地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 738-749.doi: 10.12082/dqxxkx.2022.210181
谌稳1,2(), 孙立群1,*(
), 李晴岚1, 陈晨3, 李家叶3
收稿日期:
2021-04-06
修回日期:
2021-07-06
出版日期:
2022-04-25
发布日期:
2022-06-25
通讯作者:
*孙立群(1981— ),男,博士,江苏常州人,助理研究员,主要从事生态遥感研究。E-mail: lq.sun@siat.ac.cn作者简介:
谌 稳(1996— ),男,硕士生,湖南怀化人,主要从事气象相关方面研究。E-mail: 201821511214@smail.xtu.edu.cn
基金资助:
CHEN Wen1,2(), SUN Liqun1,*(
), LI Qinglan1, CHEN Chen3, LI Jiaye3
Received:
2021-04-06
Revised:
2021-07-06
Online:
2022-04-25
Published:
2022-06-25
Supported by:
摘要:
MODIS的增强型植被指数(EVI)时间序列数据早已广泛应用于植被观测、生态环境和全球气象变化等研究领域,但即使EVI时间序列数据已经经过严格的预处理,其中仍然存在着一些噪声。因此,本文开发了一种简单有效的方法来重构EVI时间序列数据,减少EVI时间序列数据中的噪声,尤其是一些由大气云层和冰雪覆盖产生的噪声。新方法的理论来源于图论,利用拉普拉斯矩阵的关系对EVI中选定的邻域窗口的像元权重进行赋值,得到中心像元的拟合。新方法已应用于2016—2018年的MODIS MOD13A1产品,并与S-G滤波法、谐波函数法、双逻辑斯蒂拟合法和非对称高斯函数法进行了比较。结果表明,在荒漠、草原和林地中,新方法留一验证测试的绝对差值最小,相较于其他方法效果较优;在拟合不同植被类型的EVI时间序列数据时,图论邻点方法呈现出更好的细节拟合曲线;其在5类植被类型中的RMSE值分别为200.59、46.58、63.48、165.47和40.95,在5种方法中均为最小值,在获取高保真和高质量的EVI时间序列数据方面优势更明显有效。本文的方法研究可以给植被遥感时序数据的去噪和生态环境的研究提供有益借鉴。
谌稳, 孙立群, 李晴岚, 陈晨, 李家叶. 一种基于图论重构MODIS EVI时间序列数据集的新方法[J]. 地球信息科学学报, 2022, 24(4): 738-749.DOI:10.12082/dqxxkx.2022.210181
CHEN Wen, SUN Liqun, LI Qinglan, CHEN Chen, LI Jiaye. A New Method to Reconstruct MODIS EVI Time Series Data Set based on Graph Theory[J]. Journal of Geo-information Science, 2022, 24(4): 738-749.DOI:10.12082/dqxxkx.2022.210181
表2
5类测试点留一验证的对比数据
类型 | 普通值 | 最大值 | 最小值 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
真实值 | 邻点插值 | 绝对差值 | 真实值 | 邻点插值 | 绝对差值 | 真实值 | 邻点插值 | 绝对差值 | |||
林地 | 5619 | 5579.871 | 39.129 | 7124 | 7049.797 | 74.203 | 1058 | 941.051 | 116.949 | ||
灌木 | 1116 | 1166.937 | 50.937 | 1888 | 1745.814 | 142.186 | 657 | 704.244 | 47.244 | ||
草原 | 857 | 895.397 | 38.397 | 1826 | 1655.251 | 170.749 | 430 | 526.679 | 96.679 | ||
农作物 | 3128 | 3474.913 | 346.913 | 5715 | 5487.952 | 227.048 | 897 | 944.294 | 47.294 | ||
荒漠 | 886 | 882.815 | 3.185 | 982 | 958.190 | 23.810 | 548 | 605.805 | 57.805 |
表3
5类测试点在不同方法的留一验证
类型 | 真实值 | 线性插值数据 | 邻点插值 | S-G方法 | HANTS方法 | A-G方法 | D-L方法 | 最小绝对差 |
---|---|---|---|---|---|---|---|---|
林地 | 5619 | 5488.500 | 5579.871 | 5481.347 | 5211.238 | 5476.249 | 5684.763 | 邻点插值 |
灌木 | 1116 | 1132.000 | 1166.937 | 1127.458 | 996.877 | 1137.098 | 1149.358 | S-G方法 |
草原 | 857 | 867.500 | 895.397 | 801.331 | 743.219 | 799.136 | 786.916 | 邻点插值 |
农作物 | 3128 | 3291.000 | 3474.913 | 3318.579 | 2879.664 | 3218.487 | 3279.521 | A-G方法 |
荒漠 | 886 | 875.000 | 882.815 | 863.136 | 923.531 | 844.394 | 837.469 | 邻点插值 |
[1] |
Marcos B, Gonçalves J, Alcaraz-Segura D, et al. Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: A case study in northern Portugal[J]. International Journal of Applied Earth Observation and Geoinformation, 2019,78:77-85.
