Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Subendocardial fibrosis in remote myocardium results from reduction of coronary driving pressure during acute infarction in rats

OBJECTIVE: To investigate the role of hemodynamic changes occurring during acute MI in subsequent fibrosis deposition within non-MI. METHODS: By using the rat model of MI, 3 groups of 7 rats each [sham, SMI (MI <30%), and LMI (MI >30%)] were compared. Systemic and left ventricular (LV) hemodynamics were recorded 10 minutes before and after coronary artery ligature. Collagen volume fraction (CVF) was calculated in picrosirius red-stained heart tissue sections 4 weeks later. RESULTS: Before surgery, all hemodynamic variables were comparable among groups. After surgery, LV end-diastolic pressure increased and coronary driving pressure decreased significantly in the LMI compared with the sham group. LV dP/dt max and dP/dt min of both the SMI and LMI groups were statistically different from those of the sham group. CVF within non-MI interventricular septum and right ventricle did not differ between each MI group and the sham group. Otherwise, subendocardial (SE) CVF was statistically greater in the LMI group. SE CVF correlated negatively with post-MI systemic blood pressure and coronary driving pressure, and positively with post-MI LV dP/dt min. Stepwise regression analysis identified post-MI coronary driving pressure as an independent predictor of SE CVF. CONCLUSION: LV remodeling in rats with MI is characterized by predominant SE collagen deposition in non-MI and results from a reduction in myocardial perfusion pressure occurring early on in the setting of MI.

Espectroscopía de emisión de rayos x. Aplicación a la caracterización de materiales. X-ray emission spectroscopy. Application to materials characterization

La idea principal del proyecto abarca el estudio de parámetros y fenómenos físicos. Los avances logrados se aplicarán al desarrollo de software y metodologías para cuantificación de materiales mediante microanálisis con sonda de electrones y microscopía electrónica de barrido. El microanálisis no es una técnica absoluta, sino que requiere de estándares de referencia, para obviar el uso de ciertos parámetros geométricos y atómicos difíciles de conocer con una precisión adecuada. Para contar con un método sin estándares debe abordarse la determinación de parámetros atómicos e instrumentales, que es uno de los aspectos que se desea encarar en este proyecto. Por otro lado, también se pretende incluir los parámetros estudiados en un software de cuantificación desarrollado por integrantes del proyecto. Otro de los propósitos del plan de trabajo es estudiar la potencialidad de la resolución espacial de una microsonda de electrones con el fin de desarrollar una metodología para caracterizar interfases, bordes de granos e inclusiones, con resolución submicrométrica, ya que los métodos tradicionales de cuantificación se restringen al caso de muestras planas y homogéneas dentro del volumen de interacción, pero la caracterización de inhomogeneidades a nivel micrométrico no ha sido desarrollada todavía, salvo algunas excepciones. The main idea of this project involves the study of physical parameters and phenomena. The concretion of the different goals will permit the elaboration of softeare and methodologies for materials characterization by means of electron probe microanalysis and scanning microscopy. Electron probe microanalysis is not an absolute technique, but requires reference standards in order not to involve certain geometrical and atomic parameters for which high uncertainties cannot be avoided. In order to have standardless method, the determination of atomic and instrumental parameters must be accomplished, as will be faced through this project. Complementary, the parameters studied will be included in a quantification software developed in our research group of FaMAF. Another objective of this activity plan is to study the spatial resolution potentiality of a focalized electron beam, with the aim of characterizing interphases, grain boundaries and inclusions with submicron sensitivity, since the traditional quantification procedures are restricted to flat homogeneous samples, whereas the characterization of inhomogeneities has not been developed yet.

Development of a life cycle management system for remote products.

The research described in this thesis has been developed as a part of the Reliability and Field Data Management for Multi-Component Products (REFIDAM) Project. This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway-Mayo Institute of Technology and an industrial company, Thermo King Europe. The project aimed to develop a system to manage the information required for maintenance costing, cost of ownership, reliability assessment and improvement of multi-component products, by establishing information flows between the customer network and across the Thermo King organisation.

Study of human brain metabolites using magnetic resonance spectroscopy methods at 7 TESLA

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2012

The investigation of glutamine and glutamate in the human brain using MR spectroscopy at 7 Tesla

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2014

The Discrimination of Colored Acrylic, Cotton, and Wool Textile Fibers Using Micro-Raman Spectroscopy. Part 1 : In Situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633-nm laser did not provide Raman information. The 514-nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830-nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.

