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机载激光雷达（LiDAR）强度数据在获取过程中受多种因素影响,各因素影响的有效量化及校正对机载LiDAR强度校正及应用具有重要意义。本文以雷达方程为基础,分别采用距离、入射角及距离和入射角对LiDAR点云强度进行校正,从中提取冠层总强度和强度比值两类参数,用于估测森林叶面积指数（LAI）,以期量化各影响因素强度校正对不同类型参数估测森林LAI的影响。结果表明：强度经距离校正能够提高森林LAI的估测精度,而强度经数字高程模型衍生入射角校正非但没能提高估测精度,反而降低了估测精度。强度经距离和入射角综合校正虽能提高森林LAI的估测精度,但结果却低于距离单独校正的结果。与此同时,对冠层总强度参数而言,强度校正前后森林LAI估测结果的差异较为明显,而对强度比值参数而言,强度校正前后森林LAI估测结果差异不大。综上可知,不同因素强度校正对森林LAI估测的影响不同,且影响程度与所用参数变量类型密切相关。因此,在未来强度应用研究中,应根据变量参数类型选择合适的校正方式,以避免不恰当校正造成的成本浪费及精度降低。 相似文献
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基于SPOT5遥感影像的城市森林叶面积指数反演 总被引：7，自引：0，他引：7
本文以上海城市森林为研究对象,采用地面实验与遥感技术相结合方法,开展SPOT5遥感影像在估测城市森林LAI中的应用研究。结果表明,地面实测LAI与三种植被指数均具有很好的线性回归关系,相关系数(r)均大于0.6,其中MSAVI的相关系数最高(r=0.66),其次为MCARI(r=0.64)和NDVI(r=0.62)。说明ND-VI仍受到背景等因素不同程度的影响,而植被指数MSAVI和MCARI,由于能进一步消除土壤背景和叶绿素的影响,对叶面积指数比较敏感,能更好地与叶面积指数建立关系,能更好地用于城市森林叶面积指数的遥感反演。本研究可为快速定量评估城市森林的结构和功能提供依据。 相似文献
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多参数SAR数据森林应用潜力分析 总被引：2，自引：0，他引：2
利用多参数机载全球雷达(GlobeSAR)数据和航天飞机成像雷达(SIR-C/X-SAR)数据,分别在我国南、北方两个试验区进行森林识别与分类,以及蓄积量估测的试验.为了更好地了解雷达后向散射与森林结构特征的关系,分别从雷达图像上提取了后向散射系数和强度,进行森林类型识别效果的分析,以及森林结构参数与雷达后向散射强度的相关分析.结果显示多波段、多极化SAR数据能有效地识别不同类型的森林.雷达的后向散射强度对森林的结构参数,尤其是森林的平均胸径和高度较为敏感,据此对试验区的森林蓄积量进行了估测,并分析了多参数SAR在森林应用中的潜力. 相似文献
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研究了结合雷达影像纹理特征进行作物叶面积指数(LAI)估测的可行性,分析了作物LAI与多极化雷达纹理特征的相关性。将ENVISATASAR影像6种纹理特征与实测玉米的LAI进行相关分析发现,HH极化影像的灰度共生矩阵纹理特征与玉米LAI的相关性较VV极化的高;HH极化影像的对比度、异质性,VV极化影像的偏度、均质性等纹理特征与实测LAI均有较大的相关性。分别对两种极化影像雷达波散射强度及纹理特征与实测LAI进行多元回归分析,发现HH极化方式的相关系数达到0.68、VV极化的为0.87。说明结合纹理特征的雷达估测作物LAI方法具有一定的可行性。 相似文献
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基于叶片光谱的森林叶绿素浓度反演研究 总被引：2，自引：0，他引：2
准确估测森林叶片叶绿素浓度有助于进一步理解和模拟森林生态系统。基于江西省千烟州试验区主要树种光谱数据和相应叶绿素浓度等化学参量数据,使用高光谱指数和偏最小二乘(PLS)回归方法进行森林叶绿素浓度的反演,对具代表性的几种叶绿素相关的光谱指数进行反演能力评价,构造出表征叶绿素吸收特征的叶绿素吸收面积指数(CAAI),发现TCAR I(改进型的叶绿素吸收比率指数)和CAAI能较好地估测试验区森林主要树种的叶绿素浓度。针对叶片生化参量之间强相关现象,首先使用了叶片生化参量吸收特征分析方法,选定特定波段。然后对叶绿素浓度进行PLS回归估测,并从PLS获得估测模型的结果来评价和解释几种高光谱指数的叶绿素反演能力。 相似文献
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森林生物量是地表碳循环研究的重要组成部分,对其进行准确估测对区域乃至全球的森林状况和气候环境问题有重要意义.合成孔径雷达(SAR)以其独特的成像机制、全天候全天时的成像特点以及对森林冠层的穿透能力,在森林资源调查和森林制图研究领域发挥着巨大的作用.该文首先总结了林业上传统的森林生物量估测方法和基于光学遥感和激光雷达(LiDAR)数据的森林生物量估测方法;然后重点阐述了利用SAR后向散射(不同极化方式)、干涉相干性及极化干涉等信息进行森林生物量反演的方法;最后总结了利用SAR数据进行森林生物量估测的发展趋势. 相似文献
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本文介绍了利用卫星遥感编制西双版纳州热带森林植被图的研究方法。论述了以森林生态规律为导向,以多种传感器遥感信息为基础,通过计算机图像处理和森林植被分类判读,编制了热带森林植被图的技术过程。研究结果表明,利用多种传感器卫星遥感数据SPOT XS和TM,结合部份航空像片编制热带森林植被图是可行的。该图件的编制为西双版纳珍贵的热带森林植被的保护管理提供了重要的基础信息。 相似文献
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基于冠层反射光谱的水稻产量预测模型 总被引：21，自引：0，他引：21
基于地面实测的水稻冠层反射光谱,计算了常用的8个植被指数,并在产量形成生理特征的基础上,系统分析了水稻籽粒产量及其构成因素与各植被指数之间的关系。结果表明,通过单一生育时期或某个生育阶段的光谱植被指数来直接估测产量精度较低。发现叶面积氮指数(叶片氮百分含量与叶面积指数的乘积)的变化趋势很好地反映了产量的形成过程,且与光谱植被指数极显著正相关,基于此建立了水稻的光谱植被指数-累积叶面积氮指数-产量估测模型(VICLANIYieldModel)。并将其与LAD-产量模型、多生育期复合估产模型进行了比较,表明本模型预测精度最高。 相似文献