doi: 10.1016/j.jag.2018.12.003 |
[2] | 王正兴, 刘闯, Huete Alfredo. 植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J]. 生态学报, 2003,23(5):979-987. |
[ Wang Z X, Liu C, Huete Alfredo. Research progress of vegetation Index: from AVHRR-NDVI to MODIS-EVI[J]. Acta Ecologica Sinica, 2003,23(5):979-987. ] | |
[3] | 孙立双, 马运涛, 毕天平, 等. 辽宁地区不同地表覆盖类型EVI和NDVI特征[J]. 沈阳建筑大学学报(自然科学版), 2013,29(6):1024-1029. |
[ Sun L S, Ma Y T, Bi T P, et al. EVI and NDVI characteristics of different surface cover types in Liaoning area[J]. Journal of Shenyang Construction University (Natural Science Edition), 2013,29(6):1024-1029. ] | |
[4] |
周惠慧, 王楠, 黄瑶, 等. 不同时间间隔下的遥感时间序列重构模型比较分析[J]. 地球信息科学学报, 2016,18(10):1410-1417.
doi: 10.3724/SP.J.1047.2016.01410 |
[ Zhou H H, Wang N, Huang Y, et al. Comparison and analysis of remote sensing time series reconstruction models under different time intervals[J]. Journal of Geo-information Science, 2016,18(10):1410-1417. ] | |
[5] |
刘建文, 周玉科. 站点尺度的青藏高原时序NDVI重构方法比较与应用[J]. 地理科学进展, 2018,37(3):427-437.
doi: 10.18306/dlkxjz.2018.03.013 |
[ Liu J W, Zhou Y K. Comparison and application of site-scale NDVI reconstruction methods for Qinghai-Tibet Plateau time series[J]. Progress in Geography, 2018,37(3):427-437. ] | |
[6] | 贾若楠, 杜鑫, 李强子, 等. 近15年锡林郭勒盟植被变化时空特征及其对气候的响应[J]. 中国水土保持科学, 2016,14(5):47-56. |
[ Jia R N, Du X, Li Q Z, et al. Spatio-temporal characteristics of vegetation change and its response to climate in Xilingol League in recent 15 years[J]. Science of Soil and Water Conservation, 2016,14(5):47-56. ] | |
[7] | 刘倩楠, 岳彩荣, 欧阳志云, 等. 基于MODIS—NDVI时序数据的重庆市植被变化研究[J]. 测绘与空间地理信息, 2012,35(3):99-102. |
[ Liu Q N, Yue C R, Ouyang Z Y, et al. Study on vegetation change in Chongqing based on MODIS-NDVI time series data[J]. Geomatics & Spatial Information Technology, 2012,35(3):99-102. ] | |
[8] |
Holben B N. Characteristics of maximum-value composite images from temporal AVHRR data[J]. International journal of remote sensing, 1986,7(11):1417-1434.
doi: 10.1080/01431168608948945 |
[9] |
Viovy N, Arino O, Belward A S. The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series[J]. International Journal of remote sensing, 1992,13(8):1585-1590.
doi: 10.1080/01431169208904212 |
[10] |
Lovell J L, Graetz R D. Filtering pathfinder AVHRR land NDVI data for Australia[J]. International Journal of Remote Sensing, 2001,22(13):2649-2654.
doi: 10.1080/01431160116874 |
[11] |
Chen Jin, Jönsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote sensing of Environment, 2004,91(3-4):332-344.
