Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

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 Remot...

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Bibliographic Details
Other Authors: Aranha, José (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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520 |a 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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546 |a English 
650 7 |a Research & information: general  |2 bicssc 
650 7 |a Geography  |2 bicssc 
653 |a AGB estimation and mapping 
653 |a mangroves 
653 |a UAV LiDAR 
653 |a WorldView-2 
653 |a terrestrial laser scanning 
653 |a above-ground biomass 
653 |a nondestructive method 
653 |a DBH 
653 |a bark roughness 
653 |a Landsat dataset 
653 |a forest AGC estimation 
653 |a random forest 
653 |a spatiotemporal evolution 
653 |a aboveground biomass 
653 |a variable selection 
653 |a forest type 
653 |a machine learning 
653 |a subtropical forests 
653 |a Landsat 8 OLI 
653 |a seasonal images 
653 |a stepwise regression 
653 |a map quality 
653 |a subtropical forest 
653 |a urban vegetation 
653 |a biomass estimation 
653 |a Sentinel-2A 
653 |a Xuzhou 
653 |a forest biomass estimation 
653 |a forest inventory data 
653 |a multisource remote sensing 
653 |a biomass density 
653 |a ecosystem services 
653 |a trade-off 
653 |a synergy 
653 |a multiple ES interactions 
653 |a valley basin 
653 |a norway spruce 
653 |a LiDAR 
653 |a allometric equation 
653 |a individual tree detection 
653 |a tree height 
653 |a diameter at breast height 
653 |a GEOMON 
653 |a ALOS-2 L band SAR 
653 |a Sentinel-1 C band SAR 
653 |a Sentinel-2 MSI 
653 |a ALOS DSM 
653 |a stand volume 
653 |a support vector machine for regression 
653 |a ordinary kriging 
653 |a forest succession 
653 |a leaf area index 
653 |a plant area index 
653 |a machine learning algorithms 
653 |a forest growing stock volume 
653 |a SPOT6 imagery 
653 |a Pinus massoniana plantations 
653 |a sentinel 2 
653 |a landsat 
653 |a remote sensing 
653 |a GIS 
653 |a shrubs biomass 
653 |a bioenergy 
653 |a vegetation indices 
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