面向防洪救灾的人口空间分布模拟——以大安市为例

人是最重要的受灾体，准确的人口空间模拟是衡量洪涝灾情和实施防灾救灾的重要依据。利用遥感数据模拟人口空间分布需解决两大难题：（1）居民地类型、面积、位置的精确获取；（2）居民地类型、面积与人口数量的关系。基于地理意义和数学意义，对经典的城市人口一面积异速生长模型进行了扩展，导出了城乡人口一面积统一模型；并以大安市为例，建立了居民地分类体系，以1：5万地形图为辅助数据，利用ETM影像提取居民地信息，在上述模型的基础上建立了人口分布的反演模型，模拟人口的空间分布。通过精度评价，进一步证明了基于遥感数据的人口统计数据空间化的研究思路是切实可行的。     

Hydrologic Modeling Uncertainty Resulting From Land Cover Misclassification1

Abstract: A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One‐hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omission in land cover classes and is used to assess the uncertainty in hydrologic runoff simulation resulting from parameter estimation based on land cover. These land cover realizations were used in the GIS‐based Automated Geospatial Watershed Assessment tool in conjunction with topography and soils data to generate input to the physically‐based Kinematic Runoff and Erosion model. Uncertainties in modeled runoff volumes resulting from these land cover realizations were evaluated in the Upper San Pedro River basin for 40 watersheds ranging in size from 10 to 100 km² under two rainfall events of differing magnitudes and intensities. Simulation results show that model sensitivity to classification error varies directly with respect to watershed scale, inversely to rainfall magnitude and are mitigated or magnified by landscape variability depending on landscape composition.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Remotely Sensed Data for Ecosystem Analyses: Combining Hierarchy Theory and Scene Models

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost–benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.     

Vegetation Type Conversion in Los Peñasquitos Lagoon,California: An Examination of the Role of Watershed Urbanization

Environmental Management - The objective of this study was to examine the role that watershed urbanization has played in changes to the vegetation types within Los Peñasquitos Lagoon, San...     

Decision Support System for Managing Oil Spill Events

The Mediterranean environment is exposed to various hazards, including oil spills, forest fires, and floods, making the development of a decision support system (DSS) for emergency management an objective of utmost importance. The present work presents a complete DSS for managing marine pollution events caused by oil spills. The system provides all the necessary tools for early detection of oil-spills from satellite images, monitoring of their evolution, estimation of the accident consequences and provision of support to responsible Public Authorities during clean-up operations. The heart of the system is an image processing–geographic information system and other assistant individual software tools that perform oil spill evolution simulation and all other necessary numerical calculations as well as cartographic and reporting tasks related to a specific management of the oil spill event. The cartographic information is derived from the extant general maps representing detailed information concerning several regional environmental and land-cover characteristics as well as financial activities of the application area. Early notification of the authorities with up-to-date accurate information on the position and evolution of the oil spill, combined with the detailed coastal maps, is of paramount importance for emergency assessment and effective clean-up operations that would prevent environmental hazard. An application was developed for the Region of Crete, an area particularly vulnerable to oil spills due to its location, ecological characteristics, and local economic activities.     

COMPARATIVE EVALUATION OF LAND COVER DATA SOURCES FOR EROSION PREDICTION1

ABSTRACT: A fundamental problem in protecting surface drinking water supplies is the identification of sites highly susceptible to soil erosion and other forms of nonpoint source (NPS) pollution. The New York City Department of Environmental Protection is trying to identify erodible sites as part of a program aimed at avoiding costly filtration. New York City's 2,000 square mile watershed system is well suited for analysis with geographic information systems (GIS); an increasingly important tool to determine the spatial distribution of sensitive NPS pollution areas. This study used a GIS to compare three land cover sources for input into the Modified Universal Soil Loss Equation (MUSLE), a model estimating soil loss from rangeland and forests, for a tributary watershed within New York City's water supply system. Sources included both conventional data (aerial photography) and Landsat data (MSS and TM images). Although land cover classifications varied significantly across these sources, location-specific and aggregate watershed predictions of the MUSLE were very similar. We conclude that using Landsat TM imagery with a hybrid classification algorithm provides a rapid, objective means of developing large area land cover databases for use in the MUSLE, thus presenting an attractive alternative to photo interpretation.     

The Assessment of Atmospheric Pollution using Satellite Remote Sensing Technology in Large Cities in the Vicinity of Airports

This paper investigates the potential of usingsatellite remotely sensed imagery for assessing atmosphericpollution. A novel approach, which comprised radiativetransfer calculations and pseudo-invariant targets fordetermining aerosol optical thickness has been developed.The key parameter for assessing atmospheric pollution inphotochemical air pollution studies is the aerosol opticalthickness. The need for identifying suitable pseudo-invariantobjects in satellite images of urban areas is of great interest for increasing the potential of earthobservation for monitoring air pollution in such areas. Theidentification of large water bodies and concrete apronsthat can serve as suitable dark and bright targetsrespectively in different geographical areas wasdemonstrated in this study. This study added evidence onthe correlation found between the visibility valuesmeasured at Heathrow Airport area during satellite overpassand aerosol optical thickness derived from Landsat-5 TMband 1 images.     

建筑物震害遥感图像的变化检测与震害评估

随着卫星遥感成为一种便捷的信息源，遥感手段将能在震害快速评估中发挥重要的作用。本文详细介绍了地震前后遥感图像的变化检测方法，并以张北地震为例，经精确配准，利用定性显示和定量计算的方法，对地震前后张北地震区的SAR图像进行了变化检测处理，确定了村庄建筑物的地震破坏及震害程度,为利用遥感图像检测建筑物的地震破坏进行了有益的探索。     

极轨气象卫星遥感资料用于陕西洪涝灾害监测

介绍了利用极轨气象卫星遥感监测洪涝灾害的原理、监测方法和资料处理流程，以及对1996年初黄河陕西段凌汛的成功动态监测的实例。