| 黑河流域中游地区作物种植结构的遥感提取 |
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| 引用本文： | 边增淦,王文,江渊.黑河流域中游地区作物种植结构的遥感提取[J].地球信息科学,2019,21(10):1629-1641. |
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| 作者姓名： | 边增淦 王文 江渊 |
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| 作者单位： | 河海大学水文水资源与水利工程科学国家重点实验室,南京 210098 |
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| 基金项目： | 国家重点研发计划课题(2017YFC0405801-02);国家自然科学基金国际地区合作与交流项目(41961134003) |
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| 摘 要： | 及时、准确地获取农作物种植结构对区域水资源管理与作物产量估测等具有重要意义。随着对通过遥感手段获得作物种植结构的深入研究,如何优选遥感数据和分类器成为需要重点考虑的关键问题。针对黑河流域中游地区的作物分布与种植特点,提出一种基于多时相遥感影像与多分类器组合的作物种植结构提取方法。利用2018年18景16 m分辨率的GF-1 WFV影像,构建NDVI时间序列。根据NDVI时间序列表征的作物季相节律和物候变化规律特点,采用分层的策略,首先解译一级土地覆被类型,再解译二级土地覆被类型。一级土地覆被类型解译中,使用决策树分类方法先将NDVI特殊且易提取的水体进行解译,再使用面向对象分类方法通过分区将需借助NDVI纹理信息提取的建设用地进行解译,最后使用随机森林分类方法解译耕地、林地、草地、裸地和湿地。在对耕地的进一步分类中,使用决策树分类方法首先将具有特殊物候规律且易于区分的苜蓿类别解译出来,再将与其他类别物候差异较大的小麦解译,最后将物候相似的玉米、蔬菜及其他解译。黑河流域中游研究区内一级土地覆被分类总体精度为97.24%,卡帕系数为0.96;作物种植结构解译总体精度为86.58%,卡帕系数为0.80。此外,还分析了影响黑河流域中游研究区解译精度的4个因素：对土地覆被类别的定义、混合像元、影像分割时基础影像的选择以及分类方法的选择。通过对不同分类方法的比较发现,与仅使用最大似然分类方法、支持向量机分类方法或随机森林分类方法相比,本文提出方法的解译结果更好,解译精度更高。
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| 关 键 词： | 遥感提取 作物种植结构 归一化植被指数 时间序列 黑河流域中游地区 |
| 收稿时间： | 2019-04-22 |
Remote Sensing of Cropping Structure in the Middle Reaches of the Heihe River Basin |
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| BIAN Zenggan,WANG Wen,JIANG Yuan.Remote Sensing of Cropping Structure in the Middle Reaches of the Heihe River Basin[J].Geo-information Science,2019,21(10):1629-1641. |
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| Authors: | BIAN Zenggan WANG Wen JIANG Yuan |
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| Institution: | State Key Laboratory of Hydrology-water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China |
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| Abstract: | Timely and accurate acquisition of cropping structure is crucial for regional water resource management and crop yield estimation. Remote sensing of cropping structure based on multiple-temporal imagery can make full use of temporal featuresfor especially regions with complicated cropping structure.However, how to select remote sensing data and classifier remains a challenge.In this paper, to map the crop distribution and planting characteristicsin the middle reaches of the Heihe River Basin, we proposed a new method by combining multi-temporal remote sensing imagery and multiple classifiers. Eighteen images available from the Gaofen-1（GF-1） satellite in 2018 were applied to construct the time series of normalized differential vegetation index （NDVI） according to the following hierarchical principles: from easy to difficult, from specific to general. The level-1 and level-2 land cover categories were interpreted successively. Specifically, in interpreting level-1 land cover categories, the decision tree classification method was used to ide.pngywater bodies based on NDVI; and then, the object-oriented classification method was used to interpret construction land by NDVI texture information after zoning; and finally, the random forest classification method was used to classify cropland, forest land, grassland, bare land, and wetland. To further classify cropland, the decision tree classification method was used firstly to interpret alfalfa which has special phenology regularity and is easy to distinguish, and then to interpret wheat categories which possess large phenological differences from other categories, and finally to interpret corn, vegetables, and other crops that have similar phenological conditions. The overall accuracy of the level-1 land cover classification and cropping structure interpretation in the study area is 97.24% and 86.58%, respectively, with the kappa coefficient being 0.96 and 0.80, respectively. In addition, four factors affecting the interpretation accuracy of the research area in the middle reaches of the Heihe river basin are analyzed: definition of landcover categories, mixed pixels, selection of basic image in image segmentation and selection of classification method. By comparing different classification methods, Compared with methods which employ only a single classifier （e.g., maximum likelihood classification method, support vector machine classification method, and random forest classification）, the proposed method has higher interpretation accuracy. |
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| Keywords: | extraction of remote sensing cropping structure normalized vegetation index time series the middle reaches of the Heihe river basin |
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