影像的土地覆被快速分类

doi:10.11873/j.issn.1004-0323.2020.2.0315

遥感技术与应用

2020, Vol. 35

Issue (2): 315-325 DOI: 10.11873/j.issn.1004-0323.2020.2.0315

GEE专栏

影像的土地覆被快速分类

柴旭荣(

),李明,周义,王金风,田庆春

山西师范大学地理科学学院，山西临汾 041000

Rapid Land Cover Classification Using Landsat Time Series based on the Google Earth Engine

Xurong Chai(

),Ming Li,Yi Zhou,Jinfeng Wang,Qingchun Tian

College of Geographical Sciences, Shanxi Normal University, Linfen 041000, China

全文: PDF(9398 KB) HTML

摘要：

精确的土地覆盖信息是进行碳循环、气候变化监测、土壤退化等相关科学研究的基础。随着云计算技术的不断成熟,一些高效算法与平台被不断提出，用来充分挖掘遥感数据所包含的海量信息。基于Google Earth Engine(GEE) 云平台，利用随机森林监督分类法对1990、2000、2010、2017年的山西省土地覆被进行了分类。参考Google Earth高清影像选择的1 580个样本点，对分类结果进行了验证；同时将分类结果与CNLUCC、GlobeLand30、FROM-GLC等现有土地覆被分类产品进行比较。验证和对比发现时间序列分类结果的总体精度达到86%~94%，比同期单时相分类总体精度提高了5%~10%；本文时间序列结果达到了CNLUCC、GlobeLand30、FROM-GLC等产品的分类精度。结果表明：①在快速准确土地覆被分类方面，时间序列影像与云平台结合，显示出时效性强、时间周期短、成本低等优势；②时间序列百分位数指标能有效地区分不同土地覆被类型的物候差别，在进行土地覆被分类方面显示出简单、易用、高效等特点。该方法对于深入研究大区域尺度的土地覆被变化过程具有重要的参考价值。

关键词： 土地覆被分类; 云计算; 随机森林法; Google Earth Engine; Landsat时间序列

Abstract:

Accurate maps of land cover at high spatial resolution are fundamental to many researchs on carbon cycle, climate change monitoring and soil degradation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. It offer opportunities for generating land cover maps designed to meet the increasingly detailed information needs for science,monitoring, and reporting.In this study, we classified the land cover types in Shanxi using Landsat time series data based on the Google Earth Engine Platform. We selected 1 580 sample points be visual interpretation of the original fine spatial resolution images along with Google Earth historical images over six different cover types. We defined training data by randomly sampling 60% of the sample points. The remaining 40% was used for validation. We generated two diffirent types of Landsat composite: (1) one based on median values which is used as the input image for single-date classification; (2)one based on percentile values which is used as input images for time series classification. Random forest classification was performed with two different types of Landsat composites. Random forest classification was performed with two different types of Landsat composites.We visually compared the single-date based to the time series based cover maps of 1990, 2000, 2010 and 2017 in five local areas, and we future compared the results of time series to other products. We aslo performed an accuracy assessment on the land cover classification products. The results shown: (1) The results of time series classification had an overall accuracy of 84%~94%. The time series results improved overall accuracy by 5%~10% compared to single-date results; (2) The result of time series achieves the classification accuracy of products such as CNLUCC, GlobeLand30 and FROM-GLC.The following conclusions were drawn: (1) Cloud computing and archived Landsat data in the GEE has many advantages for land cover classification at a large geographic scale, such as s strong timeliness, short time cycle and low cost; (2) The statistics metrics from Landsat time series is a viable means for discrimination of land cover types, which is particularly useful for the time series classification.

Key words: Land cover classification Cloud computing Random Forest Google Earth Engine Landsat time series

收稿日期: 2018-11-27 出版日期: 2020-07-10

ZTFLH:

TP79

基金资助: 国家青年科学基金项目(41701223)

作者简介: 柴旭荣（1975-），男，山西曲沃人，博士，副教授，主要从事土地利用与信息技术方面的研究。E?mail: chaixr@sxnu.edu.cn

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引用本文:

柴旭荣,李明,周义,王金风,田庆春. 影像的土地覆被快速分类[J]. 遥感技术与应用, 2020, 35(2): 315-325.

Xurong Chai,Ming Li,Yi Zhou,Jinfeng Wang,Qingchun Tian. Rapid Land Cover Classification Using Landsat Time Series based on the Google Earth Engine. Remote Sensing Technology and Application, 2020, 35(2): 315-325.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0315 或 http://www.rsta.ac.cn/CN/Y2020/V35/I2/315

表1 各年份可用Landsat SR影像数量

图1 样本点空间分布图

图2 各年份不同地类的样本波谱线

图3 2010年Landsat的6个波段及NDVI的地类样本百分位统计特征曲线

图4 区域a单时相(A)和时间序列(B)分类结果对比

图6 区域c单时相(A)和时间序列(B)分类结果对比

图5 区域b单时相（A）和时间序列(B)分类结果对比

图7 山西省单时相与时间序列分类结果(单位：万hm2)

表2 山西省不同年份各地类面积汇总表

表3 单时相、时间序列分类结果的总体精度与Kappa系数

表4 单时相、时间序列分类结果各土地覆被类型的生产者精度 (%)

表5 单时相、时间序列分类结果各土地覆被类型的用户精度 (%)

表6 三个局部区域2010年不同土地覆被产品地类面积

图8 时间序列分类结果与CNLUCC、GlobeLand30、FROM-GLC等产品对比

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