Estimating canopy water content using hyperspectral remote sensing data

Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow.     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

水文资料缺乏区河流流量遥感估算模型研究

针对偏远地区河流上无水文站点,缺乏河川径流量监测数据的问题,该文以黑河中游地区为例,利用2005—2010年夏、秋两季的Landsat TM/ETM+/OLI影像,提取黑河中游S213桥断面的河水边界及河流宽度,结合已有的断面流量数据,通过建立宽度-流量曲线得到河流流量估测模型,模型估测和实测径流量具有较好的一致性(R2=0.82)。该文同时对河宽提取误差进行精度验证,结果表明提取误差对模型的影响较小(R2在0.81～0.83,绝对误差<2%)。为了证明模型的外推性,该文将模型应用于S213桥断面2011—2015年和高崖断面2005—2014年的流量估测,结果表明,两个断面的模拟结果与实测数据均具有较高相关性。综上结果表明,该文的河川径流遥感估算方法具有较好的精度和外推适用性。     

遥感定量反演华北平原地区冬小麦含水量

针对现有植被水分反演算法在华北平原地区适用性差、反演精度低、不能实施有效监测的问题,该文基于地面实测冬小麦植被含水量(VWC)数据,基于归一化型和比值型植被水分指数这两种常见的指数类型,提出调节植被水分指数以削弱土壤背景的影响,使用多个波段反射率数据反演VWC,提高拟合精度80%以上,发展适用于华北平原的农作物水分含量反演模型。拟合冬小麦植被含水量的决定系数为0.51,均方根误差为0.95(kg·m^-2)。结果表明:调节植被水分指数能够削弱土壤背景影响,大幅度提高植被水分反演精度;同一种指数计算形式中,在水汽吸收谷内,基于更长波段反射率的植被水分指数反演精度更高;归一化型和比值型植被水分指数在反演精度方面无明显优劣,归一化型植被水分指数反演精度。     

Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice

The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.     

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.     

多源遥感数据支持下的城市生态状况评价

城市生态环境监测具有数据来源广泛、监测要素复杂、服务对象多样等特点,整合可用的资源进行城市生态的综合监测成为目前关注的热点。本文利用高分辨率遥感影像,以及地表覆盖、城市边界等遥感产品,在现有遥感生态指数的基础上,结合黑臭水体指数,构建适合综合评估城市生态状况的集成遥感监测模型,实现对城市生态环境状态的多维度监测。该方法能够较准确客观地反映城市的生态环境质量,理论上补充了基于遥感数据的城市生态环境监测方法,可为城市生态环境监测的信息化建设提供技术支撑。     

柑橘植株冠层氮素和光合色素含量近地遥感估测

柑橘植株营养状况的遥感监测是实现果树轻简高效管理和优质丰产的重要手段,但迄今有关基于低空遥感信息的果树营养诊断研究鲜见报道。本文采用具有490 nm、550 nm、570 nm、671 nm、680 nm、700 nm、720 nm、800 nm、840 nm、900 nm、950 nm等11个波段光谱的八旋翼飞行器(UAV)载多光谱遥感系统,获取距地面100 m高度的哈姆林甜橙植株春季冠层近地遥感信息,对比分析基于多元散射校正(MSC)和标准正态变量(SNV)两种预处理光谱和原始光谱(OS)的偏最小二乘(PLS)、多元线性回归(MLR)、主成分回归(PCR)及最小二乘支持向量机(LS-SVM)等4种模型对冠层叶片氮素、叶绿素a、叶绿素b和类胡萝卜素含量预测精度的影响。结果显示,距地面100 m高度的多光谱信息,通过SNV光谱预处理和MLR建模对冠层叶片氮素、叶绿素a和叶绿素b含量的预测效果均较好,预测集相关系数(Rp)值分别达0.8036、0.8065和0.8107,预测均方根误差(RMSEP)值分别为0.1363、0.0427和0.0243;而在SNV光谱预处理基础上的LS-SVM建模对冠层类胡萝卜素含量预测效果更优,Rp值达到了0.8535,RMSEP值为0.0117。表明利用机载多光谱图像信息可实现对柑橘植株冠层全氮及叶绿素a、叶绿素b和类胡萝卜素含量的较好估算,为大规模柑橘园植株冠层营养状况的精准和高效监测提供了一条新途径。     

Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data

The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R² = 0.87, RMSE = 0.0022 g/cm², nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.     

植被覆盖地表土壤水分遥感反演

以地域特色突出的新疆渭干河-库车河三角洲绿洲为研究区,联合使用雷达数据和光学遥感数据,对干旱区绿洲土壤和植被水分信息进行提取。在同期光学遥感影像数据提取植被归一化差分水分指数基础上,利用"水-云模型"从雷达数据总的后向散射中去除植被影响,建立土壤后向散射系数与土壤含水量的关系,相关系数为HH极化R2=0.5227,HV极化R2=0.3277。结果表明利用C波段HH极化雷达影像数据结合光学影像数据,进行干旱半干旱地区棉花、玉米等农作物种植区地表土壤水分反演时,在中等覆盖条件下去除植被影响有较好的效果。     

Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data

Abstract

Geological mapping is one of the primary tasks of remote sensing. Remote sensing applications are especially useful when extreme environmental conditions inhibit direct survey such as in Antarctica. In this investigation, a satellite-based remote sensing approach was used for mapping alteration mineral zones and lithological units using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data in the Oscar II coast area, north-eastern Graham Land, Antarctic Peninsula. Specialized band ratios and band combinations were developed using visible and near infrared, shortwave infrared (SWIR) and thermal infrared spectral bands of ASTER for detecting alteration mineral assemblages and lithological units in Antarctic environments. Constrained Energy Minimization, Orthogonal Subspace Projection and Adaptive Coherence Estimator algorithms were tested to ASTER SWIR bands for detecting sub-pixels’ abundance of spectral features related to muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite. Results indicate valuable applicability of ASTER data for Antarctic geological mapping.     

