GLIBERTY-DSAIL耦合模型反演南方混交林植被LAI

针对南方丘陵地区针叶-阔叶混交林植被叶面积指数（leaf area index,LAI）反演精度低且研究较少的问题,本文提出了一种GLIBERTY-DSAIL耦合模型组合多元线性回归反演LAI的方法。本研究以GLIBERTY-DSAIL模型模拟光谱和植被实测高光谱为数据源,通过相关性分析,选取与LAI相关性高的植被指数作为反演因子,构建多元线性回归模型定量反演植被LAI并进行精度评定。结果表明：与LAI显著相关的RVI、DVI、GNDVI、MSAVI这4种植被指数作为反演因子,结合本文提出的组合模型反演LAI,模型预测决定系数R²为0.708 6,均方根误差RMSE为0.302 1,精度整体较高。该组合方法可较好地用于反演针叶-阔叶混交林植被LAI,为南方地区混交林LAI的研究提供新思路。     

基于森林模型参数先验知识估算高分辨率叶面积指数

目前,估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型,再用统计模型估算LAI。然而,与农田地面测量实验相比,森林地面测量实验获取的观测数据更加有限,这使得基于统计模型的森林高分辨率LAI的估算精度低,难以满足应用需求。为此,本文提出一种基于森林模型参数先验知识、使用森林研究区少量的LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAI的方法。首先,获取全球20个森林实验区的LAI地面测量数据和NDVI数据,建立LAI-NDVI统计模型并提取森林模型参数的先验知识。然后,以一个新的森林站点Concepción作为研究区,将该研究区的数据分为建模数据和验证数据两个部分。使用研究区有限的建模数据对森林模型参数先验知识进行本地化校正得到优化模型,优化模型用于估算森林高分辨率LAI,使用验证数据评价LAI的估算精度。同时,选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAI验证基于森林模型参数先验知识估算高分辨率LAI的结果精度,经验证4个森林站点的均方根误差分别为0.6680,0.4449,0.2863,0.5755。研究结果表明：在仅有少量观测数据时,采用本方法能有效地提高森林高分辨率LAI的估算精度。因此,本方法可为森林高分辨率LAI的遥感估算提供参考。     

基于真实白杨林地场景冠层辐射特性的计算机模拟

叶面积指数（LAI）和叶倾角分布（LAD）是决定植被冠层结构的重要参数。在计算机模拟植被冠层,两个参数是植被三维真实结构生成的重要控制因子。本论文中,结合计算机图像学理论,基于实验的地面实测结构参数数据利用可改写的扩展L-system方法生成草以及白杨树的真实三维场景。RGM（A radiosity-graphics combined model）模型是基于辐射度方法的计算机模拟模型,利用此模型来计算生成的三维场景可见光及近红外波段的冠层辐射特性,如冠层波谱以及方向反射特性等。在本研究中,模拟了两种不同下垫面的白杨林地：（1）下垫面只有土壤的白杨树场景;（2）下垫面包括土壤和草的白杨树。在特定的场景组分光学特性下,模拟得到两种情况的主平面冠层BRF（bi-di-rectional reflectance factor）,并对两者的差异进行了分析。可以看出,下垫面对冠层BRF的影响不可忽视。但是,由于白杨林地结构的复杂,大尺度的场景中必须由成千上万个面元组成,因此辐射度方法不能模拟大尺度的真实结构场景。为了拓展辐射度方法应用范围,根据白杨树树冠的特点,将其抽象为椭球体,从而减少场景组成面元个数,满足了辐射度方法的要求。并结合几何光学模型的思想,在对椭球体面元赋值加入了间隙率;并考虑了整个树冠的承照面以及阴影面的差异,模拟大尺度林地冠层BRF,且与GOMS模型结果符合的很好。通过以上研究,可以看出计算机模拟为遥感研究获取多角度数据信息提供了一种很好的手段。     

基于多角度遥感物理模型的叶面积指数反演

与传统遥感观测相比,多角度对地观测通过对林木多个方向的观察,可得到丰富的森林三维空结构信息,为定量遥感提供新的途径。本文根据不同遥感数据选择合适的模型并建立相应的查找表。TM/ETM+数据采用混合像元分解模型,CHRIS数据考虑不同森林场景选择不同的遥感物理模型,在选择合适的模型基础上,根据模型的不同敏感参数和试验区选择的特点设计查找表参数,并由模型正演建立查找表,再根据插值的方法由遥感图像的反射率值反演LAI。两种数据反演得到的LAI与MODIS 15A2 LAI产品比较分析。结果表明多角度遥感反演准确率有一定的提高,具有一定的理论研究和实际应用价值。     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。     

