基于GF-1卫星遥感数据识别京津冀冬小麦面积

doi:10.3724/SP.J.1006.2018.00762

摘要/Abstract

摘要：

省级尺度冬小麦面积的精准获取技术是农作物面积遥感监测研究的主要内容之一。为了获取省级尺度的冬小麦种植面积, 该文以北京市(京)、天津市(津)和河北省(冀) 3个省域范围为例, 以国家标准地形图分幅为分类的图幅单元, 利用国产GF-1/WFV数据, 构建冬小麦面积指数, 实现了省级尺度冬小麦面积的识别。本文以冬小麦全部9个月生育期的984景影像作为数据源, 依次经过数据预处理、标准图幅单元的NDVI合成、样本点选择、冬小麦面积指数构建、冬小麦作物类型确认、省域范围制图及精度验证等步骤完成研究区域内冬小麦面积的提取。采用区域网平差和6S大气校正算法对数据源预处理, 以中国1︰10万标准地形图分幅为分类图幅单元构建冬小麦面积指数, 将冬小麦面积指数按照1%的比例等分, 并将面积指数从0到100%分割为101个提取节点, 将提取节点的NDVI值依次与类型确认样本比较, 精度最高的则确认为冬小麦面积提取阈值, 同时将该阈值应用于图幅单元内冬小麦面积指数影像, 获取冬小麦种植分布。最后冬小麦面积识别的精度表明, 以标准地图分幅作为计算单元, 在GF-1影像基础上, 利用冬小麦面积指数能够显著提高冬小麦与其他地物类型的波谱差异, 且冬小麦的总体识别精度达到89.6%, 用户精度达到89.8%, 制图精度96.5%, Kappa系数0.72。在典型区域, 本文算法与监督分类算法精度结果较为一致, 除制图精度相差4.77%外, 总体精度与用户精度差都在1.00%以内, 说明本文算法具有精度高、运行效率高、分类单元识别结果一致性强的特点, 能够满足省级尺度农情遥感业务监测的需要。

关键词: GF-1卫星, 区域尺度, 冬小麦, 面积指数, 遥感监测

Abstract:

Winter wheat area accurate acquisition at provincial scale is an important aspect in remote-sensing monitoring of crop area. This study aimed at estimating winter wheat area in Beijing, Tianjin, and Hebei at provincial scale using classification map units from the national standard topographic map and the winter wheat area index (WWAI) created from the wide field view (WFV) data of GF-1 satellite. A total of 984 satellite monitoring images between October 1, 2013 and June 30, 2014 were used as data sources. The major process steps were data preprocessing, NDVI synthesis of standard map units, selection of samples, creation of winter wheat area index, confirmation of winter wheat crop type, provincial scale mapping, and accuracy verification. Multi-temporal GF-1/WFV data were preprocessed and NDVI value of images were calculated by using block adjustment and 6S atmospheric correction algorithm. By means of the 1:100 000 national standard topographic map framings of China, as the classification map units, WWAI was equally divided at a proportion of 1 percent into 101 extraction nodes from 0 to 100%. The NDVI values of extraction nodes were compared with type confirmation samples, and the most accurate NDVI was adopted as the extraction threshold value of winter wheat area. This threshold was then used into WWAI image in the map units to obtain winter wheat planting distribution. The identification result showed that, by taking standard map framing as calculation units and based on the creation of WWAI of GF-1 images, we remarkably improved the wave spectrum difference between winter wheat and other ground objects, with the overall accuracy of 89.6%, user accuracy of 89.8%, mapping accuracy of 96.5%, and Kappa coefficient of 0.72. In a typical region, the algorithm proposed had a similar accuracy with supervised classification algorithm. Except for 4.77% of the difference in mapping accuracy, the differences in overall accuracy and user accuracy were less than 1.00%. These results indicate that the used in this study is of algorithm high accuracy, efficiency and consistency in classification unit identification and applicable in agricultural monitoring at provincial level.

Key words: GF-1 satellite, region scale, winter wheat, area index, remote sensing

王利民, 刘佳, 杨福刚, 杨玲波, 姚保民, 王小龙. 基于GF-1卫星遥感数据识别京津冀冬小麦面积[J]. 作物学报, 2018, 44(05): 762-773.

Li-Min WANG, Jia LIU, Fu-Gang YANG, Ling-Bo YANG, Bao-Min YAO, Xiao-Long WANG. Acguisition of Winter Wheat Area in the Beijing-Tianjin-Hebei Region with GF-1 Satellite Data[J]. Acta Agronomica Sinica, 2018, 44(05): 762-773.

