1 Remote Sensing Applications in Agricultural Statistics at China NBS Yu Xinhua Department of Rural Surveys, National Bureau of Statistics(NBS)
2 Outline Introduction Remote Sensing Measurement Area Frame Survey Software & IT Infrastructure Challenges & Development
3 The new requirements of agricultural statistics in 21th century. Provide relevant data for policy making to ensure national food security. Food security has the highest priority for the development of modern agriculture. Agricultural statistics are vital information for grain development strategy. The international situation, the rapid development and profound change of agriculture and rural area in China brings up the new requirement. Meet the data gap for modern agricultural development. Grain Production Counties Development Strategy has implemented, which requires to set up statistical monitoring and evaluation system. The strategy of developing facility-based agriculture and standardizing horticulture production was set up, which requires corresponding statistics. The innovation of agricultural technology integration, mechanizing and informatization, new survey tools should be applied for these new farming. To enhance the agricultural production with social services supporting, agricultural statistics should be service-oriented, providing more relevant information to producers. To meet the needs of multi-dimension services. Markets needs timely, accurate, transparent information. Large and small area statistics are all needed. The data are needed for all major crops, include commodity crops and minor crops. Information for food quality, its production and marketing are all needed.
4 The new requirements of agricultural statistics in 21th century. New situation Food security Agriculture modernization Service-oriental New New goals goals More information Advanced vehicles Reliable data Better services New require ments Data collection capacity Data processing capacity Data analytical capacity Data delivery capacity Can the traditional agriculture statistical system meet the new requirements of 21th Century?
5 Traditional agricultural statistics become unadapted Survey schemes Counting units Survey tools The merging of sample survey and complete reporting. Does the sample designed for province population meet the needs of county and prefecture level? How to adapt the rapid change of farming structure? Mobility of sample farmers. Rapid change of Land tenure. Impact of sample farmer s subjectivity. Mainly traditional tool, such as self-report, compass and rope. Cannot efficiently deal with estimation under disaster or other rapid change Lack of necessary supervision and management. Traditional agricultural statistics cannot meet the new requirements well.
6 The support of new technologies Computer, network Geospatial information technologies Big data Technologies Policies Deepening reform Agriculture and rural policy Scientific outlook on development Best practices of peers Experience of other industries Experiences Importing new technologies, improving agricultural statistics.
7 Geospatial information technologies Geospatial information technologies (3S) include Remote Sensing (RS), Geography Information System (GIS)and Global Positioning System(GPS), combined with computer and network, applied for measurement, collection, storage, management, display, analysis, spreading and application of geospatial data. In USA, Geo-IT has become one of the three hottest occupations (with Biotechnology and Nanotechnology). The ability of earth observation Big range High resolution Timeliness Can be revisited The ability of geospatial data management Huge volume data Visualization The ability of positioning Location Navigation Measurement and mapping all-weather Geo-IT and agricultural Spatial analysis statistics can be Geo-reference naturally basis integrated. Remote Sensing GIS NSS
8 中国农业统计遥感发展历程 Operation phase Crop area frame survey 2009 年 Starting phase 1 4 Pilot phase Xinjiang cotton remote sensing measurement 2010 年 Crop area remote sensing measurement pilot 2009 年 Technical 3 Maturity Industry project Pilot -- Satellite-based Implementation supporting platform for agricultural surveys 2012 年 Research Earth observation project -- Pilot of high resolution phase remote sensing for statistical surveys 2011 年 Industry project -- Data source pilot of remote sensing 2 for agricultural surveys 2009 年 863 project -- The research of key technologies for statistical surveys 2006 年 863 project -- The research of norm and application of remote sensing for agricultural surveys 2003 年
9 The research of key technologies for operational implementation of agricultural statistical remote sensing National research project to produce of key technologies Crop remote sensing identification methodology. Using geo-spatial technology to build area sampling frame for agricultural surveys. Using pilot to promote the transformation of research results of key technologies Crop area remote sensing measurement pilot will improve crop identification methods with the terrain characters and imagery availability for different regions, and develop methodology operational for specific region. Use demonstration application to break through the nexus of operational application of key technologies Crop area frame sampling survey will integrate the geo-spatial frame building, geospatial sampling, and other new technology.
