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

Nitrous oxide, methane and carbon dioxide dynamics from experimental pig graves

Twelve pig carcasses were buried in single, shallow and deep (30 and 90cm, respectively) graves at an experimental site near Ottawa, Ontario, Canada, with three shallow and three deep wrapped in black plastic garbage bags. An additional six carcasses were left at the surface to decompose, three of which were bagged. Six reference pits without remains were also dug. The objective of this three-year study was to examine the biogeochemistry and utility of nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) in grave detection and whether grave depth or cadaver condition (bagged versus bare) affected soil pore air concentrations and emission of the three gases. Graves showed significantly higher (\u3b1=0.05) concentrations and surface fluxes of N2O and CO2 than reference pits, but there was no difference in CH4 between graves and reference pits. While CH4 decreased with depth in the soil profiles, N2O and CO2 showed a large increase compared to reference pits. Shallow graves showed significantly higher emissions and pore air concentrations of N2O and CO2 than deep graves, as did bare versus bagged carcasses.Peer reviewed: YesNRC publication: Ye

Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels

A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared

Friction and thermal effects of engineering plastics sliding against steel and DLN-coated counterfaces

Polymers are increasingly used in tribological applications, because of their self-lubricating ability, corrosion resistance and chemical compatibility. However, their performance depends strongly on the parameters of the total tribological system. Not only polymer characteristics, but also counterface properties become important because of their influence on friction and wear, on surface energy and on the thermal conductivity of the total system. Applying a Diamond-Like Nanocomposite (DLN) coating on a steel counterface can improve the tribological behaviour of the sliding couple under certain conditions. In the case of metal sliding against DLN, the high hardness and the wear resistance of the coating is advantageous for better tribological properties. However, for polymers sliding against DLN, the lower thermal conductivity of the DLN coating compared with a steel mating surface dominates friction and wear. In case of polyamides this results in worse tribological performance in contact with the DLN coating, because of polymer melting. In the case of more rigid polymers, such as, e.g., POM-H and PETP, lower coefficients of friction lead to lower frictional heat generation. In these cases, the thermal characteristics of the counterface are less important and the lower surface energy of the DLN coating is favourable for decreased adhesion between the polymer and the coating and consequently better tribological properties.status: publishe

Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments

Increasing dominance of lianas in many tropical forests is considered a fingerprint of global environmental change. Despite the key role they play in ecosystem functioning, lianas remain one of the least studied life forms in tropical environments. This paper contrasts leaf traits and spectral properties (400-1100 nm) of liana and tree communities from a tropical dry forest and a tropical rainforest in Panama, Central America. Differences between lianas and tree leaf traits were analyzed using spectroscopy, leaf histology and pigment extractions. Results from this study indicate that many of the biochemical, structural, and optical properties of lianas and trees are different in the dry forest site but not in rainforest sites. In the dry forest site, liana leaves exhibited significantly lower chlorophyll and carotenoid contents and were thinner than the leaves of their host trees. Specific leaf area, dry to fresh mass ratio, and mean water content of liana leaves were significantly higher when compared with tree leaves. The differences observed in the tropical dry forest site indicate that lianas may have a higher rate of resource acquisition and usage, whereas trees tend to conserve acquired resources. We suggest that our results may be indicative of the presence of a liana syndrome related to water availability and thus best exhibited in tropical dry forests. Our findings have important implications for using remote sensing to accurately map the distribution of liana communities at regional scales and for the continued expansion of lianas in tropical environments as a result of global change