多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一。本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析。结果表明:(1) 利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度。(2) 利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定。本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89)。(3) 采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%。三者与数字相机测量结果的相关系数r2分别为0.78、0.61和0.76。(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80)。利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺     

基于数字相机、ASTER和MODIS影像综合测量植被盖度

选择我国北方温带典型草原作为研究对象,基于Bottom-up方法,采用地表实测和多尺度遥感综合测量的方法,建立基于地表实测与多尺度遥感数据综合测量的两阶段植被盖度经验模型。此外,还将该模型与常用的亚像元分解模型相比较,结果表明:1)两阶段经验模型可以较好地实现将地面数据扩展到中尺度空间范围,从而完成数据空间尺度的转换,提高大区域草地植被盖度的测量精度;2)MODIS遥感影像数据,结合地面数据和ASTER遥感影像数据可以较好地在区域范围内对北方典型草原的植被盖度进行估测;3)目前常用的亚像元分解模型,应用于中空间分辨率的MODIS影像,估测北方温度典型草原植被盖度的精度不够理想。     

地面测量与ASTER影像综合计算植被盖度

选择我国北方温带典型草原作为研究对象,在研究区内分别针对植被盖度是高、中、低的地区随机选取49块样地,在每块样地内采用嵌套的方式作样方,样方尺度有60、30、15m3个尺度,嵌套的方式是60m的样方嵌套30m的样方,30m的样方嵌套15m的样方。15m的样方内作3个1m的样方,30m的样方内作5个1m的样方,60m的样方内作10个1m的样方。对于每个1m的样方,采用将数码相机架设在离草本层2m高的位置,垂直对样方进行拍摄,并记录影像的方式估计植被盖度。基于以上地面实测数据以及ASTER遥感数据,建立植被盖度经验模型。模型建立的步骤主要包括以下内容：（1）利用植被盖度信息提取模型计算野外实测的各张数码相片的植被盖度值,在15、30m以及60m3个样方尺度上计算各样方的植被盖度;（2）计算ASTER数据的各种植被指数（RVI,NDVI,NDGI）;（3）将地面样方与相应的ASTER影像像元在不同的尺度上相匹配。（4）基于某一样方尺度,计算与地面样方相对应区域内的ASTER影像像元植被指数的中值。（5）基于该样方尺度,建立地面实测植被盖度值与ASTER数据植被指数值之间的回归模型;（6）对回归模型进行显著性检验。在建立经验模型的过程中,研究植被指数（NDVI、NDGI以及RVI）与植被盖度的相关性以及地面样方尺度对经验模型的影响。此外,还将经验模型与目前广泛使用的亚像元分解模型作比较,检验亚像元分解模型在中国北方温带典型草原的适用性。结果表明,（1）对于中国北方典型草原区而言,利用植被指数NDVI监测草地的植被盖度是比较适宜的,它优于植被指数RVI,也比植被指数NDGI的效果好。（2）地面样方尺度的选择对于植被盖度经验模型的建立有很大的影响。就中国北方典型草原区来看,在地面作大样方,取其中值的方法在一定程度上可以克服由于地面的测量和遥感测量之间发生空间错配而产生的影响,有利于提高植被指数与植被盖度之间相关系数的显著水平。（3）60m×60m的样方尺度,基于NDVI的植被盖度经验模型相比,利用亚像元分解模型估测中国北方温带典型草原植被盖度的精度低于前者。     

基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。     

内蒙古温带草原区植被盖度变化及其与气象因子的关系

利用1982-1999年内蒙古地区NOAA/AVHRR的NDVI数字遥感影像,对内蒙古温带草原区植被盖度进行了反演,探讨了近20a来温带草原植被盖度的变化情况,并对植被盖度与不同组合方式的降水及气温数据进行了相关分析,探讨了植被盖度与气象因子的关系。结果表明:①近20a来温带草原植被盖度呈上升趋势,占总面积72%的草原植被盖度发生了增长,3种不同草原类型中典型草原盖度上升趋势最为明显;②温带草原生长季平均盖度、逐月盖度与降水成正相关关系,与气温呈负相关关系,其中降水对盖度的影响存在着时滞及累积效应;③3种草原类型植被盖度对气象因子的敏感性不同,荒漠草原植被盖度与气温和降水相关性最强,其次为典型草原与草甸草原。     

