上海滩涂植被资源遥感分析

利用2003年8月2日L andsat5-TM多光谱遥感影像,运用遥感处理软件ERDA S Im ag ine 8.6,经几何校正分幅裁剪等图像预处理后,采用监督分类和目视解译相结合对上海市滩涂植被进行解译分析。结合全球定位系统(GPS)样点定位,对分类结果进行全面的野外核实和修正,同时利用地理信息系统(G IS)对解译结果进行数据合成,统计出滩涂各类植被的分布区域及面积等数据。实际调查及其分析统计显示,上海滩涂植物群落总面积为21302.1hm2,主要植被组成为芦苇、海三棱草及互花米草三大群落,滩涂植物群落具有明显的高程梯度分布规律。大尺度的上海市滩涂植被的空间分布现状及其数量调查为上海市滩涂资源的合理规划、生物多样性保护和可持续开发利用提供科学依据。     

Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities,Taiwan

The purpose of this study is to apply different remote sensing techniques to monitor shifting mangrove vegetation in the Danshui River estuary in Taipei, Taiwan, in order to evaluate a long-term wetland conservation strategy compromising between comprehensive wetland ecosystem management and urban development. In the Danshui estuary, mangrove dominated by Kandelia candel is the major vegetation, and a large area of marsh with freshwater grasses has been protected in three reserves along the river shore. This study applied satellite imagery from different remote sensors of various resolutions for spectral analysis in order to compare shifting wetland vegetation communities at different times. A two-stage analytical process was used for extracting vegetation area and types. In the first-stage, a normalized difference vegetation index (NDVI) was adopted to analyze SPOT, Landsat, and QuickBird imagery to obtain the spatial distribution of vegetation covers. In the second stage, a maximum likelihood classification (MLC) program was used to classify mangrove and non-mangrove areas. The results indicated that the spatial distribution of mangroves expanded 15.18 and 40 ha in two monitoring sites in 10 years, demonstrating the success of establishing reserves for protecting mangrove habitats. The analytical results also indicated that satellite imagery can easily discern the difference in characteristics between imagery of mangrove and other vegetation types, and that the logistical disadvantages of monitoring long-term vegetation community changes as well as evaluating an inaccessible area may be overcome by applying remote sensing techniques.     

Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

森林生态系统立地指数的遥感分析

森林生态系统立地指数分析和立地质量的评估是森林生态系统经营管理和造林营林的重要理论基础与规划方法,也是研究森林生态系统生产力的重要内容.由于技术的限制,迄今为止,还没有实现森林生态系统立地空间分布格局的分析和开展立地条件随时间动态变化的研究.卫星遥感为大面积研究森林生态系统的生产力及其空间分布格局和动态提供了一条重要的途径.以云杉为对象,利用卫星遥感为研究手段,在岷江上游的四川西北部松潘镇江关流域研究森林生态系统立地指数的空间分布特点,探索有关遥感反演模型的建立,并通过有关精度的评估分析这种高技术应用的价值和潜力.研究结果表明,遥感植被指数NDVI和TNDVI与野外实测云杉立地指数（SI）基本为线性相关.通过对模型模拟结果和实际测定结果的比较研究,发现在1：1比例的分析图中,NDVI和TNDVI的遥感反演模型都有很好的拟合效果与较高的精度,说明通过遥感植被指数的方法测定森林立地指数具有较高的实用价值.     

卫星景象目视解释方法在湖区植被制图中的应用——以湖南洞庭湖区水域、洲滩植被图为例

植被(包括天然植被、人工植被)作为一种可更新资源始终是遥感应用专家们热心研究的课题。植被分类是植被研究的重要方面之一。利用卫星影象进行植被分类,国内外学者都进行了许多有意义的探索。本文以洞庭湖水域、洲滩植被为对象,试用卫星影象进行植被分类。本文从植被与环境相互依赖关系及在影象上的综合反映出发,提出了影象的景观生态学分析方法;即把影象上色调、形态特征与群落生态学规律结合起来分析,并以此作为植被目视解译的方法论。在此基础上,将洞庭湖水域,洲滩划分成五个景观生态模型,17个基本植被类型。     

卫星影象目视解译方法在湖区植被制图中的应用——以湖南洞庭湖区水域、洲滩植被图为例

植被(包括天然植被、人工植被)作为一种可更新资源始终是遥感应用专家们热心研究的课题。植被分类是植被研究的重要方面之一。利用卫星影象进行植被分类,国内外学者都进行了许多有意义的探索。本文以洞庭湖水域、洲滩植被为对象,试用卫星影象进行植被分类。本文从植被与环境相互依赖关系及在影象上的综合反映出发,提出了影象的景观生态学分析方法;即把影象上色调、形态特征与群落生态学规律结合起来分析,并以此作为植被目视解译的方法。在此基础上,将洞庭湖水域、洲滩划分成五个景观生态模型,17个基本植被类型。     

