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Picea abies --- Ips --- Pest insects --- Picea --- Ips typographus --- Lutte --- France

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Bostryche typographe --- Ips typographus Linné --- Ips typograpus Linné --- Letterzetter (Plantk --- 632.768

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L’épicéa (Picea abies) a été pendant plus d’un siècle la première essence de production en Wallonie. Aujourd’hui pourtant son avenir semble incertain. Les conditions climatiques actuelles ont été la cause de perturbations du milieu forestier. En 2018, la crise de scolyte a mis en avant le caractère inadapté des mesures de gestion actuelles. Pour y remédier, des méthodes de télédétection ont été développées dans l’optique de détecter les épicéas scolytés au stade vert. Le protocole CRSWIR notamment permet l’élaboration de cartes sanitaires de la pessière. L’objectif de ce mémoire est donc dans un premier temps la validation de la carte sanitaire de 2022. Ensuite, l’étude de l’évolution des attaques de scolyte (Ips typographus) durant l’année 2022 a pour but de souligner un schéma systématique de propagation au sein des peuplements d’épicéa. Un troisième objectif a pour but d’identifier les conditions stationnelles favorables aux attaques de scolyte. Pour ce faire, cinquante placettes ont été sélectionnées sur les massifs de Mellier et Anlier (Province du Luxembourg). Sur ces placettes, une validation de pixels « scolytés » et « sains » de la carte sanitaire a été effectuée et les attaques de scolyte ont été schématisées spatialement. De plus, des analyses abiotiques et un relevé floristique ont été réalisés pour déterminer l’aptitude écologique de l’épicéa sur les stations. Les résultats valident la carte sanitaire de 2022, ne permettent pas de mettre en évidence un « schéma d’attaque type » pour anticiper l’évolution du scolyte et n’identifient pas de correspondance entre l’aptitude écologique de l’épicéa et la présence de scolyte.

scolyte, Ips typographus, carte sanitaire, épicéa, Picea abies, télédétection, aptitude écologique, protocole CRSWIR --- Sciences du vivant > Entomologie & lutte antiravageur --- Sciences du vivant > Biologie végétale (sciences végétales, sylviculture, mycologie...)

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The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry.

Research & information: general --- forest road inventory --- total station --- global navigation satellite system --- point cloud --- precision density --- positional accuracy --- efficiency --- mangrove sustainability --- deforestation depletion --- anthropogenic --- natural water balance --- Southeast Asia --- Phoracantha spp. --- unmanned aerial vehicle (UAV) --- multispectral imagery --- vegetation index --- thresholding analysis --- Large Scale Mean-Shift Segmentation (LSMS) --- Random Forest (RF) --- forest mask --- validation --- probability sampling --- remote sensing --- earth observations --- forestry --- accuracy assessment --- forest classification --- forested catchment --- hydrological modeling --- SWAT model --- DEM --- airborne laser scanning --- deep learning --- Landsat --- national forest inventory --- stand volume --- bark beetle --- Ips typographus L. --- pest --- change detection --- forest damage --- spruce --- Sentinel-2 --- damage mapping --- multi-temporal regression --- mangrove --- replanting --- restoration --- analytic hierarchy process --- UAV --- DJI drone --- machine learning --- forest canopy --- canopy gaps --- canopy openings percentage --- satellite indices --- Elastic Net --- beech–fir forests --- pixel-based supervised classification --- random forest --- support vector machine --- gray level cooccurrence matrix (GLCM) --- principal component analysis (PCA) --- WorldView-3 --- wildfires --- MaxENT --- risk modeling --- GIS --- multi-scale analysis --- Yakutia --- Artic --- Siberia --- phenology modelling --- forest disturbance --- forest monitoring --- bark beetle infestation --- forest management --- time series analysis --- satellite imagery --- landsat time series --- growing stock volume --- forest inventory --- harmonic regression --- n/a --- beech-fir forests

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The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry.

Research & information: general --- forest road inventory --- total station --- global navigation satellite system --- point cloud --- precision density --- positional accuracy --- efficiency --- mangrove sustainability --- deforestation depletion --- anthropogenic --- natural water balance --- Southeast Asia --- Phoracantha spp. --- unmanned aerial vehicle (UAV) --- multispectral imagery --- vegetation index --- thresholding analysis --- Large Scale Mean-Shift Segmentation (LSMS) --- Random Forest (RF) --- forest mask --- validation --- probability sampling --- remote sensing --- earth observations --- forestry --- accuracy assessment --- forest classification --- forested catchment --- hydrological modeling --- SWAT model --- DEM --- airborne laser scanning --- deep learning --- Landsat --- national forest inventory --- stand volume --- bark beetle --- Ips typographus L. --- pest --- change detection --- forest damage --- spruce --- Sentinel-2 --- damage mapping --- multi-temporal regression --- mangrove --- replanting --- restoration --- analytic hierarchy process --- UAV --- DJI drone --- machine learning --- forest canopy --- canopy gaps --- canopy openings percentage --- satellite indices --- Elastic Net --- beech-fir forests --- pixel-based supervised classification --- random forest --- support vector machine --- gray level cooccurrence matrix (GLCM) --- principal component analysis (PCA) --- WorldView-3 --- wildfires --- MaxENT --- risk modeling --- GIS --- multi-scale analysis --- Yakutia --- Artic --- Siberia --- phenology modelling --- forest disturbance --- forest monitoring --- bark beetle infestation --- forest management --- time series analysis --- satellite imagery --- landsat time series --- growing stock volume --- forest inventory --- harmonic regression