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

• 遥感科学与应用技术 • 上一篇    下一篇

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   

  1. 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   

  1. 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:

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