From June 29th to July 2rd, experts from Department of Remote Sensing, Würzburg University, met colleagues of the LaVaCCA project from KRASS, SIC-ICWC, and Al´Farabi University, at the office of KRASS in Urgench, Uzbekistan. During this meeting, the second R/GIS course was done in the scope of LaVaCCA´s capacity building program. This course and aimed at fostering the methodological skills of the participants in using R. Participants have already gained first analytical skills in R during the first training in Almaty, Kazakhstan in February 2015.
A new concept for this course was applied, by letting the participants select and suggest topics, according to their specific methodological requirements in the LaVaCCA project. The course was then set up accordingly and held by Dr. Fabian Löw and Christian Bauer from Würzburg University. Three major topics were focused on:
- Working with raster data, satellite image pre-processing in R (atmospherically calibration, stacking, sub-setting of large data sets, calculation of vegetation indices for assessing vegetation dynamics and within-field assessments of vegetation growth)
- Supervised image classification for crop type mapping, based on multi-temporal Landsat images and Random Forest algorithm (crop type distribution and acreage), accuracy assessments (confusion matrices), object-based classification in R.
After three intensive days of training in R, the participants explored the subject of the LaVaCCA program “on-ground”: a field survey was organized and performed around Urgench city, where staffers from SIC-ICWC maintain several test sites on cotton, wheat, and rice fields, which are the major crops in this region. On these selected fields, SIC-ICWC staffers assess biophysical parameters (fPAR, biomass) which will later be incorporated into yield models. The R course enables them to analyze important agricultural parameter (crop yield, crop acreage) with one of the most advanced programming languages. Staffers from KRASS also maintain soil salinity sampling test sites in this region, the course enables them to perform regression analysis and supervised land use classifications.