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220822 ||| eng |
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|a 9783036512532
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|a books978-3-0365-1253-2
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| 020 |
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|a 9783036512525
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| 100 |
1 |
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|a Tomppo, Erkki
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| 245 |
0 |
0 |
|a Advances in Remote Sensing for Global Forest Monitoring
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (352 p.)
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| 653 |
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|a South Africa
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| 653 |
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|a Germany
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| 653 |
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|a Sentinel-2
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| 653 |
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|a Bowen ratio
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| 653 |
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|a time series satellite data
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| 653 |
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|a forest monitoring
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| 653 |
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|a error propagation
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| 653 |
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|a logistic regression
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| 653 |
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|a activity data
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| 653 |
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|a Landsat
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| 653 |
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|a degradation
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| 653 |
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|a canopy height model
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| 653 |
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|a random forest
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| 653 |
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|a genetic algorithm
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| 653 |
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|a temporal dynamics
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| 653 |
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|a IPCC good practice guidelines
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| 653 |
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|a n/a
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| 653 |
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|a multinomial logistic regression
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| 653 |
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|a data fusion
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| 653 |
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|a deep learning
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| 653 |
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|a improved k-NN
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| 653 |
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|a savanna
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| 653 |
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|a random forests
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| 653 |
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|a ordinary neighbor interpolation
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| 653 |
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|a compatible equation
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| 653 |
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|a uncertainty
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| 653 |
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|a boreal forest
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| 653 |
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|a stereo imagery
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| 653 |
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|a constrained neighbor interpolation
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| 653 |
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|a drought
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| 653 |
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|a statistical estimator
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| 653 |
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|a tropical peat
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| 653 |
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|a synthetic aperture radar
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| 653 |
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|a La Rioja
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| 653 |
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|a inconsistency
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| 653 |
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|a tropical forest
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| 653 |
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|a temperate forest
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| 653 |
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|a forest area change
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| 653 |
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|a carbon flux
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| 653 |
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|a Picea crassifolia Kom
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| 653 |
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|a digital terrain model
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| 653 |
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|a point cloud density
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| 653 |
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|a error-in-variable modeling
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| 653 |
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|a remotely sensed LAI
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| 653 |
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|a forest structure change
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| 653 |
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|a CUSUM
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| 653 |
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|a digital surface model
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| 653 |
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|a GF2
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| 653 |
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|a land use land cover
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| 653 |
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|a machine-learning
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| 653 |
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|a Sentinel 2
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| 653 |
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|a small area estimation
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| 653 |
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|a field measured LAI
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| 653 |
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|a dual-FCN8s
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| 653 |
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|a forest type
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| 653 |
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|a support vector machine
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| 653 |
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|a nonlinear seemingly unrelated regression
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| 653 |
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|a LiDAR
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| 653 |
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|a multitemporal LiDAR and stand-level estimates
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| 653 |
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|a forest disturbance mapping
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| 653 |
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|a CRFasRNN
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| 653 |
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|a FCN8s
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| 653 |
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|a NRT monitoring
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| 653 |
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|a Environmental economics / bicssc
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| 653 |
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|a classification
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| 653 |
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|a forest cover
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| 653 |
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|a Sentinel-1
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| 653 |
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|a windstorm damage
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| 653 |
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|a bootstrapping
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| 653 |
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|a uncertainty evaluation
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| 653 |
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|a near real-time monitoring
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| 653 |
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|a data assessment
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| 653 |
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|a validation
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| 653 |
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|a leave-one-out cross-validation
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| 653 |
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|a state space models
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| 653 |
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|a magnitude
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| 653 |
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|a removals factor
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| 653 |
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|a Research and information: general / bicssc
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| 653 |
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|a EBLUP
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| 653 |
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|a C-band
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| 653 |
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|a emissions factor
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| 653 |
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|a deforestation
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| 700 |
1 |
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|a Praks, Jaan
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| 700 |
1 |
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|a Wang, Guangxing
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| 700 |
1 |
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|a Waser, Lars T.
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
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| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-1253-2
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4173
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76724
|z DOAB: description of the publication
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| 082 |
0 |
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|a 363
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| 082 |
0 |
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|a 000
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| 082 |
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|a 658
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| 082 |
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|a 330
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| 520 |
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|a 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.
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