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Collection of Publications and access to non-open-access publications

This is a place where we can store publications relevant to our work, that might be featured in proposals or deliverables or that we simply deem useful. And hopefully it will be easier to search for them here then on a diffuse number of local hard disks…

Accessing non-open-access publications

Here is a quick and dirty hack to access non-open-access articles in case they are included in the journals that the UPC has subscriptions for and supposing that you have UPC credentials. This method is particularly useful for DOI searches (That the UPC Library tries to keep a secret it seemed to me…):

1) Visit: UPC Library DOI search portal (https://doi-org.recursos.biblioteca.upc.edu/) (You may need to login first.)

2) Then grab the DOI of interest as in this example “https://doi.org/10.1111/gcb.14917” and paste it into the search field from step 1 and hit Enter. Heureka! (hopefully).

If that fails, then try this hack UPC DOI access hack

Table of Literature potentially useful to our work...

Please insert any additions alphabetically by sir name of the first author.

Author Title, Description, DOI, Document
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, covering approx. 4 to 18 degrees North. The 24 LULC classes descriptions are provided here: https://eros.usgs.gov/westafrica/land-use-land-cover-map). The data products are available for download at no charge from: https://eros.usgs.gov/westafrica
https://doi.org/10.3133/fs20173004, https://doi.org/10.5066/F73N21JF
usgs-west_africa_land_use_land_cover_time_series_fs20173004.pdf
Livia C.P. Dias Patterns of land use, extensification, and intensification of Brazilian agriculture
2016 Periods 1940-2000 and 2000-2012: Based on Hansen's annual maps (30m resolution) pixels that changed from woody to non-woody vegetation were identified and filled with land use classes or crops in proportion to the crops reported in survey data for the area. Pixels were aggregated to obtain a 1x1km annual land use map. Brazilian census data were performed in 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, and 2006 at the municipality level. Also available are annual crop yields and cattle stocks since 1990. Census data was interpolated to annual data. They use a number of elaborate algorithms to grow or shrink crop areas by firstly varying the percentage of crops within a pixel that is not 100% natural vegetation (presumably before spilling to a neighbouring pixel).
https://doi.org/10.1111/gcb.13314
dias-patterns_of_land_use_of_brazilian_agriculture_1940-2010-_2016_.pdf
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://doi.org/10.1016/j.rse.2017.07.010
yaqian-he_a_time_series_of_annual_lulc_maps_of_china_from_1982-2013_using_avhrr-_2017_1-s2.0-s0034425717303255-main.pdf
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://doi.org/10.1111/gcb.14917
kondo_-_status_of_reconciling_top-down_and_bottom-up_approaches_for_co2_-_2019.pdf
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://doi.org/10.1029/2020EF001674
krause-legacy_effects_from_historical_environmental_changes_2020ef001674.pdf
Andreas Krause Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts
2018 krause-large_uncertainty_in_carbon_uptake_potential_of_lmts-_2018.pdf
https://doi.org/10.1111/gcb.14144
Pete Smith Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification?
2019
https://doi.org/10.1111/gcb.14878
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
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
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/10.1007/s11430-019-9606-4
xu_1980-2015-annual30-mlanduselandcovermapsforchina-2020.pdf
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://doi-org.recursos.biblioteca.upc.edu/10.1111/gcb.15117
yang_-_comparison_of_forest_above_ground_biomass-2020.pdf
working_groups/cp/collection_of_publications.1640172192.txt.gz · Last modified: 2021/12/22 11:23 by ameier