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作物学报 ›› 2024, Vol. 50 ›› Issue (3): 721-733.doi: 10.3724/SP.J.1006.2024.31036

• 耕作栽培·生理生化 • 上一篇    下一篇

基于Landsat 8影像提取豫中地区冬小麦和夏玉米分布信息的最佳时相选择

赵荣荣2(), 丛楠1,*(), 赵闯2   

  1. 1中国科学院地理科学与资源研究所拉萨高原生态试验站, 北京 100101
    2中国农业大学资源与环境学院, 北京 100193
  • 收稿日期:2023-06-05 接受日期:2023-09-13 出版日期:2024-03-12 网络出版日期:2023-10-09
  • 通讯作者: *丛楠, E-mail: congnan@igsnrr.ac.cn
  • 作者简介:E-mail: 610497240@qq.com
  • 基金资助:
    国家重点研发计划项目(2018YFA0606101);国家自然科学基金青年基金项目(42201032);国家优秀青年科学基金(海外)项目和中央高校基本科研业务费专项(15053348)

Optimal phase selection for extracting distribution of winter wheat and summer maize over central subregion of Henan Province based on Landsat 8 imagery

ZHAO Rong-Rong2(), CONG Nan1,*(), ZHAO Chuang2   

  1. 1Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
  • Received:2023-06-05 Accepted:2023-09-13 Published:2024-03-12 Published online:2023-10-09
  • Contact: *E-mail: congnan@igsnrr.ac.cn
  • Supported by:
    National Key Research and Development Program of China(2018YFA0606101);National Natural Science Youth Foundation of China(42201032);National Natural Science Fund for Excellent Young Scholars(Overseas), and the Fundamental Research Funds for the Central Universities(15053348)

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摘要：

遥感技术对大尺度农业实时监测提供了一个理想的手段, 遥感影像植被分类的最佳时相对作物种植面积遥感监测非常重要。本文选取2020年至2021年的6景Landsat 8影像, 覆盖了夏玉米从乳熟到收获、冬小麦从越冬到成熟的生育期, 以此分析不同时相的冬小麦-夏玉米与其他地类在光谱特征和NDVI上的差异, 通过决策树的方法提取豫中地区冬小麦-夏玉米的空间分布情况。结果表明, 冬小麦-夏玉米在不同生长发育时期, 提取到的面积比有所不同, 对于夏玉米而言, 乳熟时期的提取效果要优于之后的时期, 其在2020年8月26日的总体精度最高, 为83.60%, Kappa系数为0.72, 分类质量很好; 对于冬小麦而言, 最佳识别时期则处于冬小麦的越冬期, 其在2021年1月1日的总体精度最高, 为92.36%, Kappa系数为0.81, 信息提取效果很好。除了作物自身生长过程的覆盖度变化, 分类精度随成像时间而改变。多时相信息提取也发现, 受到天气等环境条件限制, 夏玉米和冬小麦的种植区域不完全重叠, 山区冬季不适合冬小麦种植从而没有与夏玉米出现重叠分布。本研究有助于我们从宏观上对作物分布及生长状况作出及时有效的判断, 对农业监测, 特别是对轮作农田的信息管理和作物物候、种植面积等研究具有广阔的应用前景。

关键词: 冬小麦-夏玉米, 光谱特征, 决策树分类, 分类精度, Landsat 8-OLI遥感影像

Abstract:

Remote sensing technology provides an ideal mean for the real-time monitoring of large-scale agriculture production. And the best phase of remote sensing images for vegetation classification is important to monitor crop area by means of remote sensing. In this study, we selected 6 Landsat 8 images from 2020 to 2021, including the growth period of summer maize from milk ripening to harvest and winter wheat from overwintering to ripening. Based on these data, we analyzed the differences in spectral characteristics and NDVI between winter wheat-summer maize and other landcovers at different phases. Then we extracted the spatial distribution of winter wheat-summer maize by the decision tree in central region of Henan province. The results showed that the area ratio of winter wheat-summer maize changed during growth period. For summer maize, the extraction effect at milky stage was better than that at the later stage, and the overall precision was the highest on August 26th, 2020, accounting for 83.60%, and the Kappa coefficient was 0.72, indicating that the classification quality was good. For winter wheat, the best identification period was in the wintering period, and its overall precision on January 1st, 2021 had the highest of 92.36%, and the Kappa coefficient was 0.81, which suggesting it was good for information extraction. In addition to the coverage change of the crop's own growth process, imaging at different stages also affected the classification accuracy. The multi-temporal information extraction also found that the planting areas of summer maize and winter wheat were not completely overlapped due to the limitation of weather and other environmental conditions. The local weather in the mountainous area was not suitable for winter wheat growth, and it was not consistent with the planting area of summer maize. This study helps us to make timely and effective judgments on crop distribution and growth status at a macro level, and has broad application prospects for agricultural monitoring, especially for information management of rotation farmland and crop phenology and planting area.

