基于GEE云平台的福建省10 m分辨率茶园专题空间分布制图

doi:10.12082/dqxxkx.2021.200583

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (7): 1325-1337.doi: 10.12082/dqxxkx.2021.200583

基于GEE云平台的福建省10 m分辨率茶园专题空间分布制图

熊皓丽(), 周小成^*(), 汪小钦, 崔雅君

福州大学空间数据挖掘和信息共享教育部重点实验室卫星空间信息技术综合应用国家地方联合工程研究中心数字中国研究院（福建）,福州 350108

收稿日期:2020-10-08 修回日期:2021-01-31 出版日期:2021-07-25 发布日期:2021-09-25
通讯作者: 周小成
作者简介:熊皓丽（1996— ）,女,安徽池州人,硕士生,研究方向为遥感信息处理与应用。E-mail: N185527029@fzu.edu.cn
基金资助:
中国科学院A类战略性先导科技专项子课题(XDA23100504);福建省高校产学合作项目(2017Y4010)

Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine

XIONG Haoli(), ZHOU Xiaocheng^*(), WANG Xiaoqin, CUI Yajun

Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, The Academy of Digital China (Fu Jian), Fuzhou University 350108, China

Received:2020-10-08 Revised:2021-01-31 Online:2021-07-25 Published:2021-09-25
Contact: ZHOU Xiaocheng
Supported by:
Subproject of Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23100504);Key Project of Production, Education and Research of Universities in Fujian Province(2017Y4010)

摘要/Abstract

摘要：

福建省作为中国的产茶大省,快速准确获取茶园的空间分布对福建省农业经济发展和生态环境保护具有重要的决策意义,然而,传统的方法难以保证大范围准确地获取茶园空间分布。本文基于GEE云平台,快速获取覆盖福建省的Sentinel-1雷达影像、Sentinel-2光学影像及地形数据,从中提取光谱特征、纹理特征、地形特征等98个特征,利用递归消除支持向量机算法（SVM_RFE）对特征变量进行筛选,通过支持向量机分类器（SVM）进行茶园提取,首次得到福建省2019年10 m分辨率茶园种植区空间分布图。结果表明：① 光谱特征在茶园信息提取中占据重要地位,纹理特征和地形特征次之;② 利用SVM_RFE可以有效筛选出最有利于茶园提取的特征子集,有效提高提取精度,总体精度为94.65%,Kappa系数为0.93,茶园的生产者精度为91.64%,用户精度为92.91%;③ 基于Sentinel-1及Sentinel-2影像获取的福建省2019年茶园种植面积为1913 km²,主要分布在安溪县、福鼎市、福安市、武夷山市和寿宁县,其茶园总面积达910 km²,约占据全省茶园面积的48%。利用云计算技术可以克服大尺度茶园监测运算能力不足的问题,结合Sentinel-1和Sentinel-2影像能够较准确地提取福建省茶园分布,对南方丘陵山区茶园及其他作物提取具有参考价值,并为政府及有关部门进行茶园管理提供支持。

关键词: Google Earth Engine, 茶园, Sentinel-1, Sentinel-2, 特征提取, 递归消除支持向量机, 支持向量机, 福建省

Abstract:

As a major tea-producing province in China, Fujian has a long history of tea culture. According to the National Bureau of Statistics in recent 10 years, the total planting area of tea in Fujian ranked the fifth among all the provinces in China. Rapid and accurate acquisition of tea plantation spatial distribution has important decision-making significance for agricultural economic development and ecological environment protection in Fujian province. However, it is difficult to obtain the spatial distribution of tea plantation in large areas accurately by traditional methods. Based on the GEE cloud platform, we firstly obtained Sentinel-1、Sentinel-2, and terrain data covering the whole province, and then extracted a total of 98 features including spectral features, texture features, and terrain features. Secondly, the Support Vector Machine-recursive Feature Elimination (SVM_RFE) was used to select features. Four groups of experiments were constructed according to different features and optimized feature subsets. Finally, the Support Vector Machine classifier (SVM) was used to extract tea plantation and obtain the spatial distribution map of tea plantation with a resolution of 10 m in Fujian province in 2019. The results show that: (1) Spectral features play an important role in tea plantation information extraction, followed by texture and terrain features. (2) It can improve the extraction accuracy by using SVM_RFE to select some features, that are useful to tea plantation extraction, from a large number of spectral, textural and topographic features. The overall accuracy is 94.65% while the kappa coefficient is 0.93. The producer accuracy and user accuracy of the tea plantation are 91.64% and 92.91%, respectively. (3) In 2019, the tea plantation area in Fujian province was 1913 km². Tea plantations were mainly distributed in Anxi County, Fuding City, Fuan City, Wuyishan City, and Shouning County, with a total area of 910 km², accounting for ~48% of the entire tea plantation area in Fujian province. The cloud computing technology based on GEE platform can overcome the problem of lacking computing power for large-scale tea plantation monitoring. This research can extract tea plantation distribution in Fujian province accurately, which has reference value for tea plantation and other crop extraction in hilly and mountainous areas of South China, and provides support for the government and related departments to manage tea plantation.

