Interactive Animation to Visually Explore Time Series of Satellite Imagery

2006 ◽  
pp. 71-82
Author(s):  
Connie A. Blok
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Interactive Animation

Crop monitoring technology based on time series of satellite imagery

2020 ◽  
Author(s):  
Nataliia Kussul ◽  
Andrii Shelestov ◽  
Bohdan Yailymov ◽  
Hanna Yailymova ◽  
Mykola Lavreniuk ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Monitoring Technology ◽  
Crop Monitoring

scholarly journals Characterizing Flood Impact on Swiss Floodplains Using Interannual Time Series of Satellite Imagery

2020 ◽  
Vol 13 ◽  
pp. 1479-1493
Author(s):  
Gillian Milani ◽  
Mathias Kneubuhler ◽  
Diego Tonolla ◽  
Michael Doering ◽  
Michael E. Schaepman
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Flood Impact

scholarly journals Validation of a Forage Production Index (FPI) Derived from MODIS fCover Time-Series Using High-Resolution Satellite Imagery: Methodology, Results and Opportunities

2015 ◽  
Vol 7 (9) ◽  
pp. 11525-11550 ◽  
Author(s):  
Antoine Roumiguié ◽  
Anne Jacquin ◽  
Grégoire Sigel ◽  
Hervé Poilvé ◽  
Olivier Hagolle ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Imagery ◽  
Forage Production ◽  
Production Index ◽  
High Resolution Satellite Imagery

scholarly journals SCIDB BASED FRAMEWORK FOR STORAGE AND ANALYSIS OF REMOTE SENSING BIG DATA

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 43-47
Author(s):  
A. Joshi ◽  
E. Pebesma ◽  
R. Henriques ◽  
M. Appel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Data Storage ◽  
Satellite Imagery ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Series Analysis

Abstract. Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size.

scholarly journals Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

2020 ◽  
Vol 12 (11) ◽  
pp. 1740
Author(s):  
Matthew J. McCarthy ◽  
Brita Jessen ◽  
Michael J. Barry ◽  
Marissa Figueroa ◽  
Jessica McIntosh ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Large Scale ◽  
Detection Methods ◽  
Mangrove Forests ◽  
Wetland Ecosystems ◽  
Southwest Florida ◽  
Hurricane Irma

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.

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