TY - BOOK ID - 135417396 TI - Advances in Remote Sensing for Global Forest Monitoring AU - Tomppo, Erkki AU - Praks, Jaan AU - Wang, Guangxing AU - Waser, Lars T. PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - forest structure change KW - EBLUP KW - small area estimation KW - multitemporal LiDAR and stand-level estimates KW - forest cover KW - Sentinel-1 KW - Sentinel-2 KW - data fusion KW - machine-learning KW - Germany KW - South Africa KW - temperate forest KW - savanna KW - classification KW - Sentinel 2 KW - land use land cover KW - improved k-NN KW - logistic regression KW - random forest KW - support vector machine KW - statistical estimator KW - IPCC good practice guidelines KW - activity data KW - emissions factor KW - removals factor KW - Picea crassifolia Kom KW - compatible equation KW - nonlinear seemingly unrelated regression KW - error-in-variable modeling KW - leave-one-out cross-validation KW - digital surface model KW - digital terrain model KW - canopy height model KW - constrained neighbor interpolation KW - ordinary neighbor interpolation KW - point cloud density KW - stereo imagery KW - remotely sensed LAI KW - field measured LAI KW - validation KW - magnitude KW - uncertainty KW - temporal dynamics KW - state space models KW - forest disturbance mapping KW - near real-time monitoring KW - CUSUM KW - NRT monitoring KW - deforestation KW - degradation KW - tropical forest KW - tropical peat KW - forest type KW - deep learning KW - FCN8s KW - CRFasRNN KW - GF2 KW - dual-FCN8s KW - random forests KW - error propagation KW - bootstrapping KW - Landsat KW - LiDAR KW - La Rioja KW - forest area change KW - data assessment KW - uncertainty evaluation KW - inconsistency KW - forest monitoring KW - drought KW - time series satellite data KW - Bowen ratio KW - carbon flux KW - boreal forest KW - windstorm damage KW - synthetic aperture radar KW - C-band KW - genetic algorithm KW - multinomial logistic regression KW - n/a UR - https://www.unicat.be/uniCat?func=search&query=sysid:135417396 AB - The 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. ER -