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Computer simulation models have seldom been applied for estimating the structural and biophysical variables of forest canopy. In this study, an approach for the estimation of leaf area index (LAI) using the information contained in hyperspectral, multi-angle images and the inversion of a computer simulation model are explored. For this purpose, L-systems combined with forest growth model ZELIG were applied to render 3-D forest architectural scenarios. The Radiosity-graphics combined model (RGM) was used to estimate forest LAI from the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) data. LAI inversion was performed using the look-up table (LUT) method. The estimated LAI was evaluated against in situ LAI measurement and compared against the LAI predictions from CHRIS data obtained using the Li-Strahler geometric-optical canopy reflectance model (GOMS). The results indicated that the method used in this study can be efficient strategy to estimate LAI by RGM model inversion. 相似文献
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《地理信息系统科学与遥感》2013,50(1):92-116
The leaf area index (LAI) of plant canopies is an important structural variable for assessing terrestrial ecosystems. This research examined the use of multitemporal Landsat TM imagery to estimate and map LAI in mixed natural forests in the southeastern USA. The performances of canonical correlation analysis (CCA) and partial least squares (PLS) regression techniques were evaluated for feature extraction to estimate forest LAI. The experimental results indicate that use of multitemporal TM imagery can improve the accuracy of estimating the forest LAI, and that CCA analysis outperforms PLS regression for feature extraction. 相似文献
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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。 相似文献
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《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
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Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI. 相似文献
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本文介绍了采用更系统的生物学方法,根据水稻在整个生长期的叶面积指数轨线,按叶面积指数的一次测量值,结合气象和光谱数据及陆地卫星MSS图像,估算大面积水稻产量的方法。 在产量与总截获的关系中,叶面积指数是最重要的参量之一。文中重点分析了由水稻在MSS波段内的光谱数据构成的绿度指数和Suits模式估算叶面积指数的结果。表明,用Suits模式计算的叶面积指数有较高的相关系数和较低的剩余标准差。并用这个方法计算了不同田块的叶面积指数,再根据这些数据和MSS图像建立关系。本文比较了卫星数据和多种绿度指数的关系,认为垂直植被指数(PVI)是估算叶面积指数的最好参量,因为它消除了土壤背景的影响,并用它求出了大面积水稻的叶面积指数分布。再根据已建立的叶面积指数轨线和作物截获的有效光合辐射(TIPAR)关系,计算了TIPAR,编制了产置分布图。文章分析了计算结果,并以1983年的例子进一步讨论了该方法的适用性。结果表明,预测的水稻产量和实测产量间的相关系数为0.9左右。 相似文献
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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. 相似文献
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Shenghui Fang Yuan Le Qi Liang Xiaojun Liu 《Journal of the Indian Society of Remote Sensing》2014,42(4):733-743
The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites. 相似文献
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针对遥感在三维可视化方面的不足,提出了用SRTM(航天飞机雷达地形测图任务)和TM(陆地资源卫星专题绘图仪)数据建立森林场景的方法。利用L系统语言和L-studio软件,通过提取的分枝规则建立了针叶树和不同叶面积指数(LAI)落叶树的几何模型,并将其作为森林场景内树木的纹理贴图。实验区的三维地形用四叉树结构建立,综合应用数据分块、动态载入、双缓存数组等措施提高大范围场景的漫游速度。从TM影像获取目视判读的遥感分类图和LAI反演图,结合控制树木的栽种位置和纹理选择,建立了单幅TM影像大小的森林场景。实验为其他遥感数据的三维显示提供了通用的建模方法。 相似文献
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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。 相似文献