水体背景对湿地水生植物冠层光谱影响研究

水生植被是湿地生态系统的核心,也是影响湿地生态系统功能的最主要因素。近年来,卫星遥感技术在湿地植被资源调查、分类和保护等领域中已得到广泛的应用。由于水生植被独特的生长环境,其冠层光谱会受到水体背景要素包括大气—水界面、水中浮游生物以及泥沙含量、透明度、水体深度、底质和其他光学活性成分的影响,因此遥感技术应用于湿地水生植被冠层光谱研究时,需考虑到水生植被不同于陆生植被的生长环境,这一点在以往的相关研究中并没有得到应有的重视。以典型的挺水植物鸢尾(Iris tectorum Maxim)为研究对象,模拟湿地水生植被的生长环境,使用地物光谱仪测定了鸢尾植被冠层在不同水深梯度背景下的光谱反射率(400~2 400 nm)。实验结果表明,背景水深与鸢尾冠层反射率之间存在着显著的负相关性,其中可见光波段绝对相关系数在0.9以上,近红外波段的绝对相关系数在0.8以上。在可见光和近红外波段,随着背景水深的增加,鸢尾冠层反射率下降均比较明显。最后分别依据可见光和近红外区域相关性最高的波段(505,717,1 075和2 383 nm)建立了背景水深与冠层反射率之间的线性方程,并得出了相关参数。     

Estimation of relative water content in rice panicle based on hyperspectral vegetation indexes under water saving irrigation

Determining grain water content is a traditional method to quantify maturity, but this method is laborious. Ground-based remote sensing with rapidness and flexibility has been widely used in evaluating rice growth, but less attention has been paid to predicting physiological maturity. A field experiment with rice was conducted to investigate the effects of water saving irrigation on physiological maturity based on hyperspectral data. The results indicated that, with higher coefficients of determination (R² = 0.93 and 0.87, respectively), higher residual prediction deviation (RPD = 3.54 and 2.58, respectively) and lower root mean square error (RMSE = 2.88 and 4.43, respectively) among the tested models, R₁₆₅₄/R₆₆₂ and R₅₄₆/R₅₆₂ were suggested as the optimal indexes for monitoring relative water content in rice panicle under flooding and wetting conditions, respectively. This finding is helpful in providing reference for monitoring rice physiological maturity.     

A three-dimensional solid-liquid two-phase turbulence model with the effect of vegetation in non-orthogonal curvilinear coordinates

A three-dimensional k-ε-Ap solid-liquid two-phase two-fluid model with the effect of vegetation is solved numerically with a finite-volume method on an adaptive grid to study water-sediment movements and bed evolution in vegetated channels. The additional drag force and additional turbulence generation due to vegetation are added to the relevant control equations for simulating the interaction between vegetation and flow. The flow structure and the bed-topography changes in a 60° partly vegetated channel be...     

Spectral features and separability of alpine wetland grass species

This study aimed to analyze the spectral features of alpine wetland grasses and evaluate three types of spectral feature computational methods. In situ spectra of alpine wetland grasses were collected in the northeastern Qinghai–Tibetan Plateau (Niaodao Wetland of International Importance and Ruoergai Wetland of International Importance), and spectral features were extracted using the revised vegetation spectral feature extraction (VSFE) model, wavelet transform, and the characteristic band selection method based on trivariate mutual information. These methods were then compared for their ability to amplify spectral separability and reflect grass properties, using the Jeffries–Matusita distance and relative vegetation parameters, respectively. The results show distinct spectral features of alpine wetland grasses within the band from 717.80 nm to 1050 nm. These differences are chiefly caused by leaf area index and chlorophyll content, but not plant taxonomy. Among the three spectral feature analysis methods, the revised VSFE model provides the most information for separating different alpine wetland grasses, as well as indices reflecting chlorophyll, leaf area index, and nitrogen. However, only 70 alpine wetland grass species pairs among 138 can be clearly classified within the band from 350 nm to 1050 nm. Further study on wetland grass species classification over wider band ranges should be implemented. This study also provides suggestions for selecting a spectral feature computational method in hyperspectral data processing.     

GF-6 WFV光谱植被指数模型的叶面积指数反演

农业是国家经济发展的基础支柱,同时也是社会发展的基础产业。我国农业遥感技术的进步和发展,大量遥感卫星发射升空,如高分1号、 2号和6号等,为我国农情监测、作物长势、农业产业结构调整提供了重要技术支撑。农业遥感成为农业科技创新和精准农业的重要手段。叶面积指数(LAI)是一种可用来衡量植被冠层生理与生化的关键指标,不仅可以用来评估植被冠层表面的最初能量交换情况,提供相应的结构定量数据,还能反映植被冠层的光谱能量信息。同时,在陆地气候变化情况下,叶面积指数是陆地生态系统和土地利用过程生产力模型的关键输入。此外,研究发现植被冠层受人为活动和气候变化的直接或间接影响时,叶面积指数也是陆地生态系统应对气候变化十分重要的衡量标准。因此,针对GF-6 WFV遥感影像叶面积指数反演研究较少和传统光谱植被指数模型机理性、稳定性较弱的问题。基于GF-6 WFV遥感影像以栾城县为试验区,通过光谱植被指数与实测叶面积指数构造5种传统光谱植被指数模型和15种红边参与的光谱植被指数模型反演乳熟期叶面积指数,采用R²和RMSE进行模型评价,同时利用未参与建模的实测叶面积指数和MODIS LAI产...     

