Estimating vertical vegetation density through a SPOT5 imagery at multiple radiometric correction levels

There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces.A widely used parameter for such density,i.e.,leaf area index (LAI),was measured in situ in Nanjing,China and then correlated with two vegetation indices (VI) derived from multiple radiometric correction levels of a SPOT5 imagery.The VIs were a normalized difference vegetation index (NDVI) and a ratio vegetation index (RVI),while the four radiometric correction levels were i) post atmospheric correction reflectance (PAC),ii) top of atmosphere reflectance (TOA),iii) satellite radiance (SR) and iv) digital number (DN).A total of 157 LAI-VI relationship models were established.The results showed that LAI is positively correlated with VI (r varies from 0.303 to 0.927,p < 0.001).The R 2 values of "pure" vegetation were generally higher than those of mixed vegetation.The average R 2 values of about 40 models based on DN data (0.688) were higher than that of the routinely used PAC (0.648).Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction levels,indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion.The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images,thus improving accuracies of LAI estimation.     

基于TM影像和统计分析的林分参数估测研究

林业研究中的主要兴趣点之一在于通过经验或半经验模型建立林分参数与遥感影像数据间的相互关系来估测林分参数.基于覆盖美国佛罗里达州东北Duval县的遥感数据和两块样地清查数据,论文探讨了所选林分参数与TM影像光谱DN值间的相关性.相关性分析结果表明,单波段或植被指数对林分参数的解释能力低于50%,为此构建了林分参数与影像多波段间多元回归模型来估测林分参数.预测结果通过另一组数据验证,除林分密度外,其它参数估测可信度达75%以上.论文最后探讨了预测模型不足和需改进的地方,并指出该研究有助于更好地理解影像光谱值和林分参数间的关系.图1表2参9.     

Estimation of forest parameters based on TM imagery and statistical analysis

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number （DN） value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index （VI） or normalized difference vegetation index （NDVI） as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics     

内蒙古鄂托克前旗地区苦豆子植被指数信息提取的研究

苦豆子是一种兼具药用、经济、生态等多方面价值的灌草,本文利用NDVI、DVI和MSAVI等7种植被指数(Vegetation Indices)对内蒙古鄂托克前旗地区的苦豆子资源进行遥感分类提取,并且利用分类精度评价和信噪比评价2种方法比较了这几种植被指数在苦豆子分类上的优劣。经过对比分析,认为修正土壤调节植被指数(MSAVI)可以根据植被的郁闭度来调节土壤调节参数,在苦豆子遥感分类提取上具有明显的优势。     

基于时空适应反射率融合模型的林区遥感应用研究

高空间、时间分辨率遥感数据在林业遥感变化监测方面具有重要的作用,然而,对于特定传感器获取的遥感影像在空间和时间分辨率上存在着不可调和的矛盾。本文针对神农架林区多云雾高时空分辨率数据缺乏的现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法。首先,利用LandSat8数据进行预处理,得到高分辨率的NDVI数据,并将MODIS NDVI数据进行重投影、重采样等预处理;其次,利用STARFM模型进行高分辨NDVI预测,利用评价因子选择最佳算法参数,并利用二分模型计算植被覆盖度;再次,以LandSat8获取的真实数据与预测数据进行精度评价;最后,选取黑龙江小兴安岭西北部林区生长季数据进行验证试验。结果表明:利用该方法可以在神农架林区获得预测日期的较好的NDVI及植被覆盖度,精度分别为90.8%、82.60%。此外,通过验证试验,可以获得同年生长季小兴安岭林场较好的NDVI以及植被覆盖度,精度分别达到92.86%、88.65%。     

Image-based reflectance conversion of ASTER and IKONOS imagery as precursor to structural assessment of plantation forests in KwaZulu-Natal,South Africa

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches.     

