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1.
森林生物化学与CASI高光谱分辨率遥感数据的相关分析 总被引：38，自引：3，他引：38
该文介绍了利用光谱范围约 417— 80 0nm的航空高光谱分辨率数据估计森叶族叶化学成分浓度的分析方法。沿横跨美国俄勒冈州中西部收集 6个研究立地上的小型机载成象光谱仪 (CASI)数据。 3个族叶化学成分 [总叶绿素 (TC)、全氮 (TN)和全磷 (TP) ]从相同的研究立地取样并在实验室测定。使用多元统计和光谱微分技术评价CASI数据用于估计冠层生化浓度的潜力和效率。 12个族叶化学成分样本被测定 ,并同时在相同立地上提取CASI图象数据。 7个单回归模型被用来探索单波段和植被指数与 3个化学成分的线性与非线性相关关系及预测效果。光谱微分技术被用来压缩背景噪音对目标光谱的影响。利用CASI原始光谱、一阶和二阶微分光谱数据和逐步回归分析来预测TC、TN和TP。结果表明采用光谱微分技术能显著地改善由拟合度 (R2 )和均方根差(SE)描述的森林冠层化学成分浓度的估计精度。由单波段分析的结果说明族叶化学与CASI数据间的相关性很低。事实上 ,对于TP ,CASI原始数据与实验室测定的数据几乎无关 相似文献
2.
高光谱数据与水稻叶面积指数及叶绿素密度的相关分析 总被引：54，自引：1，他引：53
分析了北京大屯科技站水稻叶面积指数 (LAI)、叶绿素密度 (CH .D)与高光谱分辨率遥感数据在整个生育期内的变化过程。利用微分技术处理水稻群体反射光谱以减少土壤等低频背景光谱噪音的影响。通过单相关分析和逐步回归方法研究水稻LAI、CH .D分别与光谱反射率、反射率的一阶微分光谱的相关关系 ,并建立预测回归方程。结果表明 ,微分技术能够改善光谱数据与LAI、CH .D的相关性 ,CH .D与光谱数据的相关明显优于同LAI的。 相似文献
3.
机载LiDAR和高光谱融合实现温带天然林树种识别 总被引：4，自引：1，他引：3
将机载LiDAR(Light Detection and Ranging)与高光谱CASI(Compact Airborne Spectrographic Imager)数据融合,充分利用垂直结构信息和光谱信息进行温带森林树种分类,并与仅用高光谱数据的分类结果相比较,评估融合数据的树种分类能力。结合样地实测数据,首先用LiDAR获得的3维垂直结构信息对CASI影像上的林间空隙进行掩膜,提取林木冠层子集;然后对冠层子集分层掩膜,利用光谱曲线的一阶微分及曲线匹配技术,实现各树种训练样本的自动提取;利用SVM分类器对两种数据分类并比较精度。结果表明,融合数据的树种分类总体精度和Kappa系数(83.88%,0.80)优于仅使用CASI数据(76.71%、0.71),优势树种的制图精度为78.43%—89.22%,用户精度为75.15%—95.65%,整体也优于仅使用CASI的制图精度(68.51%—84.69%)和用户精度(63.34%—95.45%)。结果表明,机载LiDAR与CASI基于像元的融合对温带森林树种识别的精度较仅高光谱数据有较大提高。 相似文献
4.
《国土资源遥感》2021,(1)
土壤中碳酸钙的含量是土壤分类及肥力评价的重要依据。研究选取陕西省黄土高原区的78个黄绵土土壤样品为研究对象,在分析碳酸钙含量的基础上,采用SVC HR-1024i便携式光谱仪获取土壤样品的可见光、近红外与短波红外高光谱反射率(350～2 500 nm)数据,对原始光谱曲线分别进行一阶微分、二阶微分和连续统去除3种数学变换,运用相关分析法和连续投影算法分别进行敏感波段的选取,采用随机森林回归建立土壤碳酸钙的估算模型。结果表明:黄绵土的光谱曲线特征相似,在1 440 nm,1 900 nm和2 200 nm等处均存在明显的吸收特征,且碳酸钙含量与光谱反射率呈现正相关态势;基于二阶微分与连续投影算法的随机森林估算模型精度最高,验证集R~2为0.82,相对分析误差(residual predictive deviation,RPD)值为2.37。 相似文献
5.
