利用TM影像反演广州市气溶胶光学厚度空间分布

利用2005年7月18日摄录的广州市TM影像,在相关研究基础上建立了适合于复杂大气状况城市尺度的气溶胶光学厚度(Aerosol Optical Depth,AOD)反演模型,研究了广州市30m空间分辨率的AOD空同分布,并与同期的广州市地面9个空气质量监测站的PM10浓度进行比较.结果表明,利用TM影像较好地反演了广州市AOD空间分布.地形、植被、建成区分布是影响广州市AOD空间分布的主要因素,AOD按照高山植被区、靠近建成区的山地植被区、建成区与平原植被区的顺序逐渐增加.地面监测的PM10浓度与AOD的相关系数为0.717,基于TM影像反演的AOD可较好地反映当日地面污染物PM10的空间分布.     

朝鲜4.13“光明星3号”卫星发射次声信号分析

朝鲜4.13"光明星3号"发射是一次包含火箭发射与爆炸次声信号的事件。对火箭发射激发次声信号的基本理论、影响火箭发射激发次声信号监测能力的因素等进行了简要阐述;在分析火箭发射历史次声信号的基础上,研究了该类信号的信号走时、频率及波形特征;对"光明星3号"发射激发的次声信号从事前预判、信号检测、爆炸区域确定、误差原因分析等方面进行了详细分析说明。研究分析结果表明:朝鲜"光明星3号"的爆炸疑似区域为北纬35.2756°～36.3887°,东经125.1675°～126.5487°。     

关于酸性粒子大小计算的研究

酸性粒子在许多环境问题中起着重要的作用.它们的大小一直用Gras公式计算.但在实际的研究中发现Gras公式有时并不合理.提出对Gras公式进行改进以粒子投影长度L与截面积直径d的比例关系,L/d<0.62用Gras公式计算,0.62～1.62用修正的Gras公式计算,＞1.62须具体对待.并用具体算例证实了新算法的合理性.     

Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image

The spatial resolution of satellite imagery suitable for earth resources studies has improved from 80 m (Landsat-MSS, launched in 1972) to 0.6 m (QuickBird, launched in 2001). The conventional pixel-based methods developed for medium resolution satellite images are not suitable for classification of very high spatial resolution images, because the spectral responses of particular habitat classes are much more variable. On the other hand, in the original Barnsley–Barr kernel-based reclassification algorithm not only the spectral information of a pixel but also the textural information in the vicinity of the pixel is used when the pixel labeling decision is made. The first step of the kernel reclassification algorithm is to perform an initial classification of the original image. In the second step, the adjacency-event matrices are computed for each pixel according to co-occurrence frequencies of the initial classes in the kernel window. The degree of matching between an adjacency-event matrix corresponding to specific pixel and the set of class-specific template matrices produced during training is the criterion for pixel re-labeling. We extend the original kernel-based reclassification algorithm with a decision tree-based reclassification, simultaneously taking into account the class-specific similarity images, which are a side-product of the original algorithm. The advantage of decision tree-extended approach over the original approach seems to be the ability of the former to consider more input information, thus increasing the Kappa classification accuracy for an Ikonos image of our study area from 0.56 to 0.60, using a nomenclature containing 10 habitat classes.     

An erosion-based land classification system for military installations

The universal soil loss equation (USLE) has been integrated with a geographic information system known as the geographical resources analysis support system (GRASS) to create a land classification system for use by military trainers and land managers to minimize the environmental impacts of military training activities. The USLE provides an estimate of current average annual sheet and rill erosion based upon factors representing climate, soil erodibility, topography, cover, and conservation support practices. The erosion estimate is compared to erosion tolerance values to produce an expression of the current erosion status. An index of inherent site erodibility is also achieved through manipulation of the USLE. Based on published soil surveys, satellite imagery, and ground-truth vegetation transects, data layers are created within GRASS for each of the component factors of the USLE. Appropriate mathematical operations are performed with the data layers, and color-coded maps are produced that represent the erosion status and erodibility index for each 50-m × 50-m area of soil surface. These maps aid military trainers and land managers in scheduling appropriate kinds and intensities of military training activities.     

