TIMESAT : A software package for time-series processing and assessment of vegetation dynamics
Eklundh, Lars LU
and Jönsson, Per
(2015)
In Remote Sensing and Digital Image Processing
22.
p.141-158
- Abstract
Large volumes of data from satellite sensors with high time-resolution exist today, e.g. Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), calling for efficient data processing methods. TIMESAT is a free software package for processing satellite time-series data in order to investigate problems related to global change and monitoring of vegetation resources. The assumptions behind TIMESAT are that the sensor data represent the seasonal vegetation signal in a meaningful way, and that the underlying vegetation variation is smooth. A number of processing steps are taken to transform the noisy signals into smooth seasonal curves, including fitting asymmetric Gaussian or double... (More)
Large volumes of data from satellite sensors with high time-resolution exist today, e.g. Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), calling for efficient data processing methods. TIMESAT is a free software package for processing satellite time-series data in order to investigate problems related to global change and monitoring of vegetation resources. The assumptions behind TIMESAT are that the sensor data represent the seasonal vegetation signal in a meaningful way, and that the underlying vegetation variation is smooth. A number of processing steps are taken to transform the noisy signals into smooth seasonal curves, including fitting asymmetric Gaussian or double logistic functions, or smoothing the data using a modified Savitzky-Golay filter. TIMESAT can adapt to the upper envelope of the data, accounting for negatively biased noise, and can take missing data and quality flags into account. The software enables the extraction of seasonality parameters, like the beginning and end of the growing season, its length, integrated values, etc. TIMESAT has been used in a large number of applied studies for phenology parameter extraction, data smoothing, and general data quality improvement. To enable efficient analysis of future Earth Observation data sets, developments of TIMESAT are directed towards processing of high-spatial resolution data from e.g. Landsat and Sentinel-2, and use of spatio-temporal data processing methods.
(Less)
- author
- Eklundh, Lars
LU
and Jönsson, Per
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Remote Sensing and Digital Image Processing
- series title
- Remote Sensing and Digital Image Processing
- volume
- 22
- pages
- 18 pages
- publisher
- Springer International Publishing
- external identifiers
-
- scopus:84980034065
- ISSN
- 22151842
- 15673200
- DOI
- 10.1007/978-3-319-15967-6_7
- language
- English
- LU publication?
- yes
- id
- fe7756f3-b0d5-4bd0-ad18-6c4aec4f1012
- date added to LUP
- 2016-12-21 12:16:16
- date last changed
- 2025-03-08 22:48:28
@inbook{fe7756f3-b0d5-4bd0-ad18-6c4aec4f1012,
abstract = {{<p>Large volumes of data from satellite sensors with high time-resolution exist today, e.g. Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), calling for efficient data processing methods. TIMESAT is a free software package for processing satellite time-series data in order to investigate problems related to global change and monitoring of vegetation resources. The assumptions behind TIMESAT are that the sensor data represent the seasonal vegetation signal in a meaningful way, and that the underlying vegetation variation is smooth. A number of processing steps are taken to transform the noisy signals into smooth seasonal curves, including fitting asymmetric Gaussian or double logistic functions, or smoothing the data using a modified Savitzky-Golay filter. TIMESAT can adapt to the upper envelope of the data, accounting for negatively biased noise, and can take missing data and quality flags into account. The software enables the extraction of seasonality parameters, like the beginning and end of the growing season, its length, integrated values, etc. TIMESAT has been used in a large number of applied studies for phenology parameter extraction, data smoothing, and general data quality improvement. To enable efficient analysis of future Earth Observation data sets, developments of TIMESAT are directed towards processing of high-spatial resolution data from e.g. Landsat and Sentinel-2, and use of spatio-temporal data processing methods.</p>}},
author = {{Eklundh, Lars and Jönsson, Per}},
booktitle = {{Remote Sensing and Digital Image Processing}},
issn = {{22151842}},
language = {{eng}},
pages = {{141--158}},
publisher = {{Springer International Publishing}},
series = {{Remote Sensing and Digital Image Processing}},
title = {{TIMESAT : A software package for time-series processing and assessment of vegetation dynamics}},
url = {{http://dx.doi.org/10.1007/978-3-319-15967-6_7}},
doi = {{10.1007/978-3-319-15967-6_7}},
volume = {{22}},
year = {{2015}},
}