DOI: 10.1016/j.jag.2013.02.001
Scopus记录号: 2-s2.0-84880120726
论文题名: Detecting long-duration cloud contamination in hyper-temporal NDVI imagery
作者: Ali A ; , de Bie C ; A ; J ; M ; , Skidmore A ; K
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 24, 期: 1 起始页码: 22
结束页码: 31
语种: 英语
英文关键词: Cloud
; Contamination
; Hyper-temporal
; Mapping
; MODIS
; NDVI
Scopus关键词: algorithm
; Aqua (satellite)
; cloud cover
; confidence interval
; detection method
; image analysis
; image classification
; mapping
; MODIS
; NDVI
; satellite imagery
; Terra (satellite)
英文摘要: Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach that goes beyond the use of quality flags and upper envelope filtering is tested to detect when and where long-duration clouds are responsible for unreliable NDVI readings, so that a user can flag those data as missing. The study is based on MODIS Terra and the combined Terra-Aqua 16-day NDVI product for the south of Ghana, where persistent cloud cover occurs throughout the year. The combined product could be assumed to have less cloud contamination, since it is based on two images per day. Short-duration cloud effects were removed from the two products through using the adaptive Savitzky-Golay filter. Then for each 'cleaned' product an unsupervised classified map was prepared using the ISODATA algorithm, and, by class, plots were prepared to depict changes over time of the means and the standard deviations in NDVI values. By comparing plots of similar classes, long-duration cloud contamination appeared to display a decline in mean NDVI below the lower limit 95% confidence interval with a coinciding increase in standard deviation above the upper limit 95% confidence interval. Regression analysis was carried out per NDVI class in two randomly selected groups in order to statistically test standard deviation values related to long-duration cloud contamination. A decline in seasonal NDVI values (growing season) were below the lower limit of 95% confidence interval as well as a concurrent increase in standard deviation values above the upper limit of the 95% confidence interval were noted in 34 NDVI classes. The regression analysis results showed that differences in NDVI class values between the Terra and the Terra-Aqua imagery were significantly correlated (p < 0.05) with the corresponding standard deviation values of the Terra imagery in case of all NDVI classes of two selected NDVI groups. The method successfully detects long-duration cloud contamination that results in unreliable NDVI values. The approach offers scientists interested in time series analysis a method of masking by area (class) the periods when pre-cleaned NDVI values remain affected by clouds. The approach requires no additional data for execution purposes but involves unsupervised classification of the imagery to carry out the evaluation of class-specific mean NDVI and standard deviation values over time. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79842
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7500 AE Enschede, Netherlands; Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), SUPARCO Road, P.O. Box No. 8402, Karachi 75270, Pakistan
Recommended Citation:
Ali A,, de Bie C,A,et al. Detecting long-duration cloud contamination in hyper-temporal NDVI imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,24(1)