中国农业科学 ›› 2021, Vol. 54 ›› Issue (11): 2302-2318.doi: 10.3864/j.issn.0578-1752.2021.11.005
周珂1,3(),柳乐1,3,张俨娜2(),苗茹1,3,杨阳1,3
收稿日期:
2020-08-01
接受日期:
2020-09-27
出版日期:
2021-06-01
发布日期:
2021-06-09
联系方式:
周珂,E-mail:zhouke@radi.ac.cn。
基金资助:
ZHOU Ke1,3(),LIU Le1,3,ZHANG YanNa2(),MIAO Ru1,3,YANG Yang1,3
Received:
2020-08-01
Accepted:
2020-09-27
Published:
2021-06-01
Online:
2021-06-09
摘要:
【目的】使用遥感技术对2017—2020年河南省冬小麦的空间分布信息进行高精度的提取,然后对2020年冬小麦的长势进行高频度的监测并结合气象条件进行分析。【方法】本文基于谷歌地球引擎(Google Earth Engine,GEE)云平台,对选取的Landsat 8影像数据根据NDVI最大值进行合成,然后进行特征构建,添加地形特征、纹理特征、NDVI以及一个新特征NDVI增幅,使用随机森林分类方法对样本数据按照构建的特征进行训练提取河南省2017—2020年冬小麦的播种面积信息;经过精度验证后对提取的河南省2020年的冬小麦种植区域生成掩膜,对掩膜区域(冬小麦种植区域)结合MODIS高时间分辨率影像数据,使用NDVI同期差值法对2020年2—4月份的冬小麦进行高频度的长势监测。【结果】使用GEE云平台能够对河南省冬小麦种植区域的空间分布信息进行快速制图;使用随机森林方法加入地形特征、纹理特征、NDVI后再加入新特征NDVI增幅,能够有效提高冬小麦的提取精度以及降低与统计数据的相对误差,基于混淆矩阵计算的平均总体分类精度为95.2%、平均kappa系数为0.909、冬小麦的平均分类精度为95.3%,与河南省统计年鉴数据相比,本文方法提取的2017—2019年河南省冬小麦播种面积相对误差均低于3%,河南省冬小麦主要种植区域的冬小麦播种面积的平均相对误差低于6%;使用MODIS影像数据结合NDVI差值模型能够对河南省2020年的冬小麦进行高频度的长势监测,河南省冬小麦在返青初期长势较往年及2019年好,到生育后期大部分区域长势与往年及2019年持平,总体上2020年冬小麦的长势较往年及2019年好。【结论】本文提出的方法能够对河南省冬小麦进行高精度的提取以及高频度的长势监测,且能够为地方政府或者一些农业部门在安排指导农事活动上提供科学依据。
周珂, 柳乐, 张俨娜, 苗茹, 杨阳. GEE支持下的河南省冬小麦面积提取及长势监测[J]. 中国农业科学, 2021, 54(11): 2302-2318.
ZHOU Ke, LIU Le, ZHANG YanNa, MIAO Ru, YANG Yang. Area Extraction and Growth Monitoring of Winter Wheat in Henan Province Supported by Google Earth Engine[J]. Scientia Agricultura Sinica, 2021, 54(11): 2302-2318.
表1
河南省冬小麦生育周期"
月份Month | 09 | 10 | 11 | 12 | 01 | 02 | 03 | 04 | 05 | 06 | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
旬 Ten days | 上 First | 中Middle | 下 Last | 上First | 中Middle | 下Last | 上First | 中Middle | 下Last | 上First | 中Middle | 下Last | 上First | 中Middle | 下Last | 上First | 中 Middle | 下 Last | 上 First | 中Middle | 下Last | 上 First | 中Middle | 下 Last | 上 First | 中 Middle | 下 Last | 上 First | 中Middle | 下 Last |
冬小麦生 育期 Winter wheat growth period | 播种期 Sowing stage | 出苗-三叶期 Regreening stage | 分蘖期 Tillering stage | 越冬期 Over-wintering stage | 返青期 Re-greening stage | 起身拔节期 Rising stage | 孕穗-抽穗期 Heading stage | 开花期 Flowering stage | 灌浆乳熟期 Mature stage |
表2
Landsat 8选取影像详情"
年份 Year | 日期 Date (M-D) | 选用影像数据年份Year of image data selected | 筛选云量 Cloud covered | 数量 Count |
---|---|---|---|---|
2017-2018 | 09-15—11-15 | 2015-2017 | <20% | 70 |
12-01—03-25 | 2015-2018 | <20% | 133 | |
2018-2019 | 09-15—11-15 | 2016-2018 | <20% | 83 |
12-01—03-25 | 2016-2019 | <20% | 125 | |
2019-2020 | 09-15—11-15 | 2017-2019 | <20% | 94 |
12-01—03-25 | 2019-2020 | <10% | 36 |
表3
样本选取依据"
样本种类 Sample type | 解译标志 Interpretation mark | 描述 Description |
---|---|---|
冬小麦 Winter wheat | 研究区域冬小麦主要分布在平原地带的农村周边,在Google earth纹理较为清晰,成片出现,有较为规则的形状(矩形),颜色为绿色或深绿色 Winter wheat in the study area is mainly distributed in the rural periphery of the plain area, the texture of Google earth is relatively clear, appears in pieces, has a more regular shape (rectangle), and the color is green or dark green | |
水体 Water | 研究区域的水体区域主要由水库、湖泊、河流等组成,纹理上边缘明显,在颜色上水体区域在颜色上的表现为青色、淡蓝以及土黄色(黄河) The water area of the study area is mainly composed of reservoirs, lakes, rivers and so on, and the upper edge of the texture is obvious. In color, the water area is cyan, light blue and earth yellow | |
不透水面Town | 研究区域内的不透水面有城镇建筑以及城镇周边道路组成,在Google earth上纹理信息较明显,也是成片出现,能够清晰的识别 The impervious water surface in the study area is composed of urban buildings and roads around towns, and the texture information is more obvious on Google earth, and it also appears in pieces, which can be clearly identified | |
