TOWARDS A TREE COVER CHANGE EARLY WARNING SYSTEM BASED ON SENTINEL-1 DATA AND A NEURAL NETWORK ARCHITECTURE

Abstract. This study aimed at exploring the potential of neural networks composed of convolutional and Long Short-Term Memory (LSTM) layers to handle dense Sentinel-1 data time series to develop a tree cover loss Early Warning System (EWS). The study area was in the Madre de Dios region in Peru that hosts a humid tropical forest. The second objective of this study was to investigate the potential of large free open-source datasets such as those from the GLAD and Geobosques alerts to calibrate and validate the models. The study demonstrated the capacity of the tested NN models to improve the detection of tree cover loss compared to a classical random forest algorithm thanks to their capacity to handle explicitly both spatial and temporal data. The accuracies of the best model compare reasonably well with those from similar studies. However, the observed overestimation of the Cover-loss class in the output map can be mainly linked to the quality of the alert datasets used as input data. Avenues to overcome the identified limits of this preliminary study are presented. This work provides a solid knowledge basis on the potential of a NN-based EWS and opens potential avenues for further improvements.