Back to Search
Start Over
Single bands leaf reflectance prediction based on fuel moisture content for forestry applications
- Source :
- Biosystems Engineering. 202:79-95
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
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 Valparaiso 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 a RMSE lower than 0.022, and a average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively.
- Subjects :
- Mean squared error
Soil Science
Soil science
01 natural sciences
medicine
Water content
geography
geography.geographical_feature_category
Spectral signature
biology
Pinus radiata
010401 analytical chemistry
04 agricultural and veterinary sciences
Spectral bands
biology.organism_classification
0104 chemical sciences
Control and Systems Engineering
Ridge
Eucalyptus globulus
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Environmental science
medicine.symptom
Vegetation (pathology)
Agronomy and Crop Science
Food Science
Subjects
Details
- ISSN :
- 15375110
- Volume :
- 202
- Database :
- OpenAIRE
- Journal :
- Biosystems Engineering
- Accession number :
- edsair.doi...........c3c5362b1a12f6ddb5d2915f893b0c73
- Full Text :
- https://doi.org/10.1016/j.biosystemseng.2020.12.003