基于Google Earth Engine的中国植被覆盖度时空变化特征分析

doi:10.11873/j.issn.1004-0323.2020.2.0326

遥感技术与应用

2020, Vol. 35

Issue (2): 326-334 DOI: 10.11873/j.issn.1004-0323.2020.2.0326

GEE专栏

基于Google Earth Engine的中国植被覆盖度时空变化特征分析

龙爽^1,³(

),郭正飞^2,³,徐粒^1,³,周华真^1,³,方伟华^1,^2,³,许映军^1,^2,³(

)

^1.环境演变与自然灾害教育部重点实验室，北京 100875
^2.地表过程与资源生态国家重点实验室，北京 100875
^3.北京师范大学地理科学学部，北京 100875

Spatiotemporal Variations of Fractional Vegetation Coverage in China based on Google Earth Engine

Shuang Long^1,³(

),Zhengfei Guo^2,³,Li Xu^1,³,Huazhen Zhou^1,³,Weihua Fang^1,^2,³,Yingjun Xu^1,^2,³(

)

^1.Key Laboratory of Environment Change and Natural Disaster, Ministry of Education, Beijing 100875, China
^2.State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing 100875, China
^3.Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

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摘要：

植被覆盖时空变化是全球及区域生态环境重要研究内容之一。基于Google Earth Engine云平台，利用2000~2017年250 m分辨率的MODIS-EVI长时间序列数据，采用像元二分模型并辅以趋势分析、去趋势标准差、Hurst指数方法定量估算中国自2000年来植被覆盖度时空变化，并从省域尺度分析中国植被覆盖度近18 a以及未来趋势变化的时空分异特征。研究结果表明：①2000年以来中国植被覆盖度的变化速率为0.09%/a（P＜0.01），平均植被覆盖度为44.63%，空间分布格局上整体呈现“东南高、西北低”的特点，但存在空间异质性；②从省级尺度来看，海南省平均植被覆盖度最高（79%），新疆维吾尔自治区最低（13%），山西省改善趋势最显著（0.4%/a），天津市年际波动最大(DSD=0.039)，位于中国最西部的3省：新疆、西藏、青海植被覆盖度年际波动最小；③全国尺度植被覆盖度Hurst指数为0.72，未来将继续保持改善的趋势。具有改善持续性的省份基本呈“T”型分布，位于东西两侧的省份应注重加强植被生态修复与防护工作，保障区域生态文明建设的持续性。

关键词： 植被覆盖度; 像元二分模型; Hurst指数; 时空变化; Google Earth Engine; MODIS?EVI

Abstract:

The spatiotemporal variation of vegetation coverage is one of the main research fields in Global and Regional eco-environment. Based on the Google Earth Engine cloud platform, using the MODIS-EVI long-term series data of 250 m resolution from 2000 to 2017. The model of dimidiate pixel was applied in estimating the spatiotemporal variations of Fractional vegetation coverage in China since 2000. The spatiotemporal variation characteristics of China's vegetation coverage for nearly 18 years and future trends from the provincial scale also be analyzed. Trend analysis, Detrended Standard Deviation and Hurst index were employed. The results showed that: (1) The rate of variation of vegetation coverage in China since 2000 is 0.09%/a (P<0.01), the average vegetation coverage is 44.63%. The overall spatial distribution pattern shows the characteristics of “south-high and low-lying northwest”, but there is space Heterogeneity; (2) Hainan Province has the highest average vegetation coverage (79%), the lowest in Xinjiang Uygur Autonomous Region (13%), the most significant improvement in vegetation coverage in Shanxi Province (0.4%/a). Tianjin has the largest inter-annual volatility (DSD=0.039), Xinjiang, Tibet and Qinhai province which located in the westernmost of China have the least annual fluctuations in vegetation coverage; (3) The Hurst Index of Vegetation Coverage at National Scale is 0.72, China Future vegetation coverage will continue to improve. The provinces with improved sustainability are basically “T”-type distribution, and the provinces on both sides of the east and west should focus on strengthening the ecological restoration and protection of vegetation to guarantee the sustainability of regional ecological civilization construction.

