Remote Sens.2014, 6(11), 10571-10592; doi:10.3390/rs61110571 (registering DOI) - published 31 October 2014 Show/Hide Abstract
Abstract: The city of Staufen im Breisgau in southwest Germany suffers from a localized land uplift, which has occurred in the past six years in relation to geothermal drilling activities in 2007. So far, severe damages at 269 buildings have been recorded. The chemical transformation of anhydrite and water to gypsum, resulting in a volume increase, has been attributed as the cause of the uplift. Previous studies provide knowledge on the spatio-temporal displacement evolution from 2008 through 2011 using leveling and spaceborne Synthetic Aperture Radar Interferometry (InSAR) measurements, but lack a detailed representation of vertical and horizontal displacement contributions as well as geophysical modeling. This study focuses not only on continued observation analysis from June 2011 through July 2013, but also on obtaining and evaluating horizontal displacements in Staufen based on combined analysis of TerraSAR-X satellite imagery from both ascending and descending orbits. Applying the Small BAseline Subset (SBAS) approach a deceleration of annual cumulative line of sight (LOS) uplift is observable from 13.8 cm ± 0.3 cm (July 2008–July 2009) to 3 cm ± 0.3 cm (July 2012–July 2013) within area of maximum deformation NNE of the drilling zone. Conducting displacement decomposition on ascending and descending data of a common period (October 2012 through July 2013) yields in an approximately symmetric east- and westward motion with maximum values approximately 1 cm and 1.4 cm, respectively. The joint inversion of ascending and descending InSAR data for the common period from October 2012 through July 2013 shows that a horizontal rectangular source with length, width and depth of 177 m ± 19 m, 69 m ± 15 m and 89 m ± 9 m, respectively, can satisfactorily model the observation. The amount of opening at depth shows a decrease in time by about 71% for the period 2011–2012 as compared to period 2008–2009.
Remote Sens.2014, 6(11), 10546-10570; doi:10.3390/rs61110546 (registering DOI) - published 31 October 2014 Show/Hide Abstract
Abstract: Accurate and fine-grained discovery by diverse Earth observation (EO) sensors ensures a comprehensive response to collaborative observation-required emergency tasks. This discovery remains a challenge in an EO sensor web environment. In this study, we propose an EO sensor observation capability metadata model that reuses and extends the existing sensor observation-related metadata standards to enable the accurate and fine-grained discovery of EO sensors. The proposed model is composed of five sub-modules, namely, ObservationBreadth, ObservationDepth, ObservationFrequency, ObservationQuality and ObservationData. The model is applied to different types of EO sensors and is formalized by the Open Geospatial Consortium Sensor Model Language 1.0. The GeosensorQuery prototype retrieves the qualified EO sensors based on the provided geo-event. An actual application to flood emergency observation in the Yangtze River Basin in China is conducted, and the results indicate that sensor inquiry can accurately achieve fine-grained discovery of qualified EO sensors and obtain enriched observation capability information. In summary, the proposed model enables an efficient encoding system that ensures minimum unification to represent the observation capabilities of EO sensors. The model functions as a foundation for the efficient discovery of EO sensors. In addition, the definition and development of this proposed EO sensor observation capability metadata model is a helpful step in extending the Sensor Model Language (SensorML) 2.0 Profile for the description of the observation capabilities of EO sensors.
Remote Sens.2014, 6(11), 10523-10545; doi:10.3390/rs61110523 (registering DOI) - published 31 October 2014 Show/Hide Abstract
Abstract: Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite  and the RapidEye constellation , further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of “linear” arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented.
Remote Sens.2014, 6(11), 10510-10522; doi:10.3390/rs61110510 - published 30 October 2014 Show/Hide Abstract
Abstract: In this paper, we present a procedure to map subsidence at the regional scale by means of persistent scatterer interferometry (PSI). Subsidence analysis is usually restricted to plain areas and where the presence of this phenomenon is already known. The proposed procedure allows a fast identification of subsidences in large and hilly-mountainous areas. The test area is the Tuscany region, in Central Italy, where several areas are affected by natural and anthropogenic subsidence and where PSI data acquired by the Envisat satellite are available both in ascending and descending orbit. The procedure consists of the definition of the vertical and horizontal components of the deformation measured by satellite at first, then of the calculation of the “real” displacement direction, so that mainly vertical deformations can be individuated and mapped.
Remote Sens.2014, 6(11), 10483-10509; doi:10.3390/rs61110483 - published 30 October 2014 Show/Hide Abstract
Abstract: Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.
Remote Sens.2014, 6(11), 10457-10482; doi:10.3390/rs61110457 - published 29 October 2014 Show/Hide Abstract
Abstract: The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.