植被指数的应用研究综述

目前,归一化植被指数NDVI作为一种遥感手段已经广泛应用于社会生产生活的多个方面．在综合大量国内外文献的基础上,总结了从遥感影像中提取NDVI的方法以及在提取过程中要注意的问题;气温、降水等气候因素对NDVI的影响以及NDVI自身合成计算、与气候因子相关系数计算的分析方法;列举了NDVI在植被监测、土地覆盖变化、荒漠化研究、净第一性植被生产力等几个方面的应用现状,并列出了NDVI在研究应用中的优势、存在的缺陷及改进NDVI缺陷的研究．     

基于多元回归模型的叶面积指数遥感反演

采用2011年8月获取的黄丰桥林场SPOT5数据为信息源,并同步开展现地样地调查,依据典型抽样,以不同的海拔、坡度、坡向进行选样,利用手持GPS和LAI-2000植物冠层分析仪,分别对选取的60块样地进行定位和叶面积指数测量。结合遥感数据和实地调查数据,对地理因子和遥感因子变量进行主成分分析,采用逐步回归法筛选出2个主成分建立多元回归方程,对该研究区域的植被叶面积指数进行模拟,精度达到84.17%。结果表明:RVI,NDVI,MSAVI,MCAVI和DVI与LAI之间存在较好的相关性。     

基于多特征的高分辨率影像松材线虫病树识别

为了提高松材线虫病树的监测效率,减少其对林业生产造成的损失,利用在高分辨率遥感影像上提取松材线虫病树的光谱特征、空间特征等多特征,然后进行Relief特征选择算法,提取的合适特征为归一化植被指数NDVI(Normalized Vegetation Index)、差值植被指数DVI(Difference Vegetation Index)、OHTA颜色模型作为病树与非病树的光谱特征,对目标影像进行自动筛选,得到疑似病树像元。运用DBscan空间聚类算法对疑似病树像元进行聚类,并以周围一定范围内有一定数量的健康树像元为空间分布参考,对拍摄地点30°1′N/111°43′E附近、分辨率为0.1 m的3幅高分辨率遥感影像筛选病树。自动筛选耗时分别是人工筛选的43.99%、51.08%和46.62%,相对于人工筛选的数量准确度分别为79.37%、77.85%和82.56%。结果表明:采用光谱特征与空间特征相结合的方法在高分辨率遥感影像上识别松材线虫病树识别效率更高。     

大气校正前后植被指数与森林蓄积量关系研究

利用FIAASH对中山陵风景区的ETM+影像进行大气校正,然后利用ETM+原始DN值、大气表观反射率、地面反射率计算归一化植被指数,并分别建立不同方法计算的归一化植被指数与单位蓄积量的函数关系。研究结果表明,地面反射率计算的归一化植被指数更能反映地面植被覆盖的真实情况,同蓄积量有更强的相关关系。     

基于RS与GIS的北京市植被覆盖度变化研究

选用对植被状态表征较好的归一化植被指数(NDVI),在GIS和RS工具支持下, 选用多时相北京Landsat TM遥感影像,解译和分析出NDVI的时空变化特征,利用植被指数与植被覆盖度的关系及遥感测量法测算植被覆盖度.结果显示:北京市整体植被覆盖度呈增长趋势,市区植被覆盖度变化不大,部分区县植被覆盖度则由于城镇化推进而略有减少.     

阿拉善左旗天然梭梭林鼠害防治的遥感监测

利用 1999年和 2 0 0 2年两期内蒙古阿拉善左旗TM遥感图像 ,进行大沙鼠鼠害防治前后梭梭林受害程度调查。对比分析了防治区与对照区植被生长状况 ,分析了健康和受害植被的光谱特征、其背景的纹理特征。用土壤调节植被指数和统计分类的方法 ,得出了受害植被的生长状况、健康植被面积增长量及对照区植被生长情况的相关数据。与实地害鼠密度的变化验证比较 ,为鼠害防治管理和动态监测提供科学依据。     

植被遥感研究的新思路--走出传统光谱理论应用的误区

对遥感中沿用几十年的植被光谱理论提出质疑,阐述了传统植被遥感中存在的误区,提出新的观点及研究思路。第一次提出干旱植被指数(LDVI)和综合植被指数(IVI)的概念,探讨了干旱地区草原植被的信息提取及定量分析方法。     

