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.     

Multi-temporal JERS SAR data in boreal forest biomass mapping

Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects.In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes.Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS-optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data.The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.     

Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest

The objective of this study is to evaluate the relationship between the response (σ°) of airborne P-band Synthetic Aperture Radar (SAR) polarimetric data versus biomass values of primary forest and secondary succession. To ensure that different landscapes of “Terra firme” tropical forest of the Brazilian Amazon were represented, a test-site was selected in the lower Tapajós river region (Pará State). The microwave signals from the P-band polarimetric images were related to the aboveground biomass data by statistical regression models (logarithmic and polynomial functions). In the field survey, physiognomic and structural aspects of primary forest and regrowth were collected and afterwards the biomass was estimated using allometric equations based on dendrometric parameters. As an example of the potential use of P-band polarimetric images, they were classified by a contextual classifier (ICM), whose thematic stratification of land use/land cover was associated with biomass class intervals for mapping purposes. The main objective of this P-band experiment is to improve this tool for regional mapping of Amazon landscape changes, due to the growing rate of land use occupation.     

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.     

Selective logging changes forest phenology in the Brazilian Amazon: Evidence from MODIS image time series analysis

We present a large-scale study of the relationships between selective logging and forest phenology in the Brazilian Amazon. Time-series analysis of MODIS satellite data of selectively logged forests in Mato Grosso, Brazil, shows that relatively low levels (5-10%) of canopy damage cause significant and long-lasting (more than 3 years) changes in forest phenology. Partial clearing slows forest green-up in the dry season, progressively dries the canopy, and induces overall seasonal deficits in canopy moisture and greenness. Given large and increasing geographic extent of selective logging throughout Amazonia, this phenological disturbance has a potential to impact carbon and water fluxes, nutrient dynamics, and other functional processes in these forests.     

Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate

There is an urgent need for techniques to rapidly and periodically measure biodiversity and ecosystem services over large landscapes. Conventional vegetation classification and mapping approaches are based on discrete arbitrary classes which do not capture gradual changes in forest type (and corresponding biodiversity and ecosystem services values) from site to site. We developed a simple multi-date NDVI based Mahalanobis distance measure (called eco-climatic distance) that quantifies forest type variability across a moisture gradient for complex tropical forested landscapes on a single ecologically interpretable, continuous scale. This Mahalanobis distance, unlike other distance measures takes into account the variability in the reference class and shared information amongst bands as it is based on the covariance matrix, and therefore is most useful to summarize ecological distance of a pixel to a reference class in multi-band remotely sensed space In this study we successfully apply this measure as a surrogate for tree biodiversity and ecosystem services at two nested scales for the Western Ghats Bio-diversity hotspot. Data from over 500 tree-plots and forest type maps was used to test the ability of this remotely sensed distance to be a surrogate for abundance based tree-species compositional turn-over and as a continuous measure of forest type and ecosystem services. Our results suggest a strong but scale dependant relationship between the remotely-sensed distance measure and floristic distance between plots. The multi-date NDVI distance measure emerges as very good quantitative surrogate for forest type and is a useful complement to existing forest classification systems. This surrogate quantifies forest type variability on a single, continuous quantitative scale and has important applications in conservation planning and mapping and monitoring of hydrologic and carbon storage and sequestration services.     

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.     

物候信息在大范围作物长势遥感监测中的应用

大范围的农作物长势监测可以为农业政策的制订和粮食贸易提供决策依据,也是农作物产量估测的必要前提。遥感估算的作物生物量是评价作物长势的主要群体特征指标,在大范围上开展作物长势监测时,不同区域的作物因为所处的物候阶段不同而导致生物量存在差异,这种差异与因作物长势状况差别而产生的差异混合在一起,增加了长势监测结果的不确定性。以中国河南、山东两省为研究区,以MODIS 250 m NDVI产品数据为主要数据源,结合改进的CASA模型实现了区域内冬小麦生物量的估算,结合冬小麦生长过程特征进行了典型物候期的监测。在此基础上,分析了扬花期前后物候差异对冬小麦生物量估算的影响,研究其特定物候阶段的变化规律,从而实现了生物量的物候归一化,初步探索了如何消除大区域物候差异对作物长势监测与评估的影响。     

Characterizing 3D vegetation structure from space: Mission requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth's sustainability.To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that:

(1)

Current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates.

(2)

The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1 km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1 km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization.

We also show from the literature that lidar profile samples together with wall-to-wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein.     

区域森林生物量遥感信息模型构建研究

森林是陆地生态系统中最大的碳汇,在调节全球碳平衡、减缓大气CO²等方面具有不可替代的作用。森林生物量是陆地生态系统碳循环过程中最主要的参数,准确估算森林生物量及森林的变动引起的生物量变化受到科学家的普遍关注,并成为碳循环科学研究中的焦点。以生态敏感区滇西北香格里拉县为研究区,在野外森林样方调查数据的支持下,综合3S技术、地理学、生态学、气象学等相关知识,筛选了9个植被指数、2波段灰度值、生长季降水、生长季积温、生长季总辐射量、海拔、坡度、坡向、坡位和土壤有机质含量等多个因子,组合成遥感综合因子层、地理综合因子层与水、光、热共同构成变量,建立了区域森林生物量估算模型,并进行了检验,模型的R、R²、aR²及F统计量分别为0.809、0.655、0.661、101.436;样地实测值与模型估测值建立线性回归方程常数项(a)和回归系数(b)分别为0.09和1.021;用22个野外实测样点生物量数据对估算模型进行独立性检验,平均估算精度达到76.43%。说明模型的估算精度总体稳定,基本满足生物量估算精度要求,可用于该区域的森林生物量估算研究。