# teamlucc [![Build Status](https://travis-ci.org/azvoleff/teamlucc.png)](https://travis-ci.org/azvoleff/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](http://www.teamnetwork.org/) 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](http://www.teamnetwork.org/) for news, and the [toolkit project page on github](https://github.com/azvoleff/teamlucc) 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](http://cran.r-project.org/bin/windows/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](http://cran.r-project.org). The easiest way to install the `teamlucc` package is using the [`devtools`](http://cran.r-project.org/web/packages/devtools/index.html) package by Hadley Wickham. To install `devtools` type: ```R install.packages('devtools') ``` at the R command prompt. This will fetch the latest version of `devtools` from CRAN. After installing `devtools` type: ```R 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](http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86.exe) or [ 64bit](http://download.osgeo.org/osgeo4w/osgeo4w-setup-x86_64.exe) [OSGeo4W](http://trac.osgeo.org/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](http://support.microsoft.com/kb/310519): 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: ``` sh sudo apt-get install gdal-bin libgdal-dev ``` ### (optional) Install IDL and ENVI [IDL](http://www.exelisvis.com/ProductsServices/IDL.aspx) and [ENVI](http://www.exelisvis.com/ProductsServices/ENVIProducts/ENVI.aspx) 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](http://www.azvoleff.com/teamlucc). The webpage includes examples of a number of specific applications of `teamlucc`, including: * [Filtering and downloading Landsat scenes](http://www.azvoleff.com/articles/filtering-landsat-with-teamlucc) * [Preprocessing imagery and DEMS](http://www.azvoleff.com/articles/preprocessing-imagery-with-teamlucc) * [Cloud removal](http://www.azvoleff.com/articles/cloud-removal-with-teamlucc) * [Image classification](http://www.azvoleff.com/articles/image-classification-with-teamlucc) ## 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: ```R library(devtools) install_github('azvoleff/teamlucc', ref="development") ``` ## Author Contact Information [Alex Zvoleff](mailto:azvoleff@conservation.org) 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.