10 Building two major agricultural statistical surveys system with remote sensing. Crop area frame surveys Beginning from year 2010, crop area frame survey has been carried out at Jiangsu, Henan, Liaoning, Jilin Hubei, Anhui with county-level as population, which has replaced the traditional list-based crop survey. The building of work base for survey have completed and seasonal survey has been carried normally. Remote sensing measurement Beginning from year 2010, remote sensing measurements have been implemented at Beijing, jiangsu, Henan, Hubei, Jinlin, Liaoning, Ningxia, East part of Inner Mongolia for summer and autumn grain crops. Unmanned aircrafts(drones) and other new survey tools were used for survey and a technical system was built for operational field survey.
11 Improving the survey tools for agricultural surveys The first agricultural survey vehicle was developed, which integrated satellite imagery, aero-photography, field observation. So high resolution images, roadside picture and video, field agro-parameters will be collected and combined together to distill useful data for estimation. The data collection capacity of geospatial samples for crop area, growing condition and yield was formed, which provide a solution for completely, rapidly and accurately data collection for estimation of crop area, crop condition and total production.
12 Outline Introduction Remote Sensing Measurement Area Frame Survey Software & IT Infrastructure Challenges & Development
13 Current crop area remote sensing measurement Crop area remote sensing methodology was developed, which includes data collection and processing, remote sensing classification, field survey, validation, error correction and producing ASCDL as major functions. PDA survey UAV survey Imagery acquirement Field survey Identification County estimation Provincial Estimation Provincial identification +
14 Technical procedures
15 Remote sensing identification Based on the current multi-phase remote sensing data, land cover ( arable land included) were extracted and crop identification were made and finally ASCDL was produced. ASCDL County-level knowledge base for identification Crop data layers Agricultural statistical land cover data layer Error analytical layers Description of county-level products. Production flow of ASCDL
16 Field survey and sample interpretation (1) For the selected field sample, with the support of region office, UAV field survey was carried and aero-photography was captured and crop plots were delineated. Field work photos UAV photo mosaic Vectorization of sample land
17 Field survey and sample interpretation (2) Photo Captures by RPV or Mobile Devices(Tablets, smartphones) Area measurement by photos Convenient Tools Randomly Determined Lots of data to be measured
19 Results of remote sensing identification
20 Outline Introduction Remote Sensing Measurement Area Frame Survey Software & IT Infrastructure Challenges & Development
21 Start point Area frame was built with 2 nd agricultural census data and 2 nd land use census data. cropland segments were created as sampling unit and enough segments were sampled to meet the precision requirement. Several direct interviews of segment planting were carried by interviewer to estimate the crop area. According to the harvest season of crops, certain planting plots was selected for cropcutting and then the yield of specific crop was collected and estimated. Finally, the crop production was estimated. Subjective survey transits to objective survey Lack of data quality evaluation & supervision Traditional tools, such as ropes. Traditional sampling methodology List frame Traditional crops survey Quality Tools Sample Frame Spatial analysis, repeatable, supervised Modern survey tools. PDA&GPS. Geospatial sampling methodology Geospatial frame Crop area frame survey
22 Crop area frame survey system A primary system for crop area frame survey was developed, which includes geospatial sampling frame, segment sampling, field survey, survey data submit, estimation, quality evaluation. Detailed data on fields are collected and measuring precision is improved. The goals to change the counting unit from list to spatial fields were realized. Province Sample county Sample village Sample plot crop planting area estimates and variances of the Pilot counties in 2009 Pilot county name 辽宁法库县河南濮阳市江苏溧阳市吉林德惠市安徽凤台县 对地抽样方法空间随机抽样空间随机抽样空间随机抽样 以规则网格为初级抽样单元的分层两阶段抽样 农普与土地利用数据相结合的两阶段抽样 标准地块面积 (ha) 标准地块数量 ( 总体 ) 行政村数量 ( 总体 ) 标准地块数量 ( 样本 ) 行政村数量 ( 样本 ) 作物名称 玉米 玉米 水稻 玉米 水稻 总体总值估计值 (m 2 ) CV(%)
23 Developing of sampling frame Cultivated land updating By visual interpretation or change detection with current satellite imagery, the cultivated land polygons from the 2 nd land use census data were updated for more accurate, complete cover and no missing. Identification of major crops Using remote sensing images to identifying major crops to acquire more ancillary information. Depending the situation of data acquirement, change detection and unsupervised classification method are used for identification of target crops. Sampling units construction Division with administrative area : The cultivated land polygons are divided with administrative boundaries and the identification results was summed up by crops for each administrative area. Division with grid delineation : Land polygons were split into grid blocks of same size, and the identification results was summed up by crops for each block.
