working_groups:cp:collection_of_publications
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| - | | Suzanne E. Cotillon | West Africa land use and land cover time series: U.S. Geological Survey Fact Sheet 2017–3004 | | ||
| - | | 2017 | The data shows snapshots of 1975, 2000 and 2013 at typically 2km resolution, is based on Landsat imagery and validated with aerial photography, | ||
| - | | | https:// | ||
| - | | | {{ : | ||
| - | | Livia C.P. Dias | Patterns of land use, extensification, | ||
| - | | 2016 | Periods 1940-2000 and 2000-2012: Based on Hansen' | ||
| - | | | https:// | ||
| - | | | {{ : | ||
| - | | Yaqian He, E.L.Timothy & A. Warner | A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data | | ||
| - | | 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:// | ||
| - | | | {{ : | ||
| - | | Masayuki Kondo | State of the science in reconciling top-down and bottom-up approaches for terrestrial CO2 budget | | ||
| - | | 2019 | Their set of atmospheric inversions and bio-sphere models, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s as well as for the regions of North America and South-east Asia. Differences in budget estimates are substantial for East Asia and South America. There is uncertainty in several regions as to whether these represent a carbon sink or source. Given these findings, caution should be taken when interpreting regional CO2 budgets.Those uncertainties continue to limit our ability to project the mitigation potential by the terrestrial biosphere. | | ||
| - | | | https:// | ||
| - | | | {{ : | ||
| - | | Andreas Krause | Legacy Effects from Historical Environmental Changes Dominate Future Terrestrial Carbon Uptake | | ||
| - | | 2020 | They use LPJ‐GUESS to quantify legacy effects for the 21st century. LUH2 (historic) and bias-corrected IPSL‐CM5A‐LR climate mode (future) are employed to provide land use forcing. The combined legacy effects of historical (1850–2015) environmental changes result in a land carbon uptake of +126 Gt C over the future (2015–2099) period. This by far exceeds the impacts of future environmental changes (range −53 Gt C to +16 Gt C for three scenarios) and is comparable in magnitude to historical carbon losses (−154 Gt C). The response of the biosphere to historical environmental changes dominates future terrestrial carbon cycling at least until mid-century. | | ||
| - | | | https:// | ||
| - | | | {{ : | ||
| - | | Andreas Krause | Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts | | ||
| - | | 2018 | {{ : | ||
| - | | | 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 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:// | ||
| - | | | {{ : | ||
| - | | 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/ | ||
| - | | | https:// | ||
| - | | | {{ : | ||
| - | | 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.| | ||
| - | | | https:// | ||
| - | | | {{ : | ||
working_groups/cp/collection_of_publications.1640173498.txt.gz · Last modified: 2021/12/22 11:44 by ameier
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