Reprint
Operationalization of Remote Sensing Solutions for Sustainable Forest Management
Edited by
June 2021
296 pages
- ISBN978-3-0365-0982-2 (Hardback)
- ISBN978-3-0365-0983-9 (PDF)
This book is a reprint of the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management that was published in
Engineering
Environmental & Earth Sciences
Summary
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.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; forestry; hydrological modeling; SWAT model; DEM; airborne laser scanning; deep learning; Landsat; national forest inventory; stand volume; bark beetle; Ips typographus L.; pest; remote sensing; change detection; forest damage; spruce; Sentinel-2; damage mapping; multi-temporal regression; mangrove; Southeast Asia; replanting; restoration; analytic hierarchy process; Sentinel-2; 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; random forest; risk modeling; GIS; multi-scale analysis; Yakutia; Artic; Siberia; phenology modelling; forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; remote sensing; satellite imagery; Sentinel-2; remote sensing; landsat time series; growing stock volume; forest inventory; harmonic regression; random forest; n/a