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Advances in Remote Sensing for Global Forest Monitoring
| Advances in Remote Sensing for Global Forest Monitoring |
| Autore | Tomppo Erkki |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (352 p.) |
| Soggetto topico |
Research & information: general
Environmental economics |
| Soggetto non controllato |
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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557338103321 |
Tomppo Erkki
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Remote Sensing of Hydro-Meteorology
| Remote Sensing of Hydro-Meteorology |
| Autore | Lee Joo-Heon |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (154 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
spatial downscaling
MODIS chlorophyll-a sentinel-2A MSI multiple polynomial regression genetic programming rainfall variability Indian Ocean Dipole (IOD) El Niño–Southern Oscillation (ENSO) intentional statistical simulation satellite-based precipitation hydrological modeling error propagation monsoon-climate watershed typhoon-induced rainfall prediction statistical model fuzzy C-means clustering China remote sensing integrated drought monitoring meteorological drought hydrological drought agricultural drought Bayesian principal component analysis (BPCA) statistical simulation extreme precipitation index PERSIANN-CDR KGE linear trend Huai River Basin Indian Ocean Dipole mode El Niño–Southern Oscillation singular spectrum analysis mutual information non-stationarity of seasonal precipitation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566458603321 |
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Lee Joo-Heon
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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