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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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Forest Fire Risk Prediction
| Forest Fire Risk Prediction |
| Autore | Nolan Rachael |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (235 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Forestry & related industries |
| Soggetto non controllato |
fire danger rating
fire management fire regime fire size fire weather Portugal critical LFMC threshold forest/grassland fire radiative transfer model remote sensing southwest China acid rain aerosol biomass burning forest fire PM2.5 direct estimation meteorological factor regression moisture content time lag forest fire driving factors forest fire occurrence random forest forest fire management China Cupressus sempervirens fire risk fuels fuel moisture content mass loss calorimeter Seiridium cardinale vulnerability to wildfires disease alien pathogen allochthonous species introduced fungus drying tests humidity diffusion coefficients wildfire prescribed burning modeling drought flammability fuel moisture leaf water potential plant traits climate change MNI fire season fire behavior crown fire fire modeling senescence foliar moisture content canopy bulk density fire danger fire weather patterns RCP FWI system SSR occurrence of forest fire machine learning variable importance prediction accuracy epicormic resprouter eucalyptus fire severity flammability feedbacks temperate forest |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557387103321 |
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Nolan Rachael
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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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