Key words: Fractional vegetation cover Dimidiate Pixel Model Hurst index Spatiotemporal variation Google Earth Engine MODIS-EVI

收稿日期: 2018-10-09 出版日期: 2020-07-10

ZTFLH:

TP79

基金资助: 国家重点研发计划支持项目(2017YFC1502503)

通讯作者: 许映军 E-mail: ls@mai.bnu.edu.cn;xyj@bnu.edu.cn

作者简介: 龙爽(1993—),女,湖南岳阳人，硕士研究生,主要从事资源生态与资源遥感研究。E?mail:ls@mai.bnu.edu.cn

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引用本文:

龙爽,郭正飞,徐粒,周华真,方伟华,许映军. 基于Google Earth Engine的中国植被覆盖度时空变化特征分析[J]. 遥感技术与应用, 2020, 35(2): 326-334.

Shuang Long,Zhengfei Guo,Li Xu,Huazhen Zhou,Weihua Fang,Yingjun Xu. Spatiotemporal Variations of Fractional Vegetation Coverage in China based on Google Earth Engine. Remote Sensing Technology and Application, 2020, 35(2): 326-334.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0326 或 http://www.rsta.ac.cn/CN/Y2020/V35/I2/326

图1 Google Earth Engine用户环境

图2 2000~2017年中国植被覆盖度变化

图3 2000~2017年中国植被覆盖度空间分布

图4 2000~2017年各省份植被覆盖度最大值、平均值与最小值

图5 2000~2017年各省份植被覆盖度年际波动情况

图6 2000~2017年中国植被覆盖度变化趋势以及Hurst指数分布

图7 2000~2017年中国植被覆盖度未来趋势变化

1	Xing Zhurong, Feng Yougui, Yang Guijun, et al. Method of Estimating Vegetation Coverage based in Remote Sensing[J]. Remote Sensing Technology and Application, 2009,24(6): 849-854.
1	邢著荣, 冯幼贵, 杨贵军,等. 基于遥感的植被覆盖度估算方法述评[J]. 遥感技术与应用, 2009, 24(6):849-854.
2	Chen Jin, Chen Yunhao, He Chunyang, et al. Sub-pixcel Model for Vegetation Fraction Estimation based on Land Cover Classification[J]. Journal of Remote Sensing, 2001,5(6): 416-422.
2	陈晋, 陈云浩, 何春阳，等. 基于土地覆盖分类的植被覆盖率估算亚像元模型与应用[J]. 遥感学报, 2001,5(6):416-422.
3	Sm Sudipta, Menas K. Interannual Variability of Vegetation over the Indian Sub-continent and Its Relation to the Different Meteorological Parameters[J]. Remote Sensing of Environment, 2004,90(2): 268-280.
4	Guo Zhixing, Zhang Xiaoning, Wang Zongming, et al. Response of Vegetation Phenology in Northeast China to Climate Change[J]. Chinese Journal of Ecology, 2010,29(3): 578-585.
4	国志兴, 张晓宁, 王宗明, 等. 东北地区植被物候对气候变化的响应[J]. 生态学杂志, 2010,29(3): 578-585.
5	Liu Yan, Li Yang, Cui Caixia, et al. Evaluation of MODIS MOD13Q1 Data in Desertification in the North Area of Xinjiang[J]. Acta Prataculturae Sinica, 2010,19(3): 14-21.
5	刘艳, 李杨, 崔彩霞,等. MODIS MOD13Q1数据在北疆荒漠化监测中的应用评价[J]. 草业学报, 2010,19(3):14-21.
6	Jia Kun, Yao Yunjun, Wei Xiangqin, et al. A Review on Fractional Vegetation Cover Estimation Using Remote Sensing[J]. Advances in Earth Science, 2013,28(7): 774-782．
6	贾坤, 姚云军, 魏香琴, 等. 植被覆盖度遥感估算研究进展[J]. 地球科学进展, 2013, 28( 7) : 774-782．
7	Miao Zhenghong, Liu Zhiming, Wang Zongming, et al. Dynamic Monitoring of Vegetation Fraction Change in Jilin Province based on MODIS NDVI[J]. Remote Sensing Technology and Appication, 2010, 25(3):387-393.
7	苗正红, 刘志明, 王宗明, 等. 基于MODIS NDVI的吉林省植被覆盖度动态遥感监测[J]. 遥感技术与应用, 2010, 25(3):387-393.