Key words: Google Earth Engine, tea plantation, Sentinel-1, Sentinel-2, feature extraction, Support Vector Machine-recursive feature elimination, Support Vector Machine, Fujian Province

熊皓丽, 周小成, 汪小钦, 崔雅君. 基于GEE云平台的福建省10 m分辨率茶园专题空间分布制图[J]. 地球信息科学学报, 2021, 23(7): 1325-1337.DOI:10.12082/dqxxkx.2021.200583

XIONG Haoli, ZHOU Xiaocheng, WANG Xiaoqin, CUI Yajun. Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine[J]. Journal of Geo-information Science, 2021, 23(7): 1325-1337.DOI:10.12082/dqxxkx.2021.200583

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植被指数简称	植被指数说明	计算公式	公式编号	参考文献
NDVI	归一化植被指数	NDVI = (B8 - B4)/(B8 + B4)	（1）	Tucker^[19]
NDWI	归一化水体指数	NDWI= (B3 - B8)/(B3 + B8)	（2）	Gao^[20]
LSWI	地表水分指数	LSWI = (B8 - B11)/(B8 + B11)	（3）	Xiao等^[21]
NDTI	归一化差异耕作指数	NDTI = (B11 - B12)/(B11 + B12)	（4）	Deventer等^[22]
MNDVI	修正型归一化植被指数	MNDVI = (B4 - B3)/(B4 + B3)	（5）	Dihkan等^[4]
IRECI	新型倒红边叶绿素指数	IRECI = (B7 - B4)/(B5/B6)	（6）	Frampton等^[24]
MTCI	地面叶绿素指数	MTCI= (B6 - B5)/(B5 - B4)	（7）	Dash等^[25]
NDVI_re₁	归一化植被指数红边1	NDVI_re1 = (B8A - B5)/(B8A + B5)	（8）	Gitelson等^[26]
NDVI_re₂	归一化植被指数红边2	NDVI_re2 = (B8A - B6)/(B8A + B6)	（9）	张磊等^[27]
NDVI_re₃	归一化植被指数红边3	NDVI_re3 = (B8A - B7)/(B8A + B7)	（10）	张磊等^[27]
ND_re₁	归一化差异红边1	ND_re1 = (B6 - B5)/(B6 + B5)	（11）	Gitelson等^[28]
ND_re₂	归一化差异红边2	ND_re2 = (B7 - B5)/(B7 + B5)	（12）	Merzlyak等^[29]
CI_re	红边叶绿素指数	CI_re = B7/B5 - 1	（13）	Gitelson等^[28]

波段	描述	波段	描述
asm	角二阶矩	var	方差
corr	相关性	savg	和平均
idm	逆差矩	sent	和熵
savr	和方差	davr	差方差
ent	熵	imcorr1/imcorr2	相关信息测度1/2
dent	差熵	inertia	惯性矩
diss	差异性	shade	聚类萌
contrast	对比度	prom	聚类突

数据源	特征名称	特征说明或介绍	特征数目
S2	原始光谱特征	B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12	13
	植被指数特征	NDVI, NDWI,LSWI, NDTI, MNDVI	5
	红边指数特征	IRECI, MTCI, NDVI_re1, NDVI_re2, NDVI_re3, ND_re1, ND_re2, CI_re	8
	B8波段纹理特征	NIR近红外波段的17个纹理特征	17
	NDTI指数纹理特征	NDTI指数的17个纹理特征	17
S1	雷达纹理特征	VV和VH的17个纹理特征	34
SRTM	地形特征	Elevation, Slope, Aspect, Hillshade	4
合计			98

实验方案	特征组合
1	光谱特征
2	光谱特征+纹理特征
3	光谱特征+纹理特征+地形特征
4	SVM_RFE特征优选

实验方案	OA/%	Kappa系数	人工地表		耕地		林地		茶园		水体
实验方案	OA/%	Kappa系数	PA/%	UA/%	PA/%	UA/%	PA/%	UA/%	PA/%	UA/%	PA/%	UA/%
1	89.42	0.85	93.70	96.07	83.28	80.38	92.28	91.74	83.45	84.31	95.33	96.62
2	93.04	0.90	94.79	96.11	87.54	90.20	95.11	94.01	90.27	90.12	96.67	96.03
3	93.87	0.92	94.79	96.11	91.15	91.45	96.09	94.70	90.27	91.52	96.00	96.64
4	94.65	0.93	95.62	96.14	93.11	92.51	96.19	95.44	91.64	92.91	96.67	96.67

