数字长江河道实验中遥感技术应用研究——以长江江苏段为例

在数字长江河道试验中充发分挥遥感技术的作用，应用多平台，多时相遥感技术，对长江河床演变、江岸变迁、河道边界条件、护岸工程等进行数据采集，建立长江河道数据库。开展南通河段水深遥感探测试验，在水深遥感机理分析的基础上，通过准同步的遥感光谱亮度值与相关水深实测数据的计算，建立该河段水深遥感模型，对河床水下地形进行反演。试验研究表明，应用陆地卫星TM数据对含沙量较高的长江河口段水深进行探测有一定的效果，其探测深度可达5m。对近岸水深条件及岸线资源利用现状进行数据采集，在GIS支持下，应用地理信息图谱方法对江岸稳定性进行综合评价，将岸线根据其稳定性划分为稳定岸、强侵蚀岸、弱侵蚀岸、强淤积岸的弱淤积岸5个类型，根据其近岸水深条件划分为深水岸、中深水岸和浅水岸三大类型，建立长江岸线资源专题数据库。在此基础上，对长江江苏段河道演变特征、江岸稳定性及不同性质岸线的利用情况进行分析研究，提出河道整治、防洪和岸线资源合理开发利用的对策建议。     

江苏省水稻遥感估产研究

结合水稻遥感估产研究工作，阐述了遥感估产全过程，根据江苏省境内水热条件、地貌、土壤条件、种植制度等，进行水稻遥感估产区划和样点布设，建立估产背景数据库。利用混合像元分解方法提取水稻种植面积，以及采用遥感植被指数单产模型，进而预测水稻产量。     

基于植被指数和地表温度的冬小麦遥感干旱监测

针对河北省冬小麦主要种植区2007年发生的一次春旱过程,利用MODIS8天合成遥感资料,结合地面站点数据,分析植被供水指数及其多年距平值在冬小麦生长旺期对站点和区域尺度的旱情响应,探求其在河北省冬小麦旱情日常监测中的可操作性。研究结果表明：在三个典型干旱站点,植被供水指数多年距平值与土壤相对湿度多年距平值有较为一致的时间动态;在区域尺度,植被供水指数对水分亏缺状况敏感,与20cm土壤相对湿度的相关性较好。利用植被供水指数建立的遥感监测模型可用于反演冬小麦种植区旱情空间格局,结果与地面观测资料基本吻合。     

基于GIS与QuickBird影像的小流域土壤侵蚀定量评价

基于GIS技术和QuickBird遥感影像,采用修正的通用土壤流失方程RUSLE作为评价模型,计算了清水沟小流域土壤侵蚀量,并结合土壤侵蚀强度分级标准,生成流域土壤侵蚀强度等级图;利用GIS的叠置分析功能,定量分析了土壤侵蚀与坡度和土地利用之间的关系,并针对不同水保措施预测了水土保持治理效果.结果表明清水沟小流域土壤侵蚀模数为75.02 t·hm-2·a-1,属强度侵蚀区,年水土流失量2.6×104 t;流域64.5%的泥沙来自于占流域面积仅28%的极强度和剧烈侵蚀区域;经分析,流域土壤侵蚀模数与坡度呈显著正相关,不同土地利用类型的土壤侵蚀模数大小排序依次为难利用地＞建设用地＞坡耕地＞园地＞草地＞林地;退耕还林是降低土壤侵蚀强度与减少水土流失量的主要途径.     

物联网远程接入通信的RFID矿井机车定位系统

提出了LoRa远程数据传输的有源RFID定位技术方案,实现矿井机车运行状态监测。基于nRF24LE1和nRF24L01+两种射频芯片构建RFID标签识别系统,利用电子标签标识对象唯一性和标签在巷道空间位置固定的条件,通过SX1278射频芯片开发的物联网远程接入平台传输数据,实现对矿井机车运行状态实时定位和监测,保障矿井机车运行安全。由于采用了LoRa物联网远程通信技术,降低了建设和运行成本,可靠性、抗干扰性和可用性都得到了较好的改善。     

远场涡流和内窥镜检测技术在换热器管束中的应用

针对某炼油厂目前换热器管束泄漏事故频发的现状,研究了基于远场涡流和内窥镜的无损检测技术在碳钢换热器管束检测中的应用,并提出了通过远场涡流和内窥镜检测技术对换热管柬堵管原则进行完整性评价的方法,实现换热管束堵管的针对性、可靠性和合理性。本文所提出的各项技术的综合应用,可以最大程度地找出在用换热器已经泄漏和尚未泄漏但损伤超过临界尺寸的换热管柬,并根据切实、可靠和有针对性的堵管原则进行堵管处理,可以有效减少运行周期内换热管束的泄漏次数,对石化企业安全生产和经济效益的提高有重要影响。     