图/表 12

图1

图2

图3

图4

图5

图6

表 1

图7

表 2

图8

图9

表3

参考文献 28

[1]	吴炳方. 中国农情遥感速报系统. 遥感学报, 2004, 8: 481-497 doi: 10.11834/jrs.20040601
	Wu B F.China crop watch system with remote sensing.J Remote Sens, 2004, 8: 481-497 (in Chinese with English abstract) doi: 10.11834/jrs.20040601
[2]	中华人民共和国统计局. 中国统计年鉴2017 (12-8 农作物播种面积). 北京: 中国统计出版社, 2017
	National Bureau of Statistics of the People’s Republic of China. China Statistical Yearbook 2017 (12-8 Sown areas of farm crops). Beijing: China Statistics Press, 2017 (in Chinese with English abstract)
[3]	杨邦杰, 裴志远. 国家级农情遥感监测系统的研究开发与运行. 农业工程学报, 2003, 19(增刊): 11-14
	Yang B J, Pei Z Y.National agricultural condition monitoring system based on satellite remote sensing: development, application and improvement.Trans CSAE, 2003, 19(suppl): 11-14 (in Chinese with English abstract)
[4]	Tarko A, De Bruin S, Fasbender D, Bregt A.Users’ assessment of orthoimage photometric quality for visual interpretation of agricultural fields. Remote Sens, 2015, 7: 4919-4936 doi: 10.3390/rs70404919
[5]	Khatami R, mountrakis G, Stehman S V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research.Remote Sens Environ, 2016, 177: 89-100 doi: 10.1016/j.rse.2016.02.028
[6]	Löw F, Duveiller G.Determining suitable image resolutions for accurate supervised crop classification using remote sensing data. In: Proceedings of the SPIE, Vol. 8893: Earth Resources and Environmental Remote Sensing/GIS Applications IV, Dresden, Germany, 2013. pp 909-927
[7]	Lewiński S A.Object-oriented classification of Landsat ETM+ satellite image. J Water & Land Develop, 2007, 10: 91-106 doi: 10.2478/v10025-007-0008-4
[8]	Kumar P, Gupta D K, Mishra V N, Prasad R.Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data.Int J Remote Sens, 2015, 36: 1604-1617 doi: 10.1080/2150704X.2015.1019015
[9]	Li J, Zhao G X, Yang P G.Knowledge-based land use/cover classification of remote sensing image in Kenli, Shandong Province, China.Chin Sci Bull, 2006, 51: 218-224 doi: 10.1007/s11434-006-8210-3
[10]	李霞, 王飞, 徐德斌, 刘清旺. 基于混合像元分解提取大豆种植面积的应用探讨. 农业工程学报, 2008, 24(1): 213-217
	Li X, Wang F, Xu D B, Liu Q W.Application research on the method for extracting soybean covered areas based on the pixel unmixing.Trans CSAE, 2008, 24(1): 213-217 (in Chinese with English abstract)
[11]	张建国, 李宪文, 吴延磊. 面向对象的冬小麦种植面积遥感估算研究. 农业工程学报, 2008, 24(5): 156-160
	Zhang J G, Li X W, Wu Y L.Object oriented estimation of winter wheat planting area using remote sensing data.Trans CSAE, 2008, 24(5): 156-160 (in Chinese with English abstract)
[12]	Lennington R K, Sorensen C T, Heydorn R P.A mixture model approach for estimating crop areas from Landsat data.Remote Sens Environ, 1984, 14: 197-206 doi: 10.1016/0034-4257(84)90015-4
[13]	Chellasamy M, Ferré T P A, Greeve M H. Automatic training sample selection for a multi-evidence based crop classification approach. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 7th edn., Vol. 40, 2014. pp 63-69
[14]	王利民, 刘佳, 高建孟, 杨玲波, 杨福刚, 王小龙. 冬小麦面积遥感识别精度与空间分辨率关系. 农业工程学报, 2016, 32(23): 152-160 doi: 10.11975/j.issn.1002-6819.2016.23.021
	Wang L M, Liu J, Gao J M, Yang L B, Yang F G, Wang X L.Relationship between accuracy of winter wheat area remote sensing identification and spatial resolution. Trans CSAE, 2016, 32(23): 152-160 (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2016.23.021
[15]	李颖, 陈秀万, 段红伟, 沈阳. 多源多时相遥感数据在冬小麦识别中的应用研究. 地理与地理信息科学, 2010, 26(4): 47-49
	Li Y, Chen X W, Duan H W, Shen Y.Application of multi-source and multi-temporal remote sensing data in winter wheat identification.Geog Geo-Inf Sci, 2010, 26(4): 47-49 (in Chinese with English abstract)
[16]	Conese C, Maselli F.Use of multi-temporal information to improve classification performance of TM scenes in complex terrain.ISPRS J Photogram Remote Sens, 1991, 46: 187-197 doi: 10.1016/0924-2716(91)90052-W
