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1.
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ＬｉＳｔｒａｈｌｅｒ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。 相似文献
2.
高光谱与多角度数据联合进行混合像元分解研究 总被引：8，自引：0，他引：8
混合像元问题是定量遥感的主要障碍之一。将混合像元问题归结为类内与类间像元混合两类，并对类内混合像元分解问题加以研究。混合像元分解的关键在于确定组分光谱，确定组分光谱的方法很多，但大多数方法基于以下假定，即从图像本身可以找到纯组分光谱，然而这一假定对于类内混合像元分解问题来说很难成立。提出采用高光谱与多角度相结合的方法，利用几何光学模型和线性光谱混合模型进行类内混合像元分解。即首先利用多角度数据反演几何光学交互遮蔽(GOMS)模型获得组分光谱，再对高光谱数据进行组分光谱分解。由于该方法直接从混合光谱产生的机理出发，因而更容易获得真正的亚像元信息。为减小反演误差，反演过程中采用改进的多阶段的反演策略，并充分利用多角度图像本身提供的先验信息。用BORE—AS试验获取的高光谱与多角度数据所作的研究表明，该方法可以获得比较理想的分解结果。 相似文献
3.
水稻冠层氮素含量光谱反演的随机森林算法及区域应用 总被引：5，自引：0，他引：5
利用地面实测数据构建高精度的水稻冠层氮素含量光谱反演点模型并将其进行尺度转换,实现了水稻冠层氮素含量准实时、大区域监测。以氮素光谱敏感指数作为输入变量,冠层氮素含量数据为输出变量,利用随机森林算法构建水稻冠层氮素含量高光谱反演模型,并用苏州市水稻农田验证区数据,检验模型的普适性和有效性;利用准同步的Hyperion数据,采用对输入、输出变量进行线性变换的简单尺度转换方法实现了点模型的区域应用。结果表明:基于随机森林算法的水稻冠层氮素含量高光谱反演模型可解释、所需样本少、不会过拟合、精度高(模型在实验区的预测精度为R2=0.82,验证区检验精度为R2=0.73)且具有普适性;点模型基于高光谱遥感卫星影像和尺度转换进行区域应用,精度较高(R2=0.81)。 相似文献
4.
叶片光谱是估算植被生化参数的重要依据。然而,遥感影像获取的光谱为像元及冠层光谱,因此,在进行植被生化参数的遥感定量估算时,需将冠层光谱转化到叶片尺度。根据几何光学模型原理,推导出植被冠层光谱和叶片光谱的尺度转换函数,将冠层光谱转换到叶片尺度。首先,采用叶片光谱模拟模型PROSPECT模拟出叶片水平的光谱;其次,在几何光学模型4-scale模型中,通过改变叶片光谱和叶面积指数（leaf area index,LAI）,模拟出不同叶片特征下的冠层光谱。最后,通过LAI建立两个查找表,一个是传感器观测到树冠光照面和背景光照面概率的查找表,另一个是多次散射因子M的查找表,从而实现冠层光谱和叶片光谱的转化。结果表明,利用4-scale模型能实现冠层光谱与叶片光谱的尺度转换,此方法有很好的适用性。 相似文献
5.
为削弱混合像元对植被参数反演的影响，提出了基于混合像元分解理论反演路域植被等量水厚度的方法。利用PRO4SAIL模型正演获得的高光谱窄波段数据，模拟Landsat 8遥感影像宽波段植被冠层光谱数据，并进行等量水厚度的敏感植被指数的筛选；对覆盖研究区域的Landsat 8遥感影像进行线性混合像元分解，获取更加精确的植被冠层光谱反射率；同时，利用支持向量机构建等量水厚度估测模型，实现对路域植被等量水厚度的遥感反演。研究结果表明，利用混合像元分解后得到的植被冠层光谱参与模型反演得到的路域植被等量水厚度更加符合实际情况，为遥感影像反演植被参数提供了有效数据。 相似文献
6.
7.
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。 相似文献
8.
《武汉大学学报(信息科学版)》2020,(4)
林下植被作为森林生态系统的重要组成部分,在维护森林生态系统植物多样性和稳定性方面发挥着巨大作用。当前冠层光谱与林下背景光谱间的尺度差异尚不明确,限制了单角度光学遥感技术在林下植被研究中的应用。借助几何光学模型4-scale分析了森林背景光谱与冠层光谱的相关关系。结果表明,森林背景光谱与冠层光谱之间的尺度差异因森林结构的不同而变化,植被指数运算并不能消除尺度差异。冠层和背景光谱之间的尺度差异可以通过线性函数描述,这种线性关系随波长和森林结构的不同而不同,而模型参数与叶面积指数高度相关,在680 nm和865 nm处的决定系数分别为0.881、0.834 3、0.890 6和0.880 3。相关研究能为削弱或消除冠层光谱与林下背景光谱间的尺度差异提供参考。 相似文献
9.
