卫星城市灾害应急模式的探索——以北京市门城卫星城为例

本文以北京市新规划的11个新城中的门城卫星城（门头沟）为例，从灾害应急模式的系统构建、前期准备、应急互动模型、系统神经网络建设、体制建设等方面探索卫星城市应急救灾系统模式的建设。     

四川盆地PM2.5与PM10高分辨率时空分布及关联分析

为深入了解四川盆地PM_2.5与PM₁₀污染情况，通过机器学习的方法，基于卫星遥感气溶胶产品（MAIAC）与国家环境空气质量监测网数据以及气象、地理、社会经济变量等，构建2个随机森林机器学习模型（R²均为0.86），反演四川盆地2013~2017年间1km网格逐日PM_2.5与PM₁₀浓度时空分布，并分析两者的时空关联性.结果表明：2013~2017年四川盆地地面PM_2.5与PM₁₀平均浓度分别为47.8，75.2μg/m³.PM_2.5与PM₁₀浓度空间上均整体呈现"倒月牙"状分布，西部与南部区域浓度值较高.5a间，区域颗粒物浓度逐年递减，总降幅均达到27%，季节上则均具有"冬高夏低"的特点；PM_2.5与PM₁₀浓度空间相关性显著（相关系数0.96），呈现"内强外弱"的格局，春夏季相关系数（0.91、0.90）低于秋冬季（0.96、0.96）.盆地西南部PM_2.5与PM₁₀比值较高，比值高低的季节性排序为冬季 > 秋季 > 夏季 > 春季.     

黄河口毛蚶生境区与资源的遥感信息解译研究

作者借助于卫星CCT数据进行了人-机对话式信息比值处理,获取了河口区高浓度的悬浮泥沙涡旋水体信息,并与现场考察资料相对证,发现其有规律的出现与输移。为此,结合毛蚶的生活习性,确认黄河入海泥沙扩散对黄河口及其邻近水域毛蚶的资源分布有直接的影响。预测了未来20年内研究区毛蚶场的扩大趋势及其范围。     

Preliminary Evidence of a Barrier Effect of a Railroad on the Migration of Mongolian Gazelles

Abstract:  Much remains to be learned about how anthropogenic structures affect migration in large mammals. We examined the potential influence of the international railroad in Mongolia on migration in Mongolian gazelles (Procapra gutturosa). We examined gazelle movements and the normalized difference vegetation index (NDVI) on both sides of the railroad. The tracked gazelles never crossed the railroad, despite movements that mainly followed the railroad in winter and higher NDVI values on the opposite side. It is likely that the railroad had a barrier effect on gazelle migration because it split the gazelles' habitat. Our results, although based on an extremely small sample, have utility in indicating approaches that might be useful for examining migration in ungulates.     

FLOOD INUNDATION IN THE SOUTHEASTERN UNITED STATES FROM AIRCRAFT AND SATELLITE IMAGERY1

ABSTRACT: Panchromatic black and white, color, and color infrared photographs and thermal infrared imagery are compared for a capability to show flood boundaries. In open agricultural and urban areas, these boundaries are easily delineated on all types of am. Boundaries are more difficult to see in wooded areas. In March, hardwood trees are dormant, but black and white photographs and color photographs show only the tops of these trees. Color infrared photographs in January and March have a distinctive color or tone in inundated woods; the limit of this tone is the flood boundary. Daytime thermal infrared imagery in March shows that inundated woods are cooler than dry land but warmer than open water. After about April 1, both color infrared photography and thermal infrared imagery show only the top of the tree canopy and do not reflect underlying flood water. Inundated areas can be delineated easily on ERTS satellite imagery from December through March. On imagery from May 4–5, 1973, however, most inundation boundaries had to be drawn as dashed lines; the tree canopy obscures flood waters in wooded areas. Despite this problem, the results of mapping flood boundaries on May imagery are believed to be reasonable for the scale of the imagery.     

Phytosociological inference and resource management

The application of vegetation information to resource management problems is reviewed in four areas: (1) the stratification of vegetation data by classification and gradient analysis, (2) the storage and retrieval of these data, (3) the sources of vegetation information, and (4) the accuracy and resolution requirements posed by different management applications. Conclusions indicate that the successful application of inference methods to resource management problems requires an appropriate integration of these four components.     

Using Satellite and Other Data to Estimate the Annual Water Demand of an Irrigation District

Locating and forecasting water needs can assist the location of water in dry regions, and improve the management of reservoirs and the canal network. Satellite, ground data, and agrometeorological data were combined to forecast the volume of irrigation water needed during 1993 and 1994 in an irrigation district of 327 km2 located in the Ebro basin, Spain. The main crops were rice, alfalfa plus forage, winter cereals (barley and wheat), sunflower and maize. Their extent was estimated every year by frame area sampling and a regression estimator with satellite data. Initial crop area statistics were obtained by expansion of the sample areas to the entire study area and then a regression estimator with the multitemporal supervised classification of two Landsat-5 TM images was applied. This procedure improved the precision of the estimates by expansion. Net water requiremets (m3 ha-1) of the above mentioned crops were computed from reference evapotranspiration estimates, crop coefficients and effective precipitation. These computations were performed for an average year, i.e. by using long-term averaged meteorological data. Crop hectarage and net crop water requirements were multiplied to obtain, for the entire study area, the volume (hm3 106 m3) of the net crop water requirements. After subtraction of water taken directly from the rivers and non-productive sunflower, the irrigation water volumes were estimated. The comparison of these forecasts with the volumes of water invoiced by the Ebro Basin Water Authority confirmed the feasibility of forecasting the volume of water applied to an individual irrigation district. This is an objective and practical method for estimating the irrigation water volume applied in an irrigated area.     

电磁监测卫星磁洁净控制方法研究

提出了卫星磁洁净控制的主要方法,覆盖从卫星可行性设计到最终系统级磁试验的各个阶段。结合电磁监测卫星的研制流程,介绍了在可行性论证、方案阶段和初样阶段卫星磁洁净控制工作的进展。开展的工作表明,卫星平台在绝对磁场校准装置位置产生的磁场干扰不确定量可以控制在0.5 nT,能够满足卫星在轨科学探测任务需求。     

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R², root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R² and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R² and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R² increase of only 0.08. Similarly, improvement in R² was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.     

Measuring Suspended‐Sediment Concentration and Turbidity in the Middle Mississippi and Lower Missouri Rivers Using Landsat Data

The Middle Mississippi River (MMR) and lower Missouri River (MOR) provide critical navigation waterways, ecological habitat, and flood conveyance. They are also directly linked to processes affecting geomorphic and ecological conditions in the lower MR and Delta. For this study, a method was developed to measure suspended‐sediment concentration (SSC) and turbidity along the MMR and the lower MOR using Landsat imagery. Data from nine United States Geological Survey water‐quality monitoring stations were used to create a model‐development dataset and a model‐validation dataset. Concurrent gaging data were identified for available Landsat images to generate the datasets. Surface‐reflectance filters were developed to eliminate images with cirrus cloud coverage or vessel traffic. Using the filtered model‐development dataset, unique reflectance‐SSC and reflectance‐turbidity models were developed for three Landsat sensors: Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 4–5 Thematic Mapper. Coefficient of determination values for the models ranged from 0.72 to 0.88 for the model‐development dataset. The model‐validation dataset was used to evaluate the performance of the models and had coefficient of determination values ranging from 0.62 to 0.79.