| 黑土区田块尺度遥感精准管理分区 |
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| 引用本文： | 刘焕军,邱政超,孟令华,付强,姜佰文,闫岩,徐梦园.黑土区田块尺度遥感精准管理分区[J].遥感学报,2017,21(3):470-478. |
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| 作者姓名： | 刘焕军 邱政超 孟令华 付强 姜佰文 闫岩 徐梦园 |
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| 作者单位： | 东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030,东北农业大学 资源与环境学院, 哈尔滨 150030 |
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| 基金项目： | 国家自然科学基金（编号：41501357）；黑龙江省普通高等学校新世纪优秀人才培养计划（编号：1254-NCET-002）；黑龙江省教育厅科学技术研究项目（编号：11551038） |
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| 摘 要： | 基于格网采样与空间插值的精准管理分区方法精度高,但时效性差、成本高。本文以东北农垦地区红星农场农田为研究对象,提出一种基于遥感影像的精准管理分区方法:以裸土高空间分辨率遥感影像作为数据源,结合田间格网采样数据,基于裸土反射光谱特征与黑土主要理化性质的显著相关关系,运用面向对象分割、空间统计分析方法,对典型黑土区田块进行精准管理分区研究,并利用土壤理化性质和农作物生理参数,对分区结果进行评价。得出如下结论:(1)典型黑土区田块内部土壤养分含量空间变异显著;(2)基于裸土影像与面向对象的精准管理分区方法精度高,增强了分区之间的土壤养分与归一化植被指数(NDVI)差异性、分区内部各属性的一致性;(3)基于2015年4月1日和2015年5月20日单期影像分区和两期影像波段叠加(Layer stacking)分区,区间变异系数与区内变异系数之比分别为1.42、1.39和7.63,基于两期影像综合信息的分区结果显著优于基于单期影像分区;(4)基于裸土影像面向对象分割的精准管理分区方法时效性强、成本低、精度高。研究成果为田间变量施肥、发展精准农业、实现农业可持续发展提供依据。
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| 关 键 词： | 裸土 遥感影像 精准管理分区 面向对象 精准农业 |
| 收稿时间： | 2016/4/19 0:00:00 |
| 修稿时间： | 2016/11/28 0:00:00 |
Site-specific management zone of field scale based on remote sensing image in a black soil area |
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| LIU Huanjun,QIU Zhengchao,MENG Linghu,FU Qiang,JIANG Baiwen,YAN Yan and XU Mengyuan.Site-specific management zone of field scale based on remote sensing image in a black soil area[J].Journal of Remote Sensing,2017,21(3):470-478. |
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| Authors: | LIU Huanjun QIU Zhengchao MENG Linghu FU Qiang JIANG Baiwen YAN Yan and XU Mengyuan |
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| Institution: | College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China,College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China,College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China,College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China,College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China,College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China and College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China |
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| Abstract: | Although the precision management-partition method, which is based on grid sampling and spatial interpolation, is highly precise, It has poor timeliness and high costs. Thus, this study proposes a precise management-partition method based on remote sensing images. Hongxing farm fields in the northeast agricultural reclamation area are used as the research objects. High-resolution remote sensing images of bare soil and grid sampling data in the field are utilized as data sources. This study focuses onsite-specific management zone in atypical black soil area. Management is based on the significant correlation between the spectral reflectance characteristics of bare soil and the main physical and chemical properties of black soil. In addition, the zoning result is evaluated using the soil''s physical and chemical properties and physical physiological parameters of crops. The object-oriented segmentation and spatial statistical analysis methods are utilized in this study. The following conclusions are drawn:(1) The spatial variation of soil nutrients is significantly distinct in the typical black soil field. (2) The site-specific management zone, which is based on a bare soil image and highly accurate object-oriented segmentation, enhances the differences of soil nutrients, the normalized difference vegetation indexes between zones, and the consistency in zones. (3) The two images obtained on May 20th and April 1stare processed into single image and double image partitions. The ratios of inter zone to in zone variable coefficients is 1.42, 1.39, and 7.63, which prove that the result of the site-specific management zone that is based on double images is better than those based on a single image. (4) The precision management-partition method that is based on the bare soil image and segmentation technique has strong timeliness, low cost, and good accuracy. Results of the study provide the basis for field variable fertilization, development of intelligent precision agriculture, and sustainable agricultural development. |
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| Keywords: | bare soil remote sensing images precise-management zoning object oriented precision agriculture |
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