利用交叉验证的小麦LAI反演模型研究 总被引：4，自引：0，他引：4
《国土资源遥感》2015,(4)
叶面积指数(leave area index,LAI)是表征植被冠层结构和生长状况的关键参数,采用遥感技术进行LAI反演是遥感反演领域的热点和难点之一。利用小麦关键生育期的高光谱数据,计算其一阶和二阶导数,并构建植被指数(RVI,NDVI,EVI,DVI和MSAVI)及三边变量参数等高光谱变量;将上述参数与小麦LAI数据进行相关性分析,并利用交叉验证法进行多种回归分析,确定反演小麦LAI的敏感参数,选择反演模型;最后使用敏感参数构建所有样本的小麦LAI反演模型,并比较其拟合效果。研究结果表明:经过交叉验证的反演建模,其拟合结果的均方根误差(RMSE)整体上较未经交叉验证反演建模结果的RMSE小;在用敏感参数构建的回归模型中,RVI立方回归模型是用遥感数据反演小麦LAI的最优模型。 相似文献
6.
相对于遥感影像,高光谱遥感影像具有光谱信息,为精准识别植被提供了新的技术支持.对高分五号(GF-5)高光谱数据进行光谱变换,结合植被指数,分析各种光谱变换方法对植被的识别能力.首先提取研究区主要的两种植被端元光谱,对实验区进行分类,依据植被分布位置,确定这两种植被分别为桉树和车桑子;然后对植被的反射率光谱进行一阶、二阶微分,连续统去除和对数变换处理;最后基于4种光谱变换及反射率光谱计算两种植被的11种植被指数,依据两种植被对应植被指数的J-M距离,判断不同光谱曲线对植被的识别能力.结果表明,5种光谱曲线对植被识别能力由强到弱依次为二阶微分、一阶微分、连续统去除、原始光谱、对数变换. 相似文献
7.
基于中国农业科学院在呼伦贝尔草原实测的120组草地冠层光谱反射率及相应的叶面积指数(LAI)数据,在进行主成分分析(PCA)实现降维处理的基础上,利用径向基函数(radial basis function,RBF)神经网络方法对草地LAI进行了高光谱反演研究.PCA结果表明,前9个主成分的累积贡献率达到了99.782％,能包含原光谱数据的绝大部分信息.将120组LAI及相应的9个主成分样本数据随机分为校正集数据(90组)和预测集数据(30组),分别用于神经网络模型的建立和LAI的预测.所构建的神经网络模型的模拟结果表明,RBF神经网络模型对校正集样本的模拟准确率达到100％(RMSE =0.009 6,R2 =0.999);预测集样本的实测LAI和模拟LAI之间的均方误差和决定系数分别为0.218 6和0.839,取得了较好的模拟效果,有效提高了传统的多元线性回归方程(RMSE =0.416 5,R2=0.570)的计算精度. 相似文献
8.
9.
《国土资源遥感》2017,(4)
叶面积指数(leaf area index,LAI)作为植被冠层的重要参数,对作物长势监测及产量估算具有重要意义。本研究以黑河流域张掖绿洲试验区为例,基于机载航空高光谱遥感影像(compact airborne spectrographic imager,CASI)数据,利用物理模型与统计模型对研究区的LAI进行估测反演。首先,利用归一化植被指数(normalized difference vegetation index,NDVI)与相应实测LAI数据建立最佳线性回归模型;然后,基于混合像元分解模型和多次散射植被冠层模型构建物理模型;最后,以线性回归模型为参比修正多次散射植被冠层模型,构建半经验LAI反演模型,并比较上述模型拟合效果。研究结果表明,半经验模型为绿洲区LAI反演最优模型,模型估算精度R2达到0.89,精度提高较显著。研究对提升作物LAI的估算精度有一定意义,并将进一步推动精细农业定量遥感理论的研究与应用。 相似文献
10.
