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In this paper, we use perpendicular drought indices (PDIs) and precipitation patterns to identify hydrological repartitioning in agricultural regions located in central and south-west Asia over the last decade. The spatio-temporal patterns of surface drought derived from perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) from 250-m MODerate Resolution Imaging Spectroradiometer (MODIS) data in 8-day time steps are compared against two other drought indices: the Standardized Precipitation Index (SPI) as a meteorological drought index and the potential evapotranspiration (ET 0 ) as an agro-meteorological drought index, along with 8-day MODIS Normalized Difference Vegetation Index (NDVI). The coefficient of variation as the ratio of standard deviation over inter-annual mean drought indices, precipitation from Tropical Rainfall Measuring Mission (TRMM) data are employed to analyse trends and shifts of hydrological regimes and its connection to both climate change and anthropogenic activities in the study area. Significant correlations are found between the PDI, the MPDI and precipitation and other applied meteorological and agro-meteorological drought indices. The results show that Central Asia is one of the areas across the globe experiencing dramatic shifts in hydrological regimes, and is being severely impacted by population growth and climate change. While acknowledging previous studies that used the PDI and the MPDI over some study regions in Iran, this paper, however, confirms implementation of higher resolution data (MOD09Q1) in both spatial (250 m) and temporal (8-days) dimensions shows a greater agreement between the drought information extracted by the PDI, MPDI, and field meteorological measurements. Supported by satellite based precipitation observations, we conclude that the perpendicular drought indices can be used as a drought early warning system over arid and semi-arid climatological conditions. [ABSTRACT FROM PUBLISHER]

High-quality time series of remote sensing data are needed for long-term global change studies. Since newer sensors such as MODIS provide pixel-level data quality indicators, these datasets can be employed to filter time series and interpolate invalid data with statistical or contextual methodologies. This study presents a novel automated technique for time-series generation using ranked data quality indicators and stepwise temporal interpolation of short data gaps. The methodology focuses exclusively on the temporal characteristics of each pixel as they would have been observed with good observations. The methodology is exemplarily applied to MODIS NDVI data of the entire country of Germany. Multiple time series, also those generated with other techniques, were compared with a reference set to evaluate the performance of selected parameters. The automated time-series generation approach is less time consuming, and, if parameters are specified with care, the quality is comparable to other approaches. [ABSTRACT FROM PUBLISHER]

Time series generated from remotely sensed data are important for regional to global monitoring, estimating long-term trends, and analysis of variations due to droughts or other extreme events such as El Niño. Temporal vegetation patterns including phenological states, photosynthetic activity, or biomass estimations are an essential input for climate modeling or the analysis of the carbon cycle. However, long-term analysis requires accurate calibration and error estimation, i.e., the quality of the time series determines its usefulness. Although previous attempts of quality assessment have been made with NOAA-AVHRR data, a first rigorous concept of data quality and validation was introduced with the MODIS sensors. This paper presents the time-series generator (TiSeG), which analyzes the pixel-level quality-assurance science data sets of all gridded MODIS land (MODLand) products suitable for time-series generation. According to user-defined settings, the tool visualizes the spatial and temporal data availability by generating two indices, the number of invalid pixels and the maximum gap length. Quality settings can be modified spatially and temporally to account for regional and seasonal variations of data quality. The user compares several quality settings and masks or interpolates the data gaps. This paper describes the functionality of TiSeG and shows an example of enhanced vegetation index time-series generation with numerous settings for Germany. The example indicates the improvements of time series when the quality information is employed with a critical weighting between data quality and the necessary quantity for meaningful interpolation. [ABSTRACT FROM AUTHOR]

The problem of interslice magnetic resonance (MR) image reconstruction arises in a broad range of medical applications. In such cases, there is a need to approximate information present in the original subject that is not reflected in contiguously acquired MR images because of hardware sampling limitations. In the context of vascular morphology reconstruction, this information is required in order for subsequent visualization and computational analysis of blood vessels to be most effective. Toward that end we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation (ACGI) to function as a precursor to visualization and computational analysis. ACGI has previously been implemented in addressing various problems including video coding and tracking. This paper focuses on the novel application of the technique to medical image processing. ACGI combines features of optical flow-based and blockbased motion estimation algorithms to enhance insufficiently dense MR data sets accurately with a minimal degree of computational complexity. The resulting enhanced data sets describe vascular geometries. These reconstructions can then be used as visualization tools and in conjunction with computational fluid dynamics (CFD) simulations to offer the pressure and velocity information necessary to quantify power loss. The proposed ACGI methodology is envisioned ultimately to play a role in surgical planning aimed at producing optimal vascular configurations for successful surgical outcomes. [ABSTRACT FROM AUTHOR]