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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 online resource (352 p.) |
| Soggetto topico |
Environmental economics
Research and information: general |
| Soggetto non controllato |
activity data
bootstrapping boreal forest Bowen ratio C-band canopy height model carbon flux classification compatible equation constrained neighbor interpolation CRFasRNN CUSUM data assessment data fusion deep learning deforestation degradation digital surface model digital terrain model drought dual-FCN8s EBLUP emissions factor error propagation error-in-variable modeling FCN8s field measured LAI forest area change forest cover forest disturbance mapping forest monitoring forest structure change forest type genetic algorithm Germany GF2 improved k-NN inconsistency IPCC good practice guidelines La Rioja land use land cover Landsat leave-one-out cross-validation LiDAR logistic regression machine-learning magnitude multinomial logistic regression multitemporal LiDAR and stand-level estimates n/a near real-time monitoring nonlinear seemingly unrelated regression NRT monitoring ordinary neighbor interpolation Picea crassifolia Kom point cloud density random forest random forests remotely sensed LAI removals factor savanna Sentinel 2 Sentinel-1 Sentinel-2 small area estimation South Africa state space models statistical estimator stereo imagery support vector machine synthetic aperture radar temperate forest temporal dynamics time series satellite data tropical forest tropical peat uncertainty uncertainty evaluation validation windstorm damage |
| 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 online resource (235 p.) |
| Soggetto topico |
Biology, life sciences
Forestry & related industries Research & information: general |
| Soggetto non controllato |
acid rain
aerosol alien pathogen allochthonous species biomass burning canopy bulk density China climate change critical LFMC threshold crown fire Cupressus sempervirens direct estimation disease drought drying tests epicormic resprouter eucalyptus fire behavior fire danger fire danger rating fire management fire modeling fire regime fire risk fire season fire severity fire size fire weather fire weather patterns flammability flammability feedbacks foliar moisture content forest fire forest fire driving factors forest fire management forest fire occurrence forest/grassland fire fuel moisture fuel moisture content fuels FWI system humidity diffusion coefficients introduced fungus leaf water potential machine learning mass loss calorimeter meteorological factor regression MNI modeling moisture content n/a occurrence of forest fire plant traits PM2.5 Portugal prediction accuracy prescribed burning radiative transfer model random forest RCP remote sensing Seiridium cardinale senescence southwest China SSR temperate forest time lag variable importance vulnerability to wildfires wildfire |
| 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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