贝叶斯网络支持的地表参数混合反演模式研究

基于贝叶斯网络理论，建立用于植被地表参数估计的混合反演模式，结合遥感物理模型实现了冬小麦叶片叶绿素含量（Cab）和冠层叶面积指数（LAI）的反演。用模型模拟数据以及2001年顺义遥感实验数据验证结果表明，LAI和Cab均有较好的反演精度。针对含噪声模拟数据反演结果中约有10％的噪声数据反演失败的情况，用不确定知识的处理方法有效地降低了失败点的比例。混合反演模式本质上是一个融合先验知识与观测数据的知识推理方案，本文实现了对反演过程中参数后验概率更新算法并引入热力学中的信息熵概念实现了参数后验信息动态定量计算，同时简单探讨了现阶段定量评价遥感反演过程中信息流控制存在的难点问题。     

地基激光雷达的玉兰林冠层叶面积密度反演

叶面积密度LAD(Leaf Area Density)是表征冠层内部叶面积垂直分布的重要参数,其分布廓线的准确反演对研究植被碳氮循环、初级生产力和生物量估算等具有重要意义。本文在电子科技大学校内建立实验样区,利用地基激光雷达Leica Scan Station C10和数码相机获取玉兰林高分辨率3维激光点云数据和真彩色影像。利用监督分类将真彩色影像中枝干等非光合组织与叶片分离,再将像素分类信息映射给点云数据,从而提取叶片点云。通过点云数据体元化,并引入2维凸包算法确定垂直方向分层树冠边界,获取激光接触冠层的频率;随机选择不同高度的多个叶片,利用特征值法进行叶片平面拟合,估算出叶倾角,并结合天顶角估算叶倾角校正因子;最后基于体元的冠层分析VCP(Voxel-based Canopy Profiling)方法实现树林冠层LAD反演。结果表明体元化的叶片点云数据能准确确定树林冠层边界和统计接触频率实现LAD反演;反演的LAD变化走势与区域林木冠层叶片垂直分布相吻合,在冠层中下部随着高度的增加叶面积密度也随之增加,在4 m高度处达到最大值1 m2/m3,之后随着高度的增加叶面积密度逐渐降低。根据LAD计算得到的累积叶面积指数LAI为3.20 m2/m2,与LAI-2200实测的叶面积指数相比,相对误差为1.26%。     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.     

作物LAI的遥感尺度效应与误差分析

以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。     

中国南方森林冠顶高度Lidar反演—以江西省为例

激光雷达(Lidar)与光学遥感的有效结合对中国南方区域森林冠顶高度反演意义重大,而国产卫星将为中国森林生态研究提供新的数据源。本文联合利用大脚印激光雷达GLA和国产MERSI数据,在实现GLAS波形数据处理和不同地形条件下森林冠顶高度反演算法基础上,建立了区域尺度不同森林类型林分冠顶高度GLAS+MERSI联合反演关系模型,进行了江西地区森林冠顶高度反演。总体上,GLAS激光雷达森林冠顶高度估算精度较高;且在与MERSI 250 m数据的联合反演模型中,针叶林模型精度较好(R²=0.7325);阔叶林次之(R²=0.6095);混交林较差(R²=0.4068)。分析发现,考虑了光学遥感生物物理参数的GLAS+MERSI联合关系模型在区域森林冠顶高度估算中有较高精度,且在空间分布上与土地覆盖数据分布特征非常一致。     

遥感模型多参数反演相互影响机理的研究

遥感数据具有覆盖范围广、时间与空间分辨率高的特点,被广泛应用于提取区域范围内的一些重要的生物物理参数.为提高参数的提取精度,需要制定正确的反演策略.了解影响参数提取精度的因素、反演过程中各反演参数之间如何相互作用是制定合理反演策略的关键.本文通过数学推导与物理机理的分析,证明了影响参数反演精度的因素不但有冠层反射率数据的质量,还有反演过程中参与反演的未知参数的个数、参与反演的每个参数的敏感性及各个参数敏感性之间的相关性.最后通过对反演不同参数个数、不同数据质量进行了叶面积指数反演的精度分析,验证影响参数反演精度的各个因素.     

Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions

Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (L_e) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–L_e regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory.     

