【摘要】：應用遙感技術(shù)提取水稻種植信息是農(nóng)業(yè)遙感的重要內(nèi)容。GF-1衛(wèi)星WFV數(shù)據(jù)為農(nóng)業(yè)信息提取提供了新的途徑,面向?qū)ο蟮姆诸惙椒ㄊ沁b感解譯的重要方法。本研究以揚州市為研究區(qū)域,基于GF-1影像WFV數(shù)據(jù),采用面向?qū)ο蟮姆诸惙椒?提取水稻種植信息,并實地調(diào)查驗證試驗結(jié)果,試圖探討GF-1數(shù)據(jù)面向?qū)ο蠓诸惙椒ㄔ谒痉N植信息提取中的可行性與影響提取精度的因素。結(jié)果表明,應用GF-1數(shù)據(jù),采用面向?qū)ο蟮姆诸惙椒軌蚝芎玫赝瓿蓳P州市水稻種植信息的提取,2016年揚州市有水稻種植面積214 524 hm~2,總體精度達到98.5%,Kappa系數(shù)0.95,面積精度達97.5%;實地考察能夠提高提取精度,地形破碎程度越低,提取精度越高。
[Abstract]:Using remote sensing technology to extract rice planting information is an important content of agricultural remote sensing. GF-1 satellite WFV data provide a new way for agricultural information extraction. Object-oriented classification method is an important method of remote sensing interpretation. In this study, Yangzhou City was used as the research area. Based on the WFV data of GF-1 image, the information of rice planting was extracted by using the object-oriented classification method, and the results of the experiment were verified by field investigation. This paper attempts to explore the feasibility of object oriented classification of GF-1 data in rice planting information extraction and the factors affecting the extraction accuracy. The results show that the extraction of rice planting information in Yangzhou can be accomplished well by using GF-1 data and object-oriented classification method. The total precision of 214,524 hm~2, of rice planting area in Yangzhou in 2016 is 98.5%. The Kappa coefficient is 0.95and the area precision is 97.5. Field investigation can improve the extraction accuracy, the lower the degree of topographic fragmentation, the higher the extraction accuracy.
【作者單位】： 揚州市耕地質(zhì)量保護站;福建師范大學地理科學學院;中國科學院地理科學與資源研究所/資源與環(huán)境信息系統(tǒng)國家重點實驗室;揚州地恒科技有限公司;
【基金】：國家重點研發(fā)計劃項目(2016YFD0200301) 國家重大科技專項項目“新能源評估研究示范”(30-Y30B13-9003-14/16-04) 農(nóng)業(yè)部耕地質(zhì)量保護項目(農(nóng)財發(fā)[2016]35)
【分類號】：S127;S511

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