多尺度遥感数据协同的干旱地区植被覆盖度提取

纯植被像元获取是植被覆盖信息遥感反演的必要环节。干旱地区植被分布零散稀疏,使用中、低分辨率遥感数据提取植被覆盖度时,难以获取纯植被像元,致使植被覆盖度提取精度较低。针对上述问题,本文提出一种基于多尺度遥感数据协同的干旱区植被覆盖度反演方法。该方法利用空间分辨率较高的Landsat-8 OLI数据确定纯植被像元,考虑到不同传感器之间的光谱差异,使用实测地物光谱数据进行光谱转换,代替中等分辨率MODIS数据的纯植被像元,应用于像元二分模型,选择典型的干旱区新疆阜康市为研究区,进行植被覆盖度反演实验,最后使用无人机航拍影像对反演结果进行精度验证。结果表明,植被覆盖度反演结果精度较高,与实测值间存在较高的相关性(R2=0.75),均方根误差较低(RMSE=0.10)。该方法能够有效提高干旱区植被覆盖度反演精度,可为利用中低分辨率数据研究干旱地区生态环境变化提供一种新思路。     

结合机载LiDAR和LANDSAT ETM+数据的温带森林郁闭度估测

森林郁闭度是森林资源调查中的一个重要因子,在森林生态系统管理中具有重要作用。研究如何有效地将激光雷达数据应用于森林郁闭度遥感估测具有重大意义。激光雷达数据的应用能够有效地弥补传统地面调查耗时、费力等不足,不仅可以快速、准确地获取郁闭度遥感估测的模型训练数据和验证数据,还有助于进一步推广应用于大区域的森林郁闭度反演,为林业资源调查提供有力的依据。该研究结合激光雷达数据和LANDSAT ETM+数据估测温带森林郁闭度。以高密度机载激光雷达(ALS)点云数据估算的郁闭度作为模型训练数据和验证数据,通过LANDSAT ETM+影像数据计算得到的8种植被指数作为自变量,使用多元逐步回归(MSR)、随机森林(RF)和Cubist 3种模型,对内蒙古大兴安岭根河林区森林郁闭度进行估测。经验证,Cubist模型的效果比较好(决定系数R2=0.722,均方根误差RMSE=0.126,相对均方根误差r RMSE=0.209,估计精度EA=79.883%)。结果表明,结合激光雷达数据和LANDSAT ETM+影像数据估算温带森林郁闭度非常有潜力。但要将其推广应用于更大区域尺度的森林郁闭度遥感估测,模型的预测能力还有待进一步改进和提高;自变量应尝试加入更多种类遥感数据和其他遥感因子参与建模,例如采用地形因子、高分辨率遥感影像提取纹理特征等,最大可能地减少光学影像、植被指数、地形阴影等带来的影响,提高反演精度;激光雷达数据计算得到的郁闭度的准确性和可靠性还需进一步验证。     