Key words: winter wheat-summer maize, spectral characteristics, decision tree, classification accuracy, Landsat 8-OLI remote sensing image

图1

2020年1月1日的研究区影像图"

表1

Landsat 8 的OLI数据参数"

序号
Serial number		 波段
Band		 波长范围
Wave length (μm)		 空间分辨率
Resolution (m)
波段1 Band 1 海岸波段 Coastal 0.433-0.453 30
波段2 Band 2 蓝波段 Blue 0.450-0.515 30
波段3 Band 3 绿波段 Green 0.525-0.600 30
波段4 Band 4 红波段 Red 0.630-0.680 30
波段5 Band 5 近红外波段 NIR 0.845-0.885 30
波段6 Band 6 短波红外 SWIR1 1.560-1.660 30
波段7 Band 7 短波红外 SWIR2 2.100-2.300 30
波段8 Band 8 全色波段 Pan 0.500-0.680 15
波段9 Band 9 卷云波段 Cirrus 1.360-1.390 30

表2

地类解译标志"

地物类型
Land-use type		 Landsat 8影像
Landsat 8 image		 解译标志
Interpretation indicator
建设用地
Build land		 图2-A
Fig. 2-A		 图斑亮度高, 颜色为混合亮色, 聚集式成片分布, 纹理结构粗糙。
The color of the spot is mixed bright color with high brightness. It is distributed in clustered patches with rough texture structure.
裸地
Bare land		 图2-B
Fig. 2-B		 颜色为褐色或黄色, 几何形状明显, 边界清晰, 主要为休耕地。
The color is brown or yellow, with distinct geometric shapes and clear borders. It is mainly fallow land.
水体
Water		 图2-C
Fig. 2-C		 假彩影像颜色为蓝黑色, 边界清晰, 呈连续条带状, 走势不规则, 拐弯处弧度相对圆滑。
The color of the false color image is blue and black, the boundary is clear and irregular, and the curve of the corner is relatively smooth.
林地
Woodland		 图2-D
Fig. 2-D		 颜色呈暗红色, 多位于山区, 纹理目视可见明暗对比, 地形起伏造成阴影判读清晰。
The color is dark red, mostly located in mountainous areas. And the contrast between light and shade of the texture can be observed, meanwhile the shadow caused by the terrain can be clearly interpreted.
夏玉米
Summer maize		 图2-E
Fig. 2-E		 颜色以鲜红色为主, 呈条带状分布, 多位于平坦的区域, 质地相较于冬小麦斑块更加粗糙。
The color is mainly bright red, with banded distribution, mostly located in flat areas. The texture is rougher than that of winter wheat.
冬小麦
Winter wheat		 图2-F
Fig. 2-F		 冬小麦: 颜色以明亮的红色为主, 相较于夏玉米, 其颜色的饱和度更高, 呈规则的田块状分布, 质地均匀, 纹理更加细腻趋同。
The color is bright red, in regular patches. Compared to summer corn, it has a higher color saturation, displaying a regular field-like distribution. The texture is uniform with finer and more homogeneous patterns.

图2

不同波段组合下的地类Landsat 8影像 A: 2020年8月26日, 真彩色(波段432组合); B: 2020年8月26日, 真彩色(波段432组合); C: 2020年8月26日, 假彩色(波段564组合); D: 2020年8月26日, 标准假彩色(波段543组合); E: 2020年8月26日, 标准假彩色(波段543组合); F: 2021年1月1日, 标准假彩色(波段543组合)。"

图3

不同时相冬小麦-夏玉米光谱特征的变化 A: 2020年8月26日; B: 2020年9月11日; C: 2020年9月30日; D: 2021年1月1日; E: 2021年3月22日; F: 2021年5月9日。"

图4

不同时相冬小麦-夏玉米NDVI的变化"

图5

决策树方法提取作物种植信息流程图"

表3

冬小麦-夏玉米面积分类结果精度验证"

日期
Date		 作物类型
Crop		 生产者精度
Producer’s accuracy (%)		 用户精度
User accuracy (%)		 总体精度
Overall accuracy (%)		 Kappa 系数
Kappa coefficient
2020-08-26 夏玉米Summer maize 88.00 76.20 83.60 0.72
非夏玉米Others 77.36 83.65
2020-09-11 夏玉米Summer maize 85.67 73.92 80.42 0.69
非夏玉米Others 89.58 89.23
2020-09-30 夏玉米Summer maize 75.36 73.00 72.60 0.65
非夏玉米Others 86.23 83.51
2021-01-01 冬小麦Winter wheat 89.36 86.39 92.36 0.81
非冬小麦Others 97.32 93.25
2021-03-22 冬小麦Winter wheat 88.92 85.66 90.33 0.80
非冬小麦Others 95.36 92.25
2021-05-09 冬小麦Winter wheat 96.32 93.56 86.54 0.77
非冬小麦Others 78.69 80.35

图6

冬小麦-夏玉米决策树分类结果 A: 2020年8月26日; B: 2020年9月11日; C: 2020年9月30日; D: 2021年1月1日; E: 2021年3月22日; F: 2021年5月9日。"

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