Detecting and mapping levels of Gonipterus scutellatus-induced vegetation defoliation and leaf area index using spatially optimized vegetation indices

Gonipterus scutellatus outbreaks may severely defoliate Eucalyptus plantations growing in South Africa. Therefore, detecting and mapping the severity and extent of G. scutellatus defoliation is essential for the deployment of suppressive measures. In this study, we tested the utility of spatially optimized vegetation indices and an artificial neural network in detecting and mapping G. scutellatus-induced vegetation defoliation, using both visual estimates of percentage defoliation and optical leaf area index (LAI) measures. We tested both field methods to determine which of the two were more superior in detecting vegetation defoliation using optimized vegetation indices. These indices were computed from a WorldView-2 pan-sharpened image, which is characterized with a 0.5-m spatial resolution and eight spectral bands. The indices were resampled to spatial resolutions that best represented levels of G. scutellatus-induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with an R² of 0.83 and an RMSE of 1.55 (2.97% of the mean measured defoliation), based on an independent test data-set. Similarly, LAI subjected to defoliation was detected with an R² of 0.80 and an RMSE of 0.03 (0.06% of the mean measured LAI), based on an independent test data-set. Therefore, the results indicate that the cheaper less-complicated visual percentage estimates of defoliation was the more superior model of the two. A sensitivity analysis revealed that NDRE, MCARI2 and ARI ranked as the top three most influential indices in developing both percentage defoliation and LAI models. Furthermore, we compared the optimized model with a model developed using the original image spatial resolution. The results indicated that the optimized model performed better than the original 0.5-m spatial resolution model. Overall, the study showed that vegetation indices optimized to specific spatial resolutions can effectively detect and map levels of G. scutellatus-induced defoliation and LAI subjected to defoliation.     

基于背景库的高质量LAI时间序列数据重建

叶面积指数LAI(Leaf Area Index)是表征植被冠层结构的重要参数,然而由于云等大气因素的影响,MODISLAI时间序列产品在时间与空间尺度的连续性仍存在问题。随着先验知识在遥感反演中的地位不断得到加强,本文将多年LAI历史数据作为先验知识,用以建立LAI背景库,并提出了基于LAI背景库的Savitzky-Golay(SG)滤波算法来实现LAI时间序列数据的降噪工作。结果表明,与传统SG滤波相比,新算法能够更加客观有效地重建LAI时间序列。     

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。     

黑河流域叶面积指数的遥感估算

研究利用Landsat7ETM⁺遥感数据获取黑河流域植被叶面积指数(LAI)空间分布的可行性。该研究是基于黑河流域分布式水文模型的一个重要输入项———LAI空间分布数据的需要而产生的。文章在详尽的野外观测数据基础上,分别探究实测LAI与同时相ETM⁺3、4、5、7波段反射率及相关植被指数(SR、NDVI、ARVI、RSR、SAV I、PVI、GESAVI)的相关关系,率定最佳的LAI遥感反演及其空间分布方案。研究发现,针对特定的自然条件,将研究区分为植被覆盖度小的稀疏立地和覆盖度大的密集立地,分别采用土壤调节植被指数(SAVI)和大气阻抗植被指数(ARVI)进行2种林地的LAI估算最为可靠,在此基础上,提出黑河地区LAI估算及其空间分布的遥感制图方案。     

高光谱数据与水稻叶面积指数及叶绿素密度的相关分析

分析了北京大屯科技站水稻叶面积指数 (LAI)、叶绿素密度 (CH .D)与高光谱分辨率遥感数据在整个生育期内的变化过程。利用微分技术处理水稻群体反射光谱以减少土壤等低频背景光谱噪音的影响。通过单相关分析和逐步回归方法研究水稻LAI、CH .D分别与光谱反射率、反射率的一阶微分光谱的相关关系 ,并建立预测回归方程。结果表明 ,微分技术能够改善光谱数据与LAI、CH .D的相关性 ,CH .D与光谱数据的相关明显优于同LAI的。     

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。     

基于HJ-1A CCD1数据的台湾相思树叶面积指数反演

基于HJ-1A CCD1环境卫星数据,以福建沿海地区普遍分布的台湾相思树为研究对象,利用回归分析法(NDVI、OSAVI、EVI、HJVI)和PROSAIL辐射传输模型,构建台湾相思树LAI反演模型。同时,利用同步野外地面实测数据,将模型估算LAI值与实测LAI值进行对比。结果表明：(1)相比归一化植被指数NDVI、优化土壤调节指数OSAVI和增强型植被指数EVI 3种常用植被指数,引入修正大气、土壤背景影响的蓝、绿波段的环境植被指数HJVI来反演相思树LAI具有更高的精度(R²=0.7344,RMSE=0.1421);(2)本研究所选4种植被指数构建的最优反演模型均为非线性模型,其中,环境植被指数HJVI反演LAI最优模型为幂函数模型,表明相思树LAI与植被指数之间呈非线性变化;(3)PROSAIL辐射传输模型法比回归分析法反演相思树LAI的精度有较大提高(R²=0.7903,RMSE=0.1303),可见PROSAIL模型法构建反演模型能更好地反演相思树LAI。     

新疆焉耆盆地LAI反演及空间分布特征

叶面积指数（LAI）是生态系统物质和能量循环过程的重要结构参数。研究植被LAI对监测估算生态环境极其脆弱的内陆干旱区具有理论和实践价值。使用LAI-2000植物冠层分析仪观测焉耆盆地植被LAI,并结合Landsat OLI的NDVI、SAVI、NDMI、NBR、NBR2、MSAVI和EVI等植被指数数据建立了LAI的反演模型,探求焉耆盆地LAI的空间分布特征及规律。结果表明：（1）各植被指数与观测的LAI均有明显的相关性。其中,SAVI与LAI的对数函数模型更好地模拟出研究区的LAI（R²=0.84）;通过LAI-SAVI模型反演研究区LAI的空间分布,估算精度可达89%（R²=0.82,RMSE=0.23）;（2）博斯腾湖小湖湿地、博斯腾湖大湖西北端的黄水沟入湖沼泽区以及焉耆盆地北部冲洪积扇平原绿洲区的LAI值较高,达3.85;焉耆盆地山区和平原区之间的过渡带和博斯腾湖南部的沙漠区LAI较低;（3）焉耆盆地LAI以博斯腾湖为中心环状分布,LAI随着研究区域与湖、河流距离的增加而逐渐降低的趋势。     

高光谱反演水稻叶面积指数的主成分分析法

为了通过水稻冠层反射光谱来提取水稻叶面积指数信息，尝试利用辐射传输模型PROSPECT+SAIL来模拟水稻冠层反射光谱， 比较了各植被指数中叶面积指数(LAI)和叶绿素浓度的相关性。在观察光谱曲线后发现，红边位置光谱可以较好地区分LAI和叶绿素 浓度二者引起光谱变化的差异。由此提出对700 nm~750 nm区间内的反射光谱做主成分变换，并利用第2主成分与LAI建立反演模型( 即主成分分析法)，取得了较好效果，表明在植被指数趋近于饱和以至于无法区分二者相关性时，主成分分析法可以作为一种简单 而有效提取水稻叶面积指数信息的补充手段。     

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