230 M. E. González et al.


FIG. 1 (a) Land-use (Table 1) and land-cover classes within a 10-km radius of the centroid of Nothofagus alessandrii forest fragments (three in the north and six in the south) in the study area in the Maule region of central Chile, and the extent of the 2017 anthropogenic Las Máquinas mega-fire. (b) Per cent of the various land-use and land-cover classes surrounding the northern and southern N. alessandrii fragments in the burnt areas. (c) The land-use and land-cover classes surrounding the N. alessandrii fragments in the southern area.


We selected pre-fire images from both satellites (January 2017) and a post-fire image from Sentinel-2 (March 2017). The radar-based Sentinel-1 image was obtained from


Google Earth (Google, Mountain View, USA) and included vertical–horizontal and vertical–vertical polarities and a speckle filter correction (Lee et al., 1994).We used the sen2r package (Ranghetti et al., 2020)in R 4.0.1 (R Core Team, 2020) to obtain and process the optical Sentinel-2 images. We applied radiometric and atmospheric corrections to convert the data from the top of the atmospheric radiance to the bottom of the atmospheric reflectance. We selected bands with 10 or 20 m spatial resolution (B2,B3,B4 and B8) corresponding to the visible, near-infrared (NIR) and shortwave-infrared (SWIR) spectra for further post-pro- cessing. Using these bands,we estimated the normalized dif- ference vegetation index, green normalized difference vegetation index and soil-adjusted vegetation index as indicators of vegetation vigour and greenness. We used the difference normalized burn ratio (dNBR) as


a fire severity proxy as it integrates pre- and post-fire vege- tation status (Fernandez-Manso et al., 2016; Mallinis et al., 2018). Firstly, we used the NIR and SWIR Sentinel-2 bands to assess the pre- and post-fire normalized burn ratio of the vegetation as:


NBR = (NIR −SWIR)/(NIR +SWIR)


post-fire greenness as: dNBR = NBRpre-fire–NBRpost-fire


(1) Secondly, we assessed the difference between pre- and (2)


Because of the low variability amongst the intermediate severity classes, we reclassified the five dNBR classes (Key & Benson, 2006) into three fire severity classes, supported by ground truthing: (1) high severity (carbonized tree trunks, stems and leaves; dNBR.0.66), (2) moderate severity (trees did not retain leaves or needles and most of the crown canopy was burnt; 0.27,dNBR,0.66), and (3) low severity (trees possess green foliage and most of their structure was not severely affected by the fire; dNBR,0.27). Finally, we used a lidar-derived digital terrain model


resampled to a resolution of 10 m from which we depict- ed the topographical position index using SAGA GIS (Conrad et al., 2015). This index indicates whether, in rela- tion to its neighbours, a pixel corresponds to a flat, concave or convex area.We uploaded all images and derived variables to Google Earth for further cloud-based post-processing.


Land use and land cover surrounding N. alessandrii forests


Weselected the nine fire-affected N. alessandrii populations mapped by Corporación Nacional Forestal (Valencia et al., 2018). We then identified the main land use and land cover (Table 1) within a 10-km radius area of influence around the centroid of N. alessandrii forest patches. Because the nine N. alessandrii populations are geographically separate, we analysed patches in the northern and southern areas of the mega-fire. We used all of the derived remote-sensing


Oryx, 2023, 57(2), 228–238 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605322000102


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