01147nam--2200385---450-99000246646020331620100121125010.088-15-10290-6000246646USA01000246646(ALEPH)000246646USA0100024664620050906d2005----km-y0itay50------baitaITy|||z|||001yy<<Il>> temposaggio di psicologia sperimentaleGiovanni Bruno VicarioBolognaIl Mulino2005271 p.22 cmRicercaPsicologia2001RicercaPsicologiaTempoPercezioneBNCF153.753VICARIO,Giovanni Bruno573799ITsalbcISBD990002466460203316II.3. 2666(VI ps B 1187)47218 G.II.3.00180097II.3. 2666a220729 L.M.II.3.00255201BKUMACHIARA9020050906USA011121COPAT29020051114USA011333ANNAMARIA9020100121USA011250Tempo1059309UNISA04457nam 2201129z- 450 991055747480332120220111(CKB)5400000000043050(oapen)https://directory.doabooks.org/handle/20.500.12854/76617(oapen)doab76617(EXLCZ)99540000000004305020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplications of Remote Sensing Data in Mapping of Forest Growing Stock and BiomassBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online 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.GeographybicsscResearch and information: generalbicsscabove-ground biomassaboveground biomassAGB estimation and mappingallometric equationALOS DSMALOS-2 L band SARbark roughnessbioenergybiomass densitybiomass estimationDBHdiameter at breast heightecosystem servicesforest AGC estimationforest biomass estimationforest growing stock volumeforest inventory dataforest successionforest typeGEOMONGISindividual tree detectionlandsatLandsat 8 OLILandsat datasetleaf area indexLiDARmachine learningmachine learning algorithmsmangrovesmap qualitymultiple ES interactionsmultisource remote sensingnondestructive methodnorway spruceordinary krigingPinus massoniana plantationsplant area indexrandom forestremote sensingseasonal imagessentinel 2Sentinel-1 C band SARSentinel-2 MSISentinel-2Ashrubs biomassspatiotemporal evolutionSPOT6 imagerystand volumestepwise regressionsubtropical forestsubtropical forestssupport vector machine for regressionsynergyterrestrial laser scanningtrade-offtree heightUAV LiDARurban vegetationvalley basinvariable selectionvegetation indicesWorldView-2XuzhouGeographyResearch and information: generalAranha Joséedt1318487Aranha JoséothBOOK9910557474803321Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass3033317UNINA