Title: Online deforestation detection,
Abstract: 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 occurrence 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.