04440nam 2201117z- 450 991055747480332120231214133206.0(CKB)5400000000043050(oapen)https://directory.doabooks.org/handle/20.500.12854/76617(EXLCZ)99540000000004305020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplications of Remote Sensing Data in Mapping of Forest Growing Stock and BiomassBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (276 p.)3-0365-0568-7 3-0365-0569-5 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.Research & information: generalbicsscGeographybicsscAGB estimation and mappingmangrovesUAV LiDARWorldView-2terrestrial laser scanningabove-ground biomassnondestructive methodDBHbark roughnessLandsat datasetforest AGC estimationrandom forestspatiotemporal evolutionaboveground biomassvariable selectionforest typemachine learningsubtropical forestsLandsat 8 OLIseasonal imagesstepwise regressionmap qualitysubtropical foresturban vegetationbiomass estimationSentinel-2AXuzhouforest biomass estimationforest inventory datamultisource remote sensingbiomass densityecosystem servicestrade-offsynergymultiple ES interactionsvalley basinnorway spruceLiDARallometric equationindividual tree detectiontree heightdiameter at breast heightGEOMONALOS-2 L band SARSentinel-1 C band SARSentinel-2 MSIALOS DSMstand volumesupport vector machine for regressionordinary krigingforest successionleaf area indexplant area indexmachine learning algorithmsforest growing stock volumeSPOT6 imageryPinus massoniana plantationssentinel 2landsatremote sensingGISshrubs biomassbioenergyvegetation indicesResearch & information: generalGeographyAranha Joséedt1318487Aranha JoséothBOOK9910557474803321Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass3033317UNINA