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Auteur Yasumasa Hirata |
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Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
[article]
Titre : Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests Type de document : Article/Communication Auteurs : Sadeepa Jayathunga, Auteur ; Toshiaki Owari, Auteur ; Satoshi Tsuyuki, Auteur ; Yasumasa Hirata, Auteur Année de publication : 2020 Article en page(s) : pp 53 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de groupement
[Termes IGN] couvert forestier
[Termes IGN] forêt de feuillus
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] photogrammétrie aérienne
[Termes IGN] photographie aérienne latérale
[Termes IGN] Pinophyta
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Forest canopy structure is an important parameter in multipurpose forest management. An understanding of forest structure plays a particularly important role in the management of uneven-aged forests. The identification of vertical and horizontal variations in forest canopy structure using a ground-based survey is resource intensive, hence often demands for alternative data sources. In this study, one of the advanced remote sensing (RS) techniques, i.e. digital aerial photogrammetry was used to characterize forest canopy structure in a mixed conifer–broadleaf forest. We used aerial imagery acquired with a fixed-wing unmanned aerial vehicle (UAV) platform to produce RS metrics that could be used to classify and map forest structure types at landscape scale. Our results demonstrated that few structural and spectral metrics derived from UAV photogrammetric data, e.g. mean height, standard deviation of height, canopy cover, and percentage broadleaf vegetation cover, could characterize the forest structure across landscapes, particularly at the forest management compartment level, in a limited amount of time. We used cluster analysis for classification of forest structure types and identified five forest structure classes with varying levels of forest canopy structural complexity: (1) short, open-canopy, conifer-dominated structure; (2) short, dense-canopy, broadleaf-dominated structure; (3) tall, closed-canopy, broadleaf-dominated structure; (4) very tall, closed-canopy, conifer-dominated structure with a relatively high degree of variation in canopy height; and (5) very tall, closed-canopy, conifer-dominated structure with a relatively low degree of variation in canopy height. These classes showed relationships with forest management activities (e.g. selection harvesting) and natural disturbances (e.g. typhoon damage). Spatial distribution of forest canopy structural complexity that was revealed in this study is capable of providing important information for forest management planning and habitat modelling. Further, the simple, and flexible data-driven method used in this study to characterize forest structure has the potential to be applied with necessary changes over larger landscapes and different forest types for characterizing and mapping forest structural complexity. Numéro de notice : A2020-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1648900 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648900 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94892
in International Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 53 - 73[article]