基于无人机高光谱遥感的荒漠草原覆盖度提取方法研究

植被覆盖度(fractional vegetation coverage, FVC)是草地退化评价的重要指标之一,实时、快速、准确地采集FVC是进行草地退化评价的基础。本文以无人机(unmanned aerial vehicle, UAV)高光谱遥感图像为数据源,提出了3D-ResNet18深度学习覆盖度提取方法,将此方法与回归模型法和ResNet18经典深度学习方法进行比较,并对提取精度进行验证。结果表明,提出的3D-ResNet18方法对荒漠草原FVC展现出较优的提取效果,总体估算精度达97.56%,相比较NDVI、SA-VI、G＿CR＿NDVI、G＿CR＿SAVI和ResNet18分别提高了8.32%、5.92%、2.20%、2.14%和1.87%,为荒漠草原FVC信息高精度和高效率的统计奠定基础。     

光束发散角对星载激光测高仪森林回波的影响

植被树高是生物量评估和森林生态监测的重要参数,但是大区域的植被树高数据获取困难.由大光斑星载激光测高系统森林回波可反演大区域植被树高,然而大坡度山区的植被和地面回波混叠严重,难以准确提取波形参数反演植被树高.为此建立森林目标回波的解析模型,推导出从混叠回波中分离植被和地面回波的必要条件,指出导致回波混叠的因素除了目标粗糙度和坡度,还有发射脉冲的发散角.分析了地表粗糙度和地形坡度对植被冠层回波的展宽效应及其对波形分解的影响,通过比较实测GLAS波形和仿真回波波形,验证了压缩光束发散角可降低森林回波对大坡度地形的敏感性.对于卫星轨道高度在500~600 km之间时,激光测高仪光束发散角一般在40~60μrad,更有利于森林植被树高的反演.所得结论为提高大坡度山地森林树高反演精度,从星载激光测高仪的系统设计角度提供了有意义的参考.     

基于高分一号与Radarsat-2的鄱阳湖湿地植被叶面积指数反演

叶面积指数(LAI)是衡量湿地生态系统健康状况的重要指标.根据鄱阳湖湿地植被生长密集、LAI动态范围大的特点,针对雷达数据的复杂散射机制,利用Freeman-Durden极化分解技术,定义了一种雷达植被指数,并考虑光学植被指数的饱和性,尝试将光学植被指数和雷达植被指数相结合,构建融合植被指数来估算植被LAI.通过实测数据和理论模型模拟数据与LAI的相关性分析,表明融合植被指数能有效地提高与LAI的相关性.利用融合植被指数、光学植被指数、雷达植被指数与LAI构建最佳拟合模型得出:光学微波融合植被指数能更准确地估算鄱阳湖湿地植被LAI.     

监测小麦叶片氮积累量的新高光谱特征波段及比值植被指数

实时、快速、无损监测作物氮素状况对于精确氮肥管理具有重要意义。传统的氮素估测方法在时间或空间上难以满足要求,新兴的高光谱遥感技术为作物氮素监测提供了有效手段和技术途径。本研究的目的是基于三个田间试验的系统观测资料,探索可用于小麦叶片氮素监测的新的高光谱敏感波段及比值指数。利用减量精细采样法,系统构建了350～2 500 nm范围内所有两两波段形成的比值光谱指数RSI(ratio spectral index),综合分析了小麦叶片氮积累量LNA(leaf nitrogen accumulation)(g N·m-2)与RSI的定量关系,发现了监测叶片氮积累量的新高光谱特征波段(990, 720)和光谱指数RSI(990, 720),建立了相应的监测模型y=5.095x-6.040,模型的决定系数(R2)为0.814。利用独立试验资料检验模型,决定系数(R2)为0.847,相对根均方差(RRMSE)为24.70%,表明模型预测值与观察值之间的符合度较高。因此,利用高光谱比值指数RSI(990, 720)来估算小麦叶片氮积累量是精确可行的。该结果为便携式小麦氮素监测仪的研制开发及遥感信息的快速提取提供了适用可行的波段选择与技术依据。     

Determination of Fluorine in Vegetation by Proton Activation Analysis

Abstract

Techniques have been developed for proton activation analysis using the ¹⁹F(p,p′y)¹⁹F reaction to measure the fluorine content of pulverized samples of vegetation which have been exposed to fluorides in the atmosphere or soil. The method is non-destructive and neither the chemical form of fluorine in the sample nor the type of vegetation analyzed appears to affect results. Calibration is performed by analyzing samples to which known amounts of fluorine are added. The fluorine content of 11 vegetation samples was determined by proton activation analysis and by standard chemical techniques. The values obtained by the two methods were in generally good agreement. Fluorine concentrations greater than 1 ppm can be measured with uncertainties ranging from about 50% at 5 ppm to less than 10% at concentrations above 50 ppm.