GIS在省级气候业务系统数据库建设中的应用

为了满足省级气候业务系统的需要,通过设计气候业务系统的数据模型,收集和整理地理数据和气象数据,建立合理的空间数据库和属性数据库;实现气候业务系统数据库中空间数据库和属性数据库的统一存储,实现了空间数据的存储和管理的高效率,为建立实现高效的基于GIS的省级气候业务系统奠定了坚实的基础。     

地理信息系统技术在基础地理信息数据库检查中的应用

北京市基础地理信息数据库通过空间数据库引擎ArcSDE,以要素类的形式存储在地理空间数据库中,后台为ORACLE数据库。本文以1:2000基础地理信息数据库为例,对数据库检查的技术路线、系统体系结构和功能进行了设计和探讨。     

移动地质灾害应急指挥系统设计与实现

本文讨论了ArcGIS空间分析、数据库管理和移动通信等技术,并分析了地质环境因子、地质灾害危险性预报预警等级图及实际灾害点信息,将其与智能手机结合,建立了移动地质灾害应急指挥系统。通过系统随时随地获取可靠的地质灾害预警预报信息,提高对突发性地质灾害的反应能力,从而达到有效防灾、减灾的目的。     

“数字丽水”地理空间框架建设主要成果与技术创新

"数字丽水"地理空间框架建设项目建立了完善的地理实体数据库、地理信息公共平台以及一系列应用示范系统,目前已为政务部门20多家用户单位提供地理空间数据服务。本文对"数字丽水"地理空间框架项目建设的主要成果与技术创新进行总结。     

广东省基础地理数据库系统建设

基础地理空间数据库系统是建设国土资源信息化和“数字省(市)”地理空间框架重要一环。本丈主要介绍了广东省基础地理数据库建设的内容、体系结构、开发模式和功能组成,并对数据库管理系统采用安全策略和特点进行描述,为省级基础地理数据库系统建设进行了有益探讨和实践。     

地理国情数据集成管理研究与实现

简要阐述了湖南省地理国情数据的内容与特点;针对数据集成管理、统计分析和应用服务的需要,从设计思想、系统架构、系统功能等方面对地理国情数据库管理与应用服务系统进行了分析和设计;结合湖南省工作实践,构建了省级地理国情空间数据库,开发了地理国情数据库管理与应用服务系统,可为类似数据集成管理平台建设提供参考和借鉴.     

基于WebGIS的气象图像数据服务系统研究

研究了基于WebGIS的气象图像数据服务系统。通过对气象图像数据跟GIS地图紧密结合的分析,将所需地图数据进行了整理和入库。对于不带空间参考的气象图像数据,以Microsoft.NET为开发平台,基于ArcGIS Engine开发了图像数据跟地图数据配准的应用程序,生成了带投影坐标等空间参考的气象图像数据。然后采用ArcGIS Server构建了气象图像数据查询显示平台,主要包括地图服务的发布与管理,系统的开发实现,大数据量并发,以及安全策略等,完成了基本地图操作、气象图像数据的查询显示、动画和分析等功能。该系统实现了气象图像数据在WebGIS环境下的发布、查询和分析,为公众了解气象图像数据和预报员制作气象预警预报提供了一个气象信息辅助决策平台,也便于结合地形进行各种气象分析。     

西藏自治区农业气象灾害WebGIS平台的设计与实现

针对西藏农业气象灾害数据库系统数据类型多样,数据管理困难、挖掘利用不充分等问题,本文以B/S架构和GIS组件技术,以Oracle数据库为数据存储管理中心,设计研发了西藏自治区农业气象灾害数据库系统。实现对该区域1987—2015年农业气象数据、灾害事件、社会经济、基础地理信息的有效管理,系统具有用户管理、数据维护、数据查询、统计分析、趋势预测和防灾措施、地图可视化表达等功能,通过业务化试验与运行,结果表明,系统功能齐全、界面友好、运行稳定,对发展农业气象灾害预测预报理论、区域农业气象灾害数据统计分析、管理决策与防灾减灾等具有重要理论和现实意义。     

利用ArcGIS Engine开发台风遥感监测分析系统

采用.NET和ARCGIS Engine开发完成的台风遥感监测分析系统,通过对FY2C、NOAA、FY3A等不同卫星遥感数据,以及雷达数据、空间地理数据和各种专题数据进行读取处理,能够实现台风定位、定强、结构诊断、路径分析、降水估计等专项分析功能以及图像处理、地理导航、专题制图、数值预报等辅助分析功能,为台风的监测和预报提供了一体化的信息支撑,是国内率先实现台风遥感监测分析数据综合和功能综合的平台系统。本文重点介绍了系统开发中所采用的基于面向对象的台风遥感综合应用系统框架分析设计、数据库设计与组织、模型集成等系统实现关键技术。     

