Please wait a minute...
 
国土资源遥感  2018, Vol. 30 Issue (1): 109-115    DOI: 10.6046/gtzyyg.2018.01.15
  本期目录 | 过刊浏览 | 高级检索 |
基于MODIS数据的东江流域云干扰时空特征分析
彭秋志1(), 秦国玲1(), 吕乐婷2, 吴亚玲1
1.昆明理工大学国土资源工程学院,昆明 650093
2.辽宁师范大学城市与环境学院,大连 116029
Spatio-temporal patterns of cloud interference in Dongjiang River basin from MODIS data
Qiuzhi PENG1(), Guoling QIN1(), Leting LYU2, Yaling WU1
1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. College of Urban and Environmental Science, Liaoning Normal University, Dalian 116029, China
全文: PDF(1093 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

为在更高精度水平上分析植被指数时空变化特征,增强去云降噪环节的针对性和科学性,以东江流域2001—2015年间MOD13Q1产品中的云标记信息为数据源,利用地理信息系统空间分析方法,分别从云干扰概率及其空间分布、云干扰像元消除率及其空间分布和云干扰持续时长季节差异3个方面分析了东江流域云干扰时空特征。结果表明,该流域整体云干扰概率随合成时段加长而迅速降低; 新增云干扰像元消除率随合成时段加长而先增加后减少; 空间上南部城市化区域的云干扰持续时段相对更长,时间上夏季和春季的云干扰持续时段相对更长。该研究结果可为优选或开发更具时空适应性的植被指数时间序列数据去云降噪方法提供科学依据。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
彭秋志
秦国玲
吕乐婷
吴亚玲
关键词 东江流域MODIS云标记云干扰    
Abstract

In this paper, the authors aim to clarify the spatio-temporal patterns of cloud interference in Dongjiang River basin and then provide scientific basis for vegetation index studies at the level of higher precision in this area. Cloud flag information of MOD13Q1 product data sets within the range of Dongjiang River basin from 2001 to 2015 were analyzed using the GIS approach. Three aspects were analyzed, i.e., the probability of cloud interference and its spatial distribution, the elimination rate of cloud interference and its spatial distribution and the seasonal differences of cloud interference duration period length. The results show that the whole probability of cloud interference was reduced rapidly with the increase of compounding period length, the new added elimination rate of cloud interference firstly increased and then decreased with the increase of compounding period length. The duration period length of cloud interference was relatively longer in southern urbanized region, and in summer and spring. The results of this study can provide scientific basis for choosing or developing better methods of removing clouds for vegetation index time-series data.

Key wordsDongjiang River basin    MODIS    cloud flag    cloud interference
收稿日期: 2016-07-11      出版日期: 2018-02-08
:  TP751.1  
基金资助:昆明理工大学自然科学研究基金省级人培项目“土地利用坡谱分布特征与演变规律研究”(编号: 14118815)资助
作者简介:

第一作者: 彭秋志(1982-),男,博士,讲师,主要从事基于3S技术的土地生态学研究。Email:pqz20002@163.com

引用本文:   
彭秋志, 秦国玲, 吕乐婷, 吴亚玲. 基于MODIS数据的东江流域云干扰时空特征分析[J]. 国土资源遥感, 2018, 30(1): 109-115.
Qiuzhi PENG, Guoling QIN, Leting LYU, Yaling WU. Spatio-temporal patterns of cloud interference in Dongjiang River basin from MODIS data. Remote Sensing for Land & Resources, 2018, 30(1): 109-115.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.01.15      或      https://www.gtzyyg.com/CN/Y2018/V30/I1/109
Fig.1  研究区域位置
Fig.2  不同合成时段内整体云干扰概率
Fig.3  不同合成时段内云干扰概率空间分布
Fig.4  不同合成时段内新增云干扰像元消除率
Fig.5  消除云干扰所需最短合成时段长度的空间分布
Fig.6  不同合成时段内剩余云干扰概率的年内分配
[1] 赵敏,张荣,尹东,等.一种新的可见光遥感图像云判别算法[J].遥感技术与应用,2012,27(1):106-110.
Zhao M,Zhang R,Yin D,et al.Cloud classification algorithm for optical remote sensing image[J].Remote Sensing Technology and Application,2012,27(1):106-110.
[2] 陈阳,范建容,文学虎,等.基于时空数据融合模型的TM影像云去除方法研究[J].遥感技术与应用,2015,30(2):312-320.
Chen Y,Fan J R,Wen X H,et al.Research on cloud removal from Landsat TM image based on spatial and temporal data fusion model[J].Remote Sensing Technology and Application,2015,30(2):312-320.
[3] 徐昔保,杨桂山.太湖流域1995—2010年耕地复种指数时空变化遥感分析[J].农业工程学报,2013,29(3):148-155.
Xu X B,Yang G S.Spatial and temporal changes of multiple cropping index in 1995—2010 in Taihu Lake basin, China[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(3):148-155.
[4] 蒙继华,杜鑫,张淼,等.物候信息在大范围作物长势遥感监测中的应用[J].遥感技术与应用,2014,29(2):278-285.
Meng J H,Du X,Zhang M,et al.Integrating crop phenophase information in large-area crop condition evaluation with remote sensing[J].Remote Sensing Technology and Application,2014,29(2):278-285.
[5] 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.
Zhang X W,Wu B F.A temporal transformation method of fractional vegetation cover derived from high and moderate resolution remote sensing data[J].Acta Ecologica Sinica,2015,35(4):1155-1164.
[6] Holben B N.Characteristics of maximum-value composite images from temporal AVHRR data[J].International Journal of Remote Sensing,1986,7(11):1417-1434.
[7] 武正丽,贾文雄,刘亚荣,等.近10a来祁连山植被覆盖变化研究[J].干旱区研究,2014,31(1):80-87.
Wu Z L,Jia W X,Liu Y R,et al.Change of vegetation coverage in the Qilian Mountains in recent 10 years[J].Arid Zone Research,2014,31(1):80-87.
[8] 宋富强,邢开雄,刘阳,等.基于MODIS/NDVI的陕北地区植被动态监测与评价[J].生态学报,2011,31(2):354-363.
Song F Q,Xing K X,Liu Y,et al.Monitoring and assessment of vegetation variation in northern Shaanxi based on MODIS/NDVI[J].Acta Ecologica Sinica,2011,31(2):354-363.
[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.
[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.
[11] 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.
[12] 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.
[13] 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.