24 Sampling Two stage PPS sampling First stage: Primary sampling units, which are villages or grid blocks, were sampled. Second stage: Land segments were selected with SRS within sampled villages or grid blocks. Sampling of villages Sampling of land segments
25 Field work package Field maps suites:hardcopy image of sampling villages, vector maps, maps of sampling segments. GPS task package : GPS devices are needed for area measurement of the screening survey. All the task base data and forms are packed into task package and loaded into GPS. Field maps GPS task package
26 Field survey methods Screen survey Sampling segment screen questionnaire: Collect data at the beginning of each survey round. Location, crop used area, non crop used area, field splitting, and users data are collected. Sampling village questionnaire : Collect data at the end of each year. Seasonal survey Cropping intention survey : Farmers planting intention and plan are carried at sampling segment before winter planting and summer planting. 10 farmers will be selected for survey at each sampling village. Crop area survey : Carried at winter planting, spring planting and early rice planting, summer planting period. Crop area are collected from sampling segments directly. Crop yield and production survey : Carried at summer harvest, early rice harvest and autumn harvest.
27 Outline Introduction Remote Sensing Measurement Area Frame Survey Software & IT Infrastructure Challenges & Development
28 Integrated service platform for agricultural rapid survey.
30 Data source system Developing the agricultural statistical remote sensing data source assurance system to provide required satellite imagery. Assura nce system Data produc t Satellite imagery Producing and providing data products for operational agricultural remote sensing surveys. Partner ship Exploring the data partnership with line ministries & business firms. Developing standard and norm of satellite imagery data product for agricultural remote sensing surveys. Standa rd & norm Demon stration Carrying out provincial demonstration application
31 Agricultural remote sensing survey infrastructure Data source assurance system Satellite imagery : High Resolution Satellite series, Resource Satellite series, Environmental Satellite series Partnership Line ministries : ministry of land resource, National Bureau of Mapping and Geo-information, National Geo-information Center Business firms : Oriental Roadnear, PeaceMap, China Science Geo-do Geo-spatial base data framework construction Completely covered satellite imagery : Multi-phase medium and high resolution satellite imagery, completely covered the major provinces of grain production. Level by level base geography data : Based on 2 nd land use census data, build up provincial, county-level, village-level, and plot-level base geography dataset. Other data types : Geo-referenced statistics, meteorology data, phenology data, hydrology data
32 Statistical remote sensing data sharing and service system It will realize the transformation of agricultural statistics to plane or threedimensional distribution form at spatial and regional level, which will improve the display methods and means of agricultural statistics, promote the standardization of statistical information, and enhance the ability of analysis, exploration and forecasting to agricultural statistics. Process Collection Management Distribution
33 Crop production survey vehicle
34 Unmanned Aircraft (drones)
35 Mobile data collection devices Smartphone (Explorer) GPS(Windows Mobile) PAD(Android)
36 Outline Introduction Remote Sensing Measurement Area Frame Survey Software & IT Infrastructure Challenges & Development
37 Challenges Challenges from the complexity of farming in China Complexity of farming structure in China : Lots of crop types, region difference, Non-scale farming, multiple crop rotation, intercropping and interplanting. Fragmented landscape in most regions : Except the northeast, the size of crop plots are very small. Complex terrain : There are cropping in all kinds of landscape type, plain, hill, or mountainous area Largely impacted by market, the farming structure change rapidly Following impact of the social and economical development, arable land changes very rapidly. Challenges from remote sensing technologies Time phase requirement : Because the crop planting is strongly seasonal, suitable satellite imagery must be acquired at specific period. Cloud and rain weather : Because of the constraint of cloud/rain weather and satellite passing period, the data availability is very limited for large scale crop area remote sensing. Identification accuracy : At complex situation, the accuracy of remote sensing identification must be researched to improve substantially. Challenges from operational implementation Cost : It includes the infrastructure, purchase and process of imagery, geo-spatial framework, purchase of survey tools. If it were applied at national wide, bulks of fund are required. Worklord : For the timeliness, huge volume satellite imagery must be processed in short time, and field work finished rapidly. The workload is huge. More manpower and resource are needed.
38 Future development Improvement of technical ability, efficiency, data quality and services Operational application Evaluation New system Available Data Fully operational application Survey means Integrated survey technologies to capture from space, air, fields Estimation Spatial sampling Small area estimation Model estimation Self-contained methodology Data basis Remote sensin g data Base geogra phic data Statis tical data Data source assurance system Social and economi c data Climat e data Agricultural statistical OneMap
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