异质性地表的叶面积指数反演的不确定性分析

叶面积指数(Leaf Area Index)可用来反映作物的生长状况,常作为主要指标应用于农作物估产。本文研究遥感中常见的混合像元问题对LAI反演所带来的不确定性问题。研究的混合像元由两种情况构成,一种是由不同长势的作物所构成的混合像元,另一种情况是由不同端元形成的混合像元。结果表明,不同长势形成的混合像元对LAI的准确反演影响不大;不同组分形成的混合像元对LAI反演影响很大。从验证的角度讲,地面实测点的LAI数据不能代表一定分辨率区域的LAI的值,对于像元LAI的验证要注意正确获得像元的LAI。     

基于土地覆盖分类的植被覆盖率估算亚像元模型与应用

如何利用遥感资料估算植被覆盖率已成为建立全球及区域气候、生态模型的基础工作之一。重点探讨了利用TM资料从植被指数（NDVI）中提取植被覆盖率的方法。根据TM像元为非均一混合像元的特点,提出了基于土地覆盖分类的综合运用“等密度模型”和“非密度模型”计算植被覆盖率的方法,通过对北京市海淀市区的植被覆盖率计算表明,该方法的估算精度可达75.4%,比单纯使用等密度亚像元模型在估算精度上可提高5.8%。可以认为,该方法为大面积植被覆盖率估算提供了一种有效的途径。     

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.     

华北一作区马铃薯生长发育及产量对干旱胁迫响应的模拟研究——以武川县为例

为探究不同程度干旱胁迫对华北一作区马铃薯生长发育及产量的影响,以内蒙古呼和浩特市武川县为例,基于多年马铃薯生长发育资料和气象资料,对APSIM-Potato模型进行调参与验证,评价模型在武川地区的适用性。利用验证后的模型模拟马铃薯叶面积指数（LAI）、地上部生物量和产量对不同发育阶段干旱胁迫的响应。结果表明：（1） 各发育阶段天数的模拟值与实测值的均方根误差（RMSE）均在3 d内;LAI、地上部生物量和产量的模拟值与实测值的归一化均方根误差（NRMSE）分别为12.82%、17.35%和14.48%,均低于20%,表明APSIM-Potato模型在武川地区具有较好的适用性。（2） 随着干旱胁迫时间和强度的增加,马铃薯LAI、地上部生物量和产量随之减小。模拟单一发育阶段干旱胁迫时,马铃薯LAI、地上部生物量和产量对分枝-开花期水分胁迫的响应最大;模拟连续发育阶段干旱胁迫时,LAI、地上部生物量和产量对全生育期水分胁迫的响应最大。     

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。     

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.     

冬小麦叶面积指数的高光谱估算模型研究

本文以山东禹城为研究区,利用地面实测光谱数据,探讨不同植被指数和红边参数建立高光谱模型反演冬小麦叶面积指数的精度。通过逐波段分析计算了4种植被指数(NDVI、RVI、SAVI、EVI),结合同步观测LAI数据,确定反演叶面积指数的最优波段;计算了5种常用的高光谱植被指数MCARI、MCARI2、OSAVI、MTVI2、MSAVI2,同时利用4种常用方法计算红边位置和红谷,与实测LAI进行回归分析,比较植被指数和红边参数模型对冬小麦LAI的估测精度。结果表明各因子与LAI均具有较高的相关性,整个研究区归一化植被指数具有最高的反演精度,确定了估算冬小麦LAI的最优模型,并使用独立的LAI观测数据对模型进行了验证。     

基于SPOT5遥感影像的城市森林叶面积指数反演

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。     

基于分期播种试验的小麦越冬前适宜指标探讨

基于2006-2007和2007-2008年度郑麦366的分期播种试验资料,结合郑州农业气象试验站多年同品种小麦业务观测资料,利用相关分析、最小显著差数等方法,对越冬前积温、小麦生长状况及产量构成进行了分析,结果表明：冬小麦越冬前的生长状况与各发育阶段积温有很强的相关性,产量构成的三要素也与越冬前各发育阶段积温及分蘖数...