不同树种组的热带森林植被生物量与遥感地学数据之间的相关性分析

以我国云南省西双版纳的热带森林为例,分别对栎类林、其它阔叶林、其它硬阔叶林的生物量与其对应的Landsat TM数据及其派生数据、气象数据和地形数据进行了相关性分析。首先,利用森林资源连续清查的林业固定样地数据,通过各树种组的各器官生物量估算模型计算出各样地的森林植被的生物量。然后,根据样地的坐标来建立样地GIS数据库。其次,将同期的遥感影像、气象数据和地形数据与GIS数据进行配准,并从遥感影像中产生出一系列的派生数据。最后,在此基础上,分别对栎类林、其它阔叶林、其它硬阔叶林的样地生物量与其遥感数据和派生数据、气象数据和地形数据进行相关性分析。研究表明,栎类林的生物量与TM1、TM2、TM3、TM4、TM5、TM7、缨帽变换的亮度、绿度、湿度、VI3、DVI、PC1和PVI在0.05的水平上显著,而与其它因子在这个水平上相关都不够显著。其它阔叶林的生物量与降雨量在0.05的水平上显著,而与其它因子在这个水平上相关都不够显著。其它硬阔叶林的生物量与降雨量在0.05的水平上显著,而与其它因子在这个水平上,其相关都不够显著。     

基于遥感植被生物量估算模型自变量相关性分析综述

应用遥感估算植被生物量越来越受到人们的关注。在利用遥感信息参数以及其它因子构建模型时,往往要挑选一些对生物量具有显著影响的因子作为自变量,因此自变量个数的确定、自变量的选择对估算模型的可靠性及精度影响很大。从单变量模型、多变量模型以及参数、非参数模型等几个方面论述了植被生物量估算遥感信息模型自变量相关性,并提出了一些建议,希望能对植被生物量估算模型自变量的选择提供参考价值。     

三峡库区植被生物量遥感估算方法研究

利用遥感手段估算三峡库区植被的生物量,综合野外观测数据,分析了Landsat TM数据的光谱信息与植被生物量的关系,通过相关性分析表明Landsat TM多光谱数据能较好地反映该地区的植被生物量水平。然后,利用Landsat TM数据分别建立了针对三峡库区的阔叶林、针叶林、针阔混交林、灌木林和草本植被5种主要植被类型的生物量遥感估算模型,并利用所得模型计算了该区域2002年植被的地上生物量总量为1.05×10⁸ Mg。
     

基于分区和多时相遥感数据的山区植被分类研究

山区地形的特殊性导致了山区植被分类的复杂性。位于不同光照区域的同种植被,其光谱亮度值具有较大差异,分区使分类规则及阈值的设计更具针对性。多时相遥感数据能够充分利用不同植被类型间光谱特征时间效应。基于此提出了利用分区和多时相遥感数据进行山区植被的分类研究。研究表明,该方法在山区植被分类中具有明显的技术优势,分类总体精度和kappa系数分别为81.3%和0.72。     

高光谱植被遥感数据光谱特征分析

利用植被的光谱数据,探讨了植被冠层的光谱反射特征和诊断性光谱吸收特征。根据植被光谱特征和连续统去除法(CR),介绍了识别植被种类和预测植被冠层营养元素等生化组分含量的可能性。运用一阶微分反射比(FDR)和从连续统去除的光谱吸收特征中获得的波段深度(BD)、连续统去除后微分反射比(CRDR)、波段深度比(BDR)和归一化波段深度指数(NBDI)等变量,利用逐步线性回归模型并基于光谱吸收特征的变量来选择波长,并通过相关分析来预测植被冠层生化组分。     

基于多时相遥感数据的徐州市植被净初级生产力估算及相关性分析

以遥感数据和气象数据为主要数据源,应用改进的光能利用率模型估算徐州市2006、2008和2010年3年间6月份的植被净初级生产力(Net Primary Productivity,NPP),研究了该区域6月份NPP的时空变化及其与气象因子的相关性。结果表明：时间上,受气候和环境等因素综合变化的影响,研究区域6月份NPP呈逐年下降趋势;空间上,NPP的分布表现为在林地、草地和农田相对集中的区域偏高,且不同植被类型6月份的NPP大小关系在不同年份可能不同,其中在2006和2008年为农田>草地>林地,而2010年为农田>林地>草地。通过分析与气象因子的相关性和偏相关性,限制NPP的主要气象因子不是固定不变的,其中2006和2008年,限制NPP的主要气象因子为太阳辐射,而2010年为降雨量和温度。不同植被类型下NPP与气象因子相关性和偏相关性差异反映了不同类型植被生长对光、热、水条件要求的差异。
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基于视觉注意的遥感图像森林植被纹理分割

树冠作为遥感图像森林植被的典型纹理单元,具有突出的结构纹理特征,但现有分割方法较少利用此类结构纹理进行分割。为此,提出一种基于视觉注意机制的遥感图像森林植被纹理分割方法。将遥感图像中树冠的形状和结构作为视觉注意目标,通过纹理滤波增强树冠纹理,使用特定的多尺度树冠显著图圆盘(SID)模型标记树冠,并将各个多尺度树冠SID作为种子,设计改进的区域生长方法分割森林植被区域。实验结果表明,该方法能够准确标记多数典型树冠,有效提高森林植被区域的分割精度。     

植被虚拟仿真的遥感数据融合研究

讨论了遥感数据融合在实现植被虚拟仿真工作中的重要作用和基本原理.着重研究并给出了遥感数据融合的一般方法、融合遥感数据的植被虚拟仿真应用接口描述和基于以上工作的三维可视化输出方法.该研究结果使植被虚拟仿真具备充分遥感数据实时传输、干预接口、为基于多传感器的遥感信息的植被虚拟仿真可视化输出提供了有效支撑.     

