干旱区研究

干旱区研究 >

2019 , Vol. 36 >Issue 3: 781 - 789

DOI: https://doi.org/10.13866/j.azr.2019.03.30

其他

基于GF-1/WFV时间序列的绿洲作物类型提取

  • 刘雅清 ,
  • 王磊 ,
  • 赵希妮 ,
  • 璩向宁 ,
  • 许兴 ,
  • 王锐
展开
  • 1.宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地,宁夏 银川 750021;
    2.宁夏大学西北退化生态系统恢复与重建教育部重点实验室,宁夏 银川 750021;
    3.南京大学国际地球系统科学研究所,江苏 南京 210093
刘雅清(1996 -),女,硕士研究生,研究方向为生态监测与评估. E-mail: 17809501703@163.com
王磊. E-mail: WL8999@163.com

收稿日期: 2018-08-17

  修回日期: 2018-09-20

  网络出版日期: 2025-10-18

基金资助

宁夏自然科学基金项目(NZ16022);宁夏高等学校科学研究重点项目(NGY2016010);国家自然科学基金(31760707);国家重点研发计划(2016YFC0501307/4-04);宁夏回族自治区西部一流学科建设项目(NXYLXK2017B06)资助

收起

Extraction of Crops in Oasis Based on GF-1/WFV Time Series

  • LIU Ya-qing ,
  • WANG Lei ,
  • ZHAO Xi-ni ,
  • QU Xiang-ning ,
  • XU Xing ,
  • WANG Rui
Expand
  • 1. Breeding Base for State Key Laboratory of Land Degradation and Ecosystem Restoration in Northwest China, Ningxia University, Yinchuan 750021,Ningxia,China;
    2. Key Laboratory for Restoration and Reconstruction of Degenerated Ecosystem in Northwest China under Ministry of Education, Ningxia University,Yinchuan 750021,Ningxia,China;
    3. International Institute for Earth System Science,Nanjing University,Nanjing 210093,Jiangsu,China

Received date: 2018-08-17

  Revised date: 2018-09-20

  Online published: 2025-10-18

Fold

摘要

当前基于中等空间分辨率时序数据的农作物种植结构提取成为研究热点,但农作物季相节律特征在不同气候背景下存在较大差异,绿洲作为干旱区具有明显小气候效应的生态景观,其农作物种植结构的遥感提取具有较强的典型性和代表性。选取宁夏河套平原绿洲典型区域,通过构建高分一号(GF-1/WFV)时间序列数据,结合不同作物耕作方式及生长物候,分析不同作物在整个生长季内的归一化植被指数(NDVI)和归一化水体指数(NDWI)的时间序列特征,构建不同决策树提取研究区农作物种植结构信息,并验证了不同方法的适用性。结果表明,对具有明显小气候效应的干旱区绿洲,利用时间分辨率和空间分辨率都较优的GF1-WFV时间序列数据,对其农作物种植结构进行遥感提取具有较强的实用性和代表性。

关键词： 农作物; 种植结构; GF-1/WFV时间序列; 决策树; 遥感提取; 绿洲; 宁夏

本文引用格式

刘雅清 , 王磊 , 赵希妮 , 璩向宁 , 许兴 , 王锐 . 基于GF-1/WFV时间序列的绿洲作物类型提取[J]. 干旱区研究, 2019 , 36(3) : 781 -789 . DOI: 10.13866/j.azr.2019.03.30

