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working_groups:cp:collection_of_publications [2021/12/22 11:49]
ameier 		working_groups:cp:collection_of_publications [2021/12/22 11:51]
ameier
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 |  | https://doi.org/10.1111/gcb.14878 | |  | https://doi.org/10.1111/gcb.14878 |
 |  | {{ :working_groups:cp:smith_which_practices_co-deliver_food_security_climate_change_mitigation_and_adaptation-2019.pdf |}} | |  | {{ :working_groups:cp:smith_which_practices_co-deliver_food_security_climate_change_mitigation_and_adaptation-2019.pdf |}} |
-| Philip Vergragt et al  | Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates |  
-| 2011 | This paper investigates if and how carbon capture and storage (CCS) could help to avoid reinforcing fossil fuel lock-in. The outcome is that a large-scale BECCS development could be feasible under certain conditions, thus largely avoiding the risk of reinforced fossil fuel lock-in. //Keywords: Carbon capture and storage, Biomass, Fossil fuel// | 
-| | https://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.15117      |  
-| | {{ :working_groups:cp:vergragt-comparison_of_forest_above-ground_biomass_from_dgvms-1-s2.0-s0959378011000215-main.pdf |}} | 
 | Yidi XU et al. | Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm | | Yidi XU et al. | Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm |
 | 2020 | Annual land use land cover (LULC) change information at 30m spatial resolution covering 1980 to 2015. The data integrates MODIS and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate annual nominal 30 m LULC maps for the whole of China. | | 2020 | Annual land use land cover (LULC) change information at 30m spatial resolution covering 1980 to 2015. The data integrates MODIS and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate annual nominal 30 m LULC maps for the whole of China. |
working_groups/cp/collection_of_publications.txt · Last modified: 2021/12/22 11:53 by ameier

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