phenopix-package: A package to process images of a vegetation cover
In gianlucafilippa/phenopix: Process Digital Images of a Vegetation Cover
Description
Details
Author(s)
References
See Also
Description
The package provides functions to process digital images, depict greennes index
trajectories and extract relevant phenological stages.
Details
Package: phenopix
Type: Package
Version: 1.0
Date: 2014-08-21
License: GPL-2
The package provides functions to process digital images, depict greennes index
trajectories and extract relevant phenological stages. The first step of the work
is to set a region of interest on the images. The function DrawROI does the work.
More than one ROI can be specified.
Second step is extracting information from the ROI(s). The function extractVIs
computes several vegetation indexes on image pixels falling within the ROI(s).
The function works recursively within a folder so that all images can be processed
and a time series of the computed indexes is extracted. A specific function
extractDateFilename retrives a timestamp from the filename of the images.
Time series of green, red and blue chromatic coordinates and brightenss are returned
for each ROI of the image.
Third step is data filtering. The function autoFilter provides several filtering
methods that can be used one at a time or in sequence.
Fourth step is fitting a curve to the data. Five methods have been included in the package.
The function SplineFit fits a smoothed cubic spline to the data. The remaining four
methods are based on the optimization of different double logistic equations retrieved
from the recent literature. An uncertainty calculation is provided, based on the residuals between
observed and predicted values. The variability in the residuals is used to generate random noise,
that is then added to raw data in a bootstrap procedure. From this procedure an ensemble of equation
parameters and/or of curves are generated.
Fifth step is the extraction of phenological thresholds. Five methods have been
included to extract phenological dates. One is based on thresholds on the spline
curve and one on breakpoints analysis, whereas other methods work on inflection points
of the derivatives in various ways.
Uncertainty on curve fitting is extended to threshold extraction, so that also
for this step uncertainty is easily estimated.
The package offers a variety of fittings and thresholdings so to be as flexible
as possible in order to apply to very different boreal (sofar) ecosystems, ranging
from high latitude/altitude grasslands to tropical forests.
Ecosystems that show multiple seasonal peaks can be splitted in subseason with
the CutSeason function and processed separately.
Dedicated plotting functions provide an easy way to look at fitting and thresholding
with annotated graphs.
The package is beeing tested on the PHENOCAM dataset (http://phenocam.sr.unh.edu/webcam/),
and constantly debugged.
In the example section the user can find a walk-through to understand main functions,
from the filtering step forward. ROI selection and index calculations are excluded
from the example because they require the use of locator from the user.
Author(s)
Gianluca Filippa, Edoardo Cremonese, Mirco Migliavacca, ...
Maintainer: Gianluca Filippa <gian.filippa@gmail.com>
References
Gu L, Post WM, Baldocchi D, Black TA, Suyker AE, Verma SB, Vesala T,
Wofsy SC. (2009) Characterizing the Seasonal Dynamics of Plant Community
Photosynthesis Across a Range of Vegetation Types. In: Phenology of Ecosystem
Processes (Ed: Noormets A, Springer New York), pp 35-58.
Klosterman ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L,
Norman R, Friedl MA, Richardson A D (2014) Evaluating remote sensing of deciduous
forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences,
11, 4305-4320, doi:10.5194/bg-11-4305-2014.
Migliavacca et al 2011
Papale et al.
Sonnentag et al.
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003)
Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471-475.
See Also
greenbrown.r-forge.r-project.org
gianlucafilippa/phenopix documentation built on Nov. 4, 2019, 1:06 p.m.
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Description Details Author(s) References See Also
Description
The package provides functions to process digital images, depict greennes index trajectories and extract relevant phenological stages.
Details
| Package: | phenopix |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-08-21 |
| License: | GPL-2 |
The package provides functions to process digital images, depict greennes index
trajectories and extract relevant phenological stages. The first step of the work
is to set a region of interest on the images. The function DrawROI does the work.
More than one ROI can be specified.
Second step is extracting information from the ROI(s). The function extractVIs
computes several vegetation indexes on image pixels falling within the ROI(s).
The function works recursively within a folder so that all images can be processed
and a time series of the computed indexes is extracted. A specific function
extractDateFilename retrives a timestamp from the filename of the images.
Time series of green, red and blue chromatic coordinates and brightenss are returned
for each ROI of the image.
Third step is data filtering. The function autoFilter provides several filtering
methods that can be used one at a time or in sequence.
Fourth step is fitting a curve to the data. Five methods have been included in the package.
The function SplineFit fits a smoothed cubic spline to the data. The remaining four
methods are based on the optimization of different double logistic equations retrieved
from the recent literature. An uncertainty calculation is provided, based on the residuals between
observed and predicted values. The variability in the residuals is used to generate random noise,
that is then added to raw data in a bootstrap procedure. From this procedure an ensemble of equation
parameters and/or of curves are generated.
Fifth step is the extraction of phenological thresholds. Five methods have been included to extract phenological dates. One is based on thresholds on the spline curve and one on breakpoints analysis, whereas other methods work on inflection points of the derivatives in various ways. Uncertainty on curve fitting is extended to threshold extraction, so that also for this step uncertainty is easily estimated.
The package offers a variety of fittings and thresholdings so to be as flexible
as possible in order to apply to very different boreal (sofar) ecosystems, ranging
from high latitude/altitude grasslands to tropical forests.
Ecosystems that show multiple seasonal peaks can be splitted in subseason with
the CutSeason function and processed separately.
Dedicated plotting functions provide an easy way to look at fitting and thresholding
with annotated graphs.
The package is beeing tested on the PHENOCAM dataset (http://phenocam.sr.unh.edu/webcam/),
and constantly debugged.
In the example section the user can find a walk-through to understand main functions, from the filtering step forward. ROI selection and index calculations are excluded from the example because they require the use of locator from the user.
Author(s)
Gianluca Filippa, Edoardo Cremonese, Mirco Migliavacca, ...
Maintainer: Gianluca Filippa <gian.filippa@gmail.com>
References
Gu L, Post WM, Baldocchi D, Black TA, Suyker AE, Verma SB, Vesala T, Wofsy SC. (2009) Characterizing the Seasonal Dynamics of Plant Community Photosynthesis Across a Range of Vegetation Types. In: Phenology of Ecosystem Processes (Ed: Noormets A, Springer New York), pp 35-58.
Klosterman ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl MA, Richardson A D (2014) Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences, 11, 4305-4320, doi:10.5194/bg-11-4305-2014.
Migliavacca et al 2011
Papale et al.
Sonnentag et al.
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471-475.
See Also
greenbrown.r-forge.r-project.org
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