Operationalization of Remote Sensing Solutions for Sustainable Forest Management

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 stud...

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Detalles Bibliográficos
Otros Autores:	Mozgeris, Gintautas (Editor), Balenović, Ivan (Otro)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Materias:	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
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009654736806719