Lava discharge rate estimates from thermal infrared satellite data at Pacaya Volcano, Guatemala

Time-averaged discharge rates (TADR) were calculated for five lava flows at Pacaya Volcano (Guatemala), using an adapted version of a previously developed satellite-based model. Imagery acquired during periods of effusive activity between the years 2000 and 2010 were obtained from two sensors of differing temporal and spatial resolutions; the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Geostationary Operational Environmental Satellites (GOES) Imager. A total of 2873 MODIS and 2642 GOES images were searched manually for volcanic “hot spots”. It was found that MODIS imagery, with superior spatial resolution, produced better results than GOES imagery, so only MODIS data were used for quantitative analyses. Spectral radiances were transformed into TADR via two methods; first, by best-fitting some of the parameters (i.e. density, vesicularity, crystal content, temperature change) of the TADR estimation model to match flow volumes previously estimated from ground surveys and aerial photographs, and second by measuring those parameters from lava samples to make independent estimates. A relatively stable relationship was defined using the second method, which suggests the possibility of estimating lava discharge rates in near-real-time during future volcanic crises at Pacaya.

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

Daytime Low Stratiform Cloud Detection on AVHRR Imagery

The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR) data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV). Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS) cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.

SeaWiFS chlorophyll imagery of the iron-fertilized area of SOIREE

The growth of populations is known to be influenced by dispersal, which has often been described as purely diffusive (Kierstead and Slobodkin, 1953; Okubo, 1980). In the open ocean, however, the tendrils and filaments of phytoplankton populations provide evidence for dispersal by stirring (Gower et al., 1980, doi:10.1038/288157a0; Holligan et al., 1993, doi:10.1029/93GB01731). Despite the apparent importance of horizontal stirring for plankton ecology, this process remains poorly characterized. Here we investigate the development of a discrete phytoplankton bloom, which was initiated by the iron fertilization of a patch of water (7 km in diameter) in the Southern Ocean (Boyd et al., 2000, doi:10.1038/35037500). Satellite images show a striking, 150-km-long bloom near the experimental site, six weeks after the initial fertilization. We argue that the ribbon-like bloom was produced from the fertilized patch through stirring, growth and diffusion, and we derive an estimate of the stirring rate. In this case, stirring acts as an important control on bloom development, mixing phytoplankton and iron out of the patch, but also entraining silicate. This may have prevented the onset of silicate limitation, and so allowed the bloom to continue for as long as there was sufficient iron. Stirring in the ocean is likely to be variable, so blooms that are initially similar may develop very differently.

Mapping decadal land cover changes in the woodlands of north eastern Namibia using the Landsat satellite archive (1975-2014)

Woodland savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to intensive land uses. This study investigates the land cover changes of 108,038 km**2 in NE Namibia using multi-temporal, multi-sensor Landsat imagery, at decadal intervals from 1975 to 2014, with a post-classification change detection method and supervised Regression Tree classifiers. We discuss likely impacts of land tenure and reforms over the past four decades on changes in land use and land cover. These changes included losses, gains and exchanges between predominant land cover classes. Exchanges comprised logical conversions between woodland and agricultural classes, implying woodland clearing for arable farming, cropland abandonment and vegetation succession. The most dominant change was a reduction in the area of the woodland class due to the expansion of the agricultural class, specifically, small-scale cereal and pastoral production. Woodland area decreased from 90% of the study area in 1975 to 83% in 2014, while cleared land increased from 9% to 14%. We found that the main land cover changes are conversion from woodland to agricultural and urban land uses, driven by urban expansion and woodland clearing for subsistence-based agriculture and pastoralism.

