Prediction of Canopy Cover Loss in German Spruce Forests Using a Spatio-Temporal Approach

Abstract

In the last decade, German forests have been decimated because of extreme events such as drought and windthrow, and bark beetle infestations that occur in the aftermath, primarily in monoculture Norway spruce stands. It is essential for decision makers in forest management to have an educated estimation of potential future loss. We have developed a model to predict future canopy cover loss in German spruce forests. Since, past canopy cover loss is a key predictor, we adapt the spatio-temporal matrix (STM) method used for predicting urban growth, to work with a canopy-cover-loss time-series product based on earth observation data. We configure a hybrid neural network model using the STM, its percentiles along with climatic and topographic data to produce the probability information of canopy cover loss in German spruce forests in the next year. The prediction results from the model show a good capacity of prediction, as validation results present an AUC of the ROC space as high as 82.3%. Our results show that future canopy cover loss can be predicted with reasonable accuracy using open-access earth-observation time-series data supplemented by environmental data without the need for site specific in situ data collection.