Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning

Forest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017-2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence.

Rosin from Pinus pinaster Portuguese forests shows a regular profile of resin acids

Pinus pinaster forestry occupies >20% of the forest ecosystem area in the continental territory of Portugal with a high impact on the national economy. This species' major derived non-wood product is oleoresin, the raw material for rosin production. Rosin comprises mainly a blend of resin acids and has broad industrial and pharmaceutical applications. Oleoresin production in Portugal has been progressively reduced due to low-cost producers in other countries; currently, it reaches only 2% of the existing P. pinaster trees. To support this value chain, the chemical fingerprint of rosin derived from the national forest requires focused analysis. In the present study, we collected oleoresin within seven geographically distinct pure P. pinaster forests in two consecutive collection years. A high-resolution nuclear magnetic resonance (NMR) method was used to quantify the diversity of resin acids in the corresponding rosin samples. Overall, the acquired data highlighted that the profile of resin acids in P. pinaster rosin produced in Portugal is highly regular, regardless of the forest location, having as the major constituents abietic acid and dehydroabietic acid. The diversity of resin acids is possibly influenced, to a minor extent, by some edaphoclimatic factors.