000121837 001__ 121837
000121837 005__ 20241125101125.0
000121837 0247_ $$2doi$$a10.3390/rs15030710
000121837 0248_ $$2sideral$$a132222
000121837 037__ $$aART-2023-132222
000121837 041__ $$aeng
000121837 100__ $$aGarcía-Galar, Aitor
000121837 245__ $$aAssessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data
000121837 260__ $$c2023
000121837 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121837 5203_ $$aAmong 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.
000121837 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000121837 590__ $$a4.2$$b2023
000121837 592__ $$a1.091$$b2023
000121837 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b34 / 254 = 0.134$$c2023$$dQ1$$eT1
000121837 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2023$$dQ1
000121837 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b10 / 36 = 0.278$$c2023$$dQ2$$eT1
000121837 591__ $$aREMOTE SENSING$$b16 / 63 = 0.254$$c2023$$dQ2$$eT1
000121837 591__ $$aENVIRONMENTAL SCIENCES$$b110 / 358 = 0.307$$c2023$$dQ2$$eT1
000121837 594__ $$a8.3$$b2023
000121837 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000121837 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M. Teresa$$uUniversidad de Zaragoza
000121837 700__ $$0(orcid)0000-0002-8362-7559$$aDomingo, Darío
000121837 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000121837 773__ $$g15, 3 (2023), 710 [17 pp.]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000121837 8564_ $$s1915132$$uhttps://zaguan.unizar.es/record/121837/files/texto_completo.pdf$$yVersión publicada
000121837 8564_ $$s2687094$$uhttps://zaguan.unizar.es/record/121837/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000121837 909CO $$ooai:zaguan.unizar.es:121837$$particulos$$pdriver
000121837 951__ $$a2024-11-22-11:57:27
000121837 980__ $$aARTICLE