利用多时相的高光谱航空图像监测冬小麦条锈病

冬小麦发生锈病 ,叶绿素被大量破坏 ,水分蒸滕量大大增加 ,叶片细胞大小、形态、叶片结构发生了改变 ,从而改变了叶片和冠层的光学特性 ,使得遥感探测与评价成为可能。利用多时相的高光谱航空飞行图像数据 ,了解、分析和发现条锈病病害对作物光谱的影响及其光谱特征 ;设计了病害光谱指数 ,成功地监测了冬小麦条锈病病害程度与范围。对比 3个生育期的条锈病与正常生长冬小麦的PHI图像光谱及光谱特征 ,发现 :5 6 0— 6 70nm黄边、红谷波段 ,条锈病病害冬小麦的冠层反射率高于正常生长的冬小麦光谱反射率 ;近红外波段 ,条锈病病害的冠层反射率低于正常生长的冬小麦光谱反射率 ;条锈病冬小麦冠层光谱红谷吸收深度和绿峰的反射峰高度都会减小     

小麦生物量和真实叶面积指数的高光谱遥感估算模型

利用大田小麦的参数数据和冠层光谱数据,基于光谱一阶微分技术和光谱响应函数,构建等效MODIS植被指数,建立小麦生物量(本文指总干生物量,下同)和真实叶面积指数的高光谱遥感估算模型.结果表明:①小麦生物量与冠层光谱在552 nm,721 nm处呈现最显著相关关系,叶面积指数与冠层光谱的相关性在400～1100 nm范围内较显著;②红边位置与生物量的关系最为显著,相关系数R为0.818;③6种等效MODIS植被指数中,增强型植被指数对生物量最为敏感;④红边位置估算小麦总生物量的指数模型最优,决定系数R2为0.829;⑤增强型植被指数与小麦叶面积指数的指数模型拟合度最强,决定系数R2为0.94.利用实测光谱模拟MODIS等效反射率构建植被指数反演小麦参数的方法,可为利用卫星数据进行大面积、无破坏和及时获取地面植被信息研究提供重要手段.     

夏玉米倒伏模拟试验及遥感监测

针对传统的玉米倒伏人工灾害评估方法效率低、随机性大等缺点,该文提出了一种基于遥感技术的方法,设计了田间模拟试验。利用ASD光谱仪采集倒伏玉米的冠层光谱数据,并与未倒伏玉米的光谱数据进行比较。结果显示了倒伏玉米和未倒伏玉米冠层光谱之间存在一定程度的差异。利用夏玉米NDVI值的改变,采用两期遥感影像对山东淄博市桓台县夏玉米倒伏情况进行了监测。结果表明,基于遥感数据的NDVI方法在一定程度上可以有效监测玉米倒伏。     

苹果冠层/叶片高光谱数据库系统的设计与建立

设计与建立苹果冠层/叶片高光谱数据库,实现苹果冠层/叶片高光谱数据的获取、整理、存储与应用分析,可以为苹果养分含量的高光谱遥感反演提供数据服务和支持。利用ASD Field Spec 3地物光谱仪采集的苹果冠层/叶片高光谱数据,在Microsoft Visual Studio 2010开发环境下,基于C#语言与SQLServer 2008关系型数据库,采用C/S开发模式,设计与建立了苹果冠层/叶片高光谱数据库系统,完成了对高光谱数据批量录入、存储、导出与数据处理多项功能。     

基于高光谱遥感数据提取森林结构参数的研究

以位于三峡库区的龙门河森林自然保护区为研究区,综合利用线性光谱混合模型和几何光学模型,基于高光谱遥感数据提取森林结构参数是本文研究的重点。在研究区地面调查数据的基础上,通过高光谱数据和混合光谱分解法,获得反演几何光学模型所需的四分量参数,根据背景光照分量与森林植被冠层各参数间的关系,反演得到森林冠层郁闭度及平均冠幅的定量分布图,并利用37个野外实测样本进行结果验证。     