doi: 10.1016/j.rse.2004.03.014 |
[12] |
Ma MingGuo, Veroustraete F. Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China[J]. Advances in Space Research, 2006,37(4):835-840.
doi: 10.1016/j.asr.2005.08.037 |
[13] |
Ryo Michishita, Zhenyu Jin, Jin Chen, et al. Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,91(5):17-28.
doi: 10.1016/j.isprsjprs.2014.01.003 |
[14] | 蒋雪冰, 胡月明, 刘振华, 等. 基于线性内插的扩展卡尔曼滤波法NDVI时间序列重构研究[J]. 科技通报, 2017,33(2):137-142. |
[ Jiang X B, Hu M H, Liu Z H, et al. Research on NDVI time series reconstruction based on Extended Kalman Filter based on linear interpolation[J]. Bulletin of Science and Technology, 2017,33(2):137-142. ] | |
[15] |
Roerink G J, Menenti M, Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000,21(9):1911-1917.
doi: 10.1080/014311600209814 |
[16] |
Beck P S A, Atzberger C, Høgda K A, et al. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI[J]. Remote sensing of Environment, 2006,100(3):321-334.
doi: 10.1016/j.rse.2005.10.021 |
[17] |
Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE transactions on Geoscience and Remote Sensing, 2002,40(8):1824-1832.
doi: 10.1109/TGRS.2002.802519 |
[18] |
Sellers P J, Randall D A, Collatz G J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs.1. Model formulation[J]. Journal of Climate, 1996,9(4):676-705.
doi: 10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2 |
[19] | Zhang X, Li R, Yue Y M, et al. Improved algorithm for reconstructing vegetation index image time series based on Fourier Harmonic Analysis[J]. Journal of Remote Sensing, 2010,14(3):437-447. |
[20] | 何月, 樊高峰, 张小伟, 等. 浙江省植被物候变化及其对气候变化的响应[J]. 自然资源学报, 2013,28(2):220-233. |
[ He Y, Pan G F, Zhang X W, et al. Phenological change of vegetation in Zhejiang province and its response to climate change[J]. Journal of Natural Resources, 2013,28(2):220-233. ] | |
[21] | 张晗, 任志远. 多种时序NDVI重建方法比较与应用分析[J]. 中国农业科学, 2014,47(15):2998-3008. |
[ Zhang H, Ren Z Y. Comparison and application analysis of several temporal NDVI reconstruction methods[J]. Scientia Agricultura Sinica, 2014,47(15):2998-3008. ] | |
[22] | Zhou J X, Chen J, Chen X H, et al. Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction[J]. Remote Sensing of Environment, 2021,252:112-130. |
[23] |
Zurita M R, Clevers J G, Schaepman M E. Unmixing-based Landsat TM and MERIS FR data fusion[J]. IEEE geoscience and remote sensing letters, 2008,5(3):453-457.
doi: 10.1109/LGRS.2008.919685 |
[24] |
Rao Y, Zhu X, Chen J, et al. An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM+images[J]. Remote Sensing, 2015,7(6):7865-7891.
doi: 10.3390/rs70607865 |
[25] | Gao F, Hilker T, Zhu X, et al. Fusing Landsat and MODIS data for vegetation monitoring[J]. IEEE Geoscience and Remote Sensing Magazine, 2015,3(3):47-60. |
[26] |
Wang Q, Peter M A. Spatio-temporal fusion for daily Sentinel-2 images[J]. Remote Sensing of Environment, 2018,204:31-42.
doi: 10.1016/j.rse.2017.10.046 |
[27] |
Huang B, Song H. Spatiotemporal reflectance fusion via sparse representation[J]. IEEE Trans. Geoscience and Remote Sensing, 2012,50(10):3707-3716.
doi: 10.1109/TGRS.2012.2186638 |
[28] |
Zhu X, Helmer E H, Gao F, et al. A flexible spatiotemporal method for fusing satellite images with different resolutions[J]. Remote Sensing of Environment, 2016,172:165-177.
doi: 10.1016/j.rse.2015.11.016 |
[29] |
Atzberger C, Eilers P H C. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America[J]. International Journal of Digital Earth, 2011,4(5):365-386.
doi: 10.1080/17538947.2010.505664 |
[30] |
Julien Y, Sobrino J A. Comparison of cloud-reconstruction methods for time series of composite NDVI data[J]. Remote Sensing of Environment, 2010,114(3):618-625.