Satellite-based daily lake areas in the Yangtze Basin, China, 2000-2011

The Yangtze River Basin downstream of China's Three Gorges Dam (TGD) (thereafter referred to as "downstream" basin) hosts the largest cluster of freshwater lakes in East Asia. These lakes are crucial water stocks to local biophysical environments and socioeconomic development. Existing studies document that individual lakes in this region have recently experienced dramatic changes under the context of enduring meteorological drought, continuous population growth, and extensive water regulation since TGD's initial impoundment (i.e., June, 2003). However, spatial and temporal patterns of lake dynamics across the complete downstream Yangtze basin remain poorly characterized. Using daily MODIS imagery and an advanced thematic mapping scheme, this study presents a comprehensive monitoring of area dynamics in the downstream lake system at a 10-day temporal resolution during 2000-2011. The studied lakes constitute ~76% (~11,400 km**2) of the total downstream lake area, including the entire +70 major lakes larger than 20 km**2. The results reveal a decadal net decline in lake inundation area across the downstream Yangtze Basin, with a cumulative decrease of 849 km**2 or 7.4% from 2000 to 2011. Despite an excessive precipitation anomaly in the year 2010, the decreasing trend was tested significant in all seasons. The most substantial decrease in the post-TGD period appears in fall (1.1%/yr), which intriguingly coincides with the TGD water storage season. Regional lake dynamics exhibit contrasting spatial patterns, manifested as evident decrease and increase of aggregated lake areas respectively within and beyond the Yangtze Plain. This contrast suggests a marked vulnerability of lakes in the Yangtze Plain, to not only local meteorological variability but also intensified human water regulations from both the upstream Yangtze main stem (e.g., the TGD) and tributaries (e.g., lakes/reservoirs beyond the Yangtze Plain). The produced lake mapping result and derived lake area dynamics across the downstream Yangtze Basin provides a crucial monitoring basis for continuous investigations of changing mechanisms in the Yangtze lake system.

A composite DOP approach to excluding bottom reflectance in mapping water parameters of shallow coastal zones from TM imagery

In this paper we proposed a composite depth of penetration (DOP) approach to excluding bottom reflectance in mapping water quality parameters from Landsat thematic mapper (TM) data in the shallow coastal zone of Moreton Bay, Queensland, Australia. Three DOPs were calculated from TM1, TM2 and TM3, in conjunction with bathymetric data, at an accuracy ranging from +/-5% to +/-23%. These depths were used to segment the image into four DOP zones. Sixteen in situ water samples were collected concurrently with the recording of the satellite image. These samples were used to establish regression models for total suspended sediment (TSS) concentration and Secchi depth with respect to a particular DOP zone. Containing identical bands and their transformations for both parameters, the models are linear for TSS concentration, logarithmic for Secchi depth. Based on these models, TSS concentration and Secchi depth were mapped from the satellite image in respective DOP zones. Their mapped patterns are consistent with the in situ observed ones. Spatially, overestimation and underestimation of the parameters are restricted to localised areas but related to the absolute value of the parameters. The mapping was accomplished more accurately using multiple DOP zones than using a single zone in shallower areas. The composite DOP approach enables the mapping to be extended to areas as shallow as <3 m. (C) 2004 Elsevier Inc. All rights reserved.

Monitoring the condition of savanna riparian zones in Northern Australia using high spatial resolution imagery and a field based rapid assessment method

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Inflammatory Sensitization Of Nociceptors Depends On Activation Of Nmda Receptors In Drg Satellite Cells.

The present study evaluated the role of N-methyl-D-aspartate receptors (NMDARs) expressed in the dorsal root ganglia (DRG) in the inflammatory sensitization of peripheral nociceptor terminals to mechanical stimulation. Injection of NMDA into the fifth lumbar (L5)-DRG induced hyperalgesia in the rat hind paw with a profile similar to that of intraplantar injection of prostaglandin E2 (PGE2), which was significantly attenuated by injection of the NMDAR antagonist D(-)-2-amino-5-phosphonopentanoic acid (D-AP-5) in the L5-DRG. Moreover, blockade of DRG AMPA receptors by the antagonist 6,7-dinitroquinoxaline-2,3-dione had no effect in the PGE2-induced hyperalgesia in the paw, showing specific involvement of NMDARs in this modulatory effect and suggesting that activation of NMDAR in the DRG plays an important role in the peripheral inflammatory hyperalgesia. In following experiments we observed attenuation of PGE2-induced hyperalgesia in the paw by the knockdown of NMDAR subunits NR1, NR2B, NR2D, and NR3A with antisense-oligodeoxynucleotide treatment in the DRG. Also, in vitro experiments showed that the NMDA-induced sensitization of cultured DRG neurons depends on satellite cell activation and on those same NMDAR subunits, suggesting their importance for the PGE2-induced hyperalgesia. In addition, fluorescent calcium imaging experiments in cultures of DRG cells showed induction of calcium transients by glutamate or NMDA only in satellite cells, but not in neurons. Together, the present results suggest that the mechanical inflammatory nociceptor sensitization is dependent on glutamate release at the DRG and subsequent NMDAR activation in satellite glial cells, supporting the idea that the peripheral hyperalgesia is an event modulated by a glutamatergic system in the DRG.

MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass

The use of remote sensing is necessary for monitoring forest carbon stocks at large scales. Optical remote sensing, although not the most suitable technique for the direct estimation of stand biomass, offers the advantage of providing large temporal and spatial datasets. In particular, information on canopy structure is encompassed in stand reflectance time series. This study focused on the example of Eucalyptus forest plantations, which have recently attracted much attention as a result of their high expansion rate in many tropical countries. Stand scale time-series of Normalized Difference Vegetation Index (NDVI) were obtained from MODIS satellite data after a procedure involving un-mixing and interpolation, on about 15,000 ha of plantations in southern Brazil. The comparison of the planting date of the current rotation (and therefore the age of the stands) estimated from these time series with real values provided by the company showed that the root mean square error was 35.5 days. Age alone explained more than 82% of stand wood volume variability and 87% of stand dominant height variability. Age variables were combined with other variables derived from the NDVI time series and simple bioclimatic data by means of linear (Stepwise) or nonlinear (Random Forest) regressions. The nonlinear regressions gave r-square values of 0.90 for volume and 0.92 for dominant height, and an accuracy of about 25 m(3)/ha for volume (15% of the volume average value) and about 1.6 m for dominant height (8% of the height average value). The improvement including NDVI and bioclimatic data comes from the fact that the cumulative NDVI since planting date integrates the interannual variability of leaf area index (LAI), light interception by the foliage and growth due for example to variations of seasonal water stress. The accuracy of biomass and height predictions was strongly improved by using the NDVI integrated over the two first years after planting, which are critical for stand establishment. These results open perspectives for cost-effective monitoring of biomass at large scales in intensively-managed plantation forests. (C) 2011 Elsevier Inc. All rights reserved.

Movements of feral camels in central Australia determined by satellite telemetry

Movements of two female one-humped camels in central Australia were tracked using satellite telemetry between March 1986 and July 1987. During that time both animals travelled a minimum distance of over 1000 km within a radius of 125 km for one animal, and 200 km for the other. However, their movements were uite punctuated and both animals spent periods of up to several months in rleatively small areas before moving over longer distances to new areas. Both camels moved at greater rates overnight. An activity index, probably measuring feeding rate, declined during the study period for both animals. Patchy and sporadic rainfall may explain some of these results.

Satellite protection and drag reduction using a purging gas flow

We have used the DSMC method to determine contamination (impingement of atmospheric molecules) and the aerodynamic forces on a cold satellite when a protective “purge gas” is ejected from a sting protruding ahead of the satellite. Forward ejection of the purge gas provides the greatest protection for a given mass of purge gas and the aerodynamic drag can be significantly reduced, thus compensating for the backward reaction from the forward ejection. If the purge gas is ejected backward from the sting (towards the satellite) the ejection provides thrust and the net retarding force can be reduced to zero. Contamination can be reduced and the mass of purging gas is less than the mass of conventional rocket propellant required to maintain the orbit of an unprotected satellite.

Motor imagery in Parkinson's disease: A PET study

We used positron emission tomography (PET) with O-15-labelled water to record patterns of cerebral activation in six patients with Parkinson's disease (PD), studied when clinically off and after turning on as a result of dopaminergic stimulation. They were asked to imagine a Finger opposition movement performed with their right hand. externally paced at a rate of 1 Hz. Trials alternating between motor imagery and rest were measured. A pilot study of three age-matched controls was also performed. We chose the task as a robust method of activating the supplementary motor area (SMA), defects of which have been reported in PD. The PD patients showed normal de-rees of activation of the SMA (proper) when both off and on. Significant activation with imagining movement also occurred in the ipsilateral inferior parietal cortex (both off and when on) and ipsilateral premotor cortex (when off only). The patients showed significantly greater activation of the rostral anterior cingulate and significantly less activation of the left lingual gyrus and precuneus when performing the task on compared with their performance when off. PD patients when imagining movement and off showed less activation of several sites including the right dorsolateral prefrontal cortex (DLPFC) when compared to the controls performing the same task. No significant differences from controls were present when the patients imagined when on. Our results are consistent with other studies showing deficits of pre-SMA function in PD with preserved function of the SMA proper. In addition to the areas of reduced activation (anterior cingulate, DLPFC), there were also sites of activation (ipsilateral premotor and inferior parietal cortex) previously reported as locations of compensatory overactivity for PD patients performing similar tasks. Both failure of activation and compensatory changes a-re likely to contribute to the motor deficit in PD. (C) 2001 Movement Disorder Society.

Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' ( Puccinia kuehnii ) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease-Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.