基于神经网络方法的芦苇叶面积指数遥感反演

提出了一种从TM图像上获取芦苇冠层叶面积指数的方法:首先对芦苇的生长背景进行分类;然后,对不同的背景光谱利用冠层反射率(FCR)模型计算得到查找表;最后,利用实测数据和查找表中的数据作为参数进行BP神经网络模型训练,从而得到芦苇冠层LAI。结果表明,人工神经网络方法有很强的非线性拟合能力,能够消除背景对反演结果的影响,有效提高LAI反演的精度。     

叶片非朗伯特性影响冠层辐射分布的辐射度模型模拟与分析

在大量的植被冠层遥感模型中,辐射度模型作为一种计算机模拟模型具有很多优点,它对理解植被—辐射相互作用过程和研究植被冠层辐射机理具有重要的理论价值。传统的辐射度模型假设冠层内叶片为朗伯体。但随着对叶片特性的深入研究发现,叶片的反射特性不能单纯的看成是理想漫反射过程。为了消除传统辐射度模型的朗伯假设,完善辐射度模型,本文利用Phong光照模型模拟叶片表面非朗伯(镜面反射)部分的分布特性,在基于真实结构冠层场景的辐射度模型(RGM)的基础上,增加了叶片镜面反射分量的计算。文中以玉米冠层为例,比较并分析了叶片的镜面反射分量对冠层辐射分布的影响。最后,从辐射度模型原理出发,进一步推导得出了计算冠层镜面反射分量的辐射度公式,从理论上证明了在可见光—近红外区的冠层镜面反射分量与波长无关。本文使用的方法具有简单、易于实现的特点。经过扩展后的辐射度模型既保留了传统辐射度模型的优点,又增加了该模型的功能和模拟范围。     

New Triangle Vegetation Indices for Estimating Leaf Area Index on Maize

Vegetation index-based methods have been widely used to determine the leaf area index (LAI). Nevertheless, under the high canopy coverage, the estimation ability of current inversion models has been profoundly decreased, due to the “saturation” phenomenon. In this study, the LAI of maize was investigated under various growth conditions. Two new triangular vegetation indices were proposed to improve the inversion ability and estimation accuracy of LAI on maize. The triangle difference vegetation index (TDVI) and triangle ratio vegetation index (TRVI) were constructed, and their accuracies were compared with the present spectral vegetation index models. The result shows that TDVI and TRVI are highly linearly correlated with LAI. The coefficients of determination (R²) and root-mean-square errors are, respectively, 0.92 and 0.94, and 1.42 and 0.92 using the simulated data, while they are, respectively, 0.83 and 0.77, and 0.98 and 1.05 using the measured data. In comparison with other vegetation indices (e.g. MSR, MTVI2, RTVI), TDVI is better able to estimate the LAI of maize. Conversely, TRVI has better inversion ability when the LAI is more than 3. Overall, TDVI is an accurate and robust approach for estimating the LAI of maize. The proposed TDVI and TRVI can be jointly used to retrieve LAI at various canopy coverages.     

利用IDL与VC++联合构建森林叶面积指数反演系统

以CHRIS多角度数据为数据源,采用IDL语言,运用控件和类技术实现系统框架,设计模型对象管理器组织模型对象,设计数据的各项处理技术,完成叶面积指数(LAI)物理模型的参数反演与经验模型反演技术,实现近真实森林三维场景的显示。结果表明,利用IDL处理多角度遥感数据是一种可行的技术方案,并实现森林叶面积指数信息自动化提取与浏览。     

地面LiDAR数据模拟及单木LAI反演

地面激光雷达Li DAR可以快速获取高精度、高密度的点云数据,在植被结构参数获取方面的应用越来越广泛。为了定量分析地面激光雷达点云数据获取单木结构参数的能力和精度,本文提出利用光线跟踪结合植被真实结构模拟地面3维激光扫描仪的单木点云数据(以RIEGL VZ-1000为例),并结合间隙率模型反演单木叶面积指数LAI。在点云模拟过程中,充分考虑了脉冲特性,包括光斑大小、波束发射角以及回波探测强度。重点分析了光斑大小和最小探测强度对LAI反演的影响,并采用根河实测单木数据进行了验证。结果表明,光斑大小和最小探测强度的设定对于LAI反演结果存在很大影响,该结论对于提高地面激光雷达点云数据反演植被结构参数精度具有重要意义。     

Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle

A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.     

森林地上生物量遥感反演方法综述

森林地上生物量反演对理解和监测生态系统及评估人类生产生活的影响有着重要作用,日益发展的遥感技术使全球及大区域的生物量估算成为可能。近年来,不同的遥感技术和反演方法被广泛用于估算森林生物量。本文首先总结了现有的全球及区域生物量产品及其不确定性,然后综述了3类方法在森林地上生物量遥感反演中的应用,即基于单源数据的参数化方法、基于多源数据的非参数化方法和基于机理模型的反演方法,阐述了各类反演方法的特点、优势及局限性。最后从机理模型研究、多源遥感数据协同、生物量季节变化研究和遥感数据源不断丰富4个方面对今后的生物量遥感反演研究进行了展望。     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.