汉江流域叶面积指数(LAI)空间尺度转换研究

为有效地整合利用不同分辨率遥感数据获取的陆面过程以及其他相关的环境变量,尺度问题越来越受到人们的重视.本文选取汉江流域为研究区,尝试对TM/ETM 影像反演的LAI升尺度转换,使用了一种基于NDVI像元分解的LAI升尺度转换算法,分两步考虑了地表异质性问题,很好的实现了900×1500大小实验区30m空间精度 LAI的向120m,300m,900m,3600m四种尺度的转换,该方法应用于整个汉江流域NASA 发布的MODIS LAI产品校正,取得了良好效果.     

黑河中游试验区不同分辨率LAI数据处理、分析和尺度转换

008年开展的黑河综合遥感联合试验获取了大量野外实测叶面积指数(LAI)数据以及遥感LAI产品。在利用LAI地面点观测数据对遥感影像进行验证或者不同分辨率遥感产品相互比较的过程中存在由于地表异质性引起的尺度效应,导致无法直接进行验证、比较,需要进行尺度转换。以基于泰勒级数展开的尺度转换模型为基础,研究不同源LAI之间的尺度转换方法。包括两部分内容：① 以高分辨率影像为辅助数据将地面实测点尺度的LAI转换到中、低分辨率遥感像元尺度;② 利用高分辨率影像作为亚像元信息对低分辨率LAI产品进行尺度纠正。结果表明,利用泰勒级数展开模型进行尺度转换是一种简单可行的方法,经尺度转换的地面实测点尺度LAI可用作像元尺度数据比较验证的参考。     

基于无人机高光谱数据的多类型混合作物LAI反演及尺度效应分析

叶面积指数(Leaf Area Index,LAI)作为表征不同作物生长状况的基本参数,是农业精细化管理及农田生态系统建模的关键。我国农田作物种植比较离散,受地表空间结构非均一性和反演模型非线性等因素影响,不同尺度遥感数据估算的作物LAI存在一定的差异,即农田作物LAI的遥感反演普遍存在尺度效应问题。以包头遥感综合验证场农业示范区为研究区,利用无人机高光谱数据结合PROSPECT+SAIL模型构建典型农作物区多类型作物的查找表(Look-Up-Table,LUT)反演农田LAI,研究查找表用于玉米、马铃薯、向日葵、瓜地等不同作物LAI反演的适用性和精度;通过无人机高光谱数据聚合获得多尺度遥感数据源,结合Taylor展开理论和计算几何模型,提出了一种既考虑类间差异又考虑类内异质性的尺度转换模型,定量描述多种作物混合的非均一地表LAI反演过程中的尺度效应特征。结果表明:基于分类和参数敏感性分析的LUT方法能很好地应用于包头典型农作物区多类型混合作物LAI反演,总估算精度为相关系数R~2=0.82、均方根误差RMSE=0.43m~2/m~2。随着反演尺度的增加,作物类间差异造成的反演偏差明显高于类内异质性,利用本文所提出的尺度转换模型均能较好纠正低分辨率LAI反演的尺度效应问题。     

黑河中游LAI产品的真实性检验研究

遥感产品的应用越来越受到各行各业的高度关注,因此定量遥感产品的真实性检验工作已是当务之急,也是近年来遥感领域研究的热点问题之一。首先总结了国内外学者在LAI遥感产品真实性检验中使用的方法和取得的研究进展,然后结合实例分析了黑河中游不同分辨率LAI产品的真实性检验。通过比较两种不同尺度转换方案得到1 km分辨率的LAI对MODIS LAI产品进行验证,并分析了LAI的尺度效应。结果表明:两种方案比较验证的相关系数为0.57和0.39,借助于高分辨率影像作为中间尺度验证MODIS LAI产品要比直接利用地面实测点尺度转换到低分辨率LAI图验证MODIS LAI产品更合理,且黑河中游MODIS LAI产品存在明显的低估现象,最大偏差达2.26。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R²增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成"病态"反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R2由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R2增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R²增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

基于Chen NDVI模型的NDVI尺度转换分形特性分析北大核心CSCD

空间尺度问题是定量遥感重要而基础的问题之一,文章针对“分形方法是否适用于定量遥感地表参数的尺度转换研究”的问题进行实验验证。基于传统及改进的Chen NDVI尺度转换模型,获得NDVI不同空间升尺度影像,进而利用分形尺度转换模型分析NDVI尺度转换结果,以获取“分形方法在NDVI尺度转换研究中的适用性结论”。以厦门为研究区,利用上述分形模型进行实验,结果表明:(1)若NDVI输入类型为均值,NDVI尺度转换特性更符合双对数直角坐标系下的线性函数关系,即分形特性;若NDVI输入类型为方差、方差/均值,NDVI尺度转换特性更符合直角坐标系下的对数函数关系;(2)农田在两种Chen NDVI模型及两种空间直角坐标系下所得拟合模型皆表现出显著的线性规律,尤其是其NDVI尺度转换分形特性表现显著;(3)综合而言,NDVI尺度转换结果具有分形特性,但NDVI尺度转换具有更为显著的直角坐标系下对数函数关系特性。文章所提出的融入精细地类信息的“类NDVI”地表参数通用尺度转换模型及尺度转换分形特性研究方法具有一定的代表性,值得参考。     

