Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper

In this study several pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment are evaluated. GeoCBI (Geo Composite Burn Index) field data of burn severity were correlated with remotely sensed measures, based on the NBR (Normalized Burn Ratio), the NDMI (Normalized Difference Moisture Index) and the NDVI (Normalized Difference Vegetation Index). In addition, the strength of the correlation was evaluated for specific fuel types and the influence of the regression model type is pointed out. The NBR was the best remotely sensed index for assessing burn severity, followed by the NDMI and the NDVI. For this case study of the 2007 Peloponnese fires, results show that the GeoCBI–dNBR (differenced NBR) approach yields a moderate–high R ²?=?0.65. Absolute indices outperformed their relative equivalents, which accounted for pre-fire vegetation state. The GeoCBI–dNBR relationship was stronger for forested ecotypes than for shrub lands. The relationship between the field data and the dNBR and dNDMI (differenced NDMI) was nonlinear, while the GeoCBI–dNDVI (differenced NDVI) relationship appeared linear.     

Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.     

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.     

Diagnosis of a case of acute chloroquine poisoning using 1H NMR spectroscopy: characterisation of drug metabolites in urine

The compromised optima for high intensity chemiluminescence (CL), using superoxide generators, were all above pH 9.0 for the CL probes luminol and lucigenin. With luminol the optima were at pH 9.0 and 9.4 for the generators KO2 and hypoxanthine/xanthine oxidase (HX/XO), respectively. Lucigenin, with the same generators, produced optima at pH 9.5 and 10.0, respectively. The probe methyl-Cypridina-luciferin analogue (MCLA) produced optima closer to neutral pH, which is preferred for physiological assessments. MCLA had optima at pH 6.0, 8.7 and 9.5 with KO2 and with HX/XO optima at pH 4.8, 6.0, 7.0 and 8.7. When CL was assessed at physiological pH, MCLA observed superoxide radicals with a sensitivity of 100- and 330-fold more than luminol or luicigenin respectively. For singlet oxygen, the sensitivity of MCLA at this pH was 45- and 5465-fold more than for the said probes respectively. H2O2 did not elicit CL between pH 4 and 9.5 with any of the probes and did not influence the production of superoxide or singlet oxygen when co-assessed. Therefore CL could only be obtained when enzymes were used as converters. The optima for the enzyme-conversion system horseradish peroxidase (HRP)/H2O2, and luminol, were at pH 8.0 and 9.2. Lucigenin and HRP/H2O2 also had a biphasic CL profile with optima at pH 7.4 and 9.6. MCLA and HRP/H2O2 had five optima, with the major ones at pH 6.1 and beyond 10. The optima for the myeloperoxidase/H2O system were at 8.6 and beyond 10.0 when luminol and 0.15 mol/L NaBr were used.     

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared-near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R² = 0.72) than one-year post-fire (R² = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.     

A study of visual processes in a case of interhemispheric disconnexion

A 75-year-old right-handed woman, after a probable cerebral infarct, developed an irregular constriction of the visual fields, a left-sided agraphia, and an anomia for objects in the left hand. Subsequent testing demonstrated an inability to name, though ability to recognize, letters and objects flashed in the homonymous left visual field. An inter-hemispheric disconnexion syndrome was inferred from these findings. The present publication concerns mainly the visual aspects of this disconnexion syndrome. Tasks were devised to test the abilities of the major and minor hemisphere: (a) the left hemisphere demonstrated a complete dominance for language expression and an incomplete dominance for written language comprehension; (b) the right hemisphere appeared to be dominant for some visuo-spatial tasks including number comprehension; (c) when the hemispheres were given contradictory visual informations on a non-verbal task (chimeric stimuli) there was a predominance of the right hemisphere. The right hemisphere appeared able to process complex information. Specialization of functional activities in each hemisphere is briefly discussed.     

