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Across the oceans shorelines, monitoring the topography of the intertidal zone is generally challenging. The present study is motivated by the recognized role of the intertidal topography in the near-shore hydrodynamics. We consider the region of Chittagong (northern Bay of Bengal) because of its propensity to powerful cyclone surges and associated inundation hazard. So as to curb the lack of in situ knowledge of intertidal topography, we present an original procedure relying on spaceborne optical imagery. Our method essentially amounts to a water line detection performed at various tidal levels. We apply our procedure to the recent PROBA-V (Project for On-Board Autonomy-Vegetation) multi-spectral imagery mission. The first step of our procedure concerns the shoreline extraction. PROBA-V imagery consists of four bands (Red, Blue, near-infrared - NIR, short-wave infrared - SWIR), which are then combined to generate an artificial red-green-blue (RGB) image. This RGB image is then converted into the hue-saturation-value (HSV) colour space. A simple thresholding is applied to hue and value channels to separate water masses from land masses. This process is applied to several images taken at different water levels (i.e. different parts of the tidal cycle) and the corresponding water lines are inferred. To estimate the altitude level of the water lines, we rely on tidal observations from two gauges located at Chittagong and Cox's Bazar. We operate an ad-hoc extrapolation of the point-wise gauge data to generate a synthetic tidal water level record all along the shoreline. These synthetic tidal heights are then combined with the shorelines to generate the final digital elevation model (DEM). The DEM we generated covers a 40-km long stretch of shoreline around Chittagong city. We assessed this DEM by comparison with two independent data sets based on in situ surveys as well as on Pléiades spaceborne stereoscopy. We conclude that our DEM is accurate within 1 m to 2 m, which is within the error bar of these validation data sets. Our procedure being essentially objective, it is easy to automate, for processing of other imagery satellite, including at high resolution and/or in real time. [ABSTRACT FROM AUTHOR]

Complementary analysis of satellite mission data (altimetry, Moderate Resolution Imaging Spectroradiometers (MODIS)) and climate fields over 2000–2009 was conducted to investigate the variability of the water cover surfaces and the geographical source of water inflowing into the Lake Poopó system. The results suggest that over the time span of 2000–2009 a great part of the variability of the Poopó system originates from geographic sources other than Lake Titicaca. Possible alternative causes include climate change inducing increased temperatures and greater evaporation rates along the Altiplano; increased glacier and snow melting over the Andes Cordillera; and a potential increased anthropogenic water use (such as irrigation, minery, etc.) throughout the path of the Desaguadero River (which connects lakes Titicaca and Poopó). This change in the hydrology of this region could lead to a collapse of water supplies and endanger the already fragile Poopó system as well as the regional socioeconomic system, which closely depends upon it. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an approach for monitoring land use over the semi-arid Tensift–Marrakech plain, a 3000 km2 intensively cropped area in Morocco. In this objective, the linear unmixing method is adapted to process a 6-year archive of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 16-day composite data at 250 m spatial resolution. The result of the processing is a description of land use in terms of fractions of three predominant classes: orchard, non-cultivated area and annual crop. The typical signatures of land classes – endmembers – are retrieved on a yearly basis using an automated algorithm that detects the most pure pixels in the study area. The algorithm first extracts typical NDVI profiles as potential endmembers, then selects the profiles that have the best ability to reproduce the variability of MODIS NDVI time series over the study area. The endmembers appear stable over the 6 years of study and coherent with the vegetation seasonality of the three targeted land classes. Validation data allow us to quantify the error on land-use fractions to about 0.10 at 1 km resolution. Land-use estimates are consistent in space and time: the orchard class is stable, and differences in water availability (irrigation and rainfall) partly explain a part of the inter-annual variations observed for the annual crop class. The advantages and drawbacks of the approach are discussed. [ABSTRACT FROM AUTHOR]

Time series of remotely sensed imagery have proven to be a valuable tool to study the dynamics of the Earth surface. Some important features have been put in evidence in time series of greenness (measured as Normalized Difference Vegetation Index (NDVI)) fields at global and continental scales, and linked to changes in atmospheric variables and/or direct human impact. Analysing the same kind of information at regional scales is more challenging, as the increased detail implies increased complexity. We present a case of study of visualization coupled to statistical analysis based on Empirical Orthogonal Functions (EOF) of the seasonal pattern of greenness in SW Europe (52° N, 11° W to 30° N, 5° E) between 1999 and 2003. Our goal was to determine whether trends could be identified in the inter-annual change of some regions. We have found that for most of the area of study, inter-annual change in seasonal pattern of greenness consisted of an oscillation along a linear trajectory on the plane defined by the first two EO components. Nevertheless, a few scattered sites and two larger regions show trends, as both the direction and the sense of the inter-annual changes remained consistent from year to year during the period of study. Considering the dependency of the seasonal course of greenness on annual patterns of temperature and precipitation, the extent covered by these areas and the diversity of land cover that they include strongly suggest a response to changes in the seasonal patterns of key atmospheric variables for the same period. While the period of study is short, the particular response of these regions deserves further attention, including the analysis of longer series of NDVI and other satellite products, as well as the exploration of the actual relationships to atmospheric variables. [ABSTRACT FROM AUTHOR]

Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each mixed pixel, we propose to address the downscaling issue through the generalization of varying-time regression models for longitudinal data and/or functional data by introducing random individual effects. The estimators are built by expanding the mixed pixels trajectories with B-splines functions and maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP formula allows then to get the 'best possible' estimations of the local temporal responses of each crop when observing mixed pixels trajectories. We show that this model has many potential applications in remote sensing, and an interesting one consists of coupling high and low spatial resolution images in order to perform temporal interpolation of high spatial resolution images (20 m), increasing the knowledge on particular crops in very precise locations. The unmixing and temporal high-resolution interpolation approaches are illustrated on remote-sensing data obtained on the South-Western France during the year 2002. [ABSTRACT FROM AUTHOR]