利用GLASS LAI数据分析1982-2012年亚马逊热带雨林变化

利用1982-2012年的GLASS LAI数据,结合世界粮农组织(FAO)2000年发布的全球生态环境分类图,对亚马逊热带雨林31年的植被变化进行了综合分析,采用点与面相结合的分析方法,全面地反映雨林植被的变化情况。不同于过去研究中固定研究范围或直接研究整个南美洲区域,本文采用动态静态边界相结合的方法,在考虑热带雨林动态范围变化的同时也强调研究区域的内部变化。结果显示,亚马逊热带雨林叶面积指数在31年中整体呈现波动变化,进入2000年以后,热带雨林范围内平均叶面积指数先下降后增加,整体相对稳定。在空间分布上,由于人类毁林开荒,巴西境内的热带雨林以及热带雨林部分边缘地带的叶面积指数在31年中明显下降,热带雨林东南边界持续收缩;除此之外,雨林内部的叶面积指数波动上升,这是受到全球气候变暖的影响。结果与过去的研究进行对比,具有较好的一致性。研究论证了利用具有中国自主知识产权的GLASS LAI数据可以进行长时间序列大尺度的地表植被状况监测。     

全球LAI地面验证方法及验证数据综述

叶面积指数(LAI)的地面验证是LAI反演算法研究及LAI产品验证的重要部分。近年来,为了验证遥感参数产品以及对大气、地表的系统性研究,国际上在全球主要大洲开展了多个针对主要植被类型的大型观测项目,逐渐形成了较完善的地面采样框架与全球尺度不同植被类型的地面验证数据集。国内外已开展的大型观测项目采用的采样框架对以后观测试验中LAI的地面验证框架的制定与研究具有重要的借鉴意义,同时,已观测到的全球LAI地面验证数据集是开展全球LAI算法及产品验证的基础数据集,因此,有必要对目前全球LAI地面验证方法及验证数据情况做个综述。首先综述全球主要LAI地面观测项目及其所采用的地面验证框架。在此基础上,分析比较各大观测项目中所采用的地面测量方法、采样方法及像元真值估算方法。最后,指出全球LAI产品地面验证中存在的不足,并对尚需进一步研究的方向进行展望。     

基于TAVI的山区毛竹林LAI反演分析

本文采用地形调节植被指数（TAVI）,以RapidEye高分辨率多光谱遥感影像为数据源,对福建省永安市毛竹林山区进行了叶面积指数（LAI）地面实测、遥感建模及反演分析。通过TAVI与归一化植被指数（NDVI）、比值植被指数（RVI）的对比研究,结果表明：（1）毛竹林实测LAI与TAVI、NDVI和RVI线性回归的决定系数（R²）分别为0.6085、0.3156和0.4092,最佳非线性回归的R²分别提高到0.6624、0.5280和0.6497。LAI与NDVI或RVI非线性（U曲线）模型可以很好地解释LAI-VI的散点分布规律,但难以解决LAI-VI间因地形影响导致的“同物异谱”和“异物同谱”问题,因此,在山区大面积推广应用需慎重。（2）通过实测LAI的验证表明,LAI-TAVI回归模型可有效避免因地形影响导致的“同物异谱”和“异物同谱”问题。TAVI具有良好的削减地形影响作用,可用于山区植被LAI的遥感反演。     

