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working_groups:cp:collection_of_publications [2021/12/20 10:37] ameier |
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- | ==== Collection of Publications ==== | + | ==== Collection of Publications |
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If that fails, then try this hack [[.collection_of_publications: | If that fails, then try this hack [[.collection_of_publications: | ||
- | A) If that fails, then open a new tab in your browser and paste this address | + | |
- | | + | |
- | into the address bar WITHOUT hitting enter. Then | + | |
- | B) grab the tail of your DOI html address like "10.1111/gcb.14917" from | + | == Tables |
+ | Feel free to create more sub-pages as you see fit. | ||
+ | Please insert any additions alphabetically by sir name of the first author. | ||
- | | + | [[.collection_of_publications:carbon_cycle |
- | or directly from (restrictive) publisher site like this one | + | |
- | https:// | + | |
- | C) and paste it into the same new tab's address bar to obtain something like this (from our example): | + | [[.collection_of_publications:ecearth_inner_functioning |
- | https://doi-org.recursos.biblioteca.upc.edu/ | + | |
- | and hit enter. | + | |
- | If you are lucky your publication may be found and accessed that way as in this example.... | + | |
+ | [[.collection_of_publications: | ||
- | | + | [[.collection_of_publications: |
- | == Table of Literature potentially useful to our work... == | + | [[.collection_of_publications: |
- | Please insert any additions alphabetically by sir name of the first author. | + | |
^ Author | ^ Author | ||
- | | Masayuki Kondo | State of the science in reconciling top-down | + | | Suzanne E. Cotillon |
- | | 2019 | Their set of atmospheric inversions | + | | 2017 | The data shows snapshots |
- | | | https:// | + | | | https:// |
- | | | {{ : | + | | | {{ : |
- | | Andreas Krause | + | | Livia C.P. Dias |
- | | 2020 | They use LPJ‐GUESS to quantify legacy effects for the 21st century. LUH2 (historic) | + | | 2016 | Periods 1940-2000 |
- | | | https:// | + | | | https:// |
- | | | {{ : | + | | | {{ : |
- | | Andreas Krause | + | | Yaqian He, E.L.Timothy & A. Warner |
- | | 2018 | {{ :working_groups: | + | | 2017 | 1982-2013: A 32-year annual land use and land cover (LULC) maps of China were generated. The LULC map created for 2012 has no significant differences from the corresponding MODIS map (available since 2001). The LULC classification methods can be applied to other geographical regions. | |
- | | | https:// | + | | | https:// |
+ | | | {{ : | ||
| Pete Smith | Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification? | | Pete Smith | Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification? | ||
| 2019 | | | | 2019 | | | ||
| | https:// | | | https:// | ||
| | {{ : | | | {{ : | ||
- | | Philip Vergragt | + | | Yidi XU et al. | Annual 30-m land use/land cover maps of China for 1980–2015 |
- | | 2011 | This paper investigates if and how carbon capture and storage | + | | 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. |
- | | | https://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.15117 | + | | | https://doi.org/10.1007/s11430-019-9606-4 |
- | | | {{ : | + | | | {{ : |
| Hui Yang | Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates | | | Hui Yang | Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates | | ||
| 2020 | Uses the GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs).Model estimates are 365 ± 66 Pg C compared to 275 (±13.5%) Pg C from GlobBiomass. The results suggest that TRENDY v6 DGVMs tend to underestimate biomass loss from anthropogenic disturbances.| | | 2020 | Uses the GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs).Model estimates are 365 ± 66 Pg C compared to 275 (±13.5%) Pg C from GlobBiomass. The results suggest that TRENDY v6 DGVMs tend to underestimate biomass loss from anthropogenic disturbances.| |