Online deforestation detection

Deforestation detection using satellite images can make an important contribution to forest management. Current approaches can be broadly divided into those that compare two images taken at similar periods of the year and those that monitor changes by using multiple images taken during the growing season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that builds on the latter category by implementing a year-long, continuous, time-series based approach to monitoring images. This algorithm was developed for 30m resolution, 16-day frequency reflectance data from the Landsat satellite. In this work we adapt the algorithm to 1km, 16-day frequency reflectance data from the modis sensor aboard the Terra satellite. The CMFDA algorithm is composed of two submodels which are fitted on a pixel-by-pixel basis. The first estimates the amount of surface reflectance as a function of the day of the year. The second estimates the ocurrence of a deforestation event by comparing the last few predicted and real reflectance values. For this comparison, the reflectance observations for six different bands are first combined into a forest index. Real and predicted values of the forest index are then compared and high absolute differences for consecutive observation dates are flagged as deforestation events. Our adapted algorithm also uses the two model framework. However, since the modis 13A2 dataset used, includes reflectance data for different spectral bands than those included in the Landsat dataset, we cannot construct the forest index. Instead we propose two contrasting approaches: a multivariate and an index approach similar to that of CMFDA. In the first prediction errors (form first model) for selected bands are first compared against, band-specific, thresholds to produce one deforestation flag per band. The multiple deforestation flags are then combined using an or rule to produce a general deforestation flag. In the second approach, as with the CMFDA algorithm, the reflectance observations for selected bands are combined into an index. We chose to use the local Mahalanobis distance of prediction errors for the selected bands as our index. This index will measure how atypical a given multivariate predicted error is therby helping us to detect when an intervention to the data generating mechanism has occurred, i.e. a deforestation event. We found that, in general, the multivariate approach obtained slightly better performance although the index approach, based on the Mahalanobis distance, was better at detecting deforestation early. Our training approach was different to that used in Zhu et al. (2012) in that the lower resolution of the reflectance data and the pseudo ground-truth deforestation data used allowed us to select a much larger and diverse area including nine sites with different types of forest and deforestation, and training and prediction windows spanning 2003-2010. In Zhu et al. (2012) reflectance and deforestation information from only one site and only the 2001-2003 period is used. This approach allowed us to make conclusions about how the methodology generalizes accross space (specifically pixels) and accross the day of the year. In the CMFDA and our adapted CMFDA methodology a single (possibly multivariate) threshold is applied to the prediction errors irrespective of the location or