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- | == Table of Literature potentially useful to our work... == | + | == Tables |
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Please insert any additions alphabetically by sir name of the first author. | Please insert any additions alphabetically by sir name of the first author. | ||
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^ Author | ^ Author | ||
+ | | 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, | ||
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| Livia C.P. Dias | Patterns of land use, extensification, | | Livia C.P. Dias | Patterns of land use, extensification, | ||
| 2016 | Periods 1940-2000 and 2000-2012: Based on Hansen' | | 2016 | Periods 1940-2000 and 2000-2012: Based on Hansen' | ||
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+ | | 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/ | ||
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| 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.| |