其他植被 Other vegetation | 其他植被由山体植被、城镇中的景观植物以及裸地植被等组成。在Google earth上此类地物具有清晰的特征,山体植被海拔较高成山体状;景观植物分布在城镇生活区域中;裸地植被也分布于城镇各处,表面较为稀疏 Other vegetation is composed of mountain vegetation, urban landscape plants and bare land vegetation. On Google earth, such features have clear characteristics, mountain vegetation is mountain-shaped at high altitude; Landscape plants are distributed in urban living areas; Bare land vegetation is also distributed throughout cities and towns, and the surface is relatively sparse |
表4
河南省冬小麦遥感提取的播种面积与统计的播种面积比较"
特征 Feature | 年份 Year | 提取面积 Extraction area (hm2) | 统计面积 Statistical area (hm2) | 绝对误差 Absolute error (hm2) | 相对误差 Relative error (%) |
---|---|---|---|---|---|
光谱+地形+纹理+NDVI Spectrum + terrain + texture + NDVI | 2017-2018 | 5930815 | 5714640 | 216175 | 3.78 |
2018-2019 | 6043211 | 5739850 | 303361 | 5.28 | |
光谱+地形+纹理+NDVI+NDVI增幅 Spectrum+topography+texture+NDVI+ NDVIincrease | 2017-2018 | 5548529 | 5714640 | 166111 | 2.91 |
2018-2019 | 5604970 | 5739850 | 134670 | 2.35 | |
2019-2020 | 5843632 |
表5
河南省冬小麦主要种植区域的遥感提取的播种面积与统计的播种面积比较"
特征 Feature | 2017-2018 | 2018-2019 | |||||||
---|---|---|---|---|---|---|---|---|---|
光谱+地形+纹理+NDVI Spectrum + topography + texture + NDVI | 市 City | 提取面积 Extraction area (hm2) | 统计面积 Statistical area (hm2) | 绝对误差 Absolute error (hm2) | 相对误差 Relative error (%) | 提取面积 Extraction area (hm2) | 统计面积 Statistical area (hm2) | 绝对误差 Absolute error (hm2) | 相对误差 Relative error (%) |
南阳 Nanyang | 803278 | 709020 | 94258 | 13.29 | 771926 | 724660 | 47266 | 4.09 | |
驻马店 Zhumadian | 929103 | 750520 | 178583 | 23.79 | 914174 | 781670 | 132504 | 16.95 | |
周口 Zhoukou | 846381 | 718350 | 128031 | 17.82 | 893891 | 734380 | 159511 | 21.72 | |
商丘 Shangqiu | 611644 | 588870 | 22774 | 3.87 | 705349 | 603380 | 101969 | 16.90 | |
开封 Kaifeng | 330408 | 299920 | 30488 | 10.16 | 322177 | 305200 | 16977 | 5.50 | |
许昌 Xuchang | 238458 | 228960 | 9498 | 4.15 | 226581 | 233010 | 6429 | 2.76 | |
新乡 Xinxiang | 442132 | 379410 | 62722 | 16.53 | 437147 | 387670 | 49477 | 12.76 | |
安阳 Anyang | 307150 | 320380 | 13230 | 7.22 | 305831 | 325730 | 19539 | 6.00 | |
光谱+地形+纹理+ NDVI+NDVI增幅 Spectrum + topography +texture+NDVI+ NDVIincrease | 南阳 Nanyang | 721819 | 709020 | 12799 | 1.81 | 695001 | 724660 | 29659 | 4.09 |
驻马店 Zhumadian | 849960 | 750520 | 9944 | 13.25 | 834591 | 781670 | 52921 | 6.77 | |
周口 Zhoukou | 809734 | 718350 | 91384 | 12.72 | 808415 | 734380 | 74035 | 10.08 | |
商丘 Shangqiu | 575660 | 588870 | 13210 | 2.24 | 629586 | 603380 | 26206 | 4.34 | |
开封 Kaifeng | 300274 | 299920 | 354 | 0.12 | 316275 | 305200 | 11075 | 3.63 | |
许昌 Xuchang | 230127 | 228960 | 1167 | 0.51 | 224240 | 233010 | 8770 | 3.76 | |
新乡 Xinxiang | 427995 | 379410 | 485.85 | 12.80 | 41784 | 387670 | 30170 | 7.78 | |
安阳 Anyang | 313655 | 320380 | 6725 | 2.10 | 304353 | 325730 | 11377 | 3.49 |
表6
研究区冬小麦基于混淆矩阵提取精度比较"
特征 Feature | 年份 Year | 总体精度 OA (%) | Kappa系数 Kappa | 冬小麦分类精度 Classification accuracy of winter wheat (%) |
---|---|---|---|---|
光谱+地形+纹理+NDVI Spectrum + topography + texture + NDVI | 2017-2018 | 93.3 | 0.862 | 93.5 |
2018-2019 | 94.8 | 0.891 | 95.1 | |
2019-2020 | 94.7 | 0.892 | 94.5 | |
光谱+地形+纹理+NDVI+NDVI增幅 Spectrum + topography + texture + NDVI+NDVIincrease | 2017-2018 | 94.8 | 0.893 | 94.6 |
2018-2019 | 95.6 | 0.921 | 95.3 | |
2019-2020 | 95.8 | 0.913 | 95.9 |
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