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Landsat time series clustering under modified Dynamic Time Warping
2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 2016Compared with the single remote sensing image, the time series images provide more information of ground objects, which can greatly improve the clustering accuracy. But time series clustering also has many difficulties, such as the impacts of cloud and other sharp noise. The sharp noise impacts time series clustering by affecting the calculation of the
null Yao Zhao +3 more
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Shape selection in Landsat time series: a tool for monitoring forest dynamics
Global Change Biology, 2016AbstractWe present a new methodology for fitting nonparametric shape‐restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades.
Gretchen G, Moisen +6 more
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Regional glacier mapping from time-series of Landsat type data
2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015The Sentinel-2 satellite launch in mid-2015 has similar characteristics as the Landsat TM/ETM+/OLI satellites. Together, these satellites will in the future produce a tremendous quantity of optical images worldwide, with increasing temporal coverage towards higher latitudes due to their polar orbits.
Solveig H. Winsvold +2 more
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On the relevance of radiometric normalization of dense Landsat time series for forest monitoring
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015Several radiometric preprocessing strategies to adjust multiple images are reported in the literature. These include absolute and relative correction methods. Dense time series comprising data from different seasons, have rarely been assessed so far for their sensitivity to the radiometric preprocessing.
Frank Thonfeld +3 more
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Optimal Features Selection for Wetlands Classification Using Landsat Time Series
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Accurate wetlands distribution maps could provide important information for wetland management and protection. While, wetlands have significantly inner-annual variation which makes accurate wetland mapping challenging. Time series data with high temporal resolution, such as MODIS are wildly used in monitoring wetlands, but the coarse resolution cannot ...
Liwei Xing +6 more
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Classifying fruit-tree crops by Landsat-8 time series
2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), 2017Landsat-8 time series were used to classify major crops types in Maipo and Aconcagua valleys, central Chile. In the former valley four fruit-tree crops were classified applying different machine learning techniques on feature sets comprising typical index-based temporal profiles, like those using the normalized difference vegetation index, and the ...
Marco A. Pena +2 more
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Producing Daily Landsat Snow Cover Time-Series Data
2021<p>Snow cover maps are critical for hydrological studies as well as climate change impacts assessment. Remote sensing plays a vital role in providing snow cover information. However, acquisition limitations such as clouds, shadows, or revisiting time limit accessing daily complete snow cover maps obtained from remote sensing.
Fatemeh Zakeri, Gregoire Mariethoz
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Fusing Landsat and SAR time series to detect deforestation in the tropics
Remote Sensing of Environment, 2015Fusion of optical and SAR time series imagery has the potential to improve forest monitoring in tropical regions, where cloud cover limits optical satellite time series observations. We present a novel pixel-based Multi-sensor Time-series correlation and Fusion approach (MulTiFuse) that exploits the full observation density of optical and SAR time ...
Reiche, J. +3 more
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Mapping Forest Disturbance Types in China with Landsat Time Series
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023Lian-Zhi Huo, Ping Tang
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2018
Cloud and cloud shadow detection is an inevitable preprocessing step for analyzing Landsat time series. This chapter provides a comprehensive review of all the relevant algorithms. Based on the number of Landsat images used in the algorithm, we categorize the algorithms into two groups: single-date algorithms and multitemporal algorithms.
Zhe Zhu +3 more
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Cloud and cloud shadow detection is an inevitable preprocessing step for analyzing Landsat time series. This chapter provides a comprehensive review of all the relevant algorithms. Based on the number of Landsat images used in the algorithm, we categorize the algorithms into two groups: single-date algorithms and multitemporal algorithms.
Zhe Zhu +3 more
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