基于微分变换的高光谱马尾松和杉木识别 总被引：1，自引：0，他引：1
《国土资源遥感》2018,(4)
高光谱遥感能分辨出地物间微小反射光谱差异信息,可用于解决林种遥感分类光谱识别的难题。利用Hyperion高光谱遥感影像,结合地面实测林种样地,对安徽省黄山市五城镇林区的马尾松和杉木进行识别。通过对Hyperion影像进行一阶、二阶微分变换,优化组合487～559 nm和681～742 nm光谱范围中反射差异明显的波段,再结合支持向量机(support vector machine,SVM)模型进行林种间分类识别。基于Hyperion影像像元反射率及其一阶和二阶微分光谱的分类识别总体精度分别达到76. 50%,81. 42%和88. 52%,对应Kappa系数分别为0. 528 4,0. 625 7和0. 769 1。结果表明,基于二阶微分变换的高光谱数据,通过SVM模型,可有效提高马尾松和杉木的识别精度,为高光谱遥感针叶林种分类识别提供了一种技术途径。 相似文献
11.
The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20 m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC. 相似文献
12.
基于波谱知识库的MODIS叶面积指数反演及验证 总被引：2，自引：0，他引：2
目前用物理模型反演叶面积指数普遍存在缺少先验知识的状况，如何获得准确的先验知识是遥感走向应用的一个关键环节。中国典型地物标准波谱数据库就是结合国家重大行业中的应用需求，研究制定地物波谱获取与分析的技术规范和数据标准，建立典型地物标准波谱数据库。从波谱数据库提取模型反演所需要的先验知识，实现了基于SAIL模型的MODIS数据（经过几何纠正与大气纠正）叶面积指数的反演。另外，基于TM数据，对MODIS混合像元进行了分解，用纯像元的叶面积指数与实测数据进行对比验证，同时，反演结果与NASA的LAI产品也进行了对比，结果表明基于波谱库的先验知识可以有效的提高叶面积指数的反演精度。 相似文献
13.
基于森林模型参数先验知识估算高分辨率叶面积指数 总被引：1，自引：0，他引：1
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。 相似文献
14.
Jesús Delegido Luis AlonsoGonzalo González José Moreno 《International Journal of Applied Earth Observation and Geoinformation》2010
The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm2, similar or smaller than other methods but with the improvement of applicability to a large set of different crop types. 相似文献
15.
以行道树无患子为研究目标,采用地面激光扫描(TLS)技术提取单木分回波点云数据。获取全波形数据、单目标数据、首次回波数据、其余次回波数据,建立基于多回波点云的算法,利用消光系数法提取不同投影分辨率0.01 m、0.02 m和0.03 m的的单株树叶面积指数(LAI)。利用2维影像数据数字半球影像(DHP)和LAI2200提取对应单株树的叶面积指数,进行比较分析,以检验其精度。结果表明:点云投影的分辨率与激光回波都对LAI有极显著影响,其中分辨率为0.02 m和0.03 m的估算结果与LAI2200所得估算结果相近,且差异不显著;单目标回波数据用于LAI的解算,可以同LAI2200的2维影像数据结果进行相互验证。使用单目标回波数据,0.02 m投影分辨率可以最大程度的保证单株LAI的精度,其与LAI2200测定的数据进行截距为0的线性回归,斜率达到0.827。本研究所做多回波地面激光数据计算叶面积指数的算法拓展了地面激光扫描的应用领域,为立木生长量信息准确提取和树木精确建模提供了重要的技术参考。 相似文献
16.