将植物叶片光谱模型PROSPECT、植被冠层光谱模型SAIL与大气辐射传输模型6S进行耦合,模拟不同参数条件下植被星上光谱信息在400～ 900 nm谱段的变化,并分析从地表植物叶片光谱、冠层光谱到卫星入瞳处光谱的过程中,植物叶片的叶肉结构参数、叶绿素含量、干重、叶片含水量和植物冠层的叶面积指数(LAI)、太阳天顶角、气溶胶光学厚度、地表邻近效应以及混合像元等参数对植物光谱的影响.研究结果表明,由大气引起的误差要远大于由植物本身的各种生化参数引起的误差;在叶片尺度上引起反射率发生变化的主要因素是叶绿素含量和叶肉结构参数,含水量的影响非常小,可以忽略;在冠层尺度上引起光谱发生变化的因素主要有LAI和叶片倾角. 相似文献
10.
森林生态系统在调节生态气候与碳循环方面发挥着重要作用,森林高度是衡量森林生态系统功能的重要参数。利用单一遥感数据获取森林冠层高度会受到多种制约。因此,本文使用星载激光雷达ICESat-2提供的高质量离散森林冠层高度点,结合Sentinel-1、Landsat 8及地形数据,采用随机森林方法建立不同影像特征组合森林冠层高度的回归模型,并分析各特征对森林高度反演的影响,最后将模型应用于广西森林冠层高度制图。试验结果表明,多源遥感数据可有效提高森林冠层高度反演精度,在所利用遥感数据中,特征重要性从大到小依次为光学特征、地形特征、SAR特征,“L8+SRTM+Sentinel-1+邻域均值”特征组合的反演精度最高,加入邻域均值特征进行森林冠层高度反演效果最佳,随机森林模型能精确绘制森林冠层高度。 相似文献
11.
基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较 总被引：1，自引：1，他引：0
混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。 相似文献
12.
用地基激光雷达提取单木结构参数——以白皮松为例 总被引：6，自引：1，他引：5
以白皮松(Pinus bungeana Zucc)为研究对象,针对地基激光雷达TLS扫描的3维点云数据在单株木垂直方向的分布特征,提出了一种基于体元化方法的树干覆盖度变化检测方法,获取单木枝下高;然后根据获取的枝下高引入2维凸包算法获取垂直方向分层树冠轮廓,并计算树冠体积和冠幅;同时获取的单木参数还有胸径与树高。结果表明:单木枝下高的估测精度较高,R2与RMSE分别为0.97 m和0.21 m;胸径估测结果的R2与RMSE分别为0.79 cm和1.07 cm;采用逐步线性回归方法建立单木树冠体积与其他单木参数的相关关系,模型变量包括冠幅、叶子填充树冠长度和胸径,样本数为20,模型的R2与RMSE分别是0.967 m3和2.64 m3。本文方法能较准确地估测枝下高,TLS数据具有对树冠结构3维建模的潜力。 相似文献
13.
机载激光雷达及高光谱的森林乔木物种多样性遥感监测 总被引：1，自引：0，他引：1
利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。 相似文献
14.
作为森林冠层结构的重要组成部分,树冠形状对冠层间隙率与聚集度指数的计算有重要影响。之前的研究通常将树冠假设为圆锥形、圆柱形、圆锥+圆柱形等形状计算了冠层间隙率与聚集度指数。然而,树冠生长受外部环境以及内部顶端优势等因素的影响,相较于上述理想化的树冠形状,半椭球形更符合树冠自然生长规律。事实上,半椭球形是一种十分常见的树冠形状。本文以树冠在空间呈泊松分布为前提,推导出半椭球形树冠的冠层间隙率与聚集度指数计算公式,并进一步扩展到双半椭球形树冠。同时,以半椭球形树冠为计算基准,对比分析了半椭球形树冠与其他树冠形状冠层间隙率与聚集度指数的相对差异。模拟计算中主要输入参数包括树冠密度、树冠高度、树冠半径以及叶面积指数等。最后通过虚拟场景对结果进行验证。结果表明：（1）半椭球形树冠与其他树冠形状的冠层间隙率有较大差异。随着观测天顶角增加,不同树冠形状与半椭球形树冠的冠层间隙率的相对差异也逐渐增大。当观测天顶角为70°时,圆锥形树冠与半椭球形树冠的冠层间隙率相对差异已接近100%。（2）树冠形状对聚集度指数同样有较明显影响。极端情况下,圆锥形树冠与半椭球形树冠的聚集度指数相对差异达到30%。（3）半椭球形树冠与其他树冠形状的半球空间聚集度指数期望值的差异不容忽视。 相似文献
15.
