Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
Material type:
TextLanguage: English Publication details: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021Description: 1 online resource (276 p.)Content type: - text
- computer
- online resource
- 9783036505688
- 9783036505695
- books978-3-0365-0569-5
- Geography
- Research and information: general
- above-ground biomass
- aboveground biomass
- AGB estimation and mapping
- allometric equation
- ALOS DSM
- ALOS-2 L band SAR
- bark roughness
- bioenergy
- biomass density
- biomass estimation
- DBH
- diameter at breast height
- ecosystem services
- forest AGC estimation
- forest biomass estimation
- forest growing stock volume
- forest inventory data
- forest succession
- forest type
- GEOMON
- GIS
- individual tree detection
- landsat
- Landsat 8 OLI
- Landsat dataset
- leaf area index
- LiDAR
- machine learning
- machine learning algorithms
- mangroves
- map quality
- multiple ES interactions
- multisource remote sensing
- nondestructive method
- norway spruce
- ordinary kriging
- Pinus massoniana plantations
- plant area index
- random forest
- remote sensing
- seasonal images
- sentinel 2
- Sentinel-1 C band SAR
- Sentinel-2 MSI
- Sentinel-2A
- shrubs biomass
- spatiotemporal evolution
- SPOT6 imagery
- stand volume
- stepwise regression
- subtropical forest
- subtropical forests
- support vector machine for regression
- synergy
- terrestrial laser scanning
- trade-off
- tree height
- UAV LiDAR
- urban vegetation
- valley basin
- variable selection
- vegetation indices
- WorldView-2
- Xuzhou
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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.
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