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 ^ 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, is based on Landsat imagery and validated with aerial photography, 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 |
-|  | {{ :working_groups:cp: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 |
-|  | {{ :working_groups:cp: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 |
-|  | {{ :working_groups:cp:yaqian-he_a_time_series_of_annual_lulc_maps_of_china_from_1982-2013_using_avhrr-_2017_1-s2.0-s0034425717303255-main.pdf |}} |
 | 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 |
 |  | {{ :working_groups:cp:smith_which_practices_co-deliver_food_security_climate_change_mitigation_and_adaptation-2019.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 |
-|  | {{ :working_groups:cp: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 |
-| | {{ :working_groups:cp:yang_-_comparison_of_forest_above_ground_biomass-2020.pdf |}} |

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