基于不同空间尺度遥感影像估算森林叶面积指数的差异

地面测量点对遥感像元的代表性如何,怎样获取像元的相对真值,多大的空间分辨率可以真实地反映森林区域的叶面积指数(LAI),这些都是定量遥感中的重要问题.本研究计算LAI-2200和TRAC两种冠层分析仪测量的空间范围,并结合GF-2(4.1 m)、Sentinel-2(10 m)、Landsat-8(30 m)3种不同空间分辨率遥感影像,找到各尺度下像元的相对真值,在保持真值观测面积和遥感获取面积一致的条件下,基于一元指数和多元回归模型,对比分析不同空间分辨率影像对估算森林LAI的影响,并对3种影像模型进行30和100 m尺度下的检验以及各自数据集的空间代表性评价,比较得出最适合表达研究区域森林LAI的尺度.结果表明:对于森林来说,高分辨率并不一定能充分反映森林LAI.基于3种分辨率影像的统计模型都能很好地估测森林LAI,其中,基于Sentinel-2的反演精度最高,基于GF-2的反演精度最低.30和100 m尺度下的检验结果表明,基于GF-2反演模型高估了森林LAI,基于Landsat-8的反演模型低估了森林LAI,基于Sentinel-2分辨率的统计模型可以很好地估测研究区域森林LAI.     

基于多端元光谱混合分析方法的大兴安岭火后植被盖度恢复研究

火干扰是北方针叶林结构、功能及动态的主要调节因子之一。研究火后植被恢复对理解火干扰和生态系统的交互作用具有重要意义。火烧迹地通常由植被与基质混合组成,在中低分辨率（ > 10 m）遥感影像中表现为混合像元,因此研究亚像元尺度上植被的恢复是精确量化植被恢复的关键。本研究以2000年大兴安岭呼中自然保护区中8700 hm²火烧迹地为研究区,以两期（2014年6月1日和2010年6月22日）中分辨率Landsat ETM+影像（30 m）为基础数据,比较多端元光谱混合分析（Multiple Endmember Spectral Mixture Analysis,MESMA）和归一化植被指数（Normalized Difference Vegetation Index,NDVI）获得的植被盖度,以高分辨率（2 m）WorldView-2影像（2014年7月1日）为验证数据,对两种方法计算的植被盖度精度进行比较。结果表明,MESMA方法获得的植被盖度（R²=0.691）与传统的NDVI获得的植被盖度（R²=0.700）精度无统计差异,中烈度下获得的植被覆盖精度高于低、高火烧烈度。为验证同一端元能否运用到不同时相的Landsat影像中,本研究将从2014年影像中获取的最佳端元运用到2010年影像中获得植被盖度图,结果表明2014年与2010年得到的RMSE（均方根误差）均值分别为0.0015和0.0065,说明最佳端元可用于不同时相的影像分解。本研究表明MESMA方法可有效监测北方针叶林中火后植被盖度恢复,并可运用于时间序列遥感影像监测植被恢复动态。     

基于RS和GIS的毛乌素沙地植被盖度定量估测

选取毛乌素沙地东北部的伊金霍洛旗为研究区域,以少量野外定位调查数据与其对应的遥感信息和GIS信息为基础,利用岭估计分析方法,对植被盖度估测模型及其影响因子进行系统研究.结果表明,植被盖度除受NDVI影响外,还与其他遥感信息紧密相关,岭估计方法明显地改善了最小二乘估计的缺陷,克服了变量间由于存在复共线性关系对求解待定参数所造成的不利影响,提高了估测精度.建立了以像元为单位的植被盖度估测模型,其模型检验精度达98.7%.此外,还建立了区域性植被盖度地理信息系统,实现了研究区域内任意一点(像元)或任意土地单元植被盖度的查询、更新及自动制图.     

Development of a satellite-based hazard rating system for Dendroctonus frontallis (Coleoptera: Scolytidae) in the Ouachita Mountains of Arkansas

