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Author Arevalo-Ramirez, T.; Villacres, J.; Fuentes, A.; Reszka, P.; Cheein, F.A.A. doi  openurl
  Title Moisture content estimation of Pinus radiata and Eucalyptus globulus from reconstructed leaf reflectance in the SWIR region Type
  Year 2020 Publication Biosystems Engineering Abbreviated Journal Biosyst. Eng.  
  Volume 193 Issue Pages 187-205  
  Keywords SWIR reconstruction; Machine learning; Spectral indices; Fuel moisture content; Equivalent water thickness  
  Abstract Valparaiso, a central-southern region in Chile, has one of the highest rates of wildfire occurrence in the country. The constant threat of fires is mainly due to its highly flammable forest plantation, composed of 97.5% Pinus radiata and Eucalyptus globulus. Fuel moisture content is one of the most relevant parameters for studying fire spreading and risk, and can be estimated from the reflectance of leaves in the short wave infra-red (SWIR) range, not easily available in most vision-based sensors. Therefore, this work addresses the problem of estimating the water content of leaves from the two previously mentioned species, without any knowledge of their spectrum in the SWIR band. To this end, and for validation purposes, the reflectance of 90 leaves per species, at five dehydration stages, were taken between 350 nm and 2500 nm (full spectrum). Then, two machine-learning regressors were trained with 70% of the data set to determine the unknown reflectance, in the range 1000 nm-2500 nm. Results were validated with the remaining 30% of the data, achieving a root mean square error less than 9% in the spectrum estimation, and an error of 10% in spectral indices related to water content estimation. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.  
  Address [Arevalo-Ramirez, Tito; Villacres, Juan; Auat Cheein, Fernando A.] Univ Tecn Fedrico Santa Maria, Dept Ingn Elect, Valparaiso, Chile, Email: fernando.auat@usm.cl  
  Corporate Author Thesis  
  Publisher Academic Press Inc Elsevier Science Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1537-5110 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000526114500016 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1150  
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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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  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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