8	Zhu Linfu, Xie Shiyou, Yang Hua, et al. The Response of Dynamic Change in Vegetation Coverage to Topography in Chongqing based on MODIS EVI[J]. Journal of Natural Resources, 2017,32(12): 2023-2033.
8	朱林富, 谢世友, 杨华, 等. 基于MODIS EVI的重庆植被覆盖变化的地形效应[J]. 自然资源学报, 2017,32(12):2023-2033.
9	Yuan Zhihui, Bao Gang, Yin Shan, et al. Vegetation Changes in Otindag Sand Country during 2000~2014[J]. Acta Prataculturae Sinica, 2016,25(1):33-46.
9	元志辉, 包刚, 银山,等. 2000~2014年浑善达克沙地植被覆盖变化研究[J]. 草业学报, 2016,25(1):33-46.
10	Yu Quanzhou, Liang Chunling, Liu Yujie, et al. Analysis of Vegetation Spatio-temporal Variation and Driving Factors in Shandong Province based on MODIS[J]. Ecology and Environmental Sciences, 2015,24(11):1799-1807.
10	于泉洲,梁春玲,刘煜杰,等.基于MODIS的山东省植被覆盖时空变化及其原因分析[J].生态环境学报,2015,24(11):1799-1807.
11	Zhang Xiwang, Wu Bingfang. A Temporal Transformation Method of Fraction Vegetation Cover derived from High and Moderate Resolution Remote Sensing Data[J]. Acta Ecologica Sinica, 2015,35(4): 1155-1164.
11	张喜旺, 吴炳方. 基于中高分辨率遥感的植被覆盖度时相变换方法[J]. 生态学报,2015, 35(4):1155-1164.
12	Huete A,Didan K,Miura T,et al. Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices[J].Remote Sensing of Environment,2002,83(1),195-213.
13	Dutta D, Das P K, Paul S, et al. Assessment of Ecological Disturbance in the Mangrove Forest of Sundarbans Caused by Cyclones Using MODIS Time-series Data (2001~2011) [J]. Natural Hazards, 2015,79(2): 775-790.
14	Qiu B W, Zeng C Y, Tang Z H, et al. Characterizing Spatiotemporal Non-stationary in Vegetation Dynamics in China Using MODIS EVI Dataset[J]. Environmental Monitoring and Assessment, 2013,185(11): 9019-9035.
15	Wang Zhengxing, Liu Chuang, Huete A. From AVHRR-NDVI to MODIS-EVI Adcances in Vegetation Index Research[J].Ata Ecology Sinica, 2003,23(5): 979-987.
15	王正兴, 刘闯,Alfredo Huete. 植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J].生态学报,2003,23(5):979-987.
16	Fang Jingyun, Shilong Piao, He Jinsheng, et al. Vegetation of China Invigorated in Last 20 Years[J]. Science in China: Series C, 2003,33(6):554-565.
16	方精云, 朴世龙, 贺金生, 等. 近20年来中国植被活动在增强. 中国科学, 2003,33(6):554-565.
17	Zhao Hanqing. Research on the Vegetation Coverage Changes of China based on SPOT-4/VEGETATION Data[J]. Geomatics & Spatial Information Technology, 2010,33(1): 164-166.
17	赵汉青. 基于SPOT-4/VEGETATION数据的中国植被覆盖动态变化研究[J]. 测绘与空间地理信息, 2010,33(1):164-166.
18	Li Fei, Zeng Yuan, Ling Xiaosong, et al. Remote Sensing based Monitoring of Interannual Variations in Vegetation Activity in China from 1982~2009[J]. Science China Earth Sciences, 2014,57(8): 1671-1678.
18	李飞, 曾源, 李晓松, 等. 近30年中国陆地植被活动遥感监测[J]. 中国科学:地球科学, 2014,57(8):1671-1678.
19	He Yingming, Liu Xiangpei, Wang Hanjie. Variation Characteristics of Vegetation Cover in the Latest 10 Years over China based on EVI[J]. Journal of the Meteorological Sciences, 2017,37(1): 51-59.
