基于多时相Landsat8 OLI影像的作物种植结构提取

doi:10.11873/j.issn.1004-0323.2015.4.0775

遥感技术与应用

2015, Vol. 30

Issue (4): 775-783 DOI: 10.11873/j.issn.1004-0323.2015.4.0775

图像与数据处理

基于多时相Landsat8 OLI影像的作物种植结构提取

刘吉凯¹,钟仕全²,梁文海³

(1.南京信息工程大学地理与遥感学院,江苏南京 210044;
2.广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,广西南宁 530022;
3.广西自治区林业勘测设计院,广西南宁 530011)

Extraction on Crops Planting Structure based on Multi-temporal Landsat8 OLI Images

Liu Jikai¹,Zhong Shiquan²,Liang Wenhai³

(1.School of Geography & Remote Sensing,Nanjing University of
Information Science & Technology,Nanjing 210044,China;
2.Guangxi Institute of Meteorology,Nanning/Remote Sensing Application and Test Base of
National Satellite Meteorology Centre,Nanning 530022,China;
3.Guangxi Forest Inventory & Planning Institute,Nanning 530011,China)

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摘要：

针对基于多时相遥感影像、多种特征量提取多种作物种植结构在我国研究较少的现状,利用多时相Landsat8 OLI影像数据,根据温宿县不同作物的农事历,通过分析主要地物的光谱特征和归一化植被指数的时间变化信息,构建不同作物种植结构提取的决策树模型,实现了对温宿县多种作物种植结构信息的提取。结果表明:①水稻的最佳识别依据是5月20日影像的近红外波段和7月23日影像的NDVI值;棉花和春玉米的最佳识别依据是5月20日~9月9日影像的NDVI变化值;冬小麦—夏玉米和林果的最佳识别依据是5月20日~7月23日影像的NDVI变化值;②与单时相监督分类相比,多时相决策树法对多种作物种植结构的提取效果更理想,总体精度提高了7.90%,Kappa系数提高了0.10;③Landsat8 OLI影像数据分辨率高,成本低,获取方便,是农作物遥感的良好数据源。

关键词： Landsat8 OLI影像; 光谱特征; NDVI; 决策树分类模型; 作物种植结构

Abstract:

Crops planting structure and spatiotemporal change information have become the focus of the current agricultural remote sensing application.However,using the spectral characteristics and vegetation index of multi\|temporal remote sensing data to extract the crops planting structure was still few in our country.The paper took Wensu county as the study area,used Landsat8 OLI satellite data,analyzed the difference of spectral characteristics and vegetation index of the main land cover types according to the crop phenological calendar,and modeled a decision tree to identification crops,then used the model to extract crops planting structure precisely on Wensu county.The results suggested that:① The optimal choice to identify paddy rice is near infrared band on May 20 and NDVI value on July 23;NDVI change value from May 20 to September 9,which is most helpful for the identification of cotton and corn;NDVI change value from May 20 to July 23,which is the best choice for wheat and forest fruit.② the decision tree classification based on multi\|temporal Landsat8 OLI images outperformed the single\|temporal supervised classification results with the overall accuracy increased 7.90% and Kappa coefficient increased 0.10.③Landsat8 OLI satellit data has high spatial resolution and convenient access,so it is a potential data source for crops information identification,monitoring,extracting in the future.

Key words: Landsat8 OLI images Spectral characteristics NDVI Decision tree classification Crops planting structure

收稿日期: 2014-03-10 出版日期: 2015-09-22

TP 79

基金资助:

库尔勒市香梨信息系统建设项目资助,广西科学研究与技术开发计划课题（桂科攻0816006-8）。

通讯作者: 钟仕全（1964-），男，广西桂平人，高级工程师，主要从事卫星遥感应用研究。Email:zhongshq@sina.com。

作者简介: 刘吉凯（1989-），男，甘肃武威人，硕士研究生，主要从事作物遥感监测与方法研究。Email:897285492@qq.com。

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引用本文:

刘吉凯,钟仕全,梁文海. 基于多时相Landsat8 OLI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 Landsat8 OLI Images. Remote Sensing Technology and Application, 2015, 30(4): 775-783.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.4.0775 或 http://www.rsta.ac.cn/CN/Y2015/V30/I4/775

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