Abstract

The rapid extraction of regional crops is of great significance for agricultural production management,planting structure adjustment and optimization,and food security.The use of time series data of remote sensing image for extracting crops is an important means,and the time resolution and spatial resolution are constraints.The time resolution of medium spatial resolution of remote sensing data is significantly improved with the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System.However,the seasonal rhythm features of crops differ greatly under different climate backgrounds.The Hetao Oasis is irrigated by the Yellow River.It has an ecological landscape with obvious microclimate effect.The extraction of remote sensing data of crops is typical and representative.The Hetao Oasis in Ningxia is selected as the study area to analyze and evaluate the applicability of GF-1 satellite data in extracting crops.According to the characteristics of crop planting in the study area,the four main crops including rice,corn,wheat and alfalfa were extracted.Firstly,the GF-1/WFV image was calculated by band,and the NDVI and NDWI time series of remote sensing data sets were obtained.The values of NDVI and NDWI of the main crops were extracted,and the time series curves of NDVI and NDWI were constructed.The time series data of remote sensing indexes of the main crops were analyze,the farming information was determined to extract the phases and thresholds of the crops,construct the decision tree and extract the crops.The time series data of natural grasslands,reed and grape and of other vegetation types were combined to establish the CART decision tree so as to verify the classification effect of the correlation time series and the decision tree constructed by the farming information.The results are as follows:① The morphological characteristics of time series curves were different from different farming ways,and the NDVI time series data could be used to describe the growth status of crops in different periods; the NDWI time series data obtained from GF-1/WFV could be used to describe the soil moisture status of different crops and reflect the different irrigation systems of different crops in the study area.The NDVI time series and the NDWI time series could be used to accurately express the surface dynamic change information of the surrounding areas of the study area,correlate the crop time series with farming information,and effectively recognize different crops;② Compared with the CART decision tree,the decision tree of classification phase,classification threshold and classification accuracy constructed by combining different crop farming information and main crop curve features were similar.These indicated that the farming ways were different from different crops in the study area,and the time series of corresponding NDVI and NDWI values were significantly different.

Key words： crops; cropping pattern; GF-1/WFV time series data; decision tree; remote sensing mapping; oasis; Ningxia