[17]	张喜旺, 秦耀辰, 秦奋. 综合季相节律和特征光谱的冬小麦种植面积遥感估算. 农业工程学报, 2013, 29(8): 154-163 doi: 10.3969/j.issn.1002-6819.2013.08.018
	Zhang X W, Qin Y C, Qin F.Remote sensing estimation of planting area for winter wheat by integrating seasonal rhythms and spectral characteristics.Trans CSAE, 2013, 29(8): 154-163 (in Chinese with English abstract) doi: 10.3969/j.issn.1002-6819.2013.08.018
[18]	Wardlow B D, Egbert S L.Large-area crop mapping using time- series MODIS 250m NDVI data: an assessment for the U.S. Central Great Plains.Remote Sens Environ, 2008, 112: 1096-1116 doi: 10.1016/j.rse.2007.07.019
[19]	Zheng B, Myint S W, Thenkabail P S, Aggarwal R M.A support vector machine to identify irrigated crop types using time-series Landsat NDVI data.Int J Appl Earth Observ & Geoinf, 2015, 34: 103-112 doi: 10.1016/j.jag.2014.07.002
[20]	刘佳, 王利民, 杨福刚, 杨玲波, 王小龙. 基于HJ时间序列数据的农作物种植面积估算. 农业工程学报, 2015, 31(3): 199-206 doi: 10.3969/j.issn.1002-6819.2015.03.026
	Liu J, Wang L M, Yang F G, Yang L B, Wang X L.Remote sensing estimation of crop planting area based on HJ time-series images.Trans CSAE, 2015, 31(3): 199-206 (in Chinese with English abstract) doi: 10.3969/j.issn.1002-6819.2015.03.026
[21]	Suzuki K, Takeuchi T.Classification of upland crops using multi-temporal RapidEye data.J Jpn Agric Syst Soc, 2015, 31: 1-10 doi: 10.14962/jass.31.1_1
[22]	Carfagna E, Gallego F J.Using remote sensing for agricultural statistics.Int Statistical Rev, 2005, 73: 389-404 doi: 10.1111/j.1751-5823.2005.tb00155.x
[23]	焦险峰, 杨邦杰, 裴志远. 基于分层抽样的中国水稻种植面积遥感调查方法研究. 农业工程学报, 2006, 22(5): 105-110
	Jiao X F, Yang B J, Pei Z Y.Paddy rice area estimation using a stratified sampling method with remote sensing in China.Trans CSAE, 2006, 22(5): 105-110 (in Chinese with English abstract)
[24]	王增林, 朱大明. 基于遥感影像的最大似然分类算法的探讨. 河南科学, 2010, 28: 1458-1461 doi: 10.3969/j.issn.1004-3918.2010.11.024
	Wang Z L, Zhu D M.A study of maximum likelihood classification algorithm based on remote sensing image.Henan Sci, 2010, 28: 1458-1461 (in Chinese with English abstract) doi: 10.3969/j.issn.1004-3918.2010.11.024
[25]	Mosleh M K, Hassan Q K, Chowdhury E H.Application of remote sensors in mapping rice area and forecasting its production: a review.Sensors, 2015, 15: 769-791 doi: 10.3390/s150100769 pmid: 4327048
[26]	王利民, 刘佳, 杨玲波, 杨福刚, 滕飞, 王小龙. 基于NDVI 加权指数的冬小麦种植面积遥感监测. 农业工程学报, 2016, 32(17): 127-135
	Wang L M, Liu J, Yang L B, Yang F G, Teng F, Wang X L.Remote sensing monitoring winter wheat area based on weighted NDVI index.Trans CSAE, 2016, 32(17): 127-135 (in Chinese with English abstract)
[27]	Bargiel D.A new method for crop classification combining time series of radar images and crop phenology information.Remote Sens Environ, 2017, 198: 369-383 doi: 10.1016/j.rse.2017.06.022
[28]	Massey R, Sankey T T, Congalton R G, Yadav K, Thenkabail P S, Ozdogan M, Meador A J S. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types.Remote Sens Environ, 2017, 198: 490-503 doi: 10.1016/j.rse.2017.06.033

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

地表物 Ground objective	NDVI平均值 Average NDVI	冬小麦面积指数 Winter wheat area index
冬小麦 Winter wheat	0.4250	0.3489
其他地表物 Other ground objectives	0.2624	0.1298
差异绝对值 Difference value	0.1626	0.2191
差异倍数 Difference multiple	1.62	2.69

地表物 Ground objective	冬小麦 Winter wheat	其他地表物 Other ground objectives	总计 Total	用户精度 User accuracy (%)	制图精度 Mapping accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
冬小麦 Winter wheat	1033	37	1070	89.8	96.5	89.6	0.72
其他地表物 Other ground objectives	117	292	409	88.8	71.4
总计 Total	1150	329	1479

指标 Index	最大似然监督分类 Maximum likelihood		冬小麦面积指数 Winter wheat area index
指标 Index	冬小麦 Winter wheat	其他地表物 Other ground objectives	冬小麦 Winter wheat	其他地表物 Other ground objectives
用户精度 User accuracy (%)	87.88	95.24	87.35	93.08
制图精度 Mapping accuracy (%)	95.11	88.18	90.34	90.85
总体精度 Overall accuracy (%)	91.46		90.64
Kappa系数 Kappa coefficient	0.83		0.81