ArticleSensors (Basel, Switzerland)2013
Spectroscopic determination of aboveground biomass in grasslands using spectral transformations, Support Vector Machine and Partial Least Squares Regression.
Article in Sensors (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation.Frontiers in plant science · 2022Article
- Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.Sensors (Basel, Switzerland) · 2020Article
- Article
- Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods.Surveys in geophysics · 2019Article
- Functional Redundancy in bird community decreases with riparian forest width reduction.Ecology and evolution · 2018Article
- Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.Sensors (Basel, Switzerland) · 2017Article
- Sensors and technologies in Spain: state-of-the-art.Sensors (Basel, Switzerland) · 2014Article
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Authors and funding
2 authors.
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Abstract
Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model to predict the total AGB involved PLSR and the Maximum Band Depth index derived from the continuum removed reflectance in the absorption features between 916-1,120 nm and 1,079-1,297 nm (R2 = 0.939, RMSE = 7.120 g/m2). Regarding the green fraction of the AGB, the Area Over the Minimum index derived from the continuum removed spectra provided the most accurate model overall (R2 = 0.939, RMSE = 3.172 g/m2). Identifying the appropriate absorption features was proved to be crucial to improve the performance of PLSR to estimate the total and green aboveground biomass, by using the indices derived from those spectral regions. Ordinary Least Square Regression could be used as a surrogate for the PLSR approach with the Area Over the Minimum index as the independent variable, although the resulting model would not be as accurate.
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