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Evaluating the vegetation change tracker for mapping forest cover changes in mountain environments using RapidEye imagery

Earth observation has become a key instrument for forest management and monitoring forest cover dynamics, particularly since the opening of the Landsat archives in 2008. As a result, highly automated processing chains have been developed which allow the reconstruction of forest disturbance histories and reveal important insights in the underlying change processes for many places on earth. However, the spatial scale of forest disturbances may be too broad in complex mountain regions where forest fulfills an important protection function and forest management interventions require detailed information on forest cover changes. The RapidEye satellite system provides imagery at 6.5 m spatial resolution ideally matching the scale required to map small-scale disturbances, however, it is not clear if the processing chains developed for Landsat data are transferrable to high-resolution imagery for mapping forest changes in mountainous areas. 
In our study, the overall objective was to analyze if the Vegetation Change Tracker (VCT) algorithm that is based on spectral-temporal properties of land cover and forest change processes (Huang et al., 2008, Huang et al., 2009, Huang et al., 2010) can be adapted to multi-year RapidEye data to map forest changes in an alpine region. 
The VCT follows a sequence of processing steps (Huang et al., 2008, Huang et al., 2009, Huang et al., 2010): 1.) Mapping of forest samples by identifying the characteristic forest peak in the spectral histogram using a moving window approach. 2.) Consistency check of the forest samples by integrating additional land cover information. 3.) Generating a forest probability layer using the consistency checked forest samples and a z-transformation to generate integrated forest scores (IFZ). 4.) Defining rules based on the temporal profiles of IFZ to identify persisting forest (PF), persisting non-forest (PNF) and disturbed forest (DF) as well as the year of disturbance. 
To map inter-annual forest changes, we used RapidEye images acquired on 26.07.2009, 31.07.2010, 29.06.2011, 17.08.2012 and 21.08.2013 covering a common area of around 64,000 ha within South Tyrol, Italy. RapidEye images were orthorectified, converted from DNs to top-of-atmosphere reflectances and corrected for illumination differences using the empirical rotation model (Tan et al., 2013). We then investigated the sensitivity of spectral bands and window sizes in identifying the forest peak. Additionally, we assessed the impact of different information sources for the consistency check ranging from integrating existing forest maps to image-derived layers of peak location. We also evaluated the impact of choosing different band combinations to generate IFZ values for subsequent change detection and defined temporal rules to map the classes PF, PNF and DF following the suggestions of Huang et al. (2009). To assess the accuracy of forest disturbance map, we used a stratified random sampling approach and visually interpreted the samples using orthphotos from 2008 and 2011 as well as the RapidEye imagery. 
All visible bands showed clear forest peaks in the histograms for small window sizes ( 3) or strongly varying IFZ values for all years were assigned to all non-forest land cover types. Clouds and cloud shadow affected the temporal profiles to some extent but could distinguished from forest changes where the information before and after the year of disturbance was available. The overall accuracy of the forest maps was high with similar accuracies among the three classes (PF: 97%, PNF: 95%, DF: 83%) and among all years of disturbances. 
Our results suggest that Landsat-based forest change algorithms like VCT can be transferred to RapidEye imagery which offer a limited number of 5 bands and cover only short time periods. Forest cover changes can be derived with high accuracies in complex mountain regions. The approach has great potential for operational forest change mapping as only limited preprocessing is required. While RapidEye is a commercial satellite system, high resolution will be freely available with the launch of Sentinel-2 data offering additional bands in the middle infrared which will possibly improve forest training data.