GVG农情采样系统及其应用

介绍了通过对GPS、VIDEO摄像头、GIS的综合集成 ,用于野外农作物采样的信息快速采集、定位和处理分析系统 ,简称为GVG农情采样系统。系统包括影像采集卡、视频摄像头、GPS接收卡、GPS天线和工控计算机 ,在野外采集时采用汽车为主要工作平台 ,以各级公路为样线进行动态采样。系统工作时实时采集GPS信号 ,捕捉视频影像 ,同时根据GPS位置自动获得GIS属性信息 ,并自动记录在后台数据库。野外工作结束后 ,系统提供的功能允许操作人员对每条记录的照片中各类农作物所占比例进行赋值 ,统计单元内各种作物的分类成数 ,包括采样线、县级、农业区划级和省级单元。GVG系统的自动数据采集方式和GIS支持下的图像分析和统计方法提高了数据的采集和室内数据分析的效率 ,同时保证了采样的精度 ,经过不同地区的精度检验 ,作为“中国农情遥感监测系统”的重要组成部分 ,在全国范围内对大宗农作物分类成数的监测精度达到 95 %以上。     

作物种植成数的遥感监测精度评价

以河南开封和山西太谷地区作为研究区域 ,选用LandsatTM作为农作物种植面积遥感监测的数据源。利用LandsatTM提取河南开封实验区 2 0 0 1年的夏季作物和山西太谷地区 2 0 0 3年秋季作物的作物种植成数。同时 ,利用IKONOS ,QuickBird高分辨率遥感影像 ,通过地面调查进行了地面作物填图和分类 ,同样得到实验区的农作物种植成数。最后通过两种结果对比 ,表明开封实验区夏季作物的监测精度达到 99%以上 ,太谷实验区秋季作物的监测精度达到 97%以上 ,由此推断 ,表明利用LandsatTM监测农作物种植成数的精度能够满足中国农情遥感监测的运行化要求     

基于两个独立抽样框架的农作物种植面积遥感估算方法

通过分析遥感技术在中国农作物种植面积估算中所遇到的难点 ,针对运行化的农作物遥感估产系统对主要农作物种植面积估算的需求 ,提出在农作物种植结构区划的基础上 ,采用整群抽样和样条采样技术相结合的方法 ,进行农作物种植面积估算。整群抽样技术利用遥感影像估算农作物总种植成数 ,样条采样是一种适合中国农作物种植结构特征的采样技术 ,用于调查不同农作物类别在所有播种作物中的分类成数。在中国现有的耕地数据库基础上 ,根据两次抽样获得的成数 ,计算得到具体某一种农作物类别的种植面积。最后给出了 2 0 0 3年早稻种植面积估算的实例。     

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%。     

中国农情采样网络的组织与实施

首先分析了农情采样网络建设所面临的问题 ,确定了中国农情采样网络的采样目的主要是农作物分类成数调查 ,并将全国划分为 9个采样片分别组织采样队伍进行农情采样的方案。从采样片划分及调整、农情采样队伍建设、农情采样内容、采样频次和采样时间 ,以及质量控制等角度讨论了农情采样网络的组织建设工作。从技术培训、督促、野外采样、室内汇总、质量检查和年终总结等方面介绍了中国农情采样网络的实施情况。实践证明 ,文中介绍的农情采样方案效率高 ,成本低 ,仅用 9个专业小组就完成了全国的农情采样工作 ,是一种符合中国国情的农情采样方案 ,有一定的推广价值。     

多时相遥感影像农作物识别方法的分析

作物识别是农情监测的基础,能为农业和灌溉用水管理部门提供重要参考数据。利用单景影像进行作物识别容易出现异物同谱及同物异谱现象,基于多时相影像的作物识别则可结合作物物候特征进行分类,避免该问题。介绍多时相作物识别的两种基本方法,并且对国内外多时相遥感农作物识别研究现状和新进展进行论述。     

中国农情遥感速报系统

介绍了中国农情遥感速报系统的建设情况 ,系统内容包括农作物长势监测、农作物种植面积监测、农作物单产预测与粮食产量估算、作物时空结构监测和粮食供需平衡预警等。简要介绍了 1998年以来中国农情遥感速报系统在监测内容与监测范围、监测频率、技术发展以及质量控制与过程检验体系建立等方面的进展 ,并就中国农情遥感速报系统的发展方向提出了展望。     

GVG采样线代表性检验

通过在实验区内设置不同类型的采样线,对GVG采样线的代表性进行检验.结果表明,高速公路和乡村道路类型的采样线对区域代表性的精度在95%以上;国道为86.726%;省道为65.447%.对于不同的缓冲区,高速公路、省道和乡村道路类型采样线,以200m缓冲区的代表性最好.而国道则以800m缓冲区的代表性最好.对于不同的作物而言,无论何种类型的采样线或者缓冲区,种植面积最大的棉花的精度是最好的.最低的是省道采样线1000m缓冲区,精度是78.146%,最好的是国道采样线800m缓冲区,精度是99.974%.除省道外,其他精度都在94.8%以上.这说明GVG采样线所获得的成数对于区域主要作物的代表性是很好的.     

