LEADER 04465nam 2201177z- 450 001 9910367567003321 005 20231214133029.0 010 $a3-03921-210-9 035 $a(CKB)4100000010106076 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/58170 035 $a(EXLCZ)994100000010106076 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing of Above Ground Biomass 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (264 p.) 311 $a3-03921-209-5 330 $aAbove ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local?regional?global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring. 610 $aNDLMA 610 $amulti-angle remote sensing 610 $aTerraSAR-X 610 $aabove ground biomass 610 $astem volume 610 $aregression analysis 610 $aground-based remote sensing 610 $asensor fusion 610 $apasture biomass 610 $agrazing management 610 $alivestock 610 $amixed forest 610 $aSPLSR 610 $aestimation accuracy 610 $aBidirectional Reflectance Distribution Factor 610 $aforage crops 610 $aLand Surface Phenology 610 $aclimate change 610 $avegetation index 610 $adry biomass 610 $amapping 610 $arangeland productivity 610 $avegetation indices 610 $aerror analysis 610 $abroadleaves 610 $aremote sensing 610 $aapplicability evaluation 610 $aultrasonic sensor 610 $achlorophyll index 610 $aalpine meadow grassland 610 $aforest biomass 610 $aanthropogenic disturbance 610 $afractional vegetation cover 610 $aalpine grassland conservation 610 $acarbon mitigation 610 $aconifer 610 $ashort grass 610 $agrazing exclusion 610 $aMODIS time series 610 $arandom forest 610 $aaboveground biomass 610 $aNDVI 610 $aAquaCrop model 610 $ainversion model 610 $awetlands 610 $afield spectrometry 610 $aspectral index 610 $ayield 610 $afoliage projective cover 610 $alidar 610 $acorrelation coefficient 610 $aSahel 610 $abiomass 610 $adry matter index 610 $aNiger 610 $aLandsat 610 $agrass biomass 610 $aparticle swarm optimization 610 $awinter wheat 610 $acarbon inventory 610 $arice 610 $aforest structure information 610 $aMODIS 610 $alight detection and ranging (LiDAR) 610 $aALOS2 610 $aecological policies 610 $aabove-ground biomass 610 $aWambiana grazing trial 610 $afood security 610 $aforest above ground biomass (AGB) 610 $aAtriplex nummularia 610 $aregional sustainability 610 $aCIRed-edge 700 $aMutanga$b Onisimo$4auth$01294178 702 $aKumar$b Lalit$4auth 906 $aBOOK 912 $a9910367567003321 996 $aRemote Sensing of Above Ground Biomass$93023041 997 $aUNINA