参考文献

[1] 马丽,顾晓鹤,徐新刚,等.地块数据支持下的玉米种植面积遥感测量方法[J].农业工程学报,2009,25(8):147-151.
[Ma Li,Gu Xiaohe,Xu Xingang,et al.Remote sensing measurement of corn planting area based on field-data[J].Transactions of the CSAE,2009,25(8):147-151.]
[2] 张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015,30(2):304-311.
[Zhang Huanxue,Cao Xin,Li Qiangzi,et al.Research on crop identification using multi-temporal NDVI HJ images[J].Remote Sensing Technology and Application,2015,30(2):304-311.]
[3] 张健康,程彦培,张发旺,等.基于多时相遥感影像的作物种植信息提取[J].农业工程学报,2012,28(2):134-141.
[Zhang Jiankang,Cheng Yanpei,Zhang Fawang,et al.Crops planting information extraction based on multi-temporal remote sensing images[J].Transactions of the CSAE,2012,28(2):134-141.]
[4] 宋茜.基于GF-1/WFV和面向对象的农作物种植结构提取方法研究[D].北京:中国农业科学院,2016.
[Song Qian.Object-Based Image Analysis with Machine Learning Algorithms for Cropping Pattern Mapping Using GF-1/WFV Imagery[D].Beijing: Chinese Academy of Agricultural Sciences,2016.]
[5] Wardlow Brian D,Egbert Stephen L,Kastens Jude H.Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S.Central Great Plains[J].Remote Sensing of Environment,2007,108:290-310.
[6] 刘吉凯,钟仕全,梁文海.基于多时相Landsat 8OLI影像的作物种植结构提取[J].遥感技术与应用,2015,30(4):775-783.
[Liu Jikai,Zhong Shiquan,Liang Wenhai.Extraction on crops planting structure based on multi-temporal Landsat 8OLI images[J].Remote Sensing Technology and Application,2015,30(4):775-783.]
[7] 李鑫川,徐新刚,王纪华,等.基于时间序列环境卫星影像的作物分类识别[J].农业工程学报,2013,29(2):169-176.
[Li Xinchuan,Xu Xingang,Wang Jihua,et al.Crop classification recognition based on time-series images from HJ satellite[J].Transactions of the CSAE,2013,29(2):169-176.]
[8] 张峰,吴炳方,刘成林,等.利用时序植被指数监测作物物候的方法研究[J].农业工程学报,2004,20(1):155-159.
[Zhang Feng,Wu Bingfang,Liu Chenglin,et al.Methods of monitoring crop phonological stages usingtime series of vegetation indicator[J].Transactions of the CSAE,2004,20(1):155-159.]
[9] Peña J,Gutiérrez P,Hervás-Martínez C,et al.Object-based image classification of summer crops with machine learning methods[J].Remote Sensing,2014,6(6):5019-5041.
[10] 张晶,占玉林,李如仁.高分一号归一化植被指数时间序列用于冬小麦识别[J].遥感信息,2017,32(1):50-56.
[Zhang Jing,Zhan Yulin,Li Ruren.Application of GF-1 NDVI time series in winter wheat identification[J].Remote Sensing Information,2017,32(1):50-56.]
[11] 杨闫君,占玉林,田庆久,等.基于GF-1/WFVNDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.
[Yang Yanjun,Zhan Yulin,Tian Qingjiu,et al.Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the CSAE,2015,31(24):155-161.]
[12] 李梦莹,胡勇,王征禹.基于C5.0决策树和时序HJ-1A/B CCD数据的神农架林区植被分类[J].长江流域资源与环境,2016,25(7):1070-1077.
[Li Mengying,Hu Yong,Wang Zhengyu.Study on vegetation classification in Shengnongjia forest district based on C5.0 decision tree and HJ-1 A/B data[J].Resources and Environment in the Yangtze Basin,2016,25(7):1070-1077.]
[13] 梁明,孙毅中,罗荣,等.基于综合多尺度特征决策树模型的土地利用变化分析[J].农业工程学报,2014,30(17):259-267.
[Liang Ming,Sun Yizhong,Luo Rong,et al.Analysis on land use change based on decision-tree model with comprehensive multi-scale characteristics[J].Transactions of the CSAE,2014,30(17):259-267.]
[14] 周静平,李存军,史磊刚,等.基于决策树和面向对象的作物分布信息遥感提取[J].农业机械学报,2016,47(9):318-326,333.
[Zhou Jingping,Li Cunjun,Shi Leigang,et al.Crops distribution information extracted by remote sensing based on decision tree and object-oriented method[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):318-326,333.]
[15] 朱永森,曾永年,张猛.基于HJ卫星数据与面向对象分类的土地利用/覆盖信息提取[J].农业工程学报,2017,33(14):258-265.
[Zhu Yongsen,Zeng Yongnian,Zhang Meng.Extract of land use/cover information based on HJ satellites data and object-oriented classification[J].Transactions of the CSAE,2017,33(14):258-265.]
[16] 康峻,侯学会,牛铮,等.基于拟合物候参数的植被遥感决策树分类[J].农业工程学报,2014,30(9):148-156.
[Kang Jun,Hou Xuehui,Niu Zheng,et al.Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data[J].Transactions of the CSAE,2014,30(9):148-156.]
[17] 那晓东,张树清,李晓峰,等.基于QUEST决策树兼容多源数据的淡水沼泽湿地信息提取[J].生态学杂志,2009,28(2):357-365.
[Na Xiaodong,Zhang Shuqing,Li Xiaofeng,et al.Fresh water marsh wetland information extraction based on QUEST tree integrating with multi-source data[J].Chinese Journal of Ecology,2009,28(2):357-365.]
