高光谱遥感在农作物长势监测中的应用

该研究是加拿大Saskatchewan Scott农作物轮作系统(ACS)研究的一部分.研究始于1994年,历时18 a,评价9个可耕种农作物产量系统的可靠性.由3种处理水平(organic,reduced,high)和3种作物多样性水平(low,diversified annual grains,diversified annual perennials)结合而产生的9个农作物产量系统,被用于监测和评价加拿大牧场不同处理和不同作物种植轮作下可耕种农作物的产量.在2003年生长季共收集了3次叶面积指数和光谱反射率的数据:生长季前期(6月)、生长季旺盛期(7月)、生长季后期(8月).叶面积指数是由LAI-2000植物冠层分析仪监测的,光谱测量是由覆盖了350～2500 nm波长范围共2215个波段的ADS便携式高光谱仪完成的.结果显示,光学测量可以用于监测农作物生长状况的差异.从生长季的早期到中期,光谱和叶面积指数在不同处理下有显著差异.7月中期是用遥感资料监测农作物长势的最佳季节;红光波段与近红外波段反射率的比值和基于这两个波段构造的归一化植被指数,是检测农作物长势的最佳植被指数.     

使用MODIS陆地产品LST和NDVI监测中国中、西部干旱

利用中分辨率成像光谱仪AQUA-MODIS卫星资料反演的地表温度LST和归一化差值植被指数NDVI,在中国中、西部地区应用植被温度混合状态指数VTCI方法,遥感监测干旱的空间分布状况。同时结合该地区有代表性的气象站逐月降水资料与VTCI指数进行相关分析,来验证此方法的适用性。结果表明,VTCI指数不仅与当月的降水量,也与其前期2至6个月的累积降水量有较好的线性相关性,显示VTCI方法不仅是较为有效的近实时大范围干旱监测方法,对于干旱的缓慢发展过程也有一定指示作用。     

The Würm in NW Iberia, a pollen record from Area Longa (Galicia)

The pollen record at Area Longa is the westernmost sequence available for investigation of the last glaciation in continental Europe. It is located in a region, NW Iberia, for which data from times earlier than the late glacial period are scarce. It comprises a series of exposed limnetic levels that lie above an Eemian (Oxygen Isotope Stage [OIS] 5e) beach and are separated by inorganic layers. The oldest limnetic level (Level I), attributed to the early glacial period (OIS 5a to OIS 5d), shows a dominance of woodland with high proportions of Fagus pollen and is tentatively identified with St. Germain I. The lower pleniglacial (OIS 4) Level II records a stadial landscape of grassland and shrub. Level III, from the pleniglacial interstade (OIS 3), reflects a complex period in which three warmer woodland phases alternated with periods of more open vegetation. This cyclical behavior correlates with the ice core isotope record and with the general tendencies observed in other Würmian pollen records, but the composition of our pollen profiles differs from those observed in these other records. In NW Iberia, the dominant trees were deciduous taxa, not conifers. Of particular note is the presence of lowland Fagus woodlands during the pre-Würm, and the occurrence of Carpinus considerably farther west than the boundary of its current distribution in the Iberian Peninsula.     

Younger Dryas to mid-Holocene environmental history of the lowlands of NW Transylvania, Romania

Pollen, micro-charcoal and total carbon analyses on sediments from the Turbuta palaeolake, in the Transylvanian Basin of NW Romania, reveal Younger Dryas to mid-Holocene environmental changes. The chronostratigraphy relies on AMS ¹⁴C measurements on organic matter and U/Th TIMS datings of snail shells. Results indicate the presence of Pinus and Betula open woodlands with small populations of Picea, Ulmus, Alnus and Salix before 12,000 cal yr BP. A fairly abrupt replacement of Pinus and Betula by Ulmus-dominated woodlands at ca. 11,900 cal. yr BP likely represents competition effects of vegetation driven by climate warming at the onset of the Holocene. By 11,000 cal yr BP, the woodlands were increasingly diverse and dense with the expansion of Quercus, Fraxinus and Tilia, the establishment of Corylus and the decline of upland herbaceous and shrubs taxa. The marked expansion of Quercus accompanied by Tilia between 10,500 and 8000 cal yr BP could be the result of low effective moisture associated with both low elevation of the site and with regional change towards a drier climate. At 10,000 cal yr BP, Corylus spread across the region, and by 8000 cal yr BP it replaced Quercus as a dominant forest constituent, with only little representation of Picea abies. Carpinus became established around 5500 cal yr BP, but it was only a minor constituent in local woodlands until ca. 5000 cal yr BP. Results from this study also indicate that the woodlands in the lowlands of Turbuta were never closed.     

基于手持GPS洞庭湖区植被景观格局外业调绘研究

本文以1：50000的地形图为工作底图，采用手持GPS技术、利用实地调查与分解森林资源分布图相结合的方法，对洞庭湖区植被分布状况进行了外业调绘的研究。建立了外业调绘过程中植被群落斑块最小面积和边界的确定原则及其斑块类型与编码。探讨了手持GPS实现WGS-84坐标系向1954北京坐标系的转化方法。并进行了精度估算，其点位误差≤5．8m，完全可以满足洞庭湖区植被分布外业调绘的精度要求。运用该方法进行植被分布的外业调绘，可以解决运用传统的定位测量方法在湖区定位难的问题，提高了外业作业效率。     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

A method for spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance

ＡＭｅｔｈｏｄｆｏｒＳｐａｃｅｂｏｒｎｅＳｙｎｔｈｅｔｉｃＲｅｍｏｔｅＳｅｎｓｉｎｇｏｆＡｔｍｏｓｐｈｅｒｉｃＡｅｒｏｓｏｌＯｐｔｉｃａｌＤｅｐｔｈａｎｄＶｅｇｅｔａｔｉｏｎＲｅｆｌｅｃｔａｎｃｅ①ＱｉｕＪｉｎｈｕａｎ（邱金桓）Ｉｎｓｔｉｔｕｔｅｏｆ...     

Nonlinear random wave-induced drag force on a vegetation field

This paper provides a practical method by which the drag force on a vegetation field beneath nonlinear random waves can be estimated. This is achieved by using a simple drag formula together with an empirical drag coefficient given by Mendez et al. (Mendez, F.J., Losada, I.J., Losada, M.A., 1999. Hydrodynamics induced by wind waves in a vegetation field. J. Geophys. Res. 104 (C8), 18383–18396). Effects of nonlinear waves are included by using Stokes second order wave theory where the basic harmonic motion is assumed to be a stationary Gaussian narrow–band random process. An example of calculation is also presented.     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.     

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.