利用陆地卫星TM数据评估森林病虫害

回顾和总结了近十多年来森林病虫害遥感监测的研究进展，介绍了“八五”攻关课题的最新研究成果，充分肯定了陆地卫星TM数据在森林资源质量监测和评估中的应用前景，指出了今后的发展方向。     

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Forest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network.     

利用LANDSAT TM数据对森林郁闭度进行遥感分级估测

以大兴安岭常青林场陆地卫星影像为例进行缨帽变换(Tasseled Cap),将变换后的湿度因子与森林郁闭度进行回归分析,回归分析结果表明相关性较好,最后通过回归方法建立线性方程,对遥感图像进行反演以获取森林的郁闭度,并进行遥感分级估测。     

Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.     

TM数据在桔果经济林区土地利用分类和专题信息提取中的应用

利用TM—CCT数据,对位于浙江省黄岩市境内256×256像元的试验区进行了土地利用自动分类和桔资源专题信息提取的研究。采用改进的动态聚类法和最小距离法,把非监督分类和监督分类有机地结合起来,获得了较高精度的土地利用分类图。在此基础上,运用阈值最小距离法和均值—均方差窗口法成功地实现了桔林资源的专题信息提取,精度达95.3％。从而为亚热带经济资源及其动态变化的快速检测提供了有效的新手段。     

基于激光雷达的大兴安岭典型森林生物量制图技术研究

森林生物量作为森林生态系统基本的数量表征,表明了森林的经营水平和开发利用价值,并能反映其与环境在物质循环和能量流动方面的复杂关系。同时,森林生物量也是林业问题和生态问题研究的基础。以内蒙古大兴安岭国家野外生态站为研究区域,通过对机载激光雷达(LiDAR)点云数据的预处理,利用计算机编程提取LiDAR点云数据的结构参数,以植被分位数高度变量与密度变量为自变量,结合地面调查数据,建立生物量与LiDAR结构参数的回归模型(决定系数为0.69,均方根误差为0.34)。运用IDL编程对LiDAR点云块数据进行运算并生成分辨率为20m×20m的栅格图像,拼接后得到整个区域的地上生物量分布图,对生成的地上生物量分布图进行验证的R2为0.78,RMSE为23.09t/hm2,平均估测精度达83%。     

Estimation the Biomass of Alpine Wetland Vegetation based on Multi-angle and Hyperspectral Data

Wetland vegetation biomass is a significant indicator of the health condition of wetland ecosystem,which directly mirrors the growth and productivity of vegetation communities.The estimation of vegetation biomass in alpine wetland contribute to understanding the causal feedback relations between alpine swamp ecosystem and global climate change.Longbaotan nature reserve in Sanjiangyuan area was taken as the research region.Taking advantage of hyper spectral dimension and multi\|angle stereoscopic structure information of CHRIS/PROBA,we analyzed the correlation between wetland vegetation biomass and some remote sensing factors,including spectral reflectivity,narrow band vegetation indices,red edge indices and principal component.The angular sensitivity of biomass was explored at the same time.The results showed that the biomass of vegetation on the alpine wetland was sensitive to satellite observation angle.The forward observation of the +36° degree image was significantly better than 0 and -36° degree images.Besides,the exponential model was the best regression model in the nonlinear models.Among them,REIP index of -36° degree was the fittest variable to biomass,which R2 was 0.599 and F value was 37.404 in dry biomass,and R2 was 0.685 and F value was 54.410 in fresh biomass.The maximum of dry biomass in Longbaotan area is 446.7 g/m2,and fresh biomass is 2 368.1 g/m2.     

Vegetation Dynamics and Its Relationship with Climatic Factors in Southwestern China

The response of the vegetation dynamics to climate variability in the southwestern China was analyzed based on the dataset from remotely sensed data and ground observation data,i.e.,MODIS NDVI data with the spatial resolution of 250 meters and temporal resolution of 16\|day,and meteorological data composed of monthly temperature and precipitation data collected from 121 weather stations spanning periods of 2001\|2013,respectively.Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) utilized linear regression to characterize the trends in vegetation shifts.Anomaly analysis was applied to characterize the yearly average fluctuation.Furthermore,the monthly maximum Normalized Difference Vegetation Index （MNDVI） and meteorological data were employed to calculate the correlation coefficient at different time scales.The results indicate that vegetation coverage has extensive decreased trends in the west of Sichuan Basin,the Dayao Mountain in Guangxi and part of the Yunnan\|Guizhou Plateau.Conversely,it shows an increased trend in the south of Sichuan Basin,the Daba Mountains,the west of Guizhou and the coastal areas in Guangxi.In more than half part of southwestern China,vegetation conditions are positively correlated with accumulated temperatures.But the partial correlation coefficient between vegetation condition and total precipitation has evident differences in regions.