[18] 吴健生,潘况一,彭建,等.基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例[J].地理研究,2012,31(11):1973-1980.
[Wu Jiansheng,Pan Kuangyi,Peng Jian,et al.Research on the accuracy of TM images land-use classification based on QUEST decision tree:A case study of Lijiang in Yunnan[J].Geographical Research,2012,31(11):1973-1980.]
[19] 刘晓娜,封志明,姜鲁光.基于决策树分类的橡胶林地遥感识别[J].农业工程学报,2013,29(24):163-172.
[Liu Xiao’na,Feng Zhiming,Jiang Luguang.Application of decision tree classification to rubber plantations extraction with remote sensing[J].Transactions of the CSAE,2013,29(24):163-172.]
[20] 常布辉,王军涛,罗玉丽,等.河套灌区沈乌灌域GF-1/WFV遥感耕地提取[J].农业工程学报,2017,33(23):188-195.
[Chang Buhui,Wang Juntao,Luo Yuli,et al.Cultivated land extraction based on GF-1/WFV remote sensing in Shenwu irrigation area of Hetao irrigation district[J].Transactions of the CSAE,2017,33(23):188-195.]
[21] 于文婧,刘晓娜,孙丹峰,等.基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类[J].农业工程学报,2016,32(2):212-219.
[Yu Wenjing,Liu Xiao’na,Sun Danfeng,et al.Land use classification in arid and semi-arid irrigated area based on HJ-CCD data and decision tree method[J].Transactions of the CSAE,2016,32(2):212-219.]
[22] 赖先齐,王江丽,张凤华,等.中国绿洲研究发展进程的探讨与展望[J].干旱区研究,2017,34(3):663-668.
[Lai Xianqi, Wang Jiangli, Zhang Fenghua,et al.Discussion and outlook of oasis research and development process in China[J].Arid Zone Research,2017,34(3):663-668.]
[23] 王巧焕,卢玉东,赛佳美,等.干旱区绿洲土壤盐分特征[J].干旱区研究,2018,35(3):503-509.
[Wang Qiaohuan,Lu Yudong,Sai Jiamei,et al.Characteristics of soil salinity in arid oasis[J].Arid Zone Research,2018,35(3):503-509.]
[24] 乔斌,何彤慧,于骥,等.银川平原湖泊湿地不同水深梯度下芦苇种群生长特征[J].干旱区研究,2017,34(4):762-769.
[Qiao Bin,He Tonghui,Yu Ji,et al.Growth of Phragmites australis population under different water-depth gradients in lake-wetland of the Yinchuan Plain[J].Arid Zone Research,2017,34(4):762-769.]
[25] 欧阳玲,毛德华,王宗明,等.基于GF-1与Landsat8OLI影像的作物种植结构与产量分析[J].农业工程学报,2017,33(11):147-156.
[Ouyang Ling,Mao Dehua,Wang Zongming,et al.Analysis crops planting structure and yield based on GF-1 and Landsat8 OLI images[J].Transactions of the CSAE,2017,33(11):147-156.]
[26] 周燕芳.基于作物物候特征的水稻种植面积提取研究[D].长春:东北师范大学,2015.
[Zhou Yanfang.The Study of Paddy Rice Areas Extraction Based on Vegetation Phenology Characteristics[D].Changchun:Northeast Normal University,2015.]
[27] 郑利娟.基于高分一/六号卫星影像特征的农作物分类研究[D].北京:中国科学院遥感与数字地球研究所,2017.
[Zheng Lijuan.Crop Classification Using Multi-Features of Chinese Gaofen-1/6 Sateliite Remote Sensing Images[D].Beijing:Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,2017.]
[28] 李晓东,姜琦刚.基于多时相遥感数据的农田分类提取[J].农业工程学报,2015,31(7):145-150.
[Li Xiaodong,Jiang Qigang.Extraction of farmland classification based on multi-temporal remote sensing data[J].Transactions of the CSAE,2015,31(7):145-150.]
[29] 黄启厅,曾志康,谢国雪,等.基于高时空分辨率遥感数据协同的作物种植结构调查[J].南方农业学报,2017,48(3):552-560.
[Huang Qiting,Zeng Zhikang,Xie Guoxue,et al.Investigation on crop planting structure based on synergy of high spatial-temporal resolution remote sensing data[J].Journal of Southern Agriculture,2017,48(3):552-560.]
[30] 陈利,林辉,孙华,等.基于决策树分类的森林信息提取研究[J].中南林业科技大学学报,2013,33(1):46-51.
[Chen Li,Lin Hui,Sun Hua,et al.Studies on information extraction of forest in Zhuzhou city based on decision tree classification[J].Journal of Central South University of Forestry & Technology,2013,33(1):46-51.]
[31] 申健,常庆瑞,李粉玲,等.2000—2013年关中地区耕地复种指数遥感动态监测[J].农业机械学报,2016,47(8):280-287.
[Sheng Jian,Chang Qingrui,Li Fenling,et al.Dynamic monitoring of cropping index in Guanzhong Area using remote sensing in 2000-2013[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(8):280-287.]
[32] 许青云,杨贵军,龙慧灵,等.基于MODISNDVI多年时序数据的农作物种植识别[J].农业工程学报,2014,30(11):134-144.
[Xu Qingyun,Yang Guijun,Long Huiling,et al.Crop information identification based on MODISNDVI time-series data[J].Transactions of the CSAE,2014,30(11):134-144.]
[33] Breiman L,Friedman J H,Olshen R A,et al.Classification and Regression Trees[M].California:Wadsworth International Group,1984:1-358.
Options
文章导航

/

〈 〉