地理空间数据库设计规划要点初探

地理空间数据库建设是数字城市建设中的基础,针对地理空间数据库设计规划阶段的要点和注意事项进行了初步探讨,阐述了地理空间数据库设计中应进行的工作、注意的问题,进行了初步的总结,并对其中的一些常见内容提出了建议。对今后数字城市的地理空间数据库建设有着积极的参考意义。     

全国作物种植结构快速调查技术与应用

现有种植结构的分析都是基于统计数据 ,时效性低及精度差 ,难以及时为各级政府部门提供决策支持。以“中国农情遥感速报系统”使用的GVG农情采样系统和样条采样框架为基础 ,提出了快速获取全国农作物种植结构的技术方法 ,并以 2 0 0 2年为例 ,开展全国夏粮和秋粮种植结构的调查与现状分析。全国夏粮的粮经比例为 5 8%∶2 1% ,秋粮的粮经比例为 79%∶14 % ,粮食作物仍然占有较大的比例。调查表明 ,全国范围的种植结构在时间和空间上变化很大。黑龙江省的大豆种植成数最高 ,达到38% ,是中国的大豆主产区 ;吉林和辽宁两省的春玉米种植成数相差不大 ,高达 71% ;黄淮海地区夏粮以种植冬小麦为主 ,种植成数高达 97% (河北省 ) ,秋粮以夏玉米为主 ,种植成数高达 82 % (河南 ) ;以长江为界 ,冬小麦和油料在长江南北的种植成数变化很大 ,长江以北冬小麦与油料并重 ,以南以油料为主。秋粮则以中晚稻为主 ,种植成数均超过 6 6 % ;华南夏粮和秋粮均以水稻为主 ,其中广东的蔬菜瓜果的种植成数高达 2 9% ;西南地区的秋粮以中稻和夏玉米为主 ,其中云南省的棉麻糖的种植成数高达19% ,说明云南省仍然是中国的烟草大省。经济发达或邻近经济发达地区的省份的蔬菜瓜果的种植成数较大 ,如天津市高达 34%。     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

基于GIS的热带气旋相似路径检索系统研究

本文应用GIS技术建立了热带气旋路径检索分析系统。我们首先以ESRI的Shape模型为基础设计了热带气旋路径的空间数据模型,并建立了热带气旋的空间数据库,从而实现了热带气旋的空间查询工具,提高了系统的检索效率;其次提出了热带气旋路径空间相似的概念,定义了可测度历史路径和实时路径相似程度(相似系数)的面积指数方法,并最终实现了基于空间相似性测度的相似路径检索工具。     

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.     

Use of spectral data in Markov chain model for crop yield forecasting

Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.     

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue,France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.     

Crop polarisation behavior derived from Multi-Temporal adeos-polder data

This paper reports a study on multi-temporal polarized response of wheat crop from spaceborne ADEOS-POLDER sensor over a homogeneous wheat region of Punjab, India. Both the polarized as well as total reflectance of wheat were observed at different scattering angles for two spectral bands i.e. 670 nm and 865 nm during crop growth from November to April in rabi 1996-97 season. Results show that sun-target-viewing geometry plays an important role in polarization property. The top of atmosphere (TOA) polarized reflectance is found to decrease exponentially with increasing scattering angle. Polarized reflectance of crop was found to be an order of magnitude smaller in comparison to the total reflectance. An attempt was also made to model the observed polarized behavior over an agricultural area using a theoretical simplified crop reflectance model and accounting for atmospheric molecular (Rayleigh) contribution in the single scattering approximation. It was found that there was a decrease in the polarized reflectance at the grain filling (heading) stage of wheat crop. This is in accordance with ground- based observations and can be due to the reduction in the specular component of the reflected light during post-heading stage of the crop.     

广东省特色农作物标准波谱数据库框架设计与示范应用

本研究着眼于广东省典型特色农作物的波谱，建立了波谱数据库，该库分为知识库、遥感实测波谱库、模型库、波谱分析和 荔枝园示范5大部分。 在波谱分析模块设计上提供了波谱数据查询、分析等功能，通过对荔枝园种植面积应用示范，给用户提供了极 大方便。     

马尾松毛虫危害区植被指数时序变化特征研究

本文介绍了利用虫害年度的多时相NOAA-AVHRR图像数据计算监测区归一化差植被指数（NDVI）,结合收集到的监测区的马尾松毛虫害历史资料来进行森林病虫害监测和预报的研究成果。从统计编制的分区NDVI时间序列变化曲线的对比来看,虫害区与非虫害区NDVI曲线具有一定的时序变化特征,对监测虫害有一定作用,也显示了NOAA-AVHRR资料在森林病虫害监测预报方面有一定应用前景。