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is the most damaging forest insect pest of pines (Pinus spp.) throughout the southeastern United States. Hazard rating schemes have been developed for D. frontalis, but for these schemes to be accurate and effective, they require extensive on-site measurements of stand attributes such as host density, age, and basal area. We developed a stand hazard-rating scheme for several watersheds in the Ouachita Highlands of Arkansas based upon remotely sensed data and a geographic information system. A hazard model was developed using stand attributes (tree species, stand age and density, pine basal area, and landform information) and was used to establish baseline hazard maps for the watersheds. Landsat 7 ETM+ data were used for developing new hazard maps. Two dates of Landsat imagery were used in the analyses (August 1999 and October 1999). The highest correlations between hazard rating scores and remotely sensed variables from either of the dates included individual Landsat 7 ETM+ bands in the near- and mid-infrared regions as well as variables derived from various bands (i.e., Tasseled cap parameters, principal component parameters, and vegetation indices such as the calculated simple ratio and normalized difference vegetation index). Best subset regression analyses produced models to predict stand hazard to southern pine beetle that consisted of similar variables that resembled but were more detailed than maps produced using inverse distance weighted techniques. Although the models are specific for the study area, with modifications, they should be transferable to geographically similar areas.     

Improvement, Comparison, and Application of Field Measurement Methods for Grassland Vegetation Fractional Coverage

As one of the important vegetation parameters, vegetation fractional coverage （VFC） is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the north of China was chosen for investigation in the present study and a digital camera was used to measure herb community coverage in the field, adopting methods of ocular estimation, gridding measurement, visual interpretation, supervised classification, and information extraction of color spatial transformation to calculate the VFC of images captured by the digital camera. In addition VFC calculated by various methods was analyzed and compared VFC, enabling us to propose an effective method for measuring VFC using a digital camera. The results of the present study indicate that： （i） as two common useful and effective methods of measuring VFC with a digital camera, not only does the error of estimated values of visual estimation and supervised classification vary considerably, but the degree of automatization is very low and depends, to a great extent, on the manipulator; （ii） although the method of visual interpretation may assure the precision of the calculated VFC and enable the precision of results obtained using other methods to be determined, as far as large quantities of data are concerned, this method has the disadvantages of wasting time and energy, and the applications of this method are limited; （iii） the precision and stability of VFC calculated using the grid and node method are superior to those of visual estimation and supervised classification and inferior to those of visual interpretation, but, as for visual interpretation and supervised classification, gridding measurements are difficult to apply in practice because they are not time efficient; and （iv） in terms of the precision of calculation of the VFC, an information-extracting model based on an intensity, hue, saturation （IHS） color space-multi-component series segmentation strategy is superior to methods of ocular estimation, gridding measurement, and supervised classification. In terms of practical efficiency, the information-extracting model is superior to visual interpretation, supervised classification, and gridding measurement. It has been proven that estimating the VFC of the north temperate typical steppe using this model is feasible. This is very fundamental research work in grassland ecology.     

采用广义加法模型整合数字高程模型和遥感数据进行植被分布预测

为了采用广义加法模型整合数字高程模型和遥感数据进行植被分布的预测, 并探索耦合环境变量和遥感数据作为预测变量是否能够有效地提高植被分布预测的精度, 选择海拔、坡度、至黄河最近距离、至海岸线最近距离, 以及从SPOT5遥感影像中提取的光谱变量作为预测变量, 采用广义加法模型整合环境变量和光谱变量, 建立植被分布预测模型。研究设置3种建模情景(以环境变量作为预测变量, 以光谱变量作为预测变量, 综合使用环境变量与光谱变量作为预测变量)对黄河三角洲的优势植被类型的分布进行了预测, 并对预测结果采用偏差分析、受试者工作特征曲线和野外采样点对比等3种方法进行了验证。结果表明: (1)基于广义加法模型的植被分布预测方法具有一定的实用性, 可以较为准确地预测植被的分布; 盖度较高的植被类型预测精度较高, 盖度较低的植被类型预测精度较低, 植物群落结构的特点是出现这些差异的主要原因; 综合使用环境变量和光谱变量作为预测变量的模型, 预测精度高于单独以环境变量或者光谱变量作为预测变量的模型。(2)环境变量、光谱变量大多被选入模型, 二者均对植被分布预测有重要的作用; 同一预测变量在不同植被类型的预测模型中的贡献不同, 这与植被的光谱、环境特征差异有关; 同一预测变量在不同的建模情景下对模型的贡献不同, 环境变量与光谱变量的耦合效应可能是导致预测变量对模型的贡献出现变化的原因。     