[14] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring vegetation phenology using MODIS[J].Remote Sensing of Environment,2003,84(3):471-475.
[15] Chen J,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.
[16] Ma M G,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.
[17] Atzberger C, Eilers P H C.Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements[J].International Journal of Remote Sensing,2011,32(13):3689-3709.
[18] 王坚,张继贤,刘正军,等.基于NDVI序列影像精化结果的植被覆盖变化研究[J].测绘科学,2005,30(6):43-44.
Wang J,Zhang J X,Liu Z J,et al.Vegetation cover changes based on refined NDVI image sequences[J].Science of Surveying and Mapping,2005,30(6):43-44.
[19] 边金虎,李爱农,宋孟强,等.MODIS植被指数时间序列Savitzky-Golay滤波算法重构[J].遥感学报,2010,14(4):725-741.
Bian J H,Li A N,Song M Q,et al.Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter[J].Journal of Remote Sensing,2010,14(4):725-741.
[20] 宋小宁,赵英时.MODIS图象的云检测及分析[J].中国图象图形学报,2003,8(9):1079-1083.
Song X N,Zhao Y S.Cloud detection and analysis of MODIS image[J].Journal of Image and Graphics,2003,8(9):1079-1083.
[21] 李微,方圣辉,佃袁勇,等.基于光谱分析的MODIS云检测算法研究[J].武汉大学学报(信息科学版),2005,30(5):435-438,443.
Li W,Fang S H,Dian Y Y,et al.Cloud detection in MODIS data based on spectrum analysis[J].Geomatics and Information Science of Wuhan University,2005,30(5):435-438,443.
[22] 单娜,郑天垚,王贞松.快速高准确度云检测算法及其应用[J].遥感学报,2009,13(6):1138-1155.
Shan N,Zheng T Y,Wang Z S.High-speed and high-accuracy algorithm for cloud detection and its application[J].Journal of Remote Sensing,2009,13(6):1138-1155.
[1] 胡盈盈, 戴声佩, 罗红霞, 李海亮, 李茂芬, 郑倩, 禹萱, 李宁. 2001—2015年海南岛橡胶林物候时空变化特征分析[J]. 自然资源遥感, 2022, 34(1): 210-217.
[2] 张爱竹, 王伟, 郑雄伟, 姚延娟, 孙根云, 辛蕾, 王宁, 胡光. 一种基于分层策略的时空融合模型[J]. 自然资源遥感, 2021, 33(3): 18-26.
[3] 韦耿, 侯钰俏, 查勇. 新冠疫情影响下武汉市气溶胶类型变化分析[J]. 自然资源遥感, 2021, 33(3): 238-245.
[4] 韦耿, 侯钰俏, 韩佳媚, 查勇. 基于精细模式气溶胶与WRF模式估算PM2.5质量浓度[J]. 国土资源遥感, 2021, 33(2): 66-74.
[5] 陈宝林, 张斌才, 吴静, 李纯斌, 常秀红. 历史平均值法用于MODIS影像像元云补偿——以甘肃省为例[J]. 国土资源遥感, 2021, 33(2): 85-92.
[6] 杨欢, 邓帆, 张佳华, 王雪婷, 马庆晓, 许诺. 基于MODIS EVI的江汉平原油菜和冬小麦种植信息提取研究[J]. 国土资源遥感, 2020, 32(3): 208-215.
[7] 邓刚, 唐志光, 李朝奎, 陈浩, 彭焕华, 王晓茹. 基于MODIS时序数据的湖南省水稻种植面积提取及时空变化分析[J]. 国土资源遥感, 2020, 32(2): 177-185.
[8] 金楷仑, 郝璐. 基于遥感数据与SEBAL模型的江浙沪地区地表蒸散反演[J]. 国土资源遥感, 2020, 32(2): 204-212.
[9] 赵冰, 毛克彪, 蔡玉林, 孟祥金. 中国地表温度时空演变规律研究[J]. 国土资源遥感, 2020, 32(2): 233-240.
[10] 施益强, 邓秋琴, 吴君, 王坚. 厦门市MODIS气溶胶光学厚度与空气质量指数的回归分析[J]. 国土资源遥感, 2020, 32(1): 106-114.
[11] 程宇琪, 王雨晴, 孙静萍, 张成福. 多伦县草原植被覆盖与蒸散量时空变化及其关系[J]. 国土资源遥感, 2020, 32(1): 200-208.
[12] 吴林霖, 官云兰, 李嘉伟, 袁晨鑫, 李睿. 基于MODIS影像喀斯特石漠化状况研究——以贵州省为例[J]. 国土资源遥感, 2019, 31(4): 235-242.
[13] 李恺霖, 张春桂, 廖廓, 李丽纯, 王宏. 福建省空气清新度卫星遥感监测[J]. 国土资源遥感, 2019, 31(4): 151-158.
[14] 吴佳平, 张旸, 张杰, 范胜龙, 杨超, 张小芳. 基于MODIS数据的淤泥质海岸水体指数比较与分析——以黄河三角洲海岸为例[J]. 国土资源遥感, 2019, 31(3): 242-249.
[15] 刘英, 岳辉, 侯恩科. MODIS数据在陕西省干旱监测中的应用[J]. 国土资源遥感, 2019, 31(2): 172-179.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发