doi: 10.1016/j.rse.2009.11.001 |
[31] | 耿丽英, 马明国. 长时间序列 NDVI 数据重建方法比较研究进展[J]. 遥感技术与应用, 2014,29(2):362-368. |
[ Geng L Y, Ma M G. Progress in comparative study of NDVI data reconstruction methods for long time series[J]. Remote Sensing Technology and Application, 2014,29(2):362-368. ] | |
[32] |
王乾坤, 于信芳, 舒清态, 等. MODIS EVI时序数据重建方法及拟合分析[J]. 地球信息科学学报, 2015,17(6):732-741.
doi: 10.3724/SP.J.1047.2015.00732 |
[ Wang Q K, Yu X F, Shu Q T, et al. Analysis of time-series data reconstruction method and fitting based on EVI of MODIS[J]. Journal of Geo-information Science, 2015,17(6):732-741. ] | |
[33] |
Jönsson P, Eklundh L. TIMESAT: A program for analyzing time-series of satellite sensor data[J]. Computers and Geosciences, 2004,30(8):33-845.
doi: 10.1016/j.cageo.2003.09.005 |
[1] | 刘恒孜, 吕宁, 姜侯, 姚凌. 基于DCT-PLS算法的MODIS LST缺值填补方法研究[J]. 地球信息科学学报, 2022, 24(2): 378-390. |
[2] | 卞萌, 郭树毅, 王威, 欧阳昱晖, 黄颖菁, 费腾. 融合植被遥感数据的北京市次日花粉浓度预测[J]. 地球信息科学学报, 2021, 23(9): 1705-1713. |
[3] | 葛中曦, 黄静, 赖佩玉, 郝斌飞, 赵银军, 马明国. 耕地复种指数遥感监测研究进展[J]. 地球信息科学学报, 2021, 23(7): 1169-1184. |
[4] | 管琪卉, 丁明军, 张华, 王鹏. ESTARFM算法在长江中下游平原地区的适用性研究[J]. 地球信息科学学报, 2021, 23(6): 1118-1130. |
[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] | 陆大进, 黎东, 朱笑笑, 聂胜, 周国清, 张兴忆, 杨超. 基于卷积神经网络的ICESat-2光子点云去噪分类[J]. 地球信息科学学报, 2021, 23(11): 2086-2095. |
[7] | 陈如如, 胡中民, 李胜功, 郭群. 不同数据源归一化植被指数在中国北方草原区的应用比较[J]. 地球信息科学学报, 2020, 22(9): 1910-1919. |
[8] | 李玉, 李奕燃, 王光辉, 石雪. 基于加权指数函数模型的高光谱图像分类方法[J]. 地球信息科学学报, 2020, 22(8): 1642-1653. |
[9] | 李婉, 牛陆, 陈虹, 吴骅. 基于随机森林算法的地表温度鲁棒降尺度方法[J]. 地球信息科学学报, 2020, 22(8): 1666-1678. |
[10] | 闫庆武, 厉飞, 李玲. 基于2种夜间灯光影像亮度修正指数的城市建成区提取研究[J]. 地球信息科学学报, 2020, 22(8): 1714-1724. |
[11] | 蒋世豪, 江洪, 陈慧. 基于SEVI的复杂地形山区植被FPAR遥感反演与地形效应评估[J]. 地球信息科学学报, 2020, 22(8): 1725-1734. |
[12] | 阿依尼格尔·亚力坤, 买买提艾力·买买提依明, 刘素红, 杨帆, 何清, 刘永强. 新疆沙漠地区地表宽波段比辐射率遥感估算[J]. 地球信息科学学报, 2020, 22(8): 1743-1751. |
[13] | 陆彦蓉, 刘强, 李霞, 李秀红, 刘璐, 肖洒, 孙美莹. 全球250 m反照率产品算法及验证[J]. 地球信息科学学报, 2020, 22(2): 328-335. |
[14] | 刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347. |
[15] | 洪恬林, 李云梅, 吕恒, 孟斌, 毕顺, 周玲. 基于MODIS数据的太湖浮游植物物候变化及其对水表温度的响应[J]. 地球信息科学学报, 2020, 22(10): 1935-1945. |
|