首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于GEE的山东省近30年农业大棚时空动态变化研究
引用本文:朱德海,刘逸铭,冯权泷,欧聪,郭浩,刘建涛.基于GEE的山东省近30年农业大棚时空动态变化研究[J].农业机械学报,2020,51(1):168-175.
作者姓名:朱德海  刘逸铭  冯权泷  欧聪  郭浩  刘建涛
作者单位:中国农业大学土地科学与技术学院,北京100083;自然资源部农用地质量与监控重点实验室,北京100193;中国农业大学土地科学与技术学院,北京100083;中国农业大学土地科学与技术学院,北京100083;中国农业大学资源与环境学院,北京100193;山东建筑大学测绘地理信息学院,济南250101
基金项目:中国博士后科学基金面上项目(2018M641529)和中国博士后科学基金特别资助项目(2019T120155)
摘    要:针对精确获取大尺度空间范围内农业大棚的分布情况并进行长时间的序列动态监测存在数据量大、计算效率低、精度不高等问题,利用Google Earth Engine(GEE)云平台能够实现快速存取、实时处理海量卫星数据,基于多时相Landsat影像进行农业大棚时序光谱特征和纹理特征的自动提取,采用随机森林算法实现山东省农业大棚的遥感分类,从而生成了山东省近30年农业大棚的空间分布和时空动态变化图。结果表明,本文分类流程具有较高的分类精度,其平均总体精度达到91.63%,Kappa系数均值为0.8642。经分析,山东省农业大棚从1990年的6.67 km^2增加到2018年的9919.40 km^2,增长速度为354.03 km^2/a。

关 键 词:农业大棚  时空变化  Google  Earth  Engine  大尺度范围  随机森林  Landsat
收稿时间:2019/5/9 0:00:00

Spatial-temporal Dynamic Changes of Agricultural Greenhouses in Shandong Province in Recent 30 Years Based on Google Earth Engine
ZHU Dehai,LIU Yiming,FENG Quanlong,OU Cong,GUO Hao and LIU Jiantao.Spatial-temporal Dynamic Changes of Agricultural Greenhouses in Shandong Province in Recent 30 Years Based on Google Earth Engine[J].Transactions of the Chinese Society of Agricultural Machinery,2020,51(1):168-175.
Authors:ZHU Dehai  LIU Yiming  FENG Quanlong  OU Cong  GUO Hao and LIU Jiantao
Institution:China Agricultural University;Key Laboratory for Agricultural Land Quality Monitoring and Control, Ministry of Natural Resources,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and Shandong Jianzhu University
Abstract:Shandong Province is a large agricultural province in China. In recent years, agricultural greenhouses have developed rapidly. The promotion of greenhouse technology played an important role in increasing agricultural production and efficiency in Shandong Province. Therefore, it is necessary to monitor the dynamic changes of agricultural greenhouses in Shandong Province. However, accurately obtaining the distribution of agricultural greenhouses in a large-scale space and performing dynamic monitoring of long term sequences are difficult, such as large data volume, low computational efficiency, and low precision. In response to the above problems, the Google Earth Engine (GEE) cloud platform was used to access and process massive satellite data. Based on multi-temporal Landsat images, time series spectral features and texture features were extracted. Random forests were used to complete the classification of agricultural greenhouses in Shandong Province. Finally, the thematic map of spatial distribution and spatial temporal dynamic changes of agricultural greenhouses in Shandong Province in recent 30 years were generated. The experimental results showed that the classification process proposed had better classification accuracy with the average classification accuracy of 91.63% and the Kappa coefficient of 0.8642. After analysis, the area of agricultural greenhouse in Shandong Province was increased from 6.67km2 in 1990 to 9919.40km2 in 2018, with a growth rate of 354.03km2 per year. By studying the dynamic changes of agricultural greenhouses in Shandong Province in recent 30 years, it can not only provide better planning suggestions for further development, but also provide reference for the development of agricultural greenhouses in other provinces in China.
Keywords:agricultural greenhouse  spatial-temporal changes  Google Earth Engine  large-scale  random forest  Landsat
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《农业机械学报》浏览原始摘要信息
点击此处可从《农业机械学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号