利用RADARSAT SWA SAR和LANDSAT TM的互补信息确定洪水水体范围

从RADARSAT SAR图像中自动提取洪水水体,一直是一个急需解决而又未能得到解决的问题。主要困难是由于阴影与水体严重相混淆。为此,本文研究发现,通过RADARSAT SWA SAR图像和LANDSAT TM图像上的互补信息,利用RADARSAT SWA SAR图像和LANDSAT TM图像上的水体和阴影进行复合处理,可以从RADARSAT SWA SAR图像上准确、半自动提取洪水水体范围。研究结果表明,该方法不仅适用于平地洪水水体范围的半自动提取,而且更适用于包含山区的大范围洪水水体的半自动提取。     

基于环境一号卫星的自然保护区生态系统健康评价

以生态系统健康及压力－状态响应模型作为研究方法,设计了一套基于环境一号卫星CCD数据的自然保护区生态系统健康评价评价方法、指标体系和技术流程.同时选择向海湿地自然保护区为示范区,以环境一号卫星CCD影像为数据源, 对指标, 对其生态系统健康现状进行了评价.结果表明,向海湿地自然保护区生态系统相对不健康、中等健康、较健康和相对健康的区域面积分别占整个自然保护区总面积的46.49%、18.02%、21.18%、14.31%,整个自然保护区生态系统健康水平一般,人类干扰严重,组织结构不太合理.向海湿地自然保护区生态系统健康水平一般的主要原因是人类活动日益增强和年降水量明显减少.     

酸雨对鼎湖山土壤的累积效应及荷木的反应

运用盆栽实验研究了酸雨对鼎湖山季风常绿阔叶林土壤危害的累积效应和优势树种荷木的一些生理反应.结果表明,在酸雨直接淋洗土壤的条件下,与对照相比,土壤酸度,土壤淋洗液酸度增加;土壤交换性K⁺、Na⁺上升,Ca²⁺、Mg²⁺和有效Mn、Zn下降;土壤淋洗液中K、Na含量下降,Ca、Mg、Cu、Fe、Mn和Al在pH3.05的酸雨处理下含量显著上升;但生长于模拟酸雨土壤中的荷木在土壤化学性质发生变化后仍能健康生长.     

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.     

Distribution of Selected Soil and Water Conservation Practices in the U.S. as Identified with Google Earth

The proper representation of conservation practices on agricultural lands is an important factor in large‐scale assessments of water quality in the United States. Unfortunately, there are few publicly available data sources at the local level and even fewer at the national scale. In this research, randomly selected points within agricultural lands were examined for selected conservation practices using Google Earth aerial imagery by a team of interpreters. In total, 13,530 points had field boundaries digitized, and were subsequently examined and classified. The presence of terraces, grassed waterways, contour farming, center pivot irrigation, strip cropping, ponds, riparian vegetation, filter strips, and land cover were noted. Subjectivity among interpreters was evaluated using duplicate samples and was found to be similar to image misclassification rates in other research. Conservation practice adoption rates for selected major river basins compared favorably with data collected by the Conservation Effects Assessment Project. The frequency of occurrence of each conservation practice was summarized and presented by ecoregion. To facilitate future research, point level data and software source code developed in this research are available via the web at http://nlet.brc.tamus.edu/Conservation . Aerial imagery was found to be a powerful, inexpensive, and easily accessible tool to assess large‐scale conservation practice implementation for certain conservation practices.     

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.     

Retrieving surface deformation by PSInSAR™ technology: A powerful tool in reservoir monitoring

Reservoir monitoring improves our understanding of reservoir behaviour and helps achieve more effective reservoir management and prediction of future performance with obvious economic benefits. It relies on an integrated approach involving both surveillance (well or surface based; seismic, electrical, leakage, flow and deformation measurements, etc.) and modelling. Surface deformation monitoring can provide valuable constraints on the dynamic behaviour of a reservoir enabling the evaluation of volumetric changes in the reservoir through time. Levelling campaigns, tiltmeters, GPS permanent stations and Permanent Scatterer SAR Interferometry (PSInSAR™) are the techniques most widely used to determine surface displacements. Whatever the surveying technique, the detection of millimetre-level surface deformation is required to monitor small surface displacement rates that could impact risk evaluation and land use planning. Depending on depth and reservoir/overburden rheology, volumetric changes in reservoirs due to fluid extraction and injection can induce either subsidence or uplift that could trigger fault reactivation and threaten well integrity; deformation may also be detectable at the surface. Mapping surface effects accurately requires hundreds of observation points per km² which cannot be delivered by traditional monitoring methods without unacceptably large expenditure. PSInSAR™ is one of the most promising and cost-effective techniques capable of providing high precision and high areal density displacement measurements over long periods of time. Moreover, the availability of PS data for both ascending and descending orbits enables the estimation of both vertical and E–W horizontal displacement fields. Two case histories will be presented to illustrate the advantages of PSInSAR™ technology for the detection of surface deformation induced by reservoir exploitation and monitoring of its evolution though time.     

ERS-2 SAR和Landsat ETM+数据融合提取崇明东滩典型地物信息与分类

以长江口崇明东滩为研究区,在分析崇明东滩典型地物的反射光谱特性和散射特性的基础上,对ERS-2 SAR数据和Landsat ETM＋数据分别进行了处理,其中对ERS-2 SAR数据进行了小波去噪处理和纹理特征信息的提取,对ETM＋数据进行了HIS彩色空间变换,然后在特征级层次上对两者进行了信息融合,最后对融合后的图像进行了典型地物信息的提取,并且根据我们野外实地调查数据和已有的研究成果,对本文的分类结果进行了评价,取得了满意的效果。     

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.     

地震灾区河谷滑坡检测的遥感分析——以北川县滑坡为例

震区滑坡易堵塞河道形成高危型堰塞湖,一旦坝堤溃决,将释放巨大能量,造成严重的地震次生水灾。本文利用多源遥感数据,旨在研究一种检测河道滑坡的快速有效的方法,迅速判断出滑坡位置,为救援减灾提供重要信息。首先,对于灾前CBERS-02B星CCD数据,通过归一化水体指数(NDWI)来提取灾前水体信息主要为河道;由于灾后震区天气状况恶劣,故采用Radarsat-1SAR数据。利用水体在SAR影像上的特性,经过多次试验后使用阈值法来提取河道范围。再对两幅影像提取结果中的河道区域进行交集运算,检测出河道范围内的变化部分。通过上述方法提取的河道信息较为模糊、零散,这些模糊、零散并非都由滑坡堵塞河道造成,而是受噪声影响,在此对河道交集影像做膨胀处理,达到突出滑坡信息的目的,进而得以提取河道真实堵塞范围。最后进行人机交互判读,提取滑坡区域。实验以四川省北川县滑坡灾害为例,证明了本文方法的高效性与可靠性。