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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
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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 EngineLandsat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        http://www.rsta.ac.cn/CN/Y2020/V35/I2/315

年份1990年2000年2010年2017年
传感器Landsat TMLandsat TMLandsat TMLandsat OLI
景数276341298360
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)
耕地林地草地水域建设用地未利用地
年份面积所占比例(%)面积所占比例(%)面积所占比例(%)面积所占比例(%)面积所占比例(%)面积所占比例(%)合计
时间序列结果1990623.0839.21454.1128.57390.1424.556.180.38106.846.728.660.541 589.01
2000608.7238.30439.7827.67427.2826.899.850.6294.335.939.050.571 589.01
2010515.4234.43494.3831.11487.1330.655.060.3178.494.948.530.531 589.01
2017516.3734.49481.9230.32479.9330.205.130.3292.855.8412.810.801 589.01
二 调2010448.3729.08487.2531.60411.7126.7029.431.9183.555.4281.275.271 541.58
表2  山西省不同年份各地类面积汇总表
单时相分类结果时间序列分类结果
年度19902000201020171990200020102017
总体精度/%81.6877.4486.3185.7886.4487.5793.6492.77
Kappa系数0.740.690.810.800.810.830.910.90
表3  单时相、时间序列分类结果的总体精度与Kappa系数
单时相分类结果时间序列分类结果
年度19902000201020171990200020102017
耕地87.5387.2989.7591.3093.9193.9595.1593.62
建设用地71.5666.0377.1467.8981.4883.5890.9096.17
林地97.1598.8597.5196.6698.8899.4399.4397.59
草地62.4048.8376.2782.4462.1265.8985.8478.74
水域85.1852.1785.7110077.7773.9190.9096.55
未利用地20.0030.7630.0018.3356.2550.0059.2560.00
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 (%)
单时相分类结果时间序列分类结果
年度19902000201020171990200020102017
耕地80.9771.9083.0877.5381.0484.3494.5094.57
建设用地73.6578.2180.8384.8684.2184.8991.8990.25
林地93.4496.6495.1594.0597.2597.2397.2292.04
草地75.4560.0088.2393.9188.1784.1587.3891.74
水域10010010010010094.44100100
未利用地33.3366.6775.0010085.7176.4794.1185.71
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 (%)
区域方法耕地林地草地水域建设用地未利用地
(a)本文时间序列8 530.221.3495.42111.402 764.10
FROM-GLC6 965.221.53159.574 376.16
GlobeLand308 099.3734.111.4489.643 277.92
CNLUCC7 871.7610.17211.233 409.32
(b)本文时间序列2 014.482 145.077 331.42214.2115.46
FROM-GLC1 513.214 928.845 210.1068.49
GlobeLand302 654.321 791.277 191.6283.43
CNLUCC3 521.53267.037 726.43205.65
(c)本文时间序列6 592.971 399.452 413.565.30804.96
FROM-GLC10 515.41511.5656.5236.1896.57
GlobeLand307 355.33799.742 203.473.60854.10
CNLUCC5 394.152 489.41 928.16395.821 000.348.37
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
1 Azzari G, Lobell D B. Landsat-based Classification in the Cloud: An Opportunity for a Paradigm Shift in Land Cover Monitoring [J]. Remote Sensing of Environment, 2017, 202: 64-74.
2 Luo Jianchen, Zhou Chenghu, Yang Yan. Land-cover and Land-use Classification based on Remote Rensing Intelligent Geo-Interpreting Model [J]. Journal of Natural Resources, 2001, 16(2): 180-183.
2 骆剑承, 周成虎, 杨艳. 遥感地学智能图解模型支持下的土地覆盖/土地利用分类[J]. 自然资源学报, 2001, 16(2): 180-183.
3 Chen J, Chen J, Liao A, et al. Global Land Cover Mapping at 30 m Resolution: A POK-based Operational Approach [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27.
4 Liu Jiyuan, Kuang Wenhui, Zhang Zengxiang, et al. Spatiotemporal Characteristics, Patterns and Causes of Land Use Changes in China Since the Late 1980s [J]. Acta Geographica Sinca, 2014, 24(2): 3-14.
4 刘纪远, 匡文慧, 张增祥, 等. 20 世纪80 年代以来中国土地利用变化的基本特征与空间格局[J]. 地理学报, 2014, 24(2): 3-14.
5 Gong P, Wang J, Yu L, et al. Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data [J]. International Journal of Remote Sensing, 2013, 34(7): 2607-2654.
6 Xu Xinliang, Liu Jiyuan, Zhang Shuwen, et al. China Multi-period Land Use Land Cover Remote Sensing Monitoring Data Set(CNLUCC). Data Registration and Publishing System of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences[DB/OL]. [徐新良, 刘纪远, 张树文, 等. 中国多时期土地利用土地覆被遥感监测数据集(CNLUCC).中国科学院资源环境科学数据中心数据注册与出版系统[DB/OL]. 2018. doi:10.12078/2018070201.]
doi: 10.12078/2018070201
7 Bontemps S, Defourny P, Bogaert E V, et al. Globcover 2009-Products Description and Validation Report[R]. Leuve: University of Catholique de Louvain, 2011.