基于遥感技术的退耕还林监测研究——以甘肃省清水县为例

利用1∶5万地形图的DRG(数字栅格图),采用二次多项式法和双线性内插法;对TM图像进行几何校正处理。以TM432为最佳波段组合。在ERDAS遥感数字图像处理系统支持下,对清水县1997年和2008年两期的归一化差异植被指数(NDVI)进行了统计计算,并获得了其植被指数分布图、植被指数差值图及统计表。结果表明:2008年的植被指数相对1979年有了很大程度的提高,植被指数大于0以上的面积增加了13.4%。全县60%土地的NDVI值都有不同程度的增加。     

卫星数据主成分分析估计娑罗双树生物量

Spatio-temporal assessment of the above ground biomass (AGB) is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predominantly covered by Sal (Shorea robusta C. F. Gaertn). Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components (PCs) of the bands, vegetation indices (VI), and the PCs of the VIs respectively and multiple linear regression (MLR) analysis between the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.     

基于NDVI的清水县植被变化分析

本研究以清水县为研究区域,以1979年和2008年该区的TM数据为遥感数据源,在遥感数字图像处理系统和GIS支持下,通过提取NDVI值以及NDVI与植被覆盖度之间的关系,对该县植被覆盖变化进行了研究。研究结果表明:1979～2008年清水县整体植被覆盖有所增加,植被指数在138以上的面积从1979年的52488.13hm2增加到2008年的117603.38hm2,净增65115.25hm2,占全县面积的33.80%;低盖度植被类型面积大大减少,其中,Ⅴ级植被面积减少了73780.25hm2,占全县总面积的34.22%,高盖度植被面积增加,其中,Ⅰ级植被增长了33798.07hm2,占全县总面积的17.54%,Ⅱ级、Ⅲ级植被面积均有不同程度的增加,植被盖度等级未变化的面积为62954.75hm2,占全县总面积的32.68%;植被退化面积为11768.25hm2,而植被好转面积多达117926.75hm2,是退化面积的10.02倍。通过综合治理,清水县生态环境得到大大改善。     

遥感估测森林可燃物载量的研究进展

对采用遥感图像估测森林可燃物载量的方法进行综述.首先将现有方法根据对像元载量的分配方法,分成直接分配法和间接分配法2种.直接分配法分成聚类分析法和判别分析法;间接分配法分为简单植被特征法、林分模型法和综合因子约束法3种.然后对各方法的优缺点进行评价,指出现有方法整体准确率不高的不足,并分析产生误差的3个来源:1) 从遥感图像判读中间特征所产生的误差;2) 从中间特征到可燃物载量之间的误差;3) 使用可燃物模型所产生的误差.据此提出改进现有方法、提高估测准确率的3个思路:1) 使用新图像,如更高分辨率遥感图像、雷达图像或混合图像;2) 选择更合适的中间特征以及它们与可燃物载量的关系模型;3) 使用连续变量来描述可燃物载量.     

陕北北部植被生态环境变化遥感监测及对径流的影响分析

对陕北北部植被指数增幅进行分析,本文利用1998～2004年NOAA卫星遥感资料,采用归一化植被指数方法,发现陕北植被指数增幅在50%～200%之间,其中吴旗增幅最大。通过对延安北部及榆林市年降水量资料分析,得出植被指数增加不是由于降水变化引起的,是退耕还林草工程已初见成效。对吴旗县降水量和径流量进行时间序列相关分析、典型年对比分析,表明该县植被变化对年径流通量产生了影响,年径流量和泥沙量减小近一半。     

香格里拉县云南松林遥感辅助识别特征研究

以香格里拉县云南松林为研究对象,分别从云南松林的空间分布特征、植被指数特征和像元亮度特征三个方面,提取和归纳了云南松林的遥感辅助识别特征,旨在为今后的云南松林遥感分类识别提供方法借鉴。     

基于光谱混合分析的荒漠化信息提取--以毛乌素沙地为例

采用光谱混合分析(SMA)技术,选取农地、裸沙、沙生植被、水和盐碱地作为基本组分,以位于半干旱区的毛乌素沙地的典型地区为例,进行了荒漠化土地混合像元分解和荒漠化信息提取的尝试,并与穗帽变换和监督分类的结果进行了比较,最后采用实地调查数据和NDVI方法进行了精度验证.研究结果表明:光谱混合分析技术用于荒漠化信息提取具有比较好的效果,效果明显优于常用的NDVI方法.