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Author Arevalo-Ramirez, TA.; Castillo, AHF.; Cabello, PSR.; Cheein, FAA. doi  openurl
  Title Single bands leaf reflectance prediction based on fuel moisture content for forestry applications Type
  Year 2021 Publication Biosystems Engineering Abbreviated Journal Biosyst. Eng.  
  Volume 202 Issue Pages 79-95  
  Keywords Leaf water index; Machine learning; Remote sensing; Wildfire; Wildland fuels  
  Abstract Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparai ' so forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with an RMSE lower than 0.022, and an average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively. (c) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.  
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  Series Volume Series Issue Edition  
  ISSN 1537-5110 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000613400300008 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1346  
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Author Baselli, G.; Contreras, F.; Lillo, M.; Marin, M.; Carrasco, R.A. doi  openurl
  Title Optimal decisions for salvage logging after wildfires Type
  Year 2020 Publication Omega-International Journal Of Management Science Abbreviated Journal Omega-Int. J. Manage. Sci.  
  Volume 96 Issue Pages 9 pp  
  Keywords Salvage logging; Forest harvesting; Wildfires; Workforce allocation  
  Abstract Strategic, tactical, and operational harvesting plans for the forestry and logging industry have been widely studied for more than 60 years. Many different settings and specific constraints due to legal, environmental, and operational requirements have been modeled, improving the performance of the harvesting process significantly. During the summer of 2017, Chile suffered from the most massive wildfires in its history, affecting almost half a million hectares, of which nearly half were forests owned by medium and small forestry companies. Some of the stands were burned by intense crown fires, which always spread fast, that burned the foliage and outer layer of the bark but left standing dead trees that could be salvage harvested before insect and decay processes rendered them unusable for commercial purposes. Unlike the typical operational programming models studied in the past, in this setting, companies can make insurance claims on part or all of the burnt forest, whereas the rest of the forest needs to be harvested before it loses its value. This problem is known as the salvage logging problem. The issue also has an important social component when considering medium and small forestry and logging companies: most of their personnel come from local communities, which have already been affected by the fires. Harvesting part of the remaining forest can allow them to keep their jobs longer and, hopefully, leave the company in a better financial situation if the harvesting areas are correctly selected. In this work, we present a novel mixed-integer optimization-based approach to support salvage logging decisions, which helps in the configuration of an operational-level harvesting and workforce assignment plan. Our model takes into account the payment from an insurance claim as well as future income from harvesting the remaining trees. The model also computes an optimal assignment of personnel to the different activities required. The objective is to improve the cash position of the company by the end of the harvest and ensure that the company is paying all its liabilities and maintaining personnel. We show how our model performs compared to the current decisions made by medium and small-sized forestry companies, and we study the specific case of a small forestry company located in Cauquenes, Chile, which used our model to decide its course of action. (C) 2019 Elsevier Ltd. All rights reserved.  
  Address [Baselli, Gianluca; Contreras, Felipe; Lillo, Matias; Marin, Magdalena; Carrasco, Rodrigo A.] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: gbaselli@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0305-0483 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000541944700003 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1186  
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Author Tolorza, V.; Poblete-Caballero, D.; Banda, D.; Little, C.; Leal, C.; Galleguillos, M. doi  openurl
  Title An operational method for mapping the composition of post-fire litter Type
  Year 2022 Publication Remote Sensing Letters Abbreviated Journal Remote Sens. Lett.  
  Volume 13 Issue 5 Pages 511-521  
  Keywords SOIL; BIODIVERSITY; WILDFIREI; MPACTS; CHILE  
  Abstract Recent increase in the frequency and spatial extent of wildfires motivates the quick recognition of the affected soil properties over large areas. Digital Soil Mapping is a valuable approach to map soil attributes based on remote sensing and field observations. We predicted the spatial distribution of post-fire litter composition in a 40,600 ha basin burned on the 2017 wildfire of Chile. Remotely sensed data of topography, vegetation structure and spectral indices (SI) were used as predictors of random forest (RF) models. Litter sampled in 60 hillslopes after the fire provided training and validation data. Predictors selected by the Variable Selection Using Random Forests (VSURF) algorithm resulted in models for litter composition with acceptable accuracy (coefficient of determination, R (2) = 0.51-0.64, Normalized Root Mean Square Error, NRMSE = 16.9-22.1, percentage bias, pbias = -0.35%-0.5%). Modelled litter parameters decrease in concentration respect to the degree of burn severity, and the pre-fire biomass. Because pre-fire vegetation was conditioned by land cover and by a previous (2 years old) wildfire event, our results highlight the cumulative effect of severe wildfires in the depletion of litter composition.  
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  Series Volume Series Issue Edition  
  ISSN 2150-704X ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000763106200001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1547  
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