Flexibility in daily travel routines causes regional variation in bird migration speed

We used novel Global Positioning System-based satellite telemetry to reconstruct daily time budgets on travelling days of a long-distance migrant, the Osprey Pandion haliaetus, to reveal how landscape affects migratory performance. We compared daily travel routines between the Ospreys’ passage of Europe and the Sahara. In Europe, where feeding habitat is abundant, Ospreys fed both before–after flights and during interruptions, thus, combining migration with foraging. This resulted in a 2.7-h shorter daily flight period in Europe than in the Sahara. A calculated energy budget indicated that a ‘fly-and-forage migration strategy’ is favourable in Europe because associated benefits (energy intake) more than outweigh costs (reduced flight time). The much shorter flight time in Europe was the main explanation why Ospreys covered on average 78 km less distance on a travelling day in Europe than in the Sahara. In addition, there were regional differences in hourly flight speeds that are most probably the result of variation in thermal soaring conditions. We conclude that landscape properties have a profound effect on migration through regional variation in daily routines.     

Remote Sensing and GIS Technologies as Tools to Support Sustainable Management of Areas Devastated by Landslides

IKONOS panchromatic images from a single year were used to characterise the effects of an extreme rainstorm event on six mountain catchment areas in Venezuela. Image registrations were accomplished using topographic base maps at 1 : 5000 scale giving a mapping accuracy between 37 and 40 m. It was used an 8-bit channel for correction, rectification, filteration and tone enhancements.Landsliding was discerned in the affected watersheds using morphometric criteria, including the shape of the slope failure and its position, exposure bedrock in the scar and deposition of debris down-slope. This study is restricted to the impact analysis of the distribution of landslide erosion scars and the depositional processes on the valley floor. Remote sensing data were combined into a geographic information system (GIS) with planimetric data, contour lines, hydrology and vegetation types to evaluate the distribution of the scars and their effects on the highly populated areas located on the alluvial fans. Hillslope mass wasting induced mass movements, logging and increased the mud and silt in floodwaters affecting settlement down-slope.     

Applying Satellite Imagery to Triage Assessment of Ecosystem Health

Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR) imagery to develop calibration patterns of change in the Normalized Difference Vegetation Index (NDVI) over the growing season for selected sites for which we had ground data and historical data characterizing these sites as irreversibly degraded. We used the NDVI curves for these training sites to classify and map the irreversibly degraded rangelands in southern New Mexico. We composited images into four year blocks: 1988–1991, 1989–1992, and 1990–1993. The overlap in pixels classified as irreversibly degraded ranged from 42.6% to 84.3% in year block comparisons. Quantitative data on vegetation composition and cover were collected at 13 sites within a small portion of the study area. Wide coverage reconnaissance of boundaries between vegetation types was also conducted for comparisons with year block maps. The year block 1988–1991 provided the most accurate delineation of degraded areas. The rangelands of southern New Mexico experienced above average precipitation from 1990–1993. The above average precipitation resulted in spatially variable productivity of ephemeral weedy plants on the training sites and degraded rangelands which resulted in much smaller areas classified as irreversibly degraded. We selected imagery for a single year, 1989, which was characterized by the absence of spring annual plant production in order to eliminate the confounding effect of reflectance from annual weeds. That image analysis classified more than 20% of the rangelands as irreversibly degraded because areas with shrub-grass mosaic were included in the degraded classification. The single year image included more than double the area classified as irreversibly degraded by the year blocks. AVHRR imagery can be used to make triage assessments of irreversibly degraded rangeland but such assessment requires understanding productivity patterns and variability across the landscapes of the region and careful selection of the years from which imagery is chosen.     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Multi-objective optimization of air quality monitoring

A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.