Pine plantations in Australia are subject to a range of abiotic and biotic damaging agents that affect tree health and productivity. In order to optimise management decisions, plantation managers require regular intelligence relating to the status and trends in the health and condition of trees within individual compartments. Remote sensing technology offers an alternative to traditional ground-based assessment of these plantations. Automated estimation of foliar crown health, especially in degraded crowns, can be difficult due to mixed pixels when there is low or fragmented vegetation cover. In this study we apply a linear spectral unmixing approach to high spatial resolution (50 cm) multispectral imagery to quantify the fractional abundances of the key image endmembers: sunlit canopy, shadow, and soil. A number of Pinus radiata tree crown attributes were modelled using multiple linear regression and endmember fraction images. We found high levels of significance (r2 = 0.80) for the overall crown colour and colour of the crown leader (r2 = 0.79) in tree crowns affected by the fungal pathogen Sphaeropsis sapinea, which produces both needle necrosis and chlorosis. Results for stands associated with defoliation and chlorosis through infestation by the aphid Essigella californica were lower with an r2 = 0.33 for crown transparency and r2 = 0.31 for proportion of crown affected. Similar analysis of data from a nitrogen deficient site produced an outcome somewhat in between the other two damaging agents. Overall the sunlit canopy image fraction has been the most important variable used in the modelling of forest condition for all damaging agents. 相似文献
16.
Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis 总被引：3，自引：0，他引：3
Gautam Ghosh Suresh Kumar S. K. Saha 《Journal of the Indian Society of Remote Sensing》2012,40(1):129-136
Development of salt-affected soils in the irrigated lands of arid and semi-arid region is major cause of land degradation.
Hyperion hyperspectral remote sensing data (EO-1) was used in the present study for characterization and mapping of salt-affected
soils in a part of irrigation command area of Indo-Gangetic alluvial plains. Linear spectral mixture analysis approach was
used to map various categories of salt affected soils represented by spectral endmembers of slightly, moderately and highly
salt-affected soils. These endmembers were related to surface expression of various categories of salt-affected soils in the
area. The endmembers were selected by performing minimum noise fraction (MNF) transformation and pixel purity index (PPI)
on Hyperion (EO-1) data with reference to high resolution LISS IV data and field data. The results showed that various severity
classes of salt-affected soils could be reliably mapped using linear spectral unmixing analysis. A low RMSE value (0.0193)
over the image was obtained that revealed a good fit of the model in identification and classification of endmembers of various
severities of salt affected soils. The overall classification accuracies for slight, moderate and highly salt-affected soils
were estimated of 78.57, 79.81 and 84.43% respectively. 相似文献
17.
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. 相似文献
18.
树冠形状对孔隙率及叶面积指数估算的影响分析 总被引：1，自引：1，他引：0
叶片在树冠尺度的聚集是森林场景中的重要聚集形式,模型中常假设树冠为规则的几何形体(椭球、圆锥、圆锥+圆柱等)。对树冠形状归属进行判断时界限并不明显,从而具有很强的主观性。本文首先扩展了Nilson的森林孔隙率模型,使其适用于椭球、圆锥、圆锥+圆柱等3种常见形状的树冠,并基于该模型分析了孔隙率、聚集指数对树冠形状的敏感性。同时,本文还分析了树冠形状对叶面积指数(LAI)地面间接测量精度的影响。基于不同形状树冠的模拟数据分析发现,树冠的体积、投影面积是树冠形状产生作用的主要因子,在冠层底部椭球形树冠和圆锥+圆柱形树冠的平均孔隙率、聚集指数都非常接近,而圆锥形树冠与两者存在较大差异。树冠形状的错误设置在极端情况下可导致估算的真实LAI误差超过25%。 相似文献
19.
Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun–canopy–sensor (SCS) model significantly improved over those based on the sun–terrain–sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun–crown–sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun–crown–sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantages of SCnS + C and SCnS + W on both bands are expected to facilitate forest classification and change detection applications. 相似文献
20.
《地理信息系统科学与遥感》2013,50(3):394-415
Recent advances in light detection and ranging (LIDAR) technology have enabled the estimation of valuable canopy parameters (e.g., crown diameter, leaf area, and canopy structure) that are difficult to obtain through in situ surveys. The objective of this study was to assess the utility of LIDAR-derived measurements of crown and growth parameters to model and predict the growth of sugi (Cryptomeria japonica) stands located in the University of Tokyo Forest, Chiba Prefecture, Japan. Initially, we confirmed that crown lengths and widths of trees in stands of various densities obtained from LIDAR data correlated with those measured in situ. Then, we developed a crown growth model from repeated LIDAR measurements of stands, suggesting that LIDAR data are adequate for this purpose, and indicating that crown surface area and tree volume growth were linearly related (R2 = 0.90; p < 0.01; RMSE tree volume < 0.02 m3). The model also provided robust predictions of the volume growth of local forests in 10 × 10 m plots based on LIDAR-derived estimates of crown surface areas. Future work should test the applicability of this growth model to facilitate practical forest management. 相似文献