19	赫英明, 刘向培, 王汉杰. 基于EVI的中国最近10 a植被覆盖变化特征分析[J]. 气象科学, 2017,37(1):51-59.
20	Liu Ke, Du Lingtong, Hou Jing, et al. Spatiotemporal Variations of NDVI in Terrestrial Ecosystems in China from 1982~2012[J]. Acta Ecologica Sinica, 2018,38(6): 1885-1896.
20	刘可, 杜灵通, 侯静, 等. 近30年中国陆地生态系统NDVI时空变化特征[J]. 生态学报, 2018,38(6): 1885-1896.
21	Gorelick N. Google Earth Engine[A]. In EGU General Assembly Conference Abstracts[C]∥ American Geophysical Union: Vienna, Austria, 2013:11997.
22	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.
23	Patel N N, Angiuli E, Gamba P, et al. Multitemporal Settlement and Population Mapping from Landsat Using Google Earth Engine[J]. International Journal of Applied Earth Observations & Geoinformation, 2015,35(Part B):199-208.
24	Dong J W, Xiao X M, Menarguez M A, et al. Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-based Algorithm and Google Earth Engine[J]. Remote Sensing of Environment, 2016,185:142-154.
25	Shelestov A, Lavreniuk M, Kussul N, et al. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-temporal Satellite Imagery for Crop mapping[J]. Frontiers in Earth Science, 2017,5(5): 1-10.
26	Zhang Y, Liu C, Tang Y, et al. Trends in Pan Evaporation and Reference and Actual Evapotranspiration Across the Tibetan Plateau[J]. Journal of Geophysical Research Atmospheres, 2007,112(D12).doi:10.1029/2006jd008161. doi: 10.1029/2006jd008161
27	Lan C, Zhang Y, Gao Y, et al. The Impacts of Climate Change and Land Cover/Use Transition on the Hydrology in the Upper Yellow River basin, China[J]. Journal of Hydrology, 2013,502(2):37-52.
28	Liu Xianfeng, Ren Zhiyuan, Lin Zhihui, et al. The Spatial-temporal Changes of Vegetation Coverage in the Three-river Headwater Region in Recent 12 Years[J].Acta Geographica Sinca, 2013,68(7): 897-908.
28	刘宪锋, 任志远, 林志慧,等. 2000~2011年三江源区植被覆盖时空变化特征[J]. 地理学报, 2013,68(7):897-908.
29	Yan Enping, Lin Hui, Dang Yongfeng, et al. The Spatiotemporal Changes of Vegetation Cover in Beijing-Tianjin Sandstorm Source Control Region during 2000~2012[J]. Acta Ecologica Sinca, 2014,34(17): 5007-5020.
29	严恩萍, 林辉, 党永峰,等. 2000~2012年京津风沙源治理区植被覆盖时空演变特征[J]. 生态学报, 2014，34(17):5007-5020.
30	Yi Siyang, Wu Wenjin, Li Xinwu, et al. Forest Dynamic Analysis based on Remote Sensing Data and Metrology Data in Southeast Asian[J]. Remote Sensing Technology and Application, 2019,34(1):166-175.
30	尹思阳, 吴文瑾, 李新武. 基于遥感和气象数据的东南亚森林动态变化分析[J]. 遥感技术与应用, 2019(1):166-175.
31	Neinavaz E, Skidmore A K, Darvishzadeh R, et al. Effects of Prediction Accuracy of the Proportion of Vegetation Cover on Land Surface Emissivity and Temperature Using the NDVI Threshold Method[J]. International Journal of Applied Earth Observation and Geoinformation,2020(85): 101984. doi:10.1016/j.jag.2019.101984. doi: 10.1016/j.jag.2019.101984

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