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Author (up) 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.  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
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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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