CHESTNUT COVER AUTOMATIC CLASSIFICATION THROUGH LIDAR AND SENTINEL-2 MULTI-TEMPORAL DATA

Abstract. Chestnut (Castanea sativa Mill.) managed forests in Galicia (Northwestern Spain) have important cultural, economic and ecosystem values. However, due to rural exodus chestnut stands are being degraded. In order to take restoration and conservation measures knowledge of these forests' location, expanse and stage is needed. The available Spanish official cartography is based on photointerpretation which is inaccurate in terms of chestnut forest location and classification. However, remote sensing has recently been proven to be an effective tool for this purpose. Sentinel 2 multi-temporal classification is recently acquiring importance as a method to classify tree species. This project intends to detect chestnut forests using LiDAR and Sentinel 2 multi-temporal data and to compare these results with those obtained using the official cartography. It also intends to assess how the use of different phenological stages could improve classification results. The results obtained provide an overall accuracy of 76% when a three-month combination is used: (March, July and September) leaf-off stage, flowering and leaf-on stage. Overlapping of the current map and the official cartography lead to an accuracy and precision increase; highlighting the utility of the presented methodology to acquire knowledge about chestnut forests location.