Automated Calibration of the METRIC‐Landsat Evapotranspiration Process

A remaining challenge to applying satellite‐based energy‐balance algorithms for operational estimation of evapotranspiration (ET) is the calibration of the energy‐balance model. Customized calibration for each image date is generally required to overcome biases associated with radiometric accuracy of the image, uncertainties in aerodynamic features of the landscape, background thermal conditions, and model assumptions. The CIMEC process (calibration using inverse modeling at extreme conditions) is an endpoint calibration procedure where near extreme conditions in the image are identified where the ET can be estimated and assigned. In the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC?) energy‐balance model, two endpoints represent the dry and wet ends of the ET spectrum. Generally, user‐intervention is required to select locations in the image to produce best accuracy. To bring the METRIC and similar processes into the domain of less experienced operators, a consistent, reproducible, and dependable statistics‐based procedure is introduced where relationships between vegetation amount and surface temperature are used to identify a subpopulation of locations (pixels) in an image that may best represent the calibration endpoints. This article describes the background and logic for the statistical approach, how the statistics were developed, area of interest requirements and assumptions, adjustment for dry conditions in desert climates, and implementation in a common image processing environment (ERDAS Imagine).     

Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges1

Kinzel, Paul J., Carl J. Legleiter, and Jonathan M. Nelson, 2012. Mapping River Bathymetry with a Small Footprint Green LiDAR: Applications and Challenges. Journal of the American Water Resources Association (JAWRA) 1‐22. DOI: 10.1111/jawr.12008 Abstract: Airborne bathymetric Light Detection And Ranging (LiDAR) systems designed for coastal and marine surveys are increasingly sought after for high‐resolution mapping of fluvial systems. To evaluate the potential utility of bathymetric LiDAR for applications of this kind, we compared detailed surveys collected using wading and sonar techniques with measurements from the United States Geological Survey’s hybrid topographic/bathymetric Experimental Advanced Airborne Research LiDAR (EAARL). These comparisons, based upon data collected from the Trinity and Klamath Rivers, California, and the Colorado River, Colorado, demonstrated that environmental conditions and postprocessing algorithms can influence the accuracy and utility of these surveys and must be given consideration. These factors can lead to mapping errors that can have a direct bearing on derivative analyses such as hydraulic modeling and habitat assessment. We discuss the water and substrate characteristics of the sites, compare the conventional and remotely sensed river‐bed topographies, and investigate the laser waveforms reflected from submerged targets to provide an evaluation as to the suitability and accuracy of the EAARL system and associated processing algorithms for riverine mapping applications.     

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-.     

Jamaica's disappearing forests: physical and human aspects

Jamaica is a small island that is losing its forest cover at a rapid rate. Due to the dependency of its largely poor population on the many services and functions its forests provide, this loss threatens to have substantial socioeconomic and ecological consequences for the country. Despite these basic facts, the problem of Jamaican deforestation has received very little attention from the scientific community. This article presents results of an island-wide, satellite-based study of forest change for Jamaica for the period 1987–1992, which was supplemented by a field trip to the island in 1999 to assess the overall accuracy of the estimate. Landsat MSS images, which are available only up until 1992, have proved to be an invaluable and cost-effective resource for mapping forest change in the tropics, particularly in large areas. A supervised classification indicates that Jamaica experienced an average annual deforestation rate of 3.9% for this period, a figure higher than existing estimates based on partial ground surveys but lower than the FAO's 1990 Tropical Forest Assessment of 5.3% for 1981–1990. Deforestation estimates for Jamaica's 14 parishes are also presented, based on the integration of satellite-derived forest classification maps with a parish administrative boundaries map of the island in a GIS. A correlation analysis between parish deforestation estimates and socioeconomic and land use/quality indicators derived from official sources suggests that deforestation is occurring most rapidly in highly populated areas possessing large numbers of small farmers who live and work under resource-poor conditions. By providing a sense of the magnitude of and main forest loss hotspots, it is hoped that these national and subnational level forest estimates will draw scientific attention to the problem of deforestation on the island. In addition, the socioeconomic analysis may provide policy-makers and planners with some sense of the relative contribution of underlying driving process in this deforestation as a first step toward the creation of effective social programs to combat the problem.