基于百度街景图像的行人视角城市街道植被绿化格局分析

城市街道绿化植被作为城市景观的重要组成部分, 其分布格局对城市景观美学发展及行人身心健康有显著影响, 立足行人视角准确监测街道绿植分布信息对城市规划与管理有明确的辅助作用。该文针对已有研究多采用沿天底方向垂直向下观测的遥感影像监测地表植被而对行人视角的绿色植被分布格局研究涉及不多的现状, 基于免费获取的百度街景图像, 选取绿植覆被典型的泰安市区为案例区, 结合网络信息抓取与空间地理信息处理技术, 分析百度街景图像提取侧视绿植信息的可行性, 统计并对比其计算结果与遥感影像提取结果的关系, 以期为城市规划与管理提供辅助参考信息。网络抓取案例区273个样点共3 276幅百度街景图像, 利用计算机监督分类提取图像中的绿植区域; 基于空间分析模型分析街道绿色植被的分布格局; 利用SPSS软件趋势拟合模块分析百度街景图像与遥感影像提取的植被信息的相关性。主要结果为: 百度街景图像可作为主数据源提取城市街道的侧视绿植分布情况; 案例区不同区域植被分布指数区别较大, 空间格局差异明显; 百度街道植被分布指数与基于遥感图像提取的10、20、50 m缓冲距离范围内植被覆盖面积呈显著正相关关系, 但两者的变化趋势并非完全一致。百度街道植被分布结果可作为遥感监测结果的辅助信息更好地指导城市绿色景观规划与精准管理。     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

基于遥感技术的全国生态系统分类体系

随着遥感技术的发展,以遥感数据作为生态系统监测与评价的基础已成为宏观生态学研究的重要手段。遥感数据要求每一数据集都要有相应的地物分类体系与之匹配,这也造成不同遥感数据及分类体系之间相互独立。虽然体系间多有联系和相似之处,但不同数据集的分类体系难以直接使用或替换,制约了多元数据在生态系统评价中的使用效果。为尝试解决这一问题,提高多源遥感数据的使用效率,提出了一套基于中分辨率遥感数据的生态系统分类体系。这套体系共有9个一级类、21个二级类、46个三级类,该体系主要依据类别内生态系统特征的相似性,并考虑了气候、地形等因素。最后以海南岛、内蒙古和甘肃3个省为例,探讨了以遥感数据为基础的区域生态系统构成分析方法与应用效果。研究表明,该分类体系有较好的生态学依据,可以支持更加深入的生态系统评估。但分类体系中还存在遥感数据与生态因子数据尺度不匹配、不能满足小尺度研究中对三级类进一步细分的要求以及当前数据质量和模拟技术不足以完全支持植被覆盖率反演精度要求等问题。     

三峡大老岭自然保护区森林生态系统10年(2000-2010年)质量变化

为了解湖北三峡大老岭自然保护区森林生态系统质量状况,利用多种遥感卫星影像资料并结合野外调查数据,对该保护区10年间(2000-2010年)的森林生态系统中植物地上生物量(AGB)、叶面积指数(LAI)、净初级生产力(NPP)的年际变化进行了比较研究。结果显示:(1)大老岭自然保护区10年间AGB总体呈下降趋势,但下降幅度很小,说明大老岭自然保护区森林生态系统植被结构状态比较稳定,但整个森林生态系统的结构稳定性不够,应创新管理机制,减少人为干扰;(2)年均LAI以中等(1.6~2.2)和较高等级(2.2~2.8)所占比例较大,年均LAI为1.6~2.8的植被面积占该区植被总面积的90%以上并呈增加趋势,说明该保护区内植被长势较好、生活力旺盛;(3)10年间NPP年总量具有缓慢增加的趋势,即从4.99×1010 gC(2000年)增加到5.07×1010 gC(2010年),说明自然保护区内森林生态系统林分类型多样,异质性较强,整个森林生态系统生产能力较高。本研究结果表明大老岭自然保护区森林生态系统质量总体较好,也从侧面反映出建立自然保护区进行植被保护所取得的效果。     

Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

Microphytobenthos assemblage mapping by spatial visible-infrared remote sensing in a shellfish ecosystem

The aim of this work is to assess the use of (SPOT) multispectral visible infrared remote sensing to study microphytobentos assemblages in a shellfish ecosystem (Bay of Bourgneuf, France). SPOT satellite images (acquired at low tide in spring or autumn between 1986 and 1998) were calibrated using in situ radiometric data, and the normalised vegetation index (NDVI) obtained from these images showed microphytobenthos on bay mudflats. Proliferation was mainly along a north-south strip, essentially localised around the +2 m isobath and covering a surface area of 19 to 25% of the total mudflat area studied (420 to 550 ha). Three factors seem to be responsible for the spatial structure of the assemblages: bathymetry, nutrient input from the Falleron River and its channel, and the location of oyster-farming areas. Although spatial and spectral resolutions of multispectral remote sensing data have certain limitations, this approach opens up a new field of application for hyperspectral remote sensing, particularly for synoptic mapping of biomass distribution.     

