Testing satellite and ground thermal imaging of low-temperature fumarolic fields: The dormant Nisyros Volcano (Greece)

The Nisyros Volcano (Greece) was monitored by satellite and ground thermal imaging during the period 2000–2002. Three night-scheduled Landsat-7 ETM⁺ thermal (band 6) images of Nisyros Island were processed to obtain land surface temperature. Ground temperature data were also collected during one of the satellite overpasses. Processed results involving orthorectification and 3-D atmospheric correction clearly show the existence of a thermal anomaly inside the Nisyros Caldera. This anomaly is associated mainly with the largest hydrothermal craters and has land surface temperatures 5–10 °C warmer than its surroundings. The ground temperature generally increased by about 4 °C inside the main crater over the period 2000–2002. Ground thermal images of the hydrothermal Stephanos Crater were also collected in 2002 using a portable thermal infrared camera. These images were calibrated to ground temperature data and orthorectified. A difference of about 0–2 °C was observed between the ground thermal images and the ground temperature data. The overall study demonstrates that satellite remote sensing of low-temperature fumarolic fields within calderas can provide a reliable long-term monitoring tool of dormant volcanoes that have the potential to reactivate. Similarly, a portable thermo-imager can easily be deployed for real-time monitoring using telemetric data transfer. The operational costs for both systems are relatively low for an early warning system.     

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

Simulation of the Ku-band Radar altimeter sea ice effective scattering surface

A radiative transfer model is used to simulate the sea ice radar altimeter effective scattering surface variability as a function of snow depth and density. Under dry snow conditions without layering these are the primary snow parameters affecting the scattering surface variability. The model is initialized with in situ data collected during the May 2004 GreenIce ice camp in the Lincoln Sea (73/spl deg/W; 85/spl deg/N). Our results show that the snow cover is important for the effective scattering surface depth in sea ice and thus for the range measurement, ice freeboard, and ice thickness estimation.     

The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring

A global data set of cloud occurrence probability derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua gridded daily data is analyzed to investigate the probability of obtaining at least a minimum number of cloud-free observations within various compositing periods. The probabilities derived from Terra and Aqua, with morning and afternoon overpass times, respectively, are similar and increase with compositing period. Compositing both Terra and Aqua observations results in considerably higher probabilities of obtaining a sufficient number of observations for bidirectional reflectance model-based compositing. Given that the only alternative to obtaining sufficient samples is to extend the observation period, which can cause significant problems when the surface state changes, it is concluded that using data from the two MODIS sensors provides the most effective way of generating composited products. Findings with respect to the availability of cloud-free composites when n-day composites are generated on a temporally overlapping daily rolling basis, i.e., every day, rather than every n-days, are also discussed for regional and global applications     

The Potential of the Maximum Cross-Correlation Technique to Estimate Surface Currents From Thermal AVHRR Global Area Coverage Data

Having already shown its potential of deriving the vector fields representing the ocean-surface advection from sequential 1.1-km-resolution local area coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) images, the maximum cross-correlation (MCC) technique here is applied to four 4.4-km-resolution global area coverage (GAC) AVHRR images. The resulting three vector fields are compared to the vector fields obtained from the LAC imagery corresponding to the same satellite passages. To quantify the reduction in accuracy inevitable when applying the method to the lower resolution imagery, the LAC vector fields were assumed to be error free. The deviation of the GAC vectors from the LAC vectors is expressed as percentage errors of the signal variance of meridional u and zonal v velocity components, and they are 16%/30%, respectively, for the best case and 62%/117% and 92%/111% for the other two cases. These results indicate that, in its present state, the GAC data do not allow the MCC technique to extract reliable current-vector information from it     

Remote sensing study of granulitic terrain in parts of Gujarat and Rajasthan

The geology of northwestern part of Indian peninsula is considered to be important due to complete preservation of rocks from Archaean to Upper Proterozoic. Further, these rocks have served as ideal host of varieties of economic minerals. The present work is an attempt to study the structurally deformed granulitic terrain in parts of Gujarat and Rajasthan in light of remote sensing. The study area falls under Sirohi, Banas Kantha and Sabar Kantha districts of Rajasthan and Gujarat. Remote sensing technique is utilized for the understanding of structural geology and deciphering the shear pattern. The methods adopted in this study include generation of False Color Composite (FCC) of satellite data, interpretation of lineaments from FCC and study the drainage pattern, structural basin delineation, profiling, and field mapping. It is observed that the area has undergone extensive deformation. There are two major sets of lineaments interpreted in the granulitic terrain such as WNW-ESE and NE-SW directions. Majority of the WNW-ESE lineaments are brittle in nature and N-S, NE-SW trending lineaments are ductile in nature. Overall the study area bifurcated into seven structural basins comprises of basic granulites, calc granulites and pelitic granulites.     

Plate kinematics of Nubia–Somalia using a combined DORIS and GPS solution

We have used up to 12 years of data to assess DORIS performance for geodynamics applications. We first examine the noise characteristics of the DORIS time-series of weekly station coordinates to derive realistic estimates of velocity uncertainties. We find that a combination of white and flicker noise best explains the DORIS time-series noise characteristics. Second, weekly solutions produced by the Institut Géographique National/Jet Propulsion Laboratory (IGN/JPL) DORIS Analysis Centre are combined to derive a global velocity field. This solution is combined with two independent GPS solutions, including 11 sites on Nubia and 5 on the Somalia plate. The combination indicates that DORIS horizontal velocities have an average accuracy of 3 mm/year, with best-determined sites having velocity accuracy better than 1 mm/year (one-sigma levels). Using our combined velocity field, we derive an updated plate kinematics model with a focus on the Nubia–Somalia area. Including DORIS data improves the precision of the angular velocity vector for Nubia by 15%. Our proposed model provides robust bounds on the maximum opening rates along the East African Rift (4.7–6.7 mm/year). It indicates opening rates 15 and 7% slower than values predicted by NUVEL-1A for the southern Atlantic Ocean and Indian Ocean, respectively. These differences are likely to arise from the fact that NUVEL-1A considered Africa as a single non-deforming plate, while here we use a more refined approach.     

Finding and sharing GIS methods based on the questions they answer

Geographic information has become central for data scientists of many disciplines to put their analyzes into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests.     

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well-illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing a small image-to-image difference in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p < 0.001). The original c-correction method improved the optimality of badly illuminated pixels while deteriorating the optimality of well-illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination–reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that deviated up to two months from the ideal anniversary date scheme the modified c-correction technique resulted in a more reliable change detection.