Operationalization of Remote Sensing Solutions for Sustainable Forest Management

• Autore: Mozgeris Gintautas
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (296 p.)
• Soggetto topico: Research & information: general
• Soggetto non controllato: accuracy assessment; airborne laser scanning; analytic hierarchy process; anthropogenic; Artic; bark beetle; bark beetle infestation; beech-fir forests; canopy gaps; canopy openings percentage; change detection; damage mapping; deep learning; deforestation depletion; DEM; DJI drone; earth observations; efficiency; Elastic Net; forest canopy; forest classification; forest damage; forest disturbance; forest inventory; forest management; forest mask; forest monitoring; forest road inventory; forested catchment; forestry; GIS; global navigation satellite system; gray level cooccurrence matrix (GLCM); growing stock volume; harmonic regression; hydrological modeling; Ips typographus L.; Landsat; landsat time series; Large Scale Mean-Shift Segmentation (LSMS); machine learning; mangrove; mangrove sustainability; MaxENT; multi-scale analysis; multi-temporal regression; multispectral imagery; n/a; national forest inventory; natural water balance; pest; phenology modelling; Phoracantha spp; pixel-based supervised classification; point cloud; positional accuracy; precision density; principal component analysis (PCA); probability sampling; random forest; Random Forest (RF); remote sensing; replanting; restoration; risk modeling; satellite imagery; satellite indices; Sentinel-2; Siberia; Southeast Asia; spruce; stand volume; support vector machine; SWAT model; thresholding analysis; time series analysis; total station; UAV; unmanned aerial vehicle (UAV); validation; vegetation index; wildfires; WorldView-3; Yakutia
• Persona (resp. second.): BalenovićIvan; MozgerisGintautas
• Sommario/riassunto: 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.
• Titolo autorizzato: Operationalization of Remote Sensing Solutions for Sustainable Forest Management
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557584103321
• Lo trovi qui: Univ. Federico II