松嫩平原典型土壤高光谱定量遥感研究

为实现松嫩平原典型土壤理化参数时空信息的快速获取,为定量遥感、精准农业等相关研究服务,以松嫩平原典型土壤的高光谱反射率为研究对象,分析土壤反射光谱特征及其与土壤理化参数的关系,建立基于反射光谱指数的土壤理化参数遥感估算模型;提取黑土光谱特征点,建立黑土反射光谱曲线模拟函数.结果表明:松嫩平原不同土壤光谱特征差异主要在450-600,600-800 nm两个吸收谷部分,土壤有机质是黑土反射光谱特征的决定因素;不同于南方土壤,铁对松嫩平原典型土壤反射光谱特征的影响较小;随着含水量的增加,土壤水分对土壤光谱反射率的作用过程可以用三次方程定量描述;基于土壤反射率及反射光谱特征的土壤理化参数光谱预测模型可以用于土壤相关理化参数的快速测定;基于光谱特征点的黑土反射光谱曲线模拟函数可以准确描述黑土的反射光谱特征,这一方法可以用于高光谱数据压缩和基于多光谱数据的高光谱反射率重建.     

基于Hyperion影像的涩北气田油气信息提取

 对柴达木地区涩北气田地质地理环境下的蚀变矿物进行分析，结合卫星高光谱遥感数据Hyperion的图谱，对已知气田区与背景区光谱特征进行相关分析，确定了932.64~1 346.25 nm与2 002.06~2 385.5 nm为油气信息识别的有利波长范围； 利用光谱角制图（SAM）技术提取了涩北气田油气的空间分布信息和台吉乃尔含气构造等远景区，为高光谱遥感油气勘探提供了有效技术方法与途径。     

一种三江平原湿地的植被冠层叶绿素反演

针对三江平原洪河湿地保护区内主要特征植被冠层的叶绿素含量,采用PROSAIL模型从物理角度进行反演。首先将叶面积指数、叶片结构参数、等价水厚度、叶绿素实测含量等一些植被理化参数的实测值输入模型得到模拟光谱数据,然后与实测光谱数据对比验证其准确性。在模型中,通过固定其他参量不变,取叶绿素含量为唯一值时,考察在不同叶面积指数下叶绿素含量对冠层反射率的影响。结果显示,植被冠层叶绿素含量的敏感波段为555nm和720nm。基于PROSAIL模型的叶绿素反演方法较传统的统计模型相比是较好且稳健的方法。     

成像光谱数据在城市遥感中的应用研究

采用高空间分辨率的航片与高光谱数据对城市进行遥感研究。利用高空间分辨率数据丰富的空间信息,以及高光谱分辨率数据丰富的光谱信息,提出了基于图像边缘检测和光谱分析的新型高光谱遥感图像分类方法,对城市地物覆盖以图斑为单位进行分类。从而证明对复杂的城市环境进行遥感研究,这种方法是有效的。     

几何光学冠层反射建模中的光谱分量特征的参数化(英文)

该文用几何光学与辐射传输混合模型研究不连续植被冠层的几何光学反射模型的四分量（承照树冠、承照地面、阴影树冠、阴影地面）的参数化。用一个修正的均匀介质层路径散射（反射与传输）参数的解析算法估计路径散射参数（反射与传输），其中也考虑了冠层间隙的影响。光谱分量特征是不连续植被冠层的传输与反射，背景反照率，以直射光通量与天空漫射光通量比例的函数。光谱分量特征的模型与在美国缅因州Ｈｏｌａｎｄ采集的针叶林数据吻合。基于ＬｉＳｔｒａｈｌｅｒ几何光学相互遮蔽模型，用参数化的光谱分量特征对老松林和老云杉林的方向反射进行估计，其结果与在不同太阳与观测方向上的ＰＡＲＡＢＯＬＡ测量值匹配得很好。     

对建立遥感估产模式的几点初步认识

本文从分析遥感光谱参数的生物学意义着手，论证了正确建立遥感估产模型的可能途径。对几种有代表性的遥感估产模型作了分析，作者认为把可见光、近红外波段的遥感信息与热红外信息有机结合是解决遥感估产模型的最佳方案。对ＮＯＡＡ－ＡＶＨＲＲ的第１通道与第２通道光谱数值进行非朗伯体特性的纠正是必要的。遥感估产模型不仅可以使估产的空间尺度大大缩小而且参数数目亦可大大减小，更有利于实际运行。     

高光谱遥感及其影像空间结构特征分析

分析了高光谱遥感技术相对于传统的低光谱分辨率遥感的特点,以及其在环境监测等领域的应用。然后分析了高光谱影像的空间结构特征,并指出高光谱影像的空间结构特征在实际应用中也具有很重要的意义。最后,本文使用了统计学分析方法对实验影像的空间结构特征进行了分析,并提出了一个可用于描述高光谱影像空间特征的统计学参数。     

Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.     

