The dataset contains 6 Land Cover maps at a 30m/pixel spatial resolution for the Purapel river catchment located in South-Central Chile. They were generated for the summer periods of 1986, 2000, 2005, 2010, 2015 and 2017. Maps of 1986-2015 were generated using atmospherically corrected Landsat CDR Scenes (images courtesy of the U.S. Geological Survey) including VNIR and SWIR bands from the TM5, ETM+ and OLI sensors and vegetation indices as auxiliary bands to highlight phenological differences among covers. Specifically the Normalized Difference Vegetation Index (NDVI) (Rouse et al,. 1974), the Green NDVI (Gitelson et al., 1996) and NDVI winter-summer Difference Index (ΔNDVI). Training and validation points were defined from field trips to the area in 2014-2015, various mid resolution satellite imagery sources and high-resolution Google Earth imagery (Map data ©2015 Google) when available. A topographic correction was applied using the C-Correction method (Teillet et al 1982), as proposed by Hantson and Chuvieco (2011), and the SRTM v3 DEM to account for the effect of local relief in the scene’s lighting. Accuracy assessment resulted in Overall Accuracy (OA), ranging from 82% to 92% (table 1). Table 1. Overall Accuracies for Land Cover maps from 1986 to 2017 Year OA 1986 89.7 2000 92.2 2005 91.5 2010 89.8 2015 82.7 2017 0.98 The 2017 map was generated using Random Forest classifier using several SI from Sentinel 2, Sentinel 1 C-band radar data (imagery from European Space Agency courtesy of the U.S. Geological Survey) and hydro-geomorphic indices obtained from 2009 LiDAR DTM data (Tolorza et al., 2022). Ninety polygons were used for training and thirty polygons and the classification of Zhao et al. (2016) were used for validation, obtaining an overall accuracy 0.98 (table 1). The 7 land cover classes defined following these codes and land use / covers: 0 = Unclassified 1 = Others (mainly crops and natural prairies in riverbeds) 2 = Native Forest (mainly secondary-growth deciduous Nothofagus sp. Stands) 3 = Shrubland (highly degraded formation of xerophytic and sclerophyllous shrubs such as Acacia caven, Quillaja saponaria and Lithraea caustica, among others). 4 =Tree Plantations (industrial monocultures of Pinus radiata and Eucalyptus spp. of various age and development) 5 = Seasonal grassland (annual pastures which wither in summer and urban areas) 6 = Clear cuts (bare lands within industrial forestry surface) Codes 7 to 9 are specific to 2015 y 2017 because of the occurrence of two large (>5,000 hectares) fire events, and represent different Fire Severity levels based on the dNBR index (López and Caselles, 1991) according to Key and Benson (2006). They represent the following cases: 7= Low Severity fire 8 = Moderate severity fire 9 = High severity fire Sources: Hantson, S. Chuvieco, E. 2011. Evaluation of different topographic correction methods for Landsat imagery. International Journal of Applied Earth Observation and Geoinformation 13:691-700. Rouse, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with erts. Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351, compiled and edited by S.C. Freden, E.P. Mercanti, and M.A. Becker. Washington, DC: National Aeronautics and Space Administration Gitelson, A., Y. Kaufman, and M. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298. Teillet, P., B. Guindon, and D. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8:84–106. Key, C. Benson, N. 2006. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. Pp: 1-51. López, MJ. Caselles, V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International (1) 1991: 31- 37. Tolorza, V. Poblete-Caballero, D. Banda, D. Little, C. Galleguillos, M. 2022. An operational method for mapping the composition of post-fire litter. Remote Sensing letters (13) 2022: 511-521. 10.1080/2150704X.2022.2040752 Zhao, Y. D. Feng, L. Yu, X. Wang, Y. Chen, Y. Bai, H. Hernández, et al. 2016. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data. Remote Sensing of Environment 183: 170–185. 10.1016/j.rse.2016.05.016.

{"references": ["Hantson, S. Chuvieco, E. 2011. Evaluation of different topographic correction methods for Landsat imagery. International Journal of Applied Earth Observation and Geoinformation 13:691-700.", "Rouse, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with erts. Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351, compiled and edited by S.C. Freden, E.P. Mercanti, and M.A. Becker. Washington, DC: National Aeronautics and Space Administration", "Gitelson, A., Y. Kaufman, and M. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298.", "Teillet, P., B. Guindon, and D. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8:84\u2013106.", "Key, C. Benson, N. 2006. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. Pp: 1-51.", "L\u00f3pez, MJ. Caselles, V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International (1) 1991: 31- 37.", "Tolorza, V. Poblete-Caballero, D. Banda, D. Little, C. Galleguillos, M. 2022. An operational method for mapping the composition of post-fire litter. Remote Sensing letters (13) 2022: 511-521. 10.1080/2150704X.2022.2040752", "Zhao, Y. D. Feng, L. Yu, X. Wang, Y. Chen, Y. Bai, H. Hern\u00e1ndez, et al. 2016. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data. Remote Sensing of Environment 183: 170\u2013185. 10.1016/j.rse.2016.05.016."]}

Dataset funded by ANID (FONDECYT project 11190864, FONDAP project 15110009) and the UFRO Postdoc grant VRIP20P001