R. P. Singh V. K. Dadhwal K. P. Singh R. R. Navalgund R. Sharma G. D. Bairagi S. A. Raza N. K. Sharma 《Journal of the Indian Society of Remote Sensing》2005,33(2):307-313
This paper reports estimation of the Leaf Area Index (LAI) of wheat crop from IRS-LISS-III data using Price (1993) approach.
Empirical approach for LAI estimation with different NDVI estimation procedures viz. radiance, apparent reflectance and dark
object subtraction (DOS) based atmospheric correction were also evaluated. Validation of LAI retrieval and NDVI normalizations
were carried out using field level measurements of crop LAI and spectral property using canopy analyzer and spectro-radiometer,
respectively over selected fields in Bhopal District, Madhya Pradesh. It was observed that empirical relations are sensitive
to the NDVI estimation approach and DOS method performed better as compared to other two approaches. It was also observed
that LAI estimation from Price algorithm is sensitive to the crop attenuation coefficients. Crop specific attenuation coefficients
reported in literature for Indian cultivars gave higher accuracy. The root mean square (RMS) error of 0.77 for LAI estimation
was achieved using above described approach. 相似文献
17.
遥感估算叶面积指数(LAI)时空动态变化对全球气候变化研究具有重要的意义,为了提高遥感估算时间序列叶面积指数的精度,需要耦合遥感观测数据与LAI动态过程模型。本文提出一种基于双集合卡尔曼滤波(Dual EnKF)的时间序列LAI反演方法,同时更新LAI估计值和LAI动态过程模型中的敏感性参数,得到LAI和动态过程模型敏感参数的最优估计值来优化动态过程模型。一方面使得动态过程模型可以更好地描述LAI随时间的变化过程,降低模型预测误差,从而提高LAI动态过程模型的预测能力;另一方面通过耦合动态过程模型和辐射传输模型,集成遥感观测数据与动态过程模型的预测值,进而得到优化的LAI估计值。为检验算法,分别选取作物、草地和林地等典型植被验证站点进行Dual EnKF LAI时间序列估算,并分别与MODIS LAI产品及其SG滤波曲线、集合卡尔曼滤波方法反演LAI、未优化的动态过程模型模拟LAI结果进行比较,并配以一些站点地面实测点数据作为参考。结果表明,采用Dual EnKF方法得到的LAI不但保持了时间上的连续性,而且通过改善动态过程模型的预测能力,即使在缺乏高质量遥感观测数据时,也能够获得符合LAI发展趋势的估算值,没有出现跳跃、波动现象,时间序列曲线较稳定,更符合植被LAI变化规律,表明基于Dual EnKF的时间序列LAI遥感估算方法是提取LAI时间廓线的一种有效途径。 相似文献
18.
Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2).In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation. 相似文献
19.
Linsheng Huang Furan Song Wenjiang Huang Jinling Zhao Huichun Ye Xiaodong Yang Dong Liang 《Journal of the Indian Society of Remote Sensing》2018,46(11):1907-1914
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. 相似文献
20.
《地理信息系统科学与遥感》2013,50(2):229-248
This study evaluated the utility of narrowband (EO-1 Hyperion) and broadband (Landsat ETM+) remote sensing data for the estimation of leaf area index (LAI) in a tropical environment in Sulawesi, Indonesia. LAI was inferred from canopy gap fraction measurements taken in natural tropical forest and cocoa plantations. Single and multiple spectral bands and spectral indices were used as predictor variables in reduced major axis (RMA) and ordinary least squares (OLS) regression models. The predictive power of most regression models was notably higher when employing narrowband data instead of broadband data. Highly significant relationships between LAI and spectral reflectance were observed near the red-edge region and in most shortwave infrared (SWIR) bands. In contrast to most near-infrared (NIR) narrow bands, the correlation between SWIR reflectance and LAI was not confounded when including both vegetation types and did not suffer from saturation. The results demonstrate that leaf area index of a challenging tropical environment can be estimated with satisfactory accuracy from hyperspectral remote sensing data. 相似文献