Analyzing forest effects on runoff and sediment production using leaf area index

Quantifying the effects of forests on water and soil conservation helps further understanding of ecological functions and improving vegetation reconstruction in water-eroded areas.Studies on the effects of vegetation on water and soil conservation have generally focused on vegetation types or vegetation horizontal distribution densities.However,only a few studies have used indicators that consider the vegetation vertical distribution.This study used the leaf area index(LAI) to investigate the relationship between forests and water and soil conservation in experimental plots.From 2007 to 2010,rainfall characteristics,LAI,and water and soil loss in 144 natural erosive rainfall events were measured from five pure tree plots(Pinus massoniana).These tree plots were located in Hetian Town,Changting County,Fujian Province,which is a typical water-eroded area in Southern China.Quadratic polynomial regression models for LAI and water/soil conservation effects(RE/SE) were established for each plot.The RE and SE corresponded to the ratios of the runoff depth(RD) and the soil loss(SL) of each pure tree plot to those of the control plot under each rainfall event.The transformation LAIs of the LAI–RE and LAI–SE curves,as well as the rainfall characteristics for the different water/soil conservation effects,were computed.The increasing LAI resulted in descending,descending–ascending,ascending–descending,and ascending trends in the LAI–RE and LAI–SE curves.The rainfall frequencies corresponding to each trend of LAI–RE and LAI–SE were different,and the rainfall distributions were not uniform per year.The effects of soil conservation in the plots were superior to those of water conservation.Most of the RE and SE values presented a positive effect on water and soil conservation.The main factor that caused different effects was rainfall intensity.During heavy rains(e.g.,rainfall erosivity R = 145 MJ mm/ha h and maximum 30 min intensity I30 = 13 mm/h),the main effects were positive,whereas light rains(e.g.,R = 70 MJ mm/ha h and I30 = 8 mm/h) generally led to negative effects.When the rainfall erosivity was lower than that of the positive or the negative effects to a threshold and the tree LAI reached a transformation value,the relationships between LAI and RE or SE notably transformed.Results showed that the plottransformation LAIs for water and soil conservation during rainfall events were both approximately 1.0 in our study.These results could be used to come up with a more efficient way to alleviate water and soil loss in water-eroded areas.     

一种大气订正的方法：BRDF──大气订正环

本文介绍了一种大气订正的新方法：ＢＲＤＦ──大气订正环，这种方法首先在假设地面是朗伯体的条件下进行大气订正，通过一系列在不同成象几何条件下观测值的订正结果，在ＢＲＤＦ模型库中寻找一种最能描述这些数据的模型，最后根据反演的模型参数进行基于ＢＲＤＦ的大气订正。我们设计了一系列实验，证明了这种方法的有效性和可行性。     

Principle and application of three-band gradient difference vegetation index

Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, Beijing, China shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the     

近地机载激光雷达棉花LAI提取方法研究

棉花叶面积指数（LAI）是描述其长势的重要指标,准确获取冠层结构参数是叶面积指数反演的必要条件。以ScoutB-100油动单旋翼无人机为飞行平台,搭载RIEGL VUX-1激光雷达,精确获取棉花高密度点云数据,得到研究区棉田数字表面模型（DSM）和数字高程模型（DEM）,通过差值运算获得其冠层高度模型（CHM）,进而提取有效的冠层结构参数。利用相关性分析法选取相关系数大于0.2的激光穿透力指数（LPI）、回波点云密度（D）、孔隙率（f_gap）、归一化高程值（V_nDSM）构建棉花LAI反演模型,并与实测叶面积指数进行精度验证与评价。实验结果表明：模型估算的LAI与实测LAI之间的决定系数为0.824,均方根误差为0.072,验证了模型的可靠性。     

基于MODIS的LAI时间序列谱的地物分类方法研究

利用MODIS数据所反演的每8d一景,全年共46景的时间序列叶面积指数(LAI)图像,分析江西省不同类型地物的LAI时间序列谱,并对地物进行分类。首先,利用最小噪声比变换技术(MNF)将噪声从数据中分离;然后,通过纯净像元指数(PPI)从LAI时间序列谱中提取5类主要地物类型终端单元(Endmember),从而对地物进行分类并制图;最后,结合2000年江西省兴国县1 10万比例尺的土地利用/覆盖矢量图对本研究分类结果进行检验。结果表明,该方法的地物分类精度达到74.45%,其分类方法是有效可行的。     

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r² = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.     

美国西部黄松叶面积指数与高光谱分辨率CASI数据的相关分析

本文介绍了美国俄罗冈州西部黄松的叶面积指数(LAI)与小型航空光谱制图成像仪(CASI)获取的高光谱分辨率数据进行的相关分析。在试验场地上使用LAI-2000植物冠层分析仪测得8个LAI值(0.87—2.72)。对CASI数据进行一阶和二阶微分处理,以减少土壤背景光谱对森林光谱的影响。使用逐步回归分析方法探索LAI与CASI数据的关系。由回归分析产生多元线性方程和相应的拟合度(GOF)及估计LAI的标准误(SE)。结果说明光谱微分技术能够提高LAI和CASI数据的相关性,因而可以改善LAI的估计精度。如,对于单通道LAI预测的最高GOF值是0.681,SE是0.345,而经一阶和二阶光谱微分处理后,GOF被分别提高到0.904和0.898,SE被分别降低到0.189和0.195.