Advances in Remote Sensing for Global Forest Monitoring
Tomppo, Erkki
Advances in Remote Sensing for Global Forest Monitoring - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (352 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1253-2 9783036512525 9783036512532
10.3390/books978-3-0365-1253-2 doi
Research & information: general
Environmental economics
forest structure change EBLUP small area estimation multitemporal LiDAR and stand-level estimates forest cover Sentinel-1 Sentinel-2 data fusion machine-learning Germany South Africa temperate forest savanna classification Sentinel 2 land use land cover improved k-NN logistic regression random forest support vector machine statistical estimator IPCC good practice guidelines activity data emissions factor removals factor Picea crassifolia Kom compatible equation nonlinear seemingly unrelated regression error-in-variable modeling leave-one-out cross-validation digital surface model digital terrain model canopy height model constrained neighbor interpolation ordinary neighbor interpolation point cloud density stereo imagery remotely sensed LAI field measured LAI validation magnitude uncertainty temporal dynamics state space models forest disturbance mapping near real-time monitoring CUSUM NRT monitoring deforestation degradation tropical forest tropical peat forest type deep learning FCN8s CRFasRNN GF2 dual-FCN8s random forests error propagation bootstrapping Landsat LiDAR La Rioja forest area change data assessment uncertainty evaluation inconsistency forest monitoring drought time series satellite data Bowen ratio carbon flux boreal forest windstorm damage synthetic aperture radar C-band genetic algorithm multinomial logistic regression n/a
Advances in Remote Sensing for Global Forest Monitoring - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (352 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1253-2 9783036512525 9783036512532
10.3390/books978-3-0365-1253-2 doi
Research & information: general
Environmental economics
forest structure change EBLUP small area estimation multitemporal LiDAR and stand-level estimates forest cover Sentinel-1 Sentinel-2 data fusion machine-learning Germany South Africa temperate forest savanna classification Sentinel 2 land use land cover improved k-NN logistic regression random forest support vector machine statistical estimator IPCC good practice guidelines activity data emissions factor removals factor Picea crassifolia Kom compatible equation nonlinear seemingly unrelated regression error-in-variable modeling leave-one-out cross-validation digital surface model digital terrain model canopy height model constrained neighbor interpolation ordinary neighbor interpolation point cloud density stereo imagery remotely sensed LAI field measured LAI validation magnitude uncertainty temporal dynamics state space models forest disturbance mapping near real-time monitoring CUSUM NRT monitoring deforestation degradation tropical forest tropical peat forest type deep learning FCN8s CRFasRNN GF2 dual-FCN8s random forests error propagation bootstrapping Landsat LiDAR La Rioja forest area change data assessment uncertainty evaluation inconsistency forest monitoring drought time series satellite data Bowen ratio carbon flux boreal forest windstorm damage synthetic aperture radar C-band genetic algorithm multinomial logistic regression n/a