Herbaceous biomass estimation using hyperspectral data, PLS regression and continuum removal transformation

Revista oficial de la Asociación Española de Teledetección[EN] The aim of this research work was to compare the results of two methods to estimate aboveground biomass by using field spectrometer data: (i) Partial least squares regression (PLSR), and (ii) linear regression applied to the Maximum Band Depth (MBD) and Area Over the Minimum (AOM) indices. In both cases different regions of the spectrum were transformed by Continuum Removal (CR). Since the results using PLSR (R2=0.920, RMSE=3.622 g/m2) were similar to the results achieved by the indices (R2=0.915, RMSE=3.615 g/m2 for AOM), using the indices derived from CR is recommended, since their interpretation is easier than the PLS output.[ES] El objetivo del estudio fue comparar los resultados de dos métodos para la estimación de la biomasa aérea a partir de datos de espectroradiometría de campo: (i) regresión por mínimos cuadrados parciales (Partial Least Squa-res Regression, PLSR) y (ii) regresión lineal utilizando los índices Profundidad del Mínimo (Maximum Band Depth, MBD) y Área Sobre el Mínimo (Area Over the Minimum, AOM) como descriptores. En ambos casos se llevó a cabo una previa transformación de los espectros mediante Continuum Removal (CR). Como los resultados empleando PLS (R2=0,920, RMSE=3,622 g/m2) fueron muy similares a los obtenidos con los índices (para AOM: R2=0,915, RMSE=3,615 g/m2), recomendamos los índices derivados del CR puesto que su interpretación es más sencilla que la del PLSRMarabel-García, M.; Álvarez-Taboada, F. (2014). Estimación de biomasa en herbáceas a partir de datos hiperespectrales, regresión PLS y la transformación continuum removal. Revista de Teledetección. (42):49-60. https://doi.org/10.4995/raet.2014.228649604

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Application of Kuhn’s theory of scientific revolution to the theory development of disruptive innovation

The aim of this article is to review the theory of disruptive innovation over the course of its development and analyze its theory building process. The analysis is framed through the theory of scientific development proposed by Thomas S. Kuhn (2012). The novel application of Kuhn’s framework highlights crucial developments and faults. It is assessed how the development of disruptive innovation matches the four stages of scientific development: crisis, revolution, normal science and the accumulation of anomalies. It is demonstrated that this framework is a successful means of conceptualizing the development of disruptive innovation. The theory is currently at the stage of normal science. The two potential anomalies are evaluated. It is concluded that controversies surrounding definitions are not an essential threat to the theory. Establishing predictive value on the other hand is a critical point in future development of the theory. It is shown that the future of the theory depends on whether the latter point is resolved