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.     

Research on the influence of site factors on the expansion of construction land in the Pearl River Delta,China: By using GIS and remote sensing

Landsat TM images of the Pearl River Delta taken in 1988, 1998 and 2006 are used to explore the site factors that influence the construction land expansion in this study. Several site factors, including landscape types and the distances to roads, coastlines, or city centers, had significant impacts on the expansion of construction land, influencing the direction, scale and intensity of the expansion. The site factors serve as important natural and spatial indicators of the preferable locales for construction land expansion, describing tendencies to expand to locations in suburbs, plains and areas near roads or coastlines.     

Sentinel-2与Landsat 8数据组合下的多特征冬小麦面积提取

遥感卫星的波段设置、信噪比及传感器观测角度等因素都会影响作物提取精度。为充分挖掘与发挥Sentinel-2卫星多光谱成像仪(MSI)与Landsat 8陆地成像仪(OLI)在冬小麦信息提取方面的优势,本文以商河县为研究区,基于两数据源的光谱特征、纹理特征、植被指数特征组合数据,利用随机森林(RF)与支持向量机(SVM)对冬小麦进行提取。结果表明:基于单一影像的最优Kappa系数与最优OA分别为0.89和95.13%,基于组合数据源的最优Kappa系数为0.92,最优OA为95.28%,两数据源组合的精度优于单一数据源提取精度;数据组合效果与分类器的性能有关,RF的Kappa系数相对于SVM分别提升0.04、0.20和0.11,OA分别提升2.41%、11.31%和6%,RF对冬小麦提取精度优于SVM。本文研究结果对于构建中高分辨率影像组合的典型农作物分类提取体系具有重要意义。     

以地块分类为核心的冬小麦种植面积遥感估算

以提高冬小麦种植面积估算精度为目标,选取种植结构复杂的都市农业区,采用QuickBird影像数字化农田地块边界,以多时相TM影像为核心数据源,以地块为基本分类单元,进行不同特征向量组合、不同分类器的冬小麦地块分类方法研究,并对比分析了基于地块分类和基于像元分类的冬小麦种植面积估算精度。研究结果表明,基于地块分类的冬小麦种植面积估算方法的总量精度和位置精度均高于像元分类;植被指数和纹理信息的引入有助于进一步提高地块分类精度;支持向量机与最大似然均能得到高达97%的总量精度和90%的位置精度,支持向量机地块分类所需的训练样本量远低于最大似然,因此支持向量机更加适合于冬小麦地块分类;冬小麦错分与漏分情况大多发生在细碎地块,其面积总量较小,而大地块错分和漏分较少,因此相对于像元分类,地块分类能在整个区域能得到较高的冬小麦位置精度和总量精度。     

陇东先秦遗址多光谱遥感与遗址预测模型研究

考古遗址预测模型(SPM)在考古学研究和文化资源管理中有着广泛的应用,但传统的构建方法过度依赖数字高程模型(DEM),对多光谱遥感数据的应用较少.本项研究以陇东黄土高原地区的区域考古调查为例,利用第三次全国文物普查数据、商业DEM数据和Landsat 8 OLI多光谱遥感数据,构建3种考古遗址预测模型(DEM模型、多光...     

多源控制遥感影像快速处理技术及应用

丰富的高精度基础地理信息的积累,为遥感影像快速正射纠正提供了控制源,本文从常用的正射影像、数字高程模型及立体基准网数据出发,设计了基于多源控制的遥感影像快速处理技术路线,并进行了试验验证,为遥感影像快速处理提供了思路。     

结合贝叶斯理论和MRF的主被动遥感数据协同分类

提出一种基于贝叶斯理论和马尔科夫随机场MRF(Markov Random Fields)的主被动遥感数据协同分类方法。该方法依据光学与微波遥感数据在地物提取中的各自优势,首先对ASAR后向散射系数进行入射角归一化,然后构建一种基于贝叶斯理论和MRF的分类器,以归一化后的ASAR双极化数据与TM7个波段共同参与分类。分别对ASAR入射角归一化的有效性和主被动协同的必要性进行验证,结果表明,采用本文方法的分类精度达到89.4%,较未进行角度校正的主被动数据协同分类的精度提高了4.1%,较单独TM分类的精度提高了11.5%,体现出主被动遥感数据协同在分类上的潜力。     

联合PROSAIL模型和植被水分指数的低矮植被含水量估算

植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义.本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了...     

遥感反演和站点观测的地气温度分布特征差异

地表温度和近地表大气温度是地球系统、大气系统以及地—气相互作用物理过程的重要参量.在陆地—大气的相互作用过程中,水汽含量、NDVI指数、下垫面变化等因素会对地—气热量传输造成一定的影响.本文首先利用地表温度产品(MYD11A1)以及气温站点数据(GSOD)获得全国尺度下地表温度年最大值、近地面气温年最大值.在此基础上,...