Compartmentalized Cerebral Metabolism of [1,6-(13)C]Glucose Determined by in vivo (13)C NMR Spectroscopy at 14.1 T.

Cerebral metabolism is compartmentalized between neurons and glia. Although glial glycolysis is thought to largely sustain the energetic requirements of neurotransmission while oxidative metabolism takes place mainly in neurons, this hypothesis is matter of debate. The compartmentalization of cerebral metabolic fluxes can be determined by (13)C nuclear magnetic resonance (NMR) spectroscopy upon infusion of (13)C-enriched compounds, especially glucose. Rats under light α-chloralose anesthesia were infused with [1,6-(13)C]glucose and (13)C enrichment in the brain metabolites was measured by (13)C NMR spectroscopy with high sensitivity and spectral resolution at 14.1 T. This allowed determining (13)C enrichment curves of amino acid carbons with high reproducibility and to reliably estimate cerebral metabolic fluxes (mean error of 8%). We further found that TCA cycle intermediates are not required for flux determination in mathematical models of brain metabolism. Neuronal tricarboxylic acid cycle rate (V(TCA)) and neurotransmission rate (V(NT)) were 0.45 ± 0.01 and 0.11 ± 0.01 μmol/g/min, respectively. Glial V(TCA) was found to be 38 ± 3% of total cerebral oxidative metabolism, accounting for more than half of neuronal oxidative metabolism. Furthermore, glial anaplerotic pyruvate carboxylation rate (V(PC)) was 0.069 ± 0.004 μmol/g/min, i.e., 25 ± 1% of the glial TCA cycle rate. These results support a role of glial cells as active partners of neurons during synaptic transmission beyond glycolytic metabolism.

Are glutamate and lactate increases ubiquitous to physiological activation? A (1)H functional MR spectroscopy study during motor activation in human brain at 7Tesla.

Recent studies at high field (7Tesla) have reported small metabolite changes, in particular lactate and glutamate (below 0.3μmol/g) during visual stimulation. These studies have been limited to the visual cortex because of its high energy metabolism and good magnetic resonance spectroscopy (MRS) sensitivity using surface coil. The aim of this study was to extend functional MRS (fMRS) to investigate for the first time the metabolite changes during motor activation at 7T. Small but sustained increases in lactate (0.17μmol/g±0.05μmol/g, p<0.001) and glutamate (0.17μmol/g±0.09μmol/g, p<0.005) were detected during motor activation followed by a return to the baseline after the end of activation. The present study demonstrates that increases in lactate and glutamate during motor stimulation are small, but similar to those observed during visual stimulation. From the observed glutamate and lactate increase, we inferred that these metabolite changes may be a general manifestation of the increased neuronal activity. In addition, we propose that the measured metabolite concentration increases imply an increase in ΔCMRO2 that is transiently below that of ΔCMRGlc during the first 1 to 2min of the stimulation.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

In vivo measurement of tissue oxygenation by time-resolved luminescence spectroscopy: advantageous properties of dichlorotris(1, 10-phenanthroline)-ruthenium(II) hydrate.

Measuring tissue oxygenation in vivo is of interest in fundamental biological as well as medical applications. One minimally invasive approach to assess the oxygen partial pressure in tissue (pO2) is to measure the oxygen-dependent luminescence lifetime of molecular probes. The relation between tissue pO2 and the probes' luminescence lifetime is governed by the Stern-Volmer equation. Unfortunately, virtually all oxygen-sensitive probes based on this principle induce some degree of phototoxicity. For that reason, we studied the oxygen sensitivity and phototoxicity of dichlorotris(1, 10-phenanthroline)-ruthenium(II) hydrate [Ru(Phen)] using a dedicated optical fiber-based, time-resolved spectrometer in the chicken embryo chorioallantoic membrane. We demonstrated that, after intravenous injection, Ru(Phen)'s luminescence lifetime presents an easily detectable pO2 dependence at a low drug dose (1 mg∕kg) and low fluence (120 mJ∕cm2 at 470 nm). The phototoxic threshold was found to be at 10 J∕cm2 with the same wavelength and drug dose, i.e., about two orders of magnitude larger than the fluence necessary to perform a pO2 measurement. Finally, an illustrative application of this pO2 measurement approach in a hypoxic tumor environment is presented.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.