LEADER 04440nam 2201117z- 450 001 9910557474803321 005 20231214133206.0 035 $a(CKB)5400000000043050 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76617 035 $a(EXLCZ)995400000000043050 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (276 p.) 311 $a3-0365-0568-7 311 $a3-0365-0569-5 330 $aThis 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. 606 $aResearch & information: general$2bicssc 606 $aGeography$2bicssc 610 $aAGB estimation and mapping 610 $amangroves 610 $aUAV LiDAR 610 $aWorldView-2 610 $aterrestrial laser scanning 610 $aabove-ground biomass 610 $anondestructive method 610 $aDBH 610 $abark roughness 610 $aLandsat dataset 610 $aforest AGC estimation 610 $arandom forest 610 $aspatiotemporal evolution 610 $aaboveground biomass 610 $avariable selection 610 $aforest type 610 $amachine learning 610 $asubtropical forests 610 $aLandsat 8 OLI 610 $aseasonal images 610 $astepwise regression 610 $amap quality 610 $asubtropical forest 610 $aurban vegetation 610 $abiomass estimation 610 $aSentinel-2A 610 $aXuzhou 610 $aforest biomass estimation 610 $aforest inventory data 610 $amultisource remote sensing 610 $abiomass density 610 $aecosystem services 610 $atrade-off 610 $asynergy 610 $amultiple ES interactions 610 $avalley basin 610 $anorway spruce 610 $aLiDAR 610 $aallometric equation 610 $aindividual tree detection 610 $atree height 610 $adiameter at breast height 610 $aGEOMON 610 $aALOS-2 L band SAR 610 $aSentinel-1 C band SAR 610 $aSentinel-2 MSI 610 $aALOS DSM 610 $astand volume 610 $asupport vector machine for regression 610 $aordinary kriging 610 $aforest succession 610 $aleaf area index 610 $aplant area index 610 $amachine learning algorithms 610 $aforest growing stock volume 610 $aSPOT6 imagery 610 $aPinus massoniana plantations 610 $asentinel 2 610 $alandsat 610 $aremote sensing 610 $aGIS 610 $ashrubs biomass 610 $abioenergy 610 $avegetation indices 615 7$aResearch & information: general 615 7$aGeography 700 $aAranha$b José$4edt$01318487 702 $aAranha$b José$4oth 906 $aBOOK 912 $a9910557474803321 996 $aApplications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass$93033317 997 $aUNINA