8 Chen Jun, Liao Anping, Chen jin, et al. 30-Meter Global Land Cover Data Product-GlobeLand30 [J]. Geomatic World, 2017, 24(1):1-8.
8 陈军, 廖安平, 陈晋,等. 全球30 m地表覆盖遥感数据产品-GlobeLand30[J]. 地理信息世界, 2017, 24(1):1-8.
9 Zhu Z, Woodcock C E. Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data [J]. Remote Sensing of Environment, 2017, 144: 152–171.
10 Melaas E K, Friedl M A, Zhu Z. Detecting Interannual Variation in Deciduous Broadleaf Forest Phenology Using Landsat TM/ETM+ Data [J]. Remote Sensing of Environment, 2013,132: 176-185.
11 Casu F, Manunta M, Agram P S, et al. Big Remotely Sensed Data: Tools, Applications and Experiences [J]. Remote Sensing of Environment, 2017, 202: 1-2.
12 Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone[J]. Remote Sensing of Environment, 2017, 202: 18-27.
13 Xie Z, Phinn S R, Game E T, et al. Using Landsat Observations (1988–2017) and Google Earth Engine to Detect Vegetation Cover Changes in Rangelands - A First Step Towards Identifying Degraded Lands for Conservation [J]. Remote Sensing of Environment, 2019, 232: 111317.
14 Bullock E L, Woodcock C E, Olofsson P. Monitoring Tropical Forest Degradation Using Spectral Unmixing and Landsat Time Series Analysis [J]. Remote Sensing of Environment, 2020, 238: 110968.
15 Hu Yunfeng, Shang Lingjie, Zhang Qianli, et al.Land Change Patterns and Driving Mechanism in Beijing Since 1990 based on GEE Platform [J].Remote Sensing Technology and Application, 2018,33(4):573-583.
15 胡云锋,商令杰,张千力,等.基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J].遥感技术与应用, 2018,33(4):573-583.
16 Pekel J F, Cottam A, Gorelick N, et al. High-resolution Mapping of Global Surface Water and Its Long-term Changes [J]. Nature, 2016, 540: 418–422.
17 Zhang Tao, Tang Hong. Vegetation Cover Change and Urban Expansion in Beijing-Tianjin-Hebei during 2001~2015 based on Google Earth Engine[J]. Remote Sensing Technology and Application, 2018,33(4):593-599.
17 张滔,唐宏. 基于Google Earth Engine的京津冀2001~2015年植被覆盖变化与城镇扩张研究[J].遥感技术与应用, 2018,33(4):593-599.
18 Liu X, Hu G, Chen Y, et al. High-resolution Multi-temporal Mapping of Global Urban Land Using Landsat Images based on the Google Earth Engine Platform [J]. Remote Sensing of Environment, 2018, 209: 227-239.
19 Shew A M, Ghosh A. Identifying Dry-Season Rice-planting Patterns in Bangladesh Using the Landsat Archive [J]. Remote Sensing, 2019, 11(10): 1235. doi:10.3390/rs11101235.
doi: 10.3390/rs11101235
20 Liu Chang, Li Zhen, Zhang Ping, et al. Evaluation of MODIS Snow Products in Southwestern Xinjiang Using the Google Earth Engine[J]. Remote Sensing Technology and Application, 2018,33(4):584-592.
20 刘畅,李震,张平,等.基于Google Earth Engine评估新疆西南部MODIS积雪产品[J].遥感技术与应用, 2018,33(4):584-592.
21 Hao Binfei, Han Xujun, Ma Mingguo, et al. Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Science [J]. Remote Sensing Technology and Application, 2018,33(4):600-611.
21 郝斌飞,韩旭军,马明国,等. Google Earth Engine在地球科学与环境科学中的应用研究进展[J].遥感技术与应用,2018,33(4):600-611.
22 Flood N. Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median)[J]. Remote Sensing, 2013, 5(12): 6481-6500. doi:10.3390/rs5126481.
doi: 10.3390/rs5126481
23 Gómez C, White J C, Wulder M A. Optical Remotely Sensed Time Series Data for Land Cover Classification: A Review [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116:55-72.
24 Liu Qionghuan, Zhang Yili, Liu Linshan, et al. Accuracy Evaluation of the Seven Land Cover Data in Qiangtang Plateau [J]. Geographical Research, 2017,36(11):2061-2072.
24 刘琼欢, 张镱锂,刘林山,等.七套土地覆被数据在羌塘高原的精度评价[J]. 地理研究, 2017,36(11):2061-2072.
25 Huang Yabo, Liao Shunbao. Regional Accuracy Assessments of the First Global Land Cover Dataset at 30-meter Resolution: A Case Study of Henan Province [J]. Geographical Research, 2016, 35(8): 1433-1446.
25 黄亚博, 廖顺宝. 首套全球30 m分辨率土地覆被产品区域尺度精度评价: 以河南省为例[J]. 地理研究, 2016, 35(8):1433-1446.
26 Kennedy R E, Yang Z, Gorelick N, et al. Implementation of the LandTrendr Algorithm on Google Earth Engine[J]. Remote Sensing, 2018, 10(5): 691. doi: 10.3390/rs10050691.
doi: 10.3390/rs10050691
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