基于GEE云平台的福建省10 m分辨率茶园专题空间分布制图

Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine

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实验方案	方案1（光谱）	方案2（光谱+纹理）	方案3（光谱+纹理+地形）
方案2（光谱+纹理）	7.63*
方案3（光谱+纹理+地形）	7.30*	0.90
方案4（SVM_RFE特征优选）	9.04*	3.09*	4.16*

[1]	王杏锋, 李代超, 吴升, 谢晓苇, 卢嘉奇. 水稻种植环境综合适宜性评价方法研究[J]. 地球信息科学学报, 2021, 23(8): 1484-1496.
[2]	邢晓语, 杨秀春, 徐斌, 金云翔, 郭剑, 陈昂, 杨东, 王平, 朱立博. 基于随机森林算法的草原地上生物量遥感估算方法研究[J]. 地球信息科学学报, 2021, 23(7): 1312-1324.
[3]	张春森, 贾欣, 吴蓉蓉, 崔卫红, 史书, 郭丙轩. 面向对象高分遥感影像典型自然地物半自动提取[J]. 地球信息科学学报, 2021, 23(6): 1050-1062.
[4]	陈赛楠, 蒋弥. Sentinel-1 SAR在洪水范围提取与极化分析中的应用研究[J]. 地球信息科学学报, 2021, 23(6): 1063-1070.
[5]	彭妍菲, 李忠勤, 姚晓军, 牟建新, 韩伟孝, 王盼盼. 基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析[J]. 地球信息科学学报, 2021, 23(6): 1131-1153.
[6]	姜伊兰, 陈保旺, 黄玉芳, 崔佳琪, 郭宇龙. 基于Google Earth Engine和NDVI时序差异指数的作物种植区提取[J]. 地球信息科学学报, 2021, 23(5): 938-947.
[7]	赵泉华, 冯林达, 李玉. 基于最优极化特征组合的SAR影像湿地分类[J]. 地球信息科学学报, 2021, 23(4): 723-736.
[8]	李广洋, 寇卫利, 陈帮乾, 代飞, 强振平, 吴超. 多核学习算法及其在高光谱图像分类中的应用研究进展[J]. 地球信息科学学报, 2021, 23(3): 492-504.
[9]	毛亚萍, 房世峰. 基于机器学习的参考作物蒸散量估算研究[J]. 地球信息科学学报, 2020, 22(8): 1692-1701.
[10]	崔成, 任红艳, 赵璐, 庄大方. 基于街景影像多特征融合的广州市越秀区街道空间品质评估[J]. 地球信息科学学报, 2020, 22(6): 1330-1338.
[11]	杜培军, 王欣, 蒙亚平, 林聪, 张鹏, 卢刚. 面向地理国情监测的变化检测与地表覆盖信息更新方法[J]. 地球信息科学学报, 2020, 22(4): 857-866.
[12]	李培林, 刘小平, 黄应淮, 张鸿辉. 基于GEE平台的广州市主城区不透水面时间序列提取[J]. 地球信息科学学报, 2020, 22(3): 638-648.
[13]	杨丹, 周亚男, 杨先增, 郜丽静, 冯莉. LSTM支持下时序Sentinel-1A数据的太白山区植被制图[J]. 地球信息科学学报, 2020, 22(12): 2445-2455.
[14]	刘怡媛, 李鹏, 肖池伟, 刘影, 谢正磊. 老挝北部Sentinel-2 A/B云覆盖适宜阈值界定及其影像获取概率时空差异[J]. 地球信息科学学报, 2020, 22(11): 2267-2276.
[15]	吴瑞娟, 何秀凤, 王静. 结合像元级与对象级的滨海湿地变化检测方法[J]. 地球信息科学学报, 2020, 22(10): 2078-2087.

类别	人工地表	耕地	林地	茶园	水体	总计	用户精度/%
人工地表	349	8	2	4	0	363	96.14
耕地	5	284	8	7	3	307	92.51
林地	6	2	984	37	2	1031	95.44
茶园	2	10	29	537	0	578	92.91
水体	3	1	0	1	145	150	96.67
总计	365	305	1023	586	150	2429
生产者精度/%	95.62	93.12	96.19	91.64	96.67
总体精度（OA）：94.65% Kappa系数：0.93