。以采收期生菜为试验对象,研究其在颗粒物污染生长环境下,基于高光谱技术建立并对比生菜净光合速率的反演模型的最优方法。获得生菜的净光合速率和高光谱数据;选取比值植被指数(RVI)、差值植被指数(DI)、归一化植被指数(NDVI)、可视化气压阻抗指数(VARI)、再归一化植被指数(RDVI)、垂直植被指数(PVI)和植被衰减指数(PSRI)的原始和一阶变换共14个光谱植被指数;用相关矩阵法优选出最优波长位置,而非选择既有的固定波长,计算得到最优植被指数,与光谱位置变量(红边幅值,Dr)及光谱面积变量,即红边面积(SDr)、红边面积与蓝边面积的比值(SDr/SDb)、红边面积与黄边面积的比值(SDr/SDy)共同作为光谱特征变量,建立颗粒物污染环境下生菜净光合速率的反演模型。采用多项式拟合和多元散射校正(MSC)、标准正态变量变换(SNV)、偏最小二乘(PLS)和主成分回归(PCR)光谱建模组合方法进行反演预测结果比较。研究结果表明:最优波长位置的PSRI(515,499)和DI(515,499)与净光合速率的相关性最大,可以反映颗粒物污染下生菜的更多生理信息,采用光谱预处理建模组合方法按精度从高到低排序为:SG+MSC+PCR>SG+SNV+PLS>SG+SNV+PCR>SG+MSC+PLS,其中,SG+MSC+PCR组合建模方法的精度最高,决定系数为R_(c)=0.9011,R_(p)=0.9458,基于最优光谱植被指数建模效果最佳;光谱面积变量(SDr/SDb)的拟合精度最高(R^(2)=0.9365),可以实现生菜净光合速率的可靠预测,是基于光谱位置变量和面积变量建立生菜净光合速率的最优方法。该工作对颗粒物污染环境下利用高光谱技术进行植物的生理信息反演研究具有一定参考价值。

融合无人机光谱与纹理信息的玉米氮素营养估测

【目的】作物氮素营养状况是表征玉米冠层绿色程度和健康状态的关键指标,为比较玉米氮素营养估测模型中单一光谱指数模型与融合纹理信息模型精度的差异,探究基于无人机多光谱与纹理信息融合的玉米氮素营养估测模型的精准性与可靠性。【方法】采用Matrice 300 RTK多旋翼飞行器搭载MS600 Pro多光谱传感器,获取2年间6个氮素水平下玉米抽雄-吐丝期的多光谱影像数据,提取植被指数和纹理特征信息,综合分析植被指数、单纹理特征、组合纹理指数及植被指数和纹理指数融合的相关性,优选信息量最大的植被指数、归一化差值纹理指数(NDTI)及其组合信息,利用多元逐步回归(MSR)、随机森林(RF)、支持向量机(SVM)和灰狼优化的卷积神经网络(GWO-CNN)对比估测玉米叶片氮含量(LNC)、植株氮含量(PNC)、叶片氮积累(LNA)和植株氮积累(PNA)4个氮素营养参数。【结果】(1)不同氮素处理下玉米原始光谱反射率之间存在差异,红波段R(660 nm)、蓝波段B(450 nm)和近红外波段NIR(840 nm)波段差异较为显著。(2)基于无人机多光谱影像提取的植被指数(EVI、GARI、REOSAVI、SIPI和MCARI)、单纹理特征(var_(450)、var_(660)、mean_(840)、dis_(720)和hom_(840))和组合纹理指数NDTI均可用于VT-R1阶段玉米LNC、PNC、LNA及PNA估测,其中基于植被指数的GWO-CNN模型对LNC、PNC、LNA和PNA的估测效果优于单纹理特征和纹理指数模型,R^(2)分别为0.831、0.761、0.826和0.770。(3)融合植被指数和纹理指数的GWO-CNN模型对LNC、PNC、LNA和PNA估测精度明显高于植被指数和纹理指数,R^(2)分别为0.921、0.901、0.917和0.892,较单一光谱信息最优估测模型精度R ^(2)分别提高了9.77%、15.54%、9.92%和13.68%。【结论】融合多光谱的植被指数和纹理指数能够有效提高玉米氮素营养估测精度,较好地评估玉米氮素分布情况,为田块尺度下基于无人机平台的玉米氮肥精准管理提供新思路。

基于Sentinel-2影像的黄河南岸典型改良示范区土壤含盐量反演模型

土壤盐渍化严重制约农田土壤环境的循环发展,高效准确地监测土壤盐分动态变化对盐碱地改良利用具有重要意义。为及时、有效地监测盐渍化土壤含盐量,以内蒙古黄河南岸灌区的4个典型盐碱化耕地改良示范区为例,利用Sentinel-2多光谱遥感影像,同步采集示范区内表层土壤的含盐量数据,通过相关性分析筛选敏感光谱指标,基于偏最小二乘回归(PLSR)、逐步回归(SR)、岭回归(RR)3种简单机器学习模型和深度学习Transformer模型建模,最后进行精度评价并优选出最佳含盐量反演模型。结果表明:示范区土壤反射率的可见光、红边、近红外波段反射率均与土壤含盐量呈正相关,短波红外波段反射率与土壤含盐量呈负相关,引入光谱指数能够有效提升Sentinel-2遥感影像与示范区表层土壤含盐量的相关性(相关系数绝对值不小于0.32);对比不同模型发现深度学习Transformer模型优于简单机器学习模型,验证集决定系数R~2和均方根误差(RMSE)分别为0.546和2.687 g/kg;含盐量反演结果与实地结果相吻合,为更精准反演内蒙古黄河南岸灌区盐渍化程度提供了参考。