农作物种植面积遥感估算的影响因素研究

针对不同的农作物种植结构区,研究影响遥感影像分类各因素与农作物种植面积估算精度的定性和定量关系是十分必要的。以Rapid Eye影像提取的早稻种植信息为研究对象,从农作物的种植成数、种植破碎度和地块形状指数3个角度进行了不同空间分辨率下各因素对农作物面积监测的影响研究。结果表明:随着农作物种植成数的降低,种植结构越来越破碎,种植地块趋于狭长分布,各分辨率下农作物面积估算精度均呈递减趋势;要达到85%以上的面积估算精度,当作物种植成数在50%以上时,可选取高于150 m分辨率的遥感数据;当作物种植较为破碎时,需要在提高影像空间分辨率的同时融入其他技术手段;当作物种植地块为狭长分布时,提高影像的空间分辨率并不能保证面积估算精度,必须通过其他技术手段达到精度要求;并最终得到了4种影响因素对面积估算精度的定量评估模型。研究结果为解决不同农作物种植结构区遥感数据的选择、面积估算精度的提高,以及在特定研究区和数据源条件下可达到的面积估算水平等问题提供了理论基础。     

基于遥感的大宗农产品优势产区监测研究

以HJ1卫星数据为实验数据,通过运用遥感处理技术、地理信息技术,完成实验区遥感反演作物长势指标体系研究,并将农情监测数据进行web发布。为高分数据迅速投入使用和生产提供理论基础与实现途径,同时为农作物估产的运行化遥感提供了标准、快速的农情监测方法。     

高精度作物分布图制作

中国自然条件复杂 ,农业种植结构多样 ,地块小而分散 ,利用遥感影像制作作物分布图的精度很难满足农业遥感估产的需求。该文利用目前最高分辨率的商用遥感卫星 (QuickBird)影像 ,采用面向对象的影像分析方法提取耕地种植地块图 ,结合详细的地面调查制作高精度的作物分布图 ,为农业遥感估产服务。     

Using high resolution QuickBird imagery for crop identification and area estimation

Imagery from recently launched high spatial resolution satellite sensors offers new opportunities for crop assessment and monitoring. A 2.8-m multispectral QuickBird image covering an intensively cropped area in south Texas was evaluated for crop identification and area estimation. Three reduced-resolution images with pixel sizes of 11.2 m, 19.6 m, and 30.8 m were also generated from the original image to simulate coarser resolution imagery from other satellite systems. Supervised classification techniques were used to classify the original image and the three aggregated images into five crop classes (grain sorghum, cotton, citrus, sugarcane, and melons) and five non-crop cover types (mixed herbaceous species, mixed brush, water bodies, wet areas, and dry soil/roads). The five non-crop classes in the 10-category classification maps were then merged as one class. The classification maps were filtered to remove the small inclusions of other classes within the dominant class. For accuracy assessment of the classification maps, crop fields were ground verified and field boundaries were digitized from the original image to determine reference field areas for the five crops. Overall accuracy for the unfiltered 2.8-m, 11.2-m, 19.6-m, and 30.8-m classification maps were 71.4, 76.9, 77.1, and 78.0%, respectively, while overall accuracy for the respective filtered classification maps were 83.6, 82.3, 79.8, and 78.5%. Although increase in pixel size improved overall accuracy for the unfiltered classification maps, the filtered 2.8-m classification map provided the best overall accuracy. Percentage area estimates based on the filtered 2.8-m classification map (34.3, 16.4, 2.3, 2.2, 8.0, and 36.8% for grain sorghum, cotton, citrus, sugarcane, melons, and non-crop, respectively) agreed well with estimates from the digitized polygon map (35.0, 17.9, 2.4, 2.1, 8.0, and 34.6% for the respective categories). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas.     

An Agricultural Monitoring System Based on the Use of Remotely Sensed Imagery and Field Server Web Camera Data

In this study, the authors develop an integrated agricultural monitoring system based on the use of high-spatial-resolution remote sensing imagery and Field Server data for a cabbage field in Tsumagoi, Gunma Prefecture, Japan. The use of the integrated system made it possible to verify the accuracy of cabbage coverage estimated from high-spatial-resolution QuickBird imagery using an unmixing method, because the authors were able to remotely examine cabbages growing in real-time using a Field Server web camera linked to their laboratory via the Internet. Using the developed integrated system, they produced a cabbage coverage map that provided information on cabbage growth that could be used for agricultural land management, particularly with regard to the application of fertilizer and forecasting crop production. The results support the validity of using remote sensing technology in conjunction with a Field Server to manage agricultural crop land.     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

CART and IDC – based classification of irrigated agricultural fields using multi-source satellite data

To understand water productivity of crops cultivated in the Eastern Province of Saudi Arabia, this study was conducted to generate a reliable crop type map using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and crop phenology. Classification And Regression Tree (CART) and ISO-DATA Cluster (IDC) classification techniques were utilized for the identification of crops. The Ideal Crop Spectral Curves were generated and utilized for the formulation of CART decision rules. For IDC, the stacked images of the phenology-integrated Normalized Difference Vegetation Index were utilized for the classification. The overall accuracy of the classified maps of CART was 76, 77 and 81% for ASTER, MODIS and Landsat-8, respectively. For IDC, the accuracy was determined at 67, 63 and 60% for ASTER, MODIS and Landsat-8, respectively. The developed decision rules can be efficiently used for mapping of crop types for the same agro-climatic region of the study area.