Awesome

teamlucc

Overview

The 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

  • Parsing metadata files from USGS EarthExplorer
  • Plotting available imagery for an area of interest (AOI), including AOIs that cover more than one path/row
  • Formatting an image order for upload to ESPA system
  • Downloading images from a USGS ESPA order Not working as of 7/1/2014 due to changes in the ESPA system.

• Preprocessing

  • Extraction and file conversion of surface reflectance imagery from the Landsat Climate Data Record (CDR) archive
  • Topographic correction using parallel processing (Goslee, 2011)
  • Cloud fill and gap fill (for SLC-off Landsat 7 scenes), including support for the modified Neighborhood Similar Pixel Interpolator (NSPI) and Geostatistical Neighborhood Similar Pixel Interpolator (GNSPI) by Zhu et al. (2012a, 2012b)
  • Image normalization

• 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)

The toolkit is under active development. Follow the TEAM website for news, and the toolkit project page on github for the latest updates.

Package installation

Installing teamlucc

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 below steps.

As teamlucc is still under development, it is not yet listed on CRAN. The easiest way to install the teamlucc package is using the devtools package by Hadley Wickham.

To install devtools type:

install.packages('devtools')

at the R command prompt. This will fetch the latest version of devtools from CRAN. After installing devtools type:

library(devtools)
install_github('azvoleff/teamlucc')

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.

Install GDAL

teamlucc uses the gdalUtils package to facilitate fast image reprojection and mosaicking. gdalUtils requires having a local GDAL installation. Follow the below steps to install GDAL on your system:

Windows:

Download the 32bit or 64bit OSGeo4W installer.

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).

Edit your environment variables:

1. Add "C:\OSGeo4W\bin" (or "C:\OSGeo4W64\bin" if you installed the 64bit version) to the "PATH" environment variable.
2. Add a new "GDAL_DATA" environment variable equal to "C:\OSGeo4W\share\gdal" (or "C:\OSGeo4W64\share\gdal" for the 64bit version).

Linux (ubuntu):

At a shell prompt, type:

sudo apt-get install gdal-bin libgdal-dev

(optional) Install IDL and ENVI

IDL and ENVI are required for running the CLOUD_REMOVE and CLOUD_REMOVE_FAST cloud fill algorithms in 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.

Using teamlucc

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 teamlucc, including:

• Filtering and downloading Landsat scenes

• Preprocessing imagery and DEMS

• Cloud removal

• Image classification

Installing teamlucc Development Version

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 teamlucc development branch, run:

library(devtools)
install_github('azvoleff/teamlucc', ref="development")

Author Contact Information

Alex Zvoleff
Postdoctoral Associate
Tropical Ecology Assessment and Monitoring (TEAM) Network
Conservation International
2011 Crystal Dr. Suite 500
Arlington, VA 22202
USA

References

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.