Application of Remote Sensing in Detection of Ocean Oil Pollution

It is well known that the increase of surface tension due to the presence of oil slicks causes the surface wave motion is depressed or even disappeared, thus making the surface electromagnetically smoother. Therefore, the radar backscattered energy is correspondingly decreased. These damping effects is now well understood and such effects enables the oil slicks to be discernible from the radar image. in this paper, we are concerned with the digital technique that effectively delineates the oil slicks pattern from the SAR image. the detected pattern allows us to estimate the coverage of oil spillage. the technique is based on the fact that the oil slicks make a gray value surface in the image which is a concave area with a certain size. in order to identify correctly these oil slicks and suppress the speckle noise and other natural phenomena, an image pyramid with multi-resolution layers is generated sequentially from the original image. Then a top-down approach, which applies both first and second order derivative operators, the Difference of Gaussian (DoG) and the Laplace of Gaussian (LoG), to the image pyramid, is used to detect oil patches. Two ERS-1 SAR images acquired on August 5, 1994 and September 5, 1994, around Taiwan, were used for testing. Results indicate that the proposed method depresses the speckle noise and other sea noise signals, and enhances the oil slicks pattern clearly. the robustness to the empirical parameters introduced in this scheme was also demonstrated.     

从内波SAR图像中提取跃层深度和内波振幅的非线性方法

针对SAR图像进行内波参数提取方法的不足:非线性方法将跃层深度作为已知量,将跃层深度作为未知量却用了线性方法.本文将根据非线性内波KdV方程的频散关系,结合半日潮假定并利用从SAR图像上得到的半波宽度,提出了非线性框架下同时从内波SAR图像反演内波振幅及跃层深度的方法.然后利用实测数据和内波SAR图像数据对此方法进行了分析,所得结果较理想.最后根据倾斜底地形内波波包的周期验证了文中所用半日潮假定的正确性.     

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.     

Assessing the impact of urbanization on regional net primary productivity in Jiangyin County, China

Urbanization is one of the most important aspects of global change. The process of urbanization has a significant impact on the terrestrial ecosystem carbon cycle. The Yangtze Delta region has one of the highest rates of urbanization in China. In this study, carried out in Jiangyin County as a representative region within the Yangtze Delta, land use and land cover changes were estimated using Landsat TM and ETM+ imagery. With these satellite data and the BEPS process model (Boreal Ecosystem Productivity Simulator), the impacts of urbanization on regional net primary productivity (NPP) and annual net primary production were assessed for 1991 and 2002. Landsat-based land cover maps in 1991 and 2002 showed that urban development encroached large areas of cropland and forest. Expansion of residential areas and reduction of vegetated areas were the major forms of land transformation in Jiangyin County during this period. Mean NPP of the total area decreased from 818 to 699 gCm(-2)yr(-1) during the period of 1991 to 2002. NPP of cropland was only reduced by 2.7% while forest NPP was reduced by 9.3%. Regional annual primary production decreased from 808 GgC in 1991 to 691 GgC in 2002, a reduction of 14.5%. Land cover changes reduced regional NPP directly, and the increasing intensity and frequency of human-induced disturbance in the urbanized areas could be the main reason for the decrease in forest NPP.     

GIS支持的卫星图像土地利用现状分类和制图

应用卫星图像提取专题要素，综合利用多元地理信息，是克服多光谱数据在解像力和分辨率上所存在的局限性的有效途径。ＧＩＳ技术则提供多种处理功能将图像与地理信息复合，提高分类精度。分类图像数据与地形基础要素数据复合，通过彩色扫描绘图机直接绘制高质量的土地利用现状专题地图。     

OPERATIONAL APPLICATIONS OF SATELLITE SNOW COVER OBSERVATIONS1

ABSTRACT: Several federal and state water resources agencies and NASA have recently completed an Applications Systems Verification and Transfer (ASVT) project on the operational applications of satellite snow cover observations. When satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error from 15 percent to 10 percent on three study basins; and modeling studies on the Boise River basin in Idaho indicated that satellite snow cover could be used to reduce short term forecast error by up to 9.6 percent (5 day forecast). Potential benefits from improved satellite snow cover based predictions across the 11 western states total 10 million dollars for hydropower and 28 million dollars for irrigation annually. The truly operational application of the new technology in the West, however, will only be possible when the turnaround time for all data is reduced to 72 hours, and the water management agencies can be assured of a continuing supply of operational snow cover data from space.     

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.     

Use of Radar Remote Sensing (RADARSAT) to Map Winter Wetland Habitat for Shorebirds in an Agricultural Landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. Also, Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon 97331, USA     

The application of geographic information systems and global positioning systems in humanitarian emergencies: lessons learned,programme implications and future research

Geographic information systems (GIS), global positioning systems and remote sensing have been increasingly used in public health settings since the 1990s, but application of these methods in humanitarian emergencies has been less documented. Recent areas of application of GIS methods in humanitarian emergencies include hazard, vulnerability, and risk assessments; rapid assessment and survey methods; disease distribution and outbreak investigations; planning and implementation of health information systems; data and programme integration; and programme monitoring and evaluation. The main use of GIS in these areas is to provide maps for decision-making and advocacy, which allow overlaying types of information that may not normally be linked. GIS is also used to improve data collection in the field (for example, for rapid health assessments or mortality surveys). Development of GIS methods requires further research. Although GIS methods may save resources and reduce error, initial investment in equipment and capacity building may be substantial. Especially in humanitarian emergencies, equipment and methodologies must be practical and appropriate for field use. Add-on software to process GIS data needs to be developed and modified. As equipment becomes more user-friendly and costs decrease, GIS will become more of a routine tool for humanitarian aid organisations in humanitarian emergencies, and new and innovative uses will evolve.