基于光谱相似尺度的支持向量机遥感土地利用分类

提出一种基于光谱相似尺度( spectral similarity scale, SSS ) 的支持向量机( support vector machines, SVM) 遥感土地分类新方法, 该方法选择莆田市作为遥感土地利用分类典型研究区, 利用该区域的Landsat7 ETM 遥感影像结合地面实况调查数据, 从图像上选取少量具有代表性的样本点的光谱作为参考光谱, 利用SSS 方法提取训练样本, 然后应用SVM 算法进行遥感土地利用分类, 并将分类结果与最大似然分类算法( MLC) 相比较, 实验结果表明分类精度上有了很大的提高。     

基于DART模型和随机森林估算山地叶面积指数

地形效应会使遥感影像中的地表反射率发生畸变,进而影响基于反射率估算的叶面积指数(Leaf Area Index,LAI)精度。为了减弱或消除地形对LAI反演的影响,基于三维辐射传输模型DART(Discrete Anisotropic Radiative Transfer)构建坡地反射率与LAI数据集作为训练数据。以反射率为输入,LAI为输出,利用随机森林算法进行训练,构建山地LAI反演模型。结合实际遥感影像数据实现山地LAI的估算,并利用实测数据对反演结果开展精度评价。同时,基于DART模型和随机森林构建了平地LAI反演模型作为参照以评价本文发展方法的有效性。结果表明：考虑了地形影响的山地LAI反演模型具有较强的估算能力,验证结果的精度(决定系数(R²)=0.57,均方根误差(RMSE)=0.77 m²/m²)优于平地反演模型(R2=0.46,RMSE=0.86 m2/m2);基于DART模型构建的山地反演模型能够捕捉到坡度和坡向对地表反射率的影响,其反演结果较好地还原了研究区LAI的空间分布,与地面真实情况接近。研究...     

Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) is addressed in this article. We define the goal of scaling as the process by which it is established that LAI values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI retrievals is investigated with 1-km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice versa. A physically based scaling with explicit spatial resolution-dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated. These principles underlie our approach to the production and validation of LAI product from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectroradiometer (MISR) aboard the TERRA platform.     

Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America

A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time-steps of 10 days, using data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer‐ and hardwood‐dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer‐ and hardwood‐dominated stands are separated, image spatial information estimates tree crown size much better for conifer‐dominated stands than for the hardwood‐dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data

The monitoring of earth surface dynamic processes requires global observations of the structure and the functioning of vegetation. Moderate resolution sensors (with pixel size ranging from 250 m to 7 km) provide frequent estimates of biophysical variables to characterize vegetation such as the leaf area index (LAI). However, the computation of LAI from moderate resolution remote sensing data induces a scaling bias on the LAI estimate if the moderate resolution pixel is heterogeneous and if the transfer function that relates remote sensing data to LAI is non-linear.This study provides a model to evaluate and correct the scaling bias. The model is built first for a univariate semi-empirical transfer function relating LAI directly to NDVI. The scaling bias is a function of (i) the degree of non-linearity of the transfer function quantified by its second derivative and (ii) the spatial heterogeneity of the moderate resolution pixel quantified by the variogram of the high spatial resolution (20 m) NDVI image. Then, the model is extended to a bivariate transfer function where LAI is related to red and near infrared reflectances. The scaling bias depends on (i) the Hessian matrix of the transfer function and (ii) the variograms and cross variogram of the red and near infrared reflectances.The scaling bias is investigated on several distinct landscapes from the VALERI database. Adjusting for scaling bias is critical on crop sites which are the most heterogeneous sites at the landscape level. Regarding the univariate transfer function, the magnitude of the scaling bias increases rapidly with pixel size until this size is larger than the typical spatial scale of the data. For the bivariate transfer function, it results from the addition of several components that may add up or cancel each other out. It is thus more difficult to analyze.The accuracy of the model to estimate the scaling bias is discussed. It depends mainly on the ability of the variograms and cross variogram to represent the local dispersion variances and covariance within the moderate resolution pixel. The model is generally highly accurate at 1000 m spatial resolution for the univariate transfer function and less accurate for the bivariate transfer function.