高光谱--遥感测绘的新机遇

通过对高光谱遥感技术的主要优势、基本特征和地理空间信息探测潜力的分析,展示了高光谱数据在遥感测绘领域大规模应用的可能性.同时,还分析了围绕高光谱测绘应用相关的一系列问题,包括高光谱数据的测绘需求、面向测绘应用的高光谱影像分析的关键技术、高光谱遥感测绘模式以及实用化系统建立的设想等.通过对这些问题的进一步深化研究,高光谱技术就可充分与现有的遥感测绘手段有机结合,并对地理空间信息的遥感探测技术产生有力的推动.     

高光谱──遥感测绘的新机遇

通过对高光谱遥感技术的主要优势、基本特征和地理空间信息探测潜力的分析,展示了高光谱数据在遥感测绘领域大规模应用的可能性。同时,还分析了围绕高光谱测绘应用相关的一系列问题,包括高光谱数据的测绘需求、面向测绘应用的高光谱影像分析的关键技术、高光谱遥感测绘模式以及实用化系统建立的设想等。通过对这些问题的进一步深化研究,高光谱技术就可充分与现有的遥感测绘手段有机结合,并对地理空间信息的遥感探测技术产生有力的推动。     

Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

高光谱热红外遥感：现状与展望

高光谱热红外数据中蕴含着丰富的长波光谱信息,可以更精细的揭示地气耦合过程导致的辐射变化,反映热红外谱段特有的地物诊断特征,同时高光谱特性也可以为热红外关键特征参数的病态反演问题提供更合理的假设和约束条件,具有重要的研究价值和应用前景。高光谱热红外遥感技术自诞生起,在吸纳多光谱热红外遥感技术的基础上迅速发展,成为热红外遥感领域的重要研究方向和突破点。然而,当前高光谱热红外遥感存在着可用数据不足,处理方法传统,反演精度有限,应用难以有效实施等问题。为进一步明晰高光谱热红外遥感的研究进展和现存挑战,本文在高光谱热红外相关文献深入分析的基础上,梳理了高光谱热红外研究的发展脉络和热点,介绍了现有国内外主要的高光谱热红外传感器,分析了高光谱大气效应校正、地表温度和发射率分离以及地气关键特征参数一体化反演的现状和问题,总结了相关典型行业应用,展望了高光谱热红外的发展方向,以期为未来高光谱热红外研究工作的开展提供借鉴和帮助。     

天宫一号高光谱遥感场景分类数据集及应用

天宫一号高光谱成像仪具有空间分辨率高、光谱分辨率高、图谱合一等特性，在中国航天高光谱领域具有里程碑的意义。针对一般遥感场景分类数据集尺度单一、光谱分辨率较低等问题，本文提出基于天宫一号的多谱段、高空间分辨率、多时相高光谱遥感场景分类数据集(TG1HRSSC)。利用天宫一号高光谱成像仪获取的高质量数据，经过辐射校正、几何校正、空间裁剪、波段筛选、数据质量分析与控制等，制作了一批通用的航天高光谱遥感场景分类数据集，通过载人航天空间应用数据推广服务平台（http://www.msadc.cn[2019-09-10]）进行分发和共享。该数据集包括天宫一号高光谱成像仪获取的城镇、农田、林地、养殖塘、荒漠、湖泊、河流、港口、机场等9个典型地物场景的204个高光谱影像数据，其中5 m分辨率全色谱段1个波段、10 m分辨率可见近红外谱段54个有效波段以及20 m分辨率短波红外谱段52个有效波段。研究利用AlexNet、VGG-VD-16、GoogLeNet等深度学习算法网络对构建的数据集进行场景分类的试验，结果表明该数据集的场景分类应用实现较好效果。由于该数据集具备高分辨、高光谱等特征优势，未来在语义理解、多目标检测等方面有着广泛的应用价值。     

高光谱遥感及其在青藏高原的应用潜力分析

高光谱遥感以其波段数目多、光谱分辨率高、带宽窄等特点受到了国内外研究者的广泛关注,它的发展可以说是遥感技术领域的巨大进步。本文主要介绍高光谱遥感的发展,以及针对高光谱影像独有的特点所要进行的数据处理方法。在此基础上,结合青藏高原独特的地理地质环境,探讨高光谱遥感在青藏高原的应用潜力。