teamlucc package is designed to facilitate analysis of land use and cover
change (LUCC) around the monitoring sites of the Tropical Ecology Assessment
and Monitoring (TEAM) Network. The TEAM Network
is a global network of sites in tropical forests wth standardized real-time
data collection designed to measure tropical forest responses to climate
variability and change, land cover and land use change, and other threats.
teamlucc assists with processing and analysis of remote sensing imagery.
teamlucc supports a range of preprocessing steps and analyses, including:
Image selection from USGS archive
Calculation of vegetation indices and image texture measures from grey-level co-occurrence matrices (GLCMs)
Image classification using random forests or support vector machines
Change detection using the Change Vector Analysis in Posterior Probability Space (CVAPS) and Double Window Flexible Pace Search (DFPS) algorithms (Chen et al. 2011)
Accuracy assessment using user's, producer's and overall accuracies, in addition to quantity agreement and disagreement (Pontius and Millones, 2011)
NOTE: If you are installing on Windows, you will need to install the
appropriate version of Rtools
for your version of R (as
teamlucc contains C++ code) before you follow the
at the R command prompt. This will fetch the latest version of
CRAN. After installing
at the R prompt to install the latest version of
teamlucc. Typing the above
command will also work if you already have
teamlucc installed and want to
install an updated version of the package.
teamlucc uses the
gdalUtils package to facilitate fast image reprojection
gdalUtils requires having a local GDAL installation. Follow
the below steps to install GDAL on your system:
Run the installer. Choose the "Express Desktop Install". On the "Select Packages" screen, ensure the GDAL screen package is checked. You can uncheck the boxes for QGIS and GRASS GIS if you don't want them installed (though I highly recommend QGIS).
At a shell prompt, type:
sudo apt-get install gdal-bin libgdal-dev
required for running the CLOUD_REMOVE and CLOUD_REMOVE_FAST cloud fill
teamlucc (there are also two native R cloud fill routines that
can be used without an IDL license). IDL and ENVI are also needed to run the
Landsat 7 SLC-off gap fill routine.
For more information on using
teamlucc, see the online help in R, and the
teamlucc webpage. The webpage includes
examples of a number of specific applications of
If you want the very latest version of
teamlucc, you can install the
development version. Be aware this version might not install as it is not as
well tested as the stable version. To install from the
library(devtools) install_github('azvoleff/teamlucc', ref="development")
Alex Zvoleff Postdoctoral Associate Tropical Ecology Assessment and Monitoring (TEAM) Network Conservation International 2011 Crystal Dr. Suite 500 Arlington, VA 22202 USA
Chen, J., Chen, X., Cui, X., Chen, J., 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters 8, 317--321.
Goslee, S.C., 2011. Analyzing remote sensing data in R: the landsat package. Journal of Statistical Software 43, 1--25.
Pontius, R.G., Millones, M., 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing 32, 4407--4429.
Zhu, X., Gao, F., Liu, D., Chen, J., 2012a. A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images. Geoscience and Remote Sensing Letters, IEEE 9, 521--525.
Zhu, X., Liu, D., Chen, J., 2012b. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment 124, 49--60.
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