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Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales., Competing Interests: Adrián Cardil is employed by Tecnosylva SL. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Large wildfires can cover millions of hectares of forest every year worldwide, causing losses in ecosystems and assets. Fire simulation and modeling provides an analytical scheme to characterize and predict fire behavior and spread in several and complex environments. Spatial dynamics of large wildfires can be analyzed using satellite active fire data, a cost-effective way to acquire information systematically worldwide. The simulated growth of three large wildland fires from the USA, Chile and Spain with different fire spread pattern, duration and size has been compared to satellite active fire data. Additionally, a new approach to reinitialize fire simulations in near real-time and predict a more accurate fire spread is shown in this work. Discrepancies between the simulated fire growth and satellite active data were measured spatially and temporally in the three fires, increasing along the fire duration. The reinitialization approach meaningfully improved the accuracy of fire simulations in all case studies. Satellite active fire data showed a high potential to be used in real fire incidents, improving fire monitoring and simulation and, therefore, supporting the decision-making process of the fire analyst. The reinitialization approach could be applied by using the current satellite active fire data such as MODIS or VIIRS as well as Unmanned Aerial Vehicles or GPS locations from suppression resources., (Copyright © 2018 Elsevier Ltd. All rights reserved.)