Microglial activation in multiple system atrophy: a potential role for NF-kappaB/rel proteins

Microglial activation is a prominent feature of affected brain areas in multiple system atrophy. Microglia express proinflammatory peptides, which may be a result of activation of nuclear factor-KB. We investigated the nuclear presence of RelA, the 65 kDa subunit of the NF-KB/RelA family in striatum and brain stem of patients with multiple system atrophy. Affected brain areas of patients with multiple system atrophy showed a marked immunoreactivity for nuclear Rel A p65, which was almost exclusively localized in activated microglia. Interestingly nuclear translocation of Rel A was not detected in striatal tissue of controls and Parkinson disease patients. Thus, NF-kappaB/Rel A complexes may play a role in mediating microglial activation in multiple system atrophy.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data

Hydrology is the single most important abiotic factor in the formation and functioning of a wetland. Many limitations still exist to accurately characterizing wetland hydrology over large spatial extents, especially in forested wetlands. Imaging radar has emerged as a viable tool for wetland flood mapping, although the limitations of radar data remain uncertain. The influence of incidence angle on the ability to detect flooding in different forest types was examined using C-HH Radarsat-1 data (23.5°, 27.5°, 33.5°, 39.0°, 43.5°, and 47.0°) during the leaf-off and leaf-on seasons. The ability to detect flooding under leaf-on conditions varied much more according to incidence angle while forest type (open canopy tupelo-cypress, tupelo-cypress, and bottomland hardwood) had a greater effect during the leaf-off season. When all forest types were considered together, backscatter generally decreased with increasing incidence angle under all conditions (2.45 dB between 23.5° and 47.0° flooded, leaf-off; 2.28 dB between 23.5° and 47.0° not flooded, leaf-off; 0.62 between 23.5° and 43.5° flooded, leaf-on; 1.73 dB between 23.5° and 43.5° not flooded, leaf-on; slope was not constant between incidence angles), but the distinction between flooded and non-flooded areas did not decline sharply with incidence angle. Differentiation of flooded and non-flooded forests was similar during the leaf-off and leaf-on seasons. The ability to detect inundation under forest canopies was less than expected at smaller incidence angles and greater than expected at larger incidence angles, based on the results of previous studies. Use of a wider range of incidence angles during the entire year increases the temporal resolution of imagery which may, in turn, enhance mapping of inundation beneath forest canopies.     

Change vector analysis: A technique for the multispectral monitoring of land cover and condition

Change vector analysis (CVA) is a robust approach for detecting and characterizing radiometric change in multispectral remote sensing data sets. CVA is reviewed as a useful technique to: (1) process the full dimensionality of multispectral/multi-layer data so as to ensure detection of all change present in the data; (2) extract and exploit the 'components' of change in multispectral data; and (3) facilitate composition and analysis of change images. Examples drawn from various projects are included throughout this methodological discussion, in order to illustrate the CVA approach and suggest its potential utility.     

The gridded trihedral: a new polarimetric SAR calibration reflector

The characteristics of the gridded trihedral used to calibrate polarimetric synthetic-aperture radar (SAR) systems are considered. The gridded trihedral is a normal trihedral with one of the conducting faces replaced with a grid of closely spaced (relative to a wavelength) parallel conductors over a layer of microwave absorber. This grid changes the incident wave's polarization and results in a calibration target with a significant cross-polarized reflection. The advantages of the gridded trihedral are that it has a broad backscatter beamwidth (unlike the dihedral), and that it is passive and simple to construct (unlike the active radar calibrator, or ARC). The performance characteristics of the gridded trihedral reflector are reviewed, using basic theoretical models and calibrated P-3 SAR imagery     

Analysis of the Impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan — Uganda border using multitemporal Landsat imagery

The impacts of armed conflict on ecosystems are complex and difficult to assess due to restricted access to affected areas during wartime making satellite remote sensing a useful tool for studying direct and indirect effects of conflict on the landscape. The Imatong Central Forest Reserve (ICFR) in South Sudan together with the nearby Dongotana Hills and the Agoro-Agu Forest Reserve (AFR) in Northern Uganda share a boundary and encompass a biologically diverse montane ecosystem. This study used satellite data combined with general human population trends to examine the impact of armed conflict and its outcome on similar forest ecosystems both during and after hostilities have occurred. A Disturbance Index (DI) was used to investigate the location and extent of forest cover loss and gain in three areas for two key time periods from mid-1980s to 2001 and 2003 to 2010. Results indicate that the rate of forest recovery was significantly higher than the rate of disturbance both during and after wartime in and around the ICFR and the net rate of forest cover change remained largely unchanged for the two time periods. In contrast, the nearby Dongotana Hills experienced relatively high rates of disturbance during both periods; however, post war period losses were largely offset by some gains in forest cover. For the AFR in Uganda, the rate of forest recovery was much higher during the second period, coinciding with the time people began leaving overcrowded camps. The diversity and merging of floristic regions in a very narrow band around the Imatong Mountains makes this area biologically distinct and of outstanding conservation importance; therefore, any future loss in forest cover is important to monitor — particularly in South Sudan where large numbers of people continue to return following the 2005 peace agreement and the 2011 Referendum on Independence.     

Phase calibration of polarimetric radar images

The problem of phase calibration between polarization channels of an imaging radar is studied. The causes of various types of phase errors due to the radar system architecture and system imperfections are examined. A simple model is introduced to explain the spatial variation in phase error as being due to a displacement between the phase centers of the vertical and horizontal antennas. It is also shown that channel leakage can cause a spatial variation in phase error. Phase calibration using both point and distributed ground targets is discussed and a method for calibrating phase using only distributed target is verified, subject to certain constraints. Experimental measurements using the NADC/ERIM P-3 synthetic-aperture radar (SAR) system and NASA/JPL DC-8 SAR, which operates at C-, L-, and P-bands, are presented. Both of these systems are multifrequency, polarimetric, airborne, SAR systems.<>     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.     

Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R² of the organic layer depth reduction models was 0.60 and 0.55 (p < 0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R² to 0.80 and 0.79 (p < 0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (> 200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.