Moth and Birch

Sammanfattning: The goal of this thesis is to build a near-real time defoliation detector that could be used, early in the spring, to find out which areas of a birch forest are being affected by an insect outbreak. The importance of a reliable detection at the beginning of the spring lies on the possibility for an early intervention. The data on which the study is based is the Normalized Difference Vegetation Index (NDVI), which is calculated from the 8-days interval measurements, obtained from the MODerate resolution Imaging Spectroradiometer (MODIS). Further available information that is included in the analysis are the forest fraction and the altitude of each pixel. The first step of the analysis is fitting a function to the measurements of each pixel whose parameters could capture the important aspects of the changes in NDVI values. Among them, the final NDVI value that is reached during the summer is of particular interest since an abnormally low value could be the indication of an insect infestation. Different assumptions are made on the error distribution. The first ones, more simplystic, do not manage to counteract the noise. A more complex error structure is thus taken into account, leading to a better estimate which is then used to build the estimator. The idea behind the detector is to identify those pixels for which the NDVI does not reach its high late spring/summer values fast enough, with respect to other pixels and previous years. It is known that the two years, among those available in the dataset, that have suffered a moth outbreak are 2004 and 2013. Hence, the estimation of the fitting function is run for 2000-2003, the detector is tried on 2004 and finally tested on 2013, since for this year a few locations of the outbreak were known. The discrepancy between field data and the results generated by the detector suggests further adjustments that would improve the capacity to detect moth infestation.