Reprint
Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
Edited by
July 2021
276 pages
- ISBN978-3-0365-0568-8 (Hardback)
- ISBN978-3-0365-0569-5 (PDF)
This is a Reprint of the Special Issue Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass that was published in
Biology & Life Sciences
Environmental & Earth Sciences
Summary
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
AGB estimation and mapping; mangroves; UAV LiDAR; WorldView-2; terrestrial laser scanning; above-ground biomass; nondestructive method; DBH; bark roughness; Landsat dataset; forest AGC estimation; random forest; spatiotemporal evolution; aboveground biomass; variable selection; forest type; machine learning; subtropical forests; aboveground biomass; Landsat 8 OLI; seasonal images; stepwise regression; map quality; subtropical forest; urban vegetation; biomass estimation; Sentinel-2A; stepwise regression; Xuzhou; forest biomass estimation; forest inventory data; multisource remote sensing; random forest; biomass density; ecosystem services; trade-off; synergy; multiple ES interactions; valley basin; norway spruce; LiDAR; allometric equation; individual tree detection; tree height; diameter at breast height; GEOMON; ALOS-2 L band SAR; Sentinel-1 C band SAR; Sentinel-2 MSI; ALOS DSM; stand volume; support vector machine for regression; ordinary kriging; forest succession; LiDAR; leaf area index; plant area index; machine learning algorithms; forest growing stock volume; SPOT6 imagery; Pinus massoniana plantations; sentinel 2; landsat; remote sensing; GIS; shrubs biomass; bioenergy; vegetation indices