基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布

森林碳储量对于全球气候变化具有重要影响,以往的模型估算未考虑到模型残差的空间相关性和碳储量数据的非平稳性,影响模型的预测精度.本研究基于东北林业大学帽儿山实验林场的ETM+遥感影像数据和193块固定样地,利用地理加权克里格回归(GWRK)建立森林碳储量与遥感和地形因子的回归模型,同时对比最小二乘模型(OLS)、地理加权回归模型(GWR)的预测精度.结果表明: 对于帽儿山地区的森林碳储量估算,GWRK的平均绝对误差(MAE)、均方根误差(RMSE)低于OLS模型和GWR模型,GWRK模型的平均误差(ME)低于GWR模型,与OLS模型相近.GWRK模型的预测精度为83.2%,较OLS模型(73.7%)和GWR模型(77.3%)分别提高6%和10%,拟合精度明显提高,说明GWRK模型是森林碳储量估算的有效方法.利用GWRK模型预测的研究区森林碳储量平均值为70.31 t·hm^-2,在海拔较高的地区,森林碳储量值相对较高,说明海拔对其有较大影响.     

Response of NDVI,biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra

This study explores the relationship between the normalized difference vegetation index (NDVI), aboveground plant biomass, and ecosystem C fluxes including gross ecosystem production (GEP), ecosystem respiration (ER) and net ecosystem production. We measured NDVI across long-term experimental treatments in wet sedge tundra at the Toolik Lake LTER site, in northern Alaska. Over 13 years, N and P were applied in factorial experiments (N, P and N + P), air temperature was increased using greenhouses with and without N + P fertilizer, and light intensity (photosynthetically active photon flux density) was reduced by 50% using shade cloth. Within each treatment plot, NDVI, aboveground biomass and whole-system CO(2) flux measurements were made at the same sampling points during the peak-growing season of 2001. We found that across all treatments, NDVI is correlated with aboveground biomass ( r(2)=0.84), GEP ( r(2)=0.75) and ER ( r(2)=0.71), providing a basis for linking remotely sensed NDVI to aboveground biomass and ecosystem carbon flux.     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

北京海淀区植被覆盖的遥感动态研究

 植被覆盖度fg人(植被的垂直投影面积与单位面积之比)是一个十分重要的生态气候参数。为了有效地从遥感资料中提取植被覆盖度,发展了一套计算区域植被覆盖度的亚象元分解模型法。运用该方法对北京市海淀区1975、1991和1997年的植被覆盖度进行了计算,并在此基础上,求得研究区不同植被覆盖等级的变化转移矩阵,分析了海淀区22年来植被覆盖等级变化的空间过程和变化趋势。     

Secondary Forest Detection in a Neotropical Dry Forest Landscape Using Landsat 7 ETM+ and IKONOS Imagery1

We integrate forest structure and remotely sensed data for four successional stages (pasture, early, intermediate, and late) of a tropical dry forest area located in the Sector Santa Rosa of the Guanacaste Conservation Area in northwestern Costa Rica. We used a combination of spectral vegetation indices derived from Landsat 7 ETM+ medium resolution and IKONOS high‐resolution imagery. The indices (using the red and near‐infrared bands) simple ratio and normalized difference vegetation index separated the successional stages well. Two other indices using mid‐infrared bands did not separate successional stages as well. In a comparison of the successional stages with chronological age, there was no separability in the spectral reflectance among different age classes. Successional stages, in contrast, showed distinct groups with minimal overlap. We also applied a simple validation in another dry forest located in the Palo Verde National Park in the province of Guanacaste, Costa Rica, with reasonably good results.