Amiodarone N-deethylation in human liver microsomes: involvement of cytochrome P450 3A enzymes (first report)

Experiments were conducted on three different human liver samples to identify the cytochrome P450 isozyme which is involved in the biotransformation of the class III antiarrhythmic agent, amiodarone, into its major metabolite, desethylamiodarone (DEA). The classic P450 inhibitors, SKF 525A, metyrapone, and carbon monoxide provided a significant reduction in the in vitro formation of DEA by human hepatic microsomes. Amiodarone N-deethylase activities expressed by intrinsic clearance values were similar in all the livers used, although two livers were genotyped as extensive and one as a poor metabolizer for the cytochrome P450 CYP2D6 gene. DEA production was strongly inhibited (more than 80%) by the anti-P450 3A4 antibody, but not by anti-LKM1-positive serum. It seems therefore that the P450 3A subfamily is certainly implicated in human hepatic amiodarone N-deethylation.     

A comparison of time series similarity measures for classification and change detection of ecosystem dynamics

Time series of remote sensing imagery or derived vegetation indices and biophysical products have been shown particularly useful to characterize land ecosystem dynamics. Various methods have been developed based on temporal trajectory analysis to characterize, classify and detect changes in ecosystem dynamics. Although time series similarity measures play an important role in these methods, a quantitative comparison of the similarity measures is lacking. The objective of this study was to provide an overview and quantitative comparison of the similarity measures in function of varying time series and ecosystem characteristics, such as amplitude, timing and noise effects. For this purpose, the performance was evaluated for the commonly used similarity measures (D), ranging from Manhattan (D_Man), Euclidean (D_E) and Mahalanobis (D_Mah) distance measures, to correlation (D_CC), Principal Component Analysis (PCA; D_PCA) and Fourier based (D_FFT,D_ξ,D_Fk) similarities. The quantitative comparison consists of a series of Monte-Carlo simulations based on subsets of global MODIS Normalized Difference Vegetation index (NDVI) and Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) data. Results of the simulations reveal four main groups of time series similarity measures with different sensitivities: (i) D_Man, D_E, D_PCA, D_Fk quantify the difference in time series values, (ii) D_Mah accounts for temporal correlation and non-stationarity of variance, (iii) D_CC measures the temporal correlation, and (iv) the Fourier based D_FFT and D_ξ show their specific sensitivity based on the selected Fourier components. The difference measures show relatively the highest sensitivity to amplitude effects, whereas the correlation based measures are highly sensitive to variations in timing and noise. The Fourier based measures, finally, depend highly on the signal to noise ratio and the balance between amplitude and phase dominance. The heterogeneity in sensitivity of each D stresses the importance of (i) understanding the time series characteristics before applying any classification of change detection approach and (ii) defining the variability one wants to identify/account for. This requires an understanding of the ecosystem dynamics and time series characteristics related to the baseline, amplitude, timing, noise and variability of the ecosystem time series. This is also illustrated in the quantitative comparison, where the different sensitivities of D for the NDVI, EVI, and LAI data relate specifically to the temporal characteristics of each data set. Additionally, the effect of noise and intra- and interclass variability is demonstrated in a case study based on land cover classification.     

Anatomo-clinical study of a case of "ophthalmoplegia plus" with analysis of muscular, central nervous, ocular, myocardial, and thyroid lesions

This study deals with a case of "ophtalmoplegia plus" with histochemistry and electron microscopy of a muscle biopsy and full pathological examination in a 29 year old woman. Ragged-red fibers with abnormal mitochondria, cerebral spongiosis mostly involving white matter, perimacular pigmentary retinopathy and scattered myocardial fibrosis interrupting the Hiss'bundle were found. A goiter with hypersecretion of thyroid hormones and thyroid and ocular muscles changes characteristics of Graves' disease without any hypermetabolism occurred in her late twenties. This could be related to the absence of response from the abnormal mitochondria to the thyroid hormones.     

Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.