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。     

Remote sensing as a data source to analyse regional implications of genetically modified plants in agriculture—Oilseed rape (Brassica napus) in Northern Germany

Remote sensing permits the identification of locations and area extent where particular crops are cultivated across larger regions. This requires the availability of crop- and region-specific algorithms. We developed an approach to identify oilseed rape fields in Northern Germany. The remote sensing data sources and main processing steps are described. The derived cultivation density information provides an overview of the fine-scale spatial structure and crop neighbourhood relations in Northern Germany as one of the main oilseed rape cultivation regions in Europe. Geographical Information System (GIS) analyses involving buffer operations allowed to identify those parts of the region, where the highest interaction potential of GM crops and conventional crops would occur if GM varieties were admitted for cultivation. Cultivation density and field sizes in combination were used to indicate the interaction intensity as a marker for observation requirement (environmental monitoring) and for those potential risks that relate to density parameter. Conclusions can be drawn for the feasibility of coexistence measures when neighbouring farmers have to co-operate to keep separation distances between GM crop cultivation and conventional varieties. Together with other data sources, the results of satellite image analysis can be used as input data for up-scaling small-scale model results. Remote sensing data allow to specify, which field density parameter must be chosen to cover the regional variability of cultivation conditions, in particular the regional distribution of field sizes and field density.     

表观反射率及其在植被遥感中的应用

 由于植被遥感应用定量化和监测等的需求,光学遥感数据的辐射校正更加受到重视。该文论述了辐射校正,辐射定标和大气校正的概念以及它们之间的区别及关系。特别对辐射定标的结果之一,大气层顶表观反射率,简称表观反射率(Apparent reflectance)的定义、概念、计算和它在植被遥感中的应用等方面,进行了详细的论述。     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Associations between wasp communities and forest structure: Do strong local patterns hold across landscapes?

Abstract Forest structure and habitat complexity have been used extensively to predict the distribution and abundance of insect assemblages in forest ecosystems. We tested empirically derived predictions of strong, consistent relationships between wasp assemblages and habitat complexity, using both field assessments and vegetation indices from remote sensing as measures of habitat complexity. Wasp samples from 26 paired ‘high and low’ complexity sites in two forests approximately 70 km apart, were compared with normalized difference vegetation indices (NDVIs) derived from multispectral videography of the survey sites. We describe a strong unequivocal link between habitat complexity and wasp communities, the patterns holding over coarse and fine landscape scales. NDVIs were also excellent predictors of habitat complexity and hence wasp community patterns. Sites with greater NDVIs consistently supported a greater abundance and species richness, and a different composition of wasps to sites with low NDVIs. Using vegetation indices from remote sensing to gauge habitat complexity has significant potential for ecosystem modelling and rapid biodiversity assessment.     

Implementation of a hierarchical global vegetation classification in ecosystem function models

Abstract. We propose an alternative approach for the currently used biogeographic global vegetation classifications. A hierarchical vegetation classification system is proposed for consistent and routine monitoring of global vegetation. Global vegetation is first defined into six classes based on plant canopy structure and dynamics observable by remote sensing from satellites. Additional biome variability is then represented through a remote sensing derived leaf area index map, and direct climate data sets driving an ecosystem model to compute and map net primary production and evapotranspiration. Simulation results from an ecosystem function model suggest that the six canopy structure-based classes are sufficient to represent global variability in these parameters, provided the spatio-temporal variations in Leaf Area Index and climate are characterized accurately. If a bioclimatically based classification is needed for other purposes, our six class approach can be expanded to a possible 21 classes using archived climatic zones. For example, tropical, subtropical, temperate and boreal labels are defined by absolute minimum temperature. Further separation in each class is possible through changes in water availability defined by precipitation and/or soils. The resulting vegetation classes correspond to many of the existing, conventional global vegetation schemes, yet retain the measure of actual vegetation possible because remote sensing first defines the six biome classes in our classification. Vegetation classifications are no longer an end product but a source of initializing data for global ecosystem function models. Remote sensing with biosphere models directly calculates the ecological functions previously inferred from vegetation classifications, but with higher spatial and temporal accuracy.     

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau. These include the alpine meadow and steppe and degraded alpine meadow and steppe. This study aimed at developing a method to estimate aboveground biomass (AGB) for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition (VIUPD) from the spectra to estimate AGB. First, we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type. Next, we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables. At last, we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately, all eight vegetation indices provided good estimates of AGB, with the best predictor of AGB varying among different ecosystems. When AGB of all the five ecosystems was estimated together using a simple linear model, VIUPD showed the lowest prediction error among the eight vegetation indices. The regression models containing dummy variables predicted AGB with higher accuracy than the simple models, which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index (VI). These results suggest that VIUPD is the best predictor of AGB among simple regression models. Moreover, both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models. Therefore, ground-based hyperspectral measurements are useful for estimating AGB, which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.     

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.