LEADER 04721nam 2201369z- 450 001 9910557338103321 005 20231214133206.0 035 $a(CKB)5400000000042497 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76724 035 $a(EXLCZ)995400000000042497 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Remote Sensing for Global Forest Monitoring 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (352 p.) 311 $a3-0365-1252-7 311 $a3-0365-1253-5 330 $aThe topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article. 606 $aResearch & information: general$2bicssc 606 $aEnvironmental economics$2bicssc 610 $aforest structure change 610 $aEBLUP 610 $asmall area estimation 610 $amultitemporal LiDAR and stand-level estimates 610 $aforest cover 610 $aSentinel-1 610 $aSentinel-2 610 $adata fusion 610 $amachine-learning 610 $aGermany 610 $aSouth Africa 610 $atemperate forest 610 $asavanna 610 $aclassification 610 $aSentinel 2 610 $aland use land cover 610 $aimproved k-NN 610 $alogistic regression 610 $arandom forest 610 $asupport vector machine 610 $astatistical estimator 610 $aIPCC good practice guidelines 610 $aactivity data 610 $aemissions factor 610 $aremovals factor 610 $aPicea crassifolia Kom 610 $acompatible equation 610 $anonlinear seemingly unrelated regression 610 $aerror-in-variable modeling 610 $aleave-one-out cross-validation 610 $adigital surface model 610 $adigital terrain model 610 $acanopy height model 610 $aconstrained neighbor interpolation 610 $aordinary neighbor interpolation 610 $apoint cloud density 610 $astereo imagery 610 $aremotely sensed LAI 610 $afield measured LAI 610 $avalidation 610 $amagnitude 610 $auncertainty 610 $atemporal dynamics 610 $astate space models 610 $aforest disturbance mapping 610 $anear real-time monitoring 610 $aCUSUM 610 $aNRT monitoring 610 $adeforestation 610 $adegradation 610 $atropical forest 610 $atropical peat 610 $aforest type 610 $adeep learning 610 $aFCN8s 610 $aCRFasRNN 610 $aGF2 610 $adual-FCN8s 610 $arandom forests 610 $aerror propagation 610 $abootstrapping 610 $aLandsat 610 $aLiDAR 610 $aLa Rioja 610 $aforest area change 610 $adata assessment 610 $auncertainty evaluation 610 $ainconsistency 610 $aforest monitoring 610 $adrought 610 $atime series satellite data 610 $aBowen ratio 610 $acarbon flux 610 $aboreal forest 610 $awindstorm damage 610 $asynthetic aperture radar 610 $aC-band 610 $agenetic algorithm 610 $amultinomial logistic regression 615 7$aResearch & information: general 615 7$aEnvironmental economics 700 $aTomppo$b Erkki$4edt$01322272 702 $aPraks$b Jaan$4edt 702 $aWang$b Guangxing$4edt 702 $aWaser$b Lars T$4edt 702 $aTomppo$b Erkki$4oth 702 $aPraks$b Jaan$4oth 702 $aWang$b Guangxing$4oth 702 $aWaser$b Lars T$4oth 906 $aBOOK 912 $a9910557338103321 996 $aAdvances in Remote Sensing for Global Forest Monitoring$93034740 997 $aUNINA