2026-04-28T19:32:39Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/634082024-12-19T09:30:11Zcom_10324_1137com_10324_931com_10324_894col_10324_1222

García Galar, Aitor Lamelas, María Teresa Domingo Ruiz, Darío 2023-12-01T12:52:47Z 2023-12-01T12:52:47Z 2023 Remote Sensing, 2023, Vol. 15, Nº. 3, 710 2072-4292 https://uvadoc.uva.es/handle/10324/63408 10.3390/rs15030710 710 3 Remote Sensing 15 2072-4292 Producción Científica Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m2) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats. Ayudas Margarita Salas, European Union-Next GenerationEU - (grant MS-240621) application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2023 The authors Atribución 4.0 Internacional Nature conservation Naturaleza - Conservación Landscape Ecology Ecología del paisaje Environnement - Gestion - Europe Medio ambiente - Países de la Unión Europea Bosques - Europa Bosques - Gestión - Europa Forest management - Europe Forests and forestry Bosques y silvicultura Forest mapping Optical radar Machine learning Aprendizaje automático Artificial intelligence Bosques - Conservación - España - Navarra LiDAR Sentinel -2 3106 Ciencia Forestal 3106.08 Silvicultura 5902.08 Política del Medio Ambiente 1203.04 Inteligencia Artificial Assessment of oak groves conservation statuses in Natura 2000 sacs with single photon Lidar and Sentinel-2 data info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/2072-4292/15/3/710 SI