On the synergistic use of Envisat/ASAR imagery and ancillary spatial data for monitoring Doñana wetlands

A time series of Envisat/ASAR images was acquired for monitoring the inundation evolution in Doñana National Park wetlands. Flood mapping from the ASAR data alone was unfeasible due to the complex casuistic of Doñana’s covers backscattering. A digital terrain model and a vegetation map were then utilized to complement the ASAR data. The use of irregular filtering neighborhoods adapted to the terrain elevation contours drastically improved the ASAR images filtering. Pixels highly likely to belong to the different cover classes were selected by combining the vegetation map, the DTM-based sub-basin segmentation and previous knowledge on the covers backscattering. The regions were grown based on the Mahalanobis distance, yielding accurate classification maps of flooded and emerged cover types.Postprint (published version

Local isotropy indicator for SAR image filtering: application to Envisat/ASAR images of the Doñana Wetland

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper explores a geometrical and computationally simple operator, named Ds, for local isotropy assessment on SAR images. It is assumed that isotropic intensity distributions in natural areas, either textured or nontextured, correspond to a single cover class. Ds is used to measure isotropy in processing neighborhoods and decide if they can be considered as belonging to a unique cover class. The speckle statistical properties are used to determine suitable Ds thresholds for discriminating heterogeneous targets from isotropic cover types at different window sizes. An assessment of Ds as an edge detector showed sensitivities similar to those of the ratio edge operator for straight, sharp boundaries, centered in the processing window, but significantly better sensitivity for detecting heterogeneities during the window expansion in multiresolution filtering. Furthermore, Ds presents the advantage versus the ratio edge coefficient of being rotationally invariant, and its computation indicates the direction of the main intensity gradient in the processing window. The Ds operator is used in a multiresolution fashion for filtering ASAR scenes of the Doñana wetland. The intensities in isotropic areas are averaged in order to flatten fluctuations within cover types and facilitate a subsequent land cover classification. The results show high degree of smoothing within textured cover classes, plus effective spatial adaptation to gradients and irregular boundaries, substantiating the usefulness of this operator for filtering SAR data of natural areas with the purpose of classification.Peer ReviewedPostprint (author's final draft

Local texture stationarity indicator for filtering Doñana wetlands SAR images

This paper defines a new operator, named Ds, for local texture stationarity assessment on SAR images. . The aim is to discriminate heterogeneous targets from land cover types of high normalized variance values, as those observed in flooded vegetation areas of Doñana wetlands. Suitable Ds thresholds for such discrimination were estimated for different window sizes through Monte Carlo simulations of synthetic textures. Maximum stationary texture windows were then determined on Doñana ASAR scenes by Ds multi-resolution thresholding and averaging was applied within. Results reveal the substantial degree of smoothing achieved over high variance cover types, while edges among different targets were properly preserved.Postprint (published version

NRCS-CN estimation from onsite and remote sensing data for management of a reservoir in the Eastern Pyrenees

Onsite and Earth observation (EO) data are used for the calibration of the Natural Resources Conservation Service curve number (NRCS-CN) value in a hydrological simulation model. The model was developed for La Muga catchment (eastern Pyrenees) highly vulnerable to flood and drought episodes. It is an integral part of a regional reservoir management tool, which aims at minimizing the flood risk while maximizing the preservation of water storage. The CN values were optimized for five recorded events for the model to match the observed hydrographs at the reservoir when supported with the measured rainfall intensities. This study also investigates the possibilities of using antecedent moisture conditions (AMC) retrieved from satellite data to inform the selection of the NRCS-CN losses parameter. A good correlation was found between the calibrated CN values and the AMC obtained from satellite data. This correlation highlights the interest in using EO data to update NRCS-CN estimates. This advances in hydrologic-hydraulic coupled modeling combined with new remote sensing datasets present valuable opportunities and potential benefits for flood risk management and water resources preservation

Modelación numérica (2D y 3D) de la hidrodinámica en el entorno de la confluencia Ebro-Segre

Postprint (published version

On the synergistic use of Envisat/ASAR imagery and ancillary spatial data for monitoring Doñana wetlands

A time series of Envisat/ASAR images was acquired for monitoring the inundation evolution in Doñana National Park wetlands. Flood mapping from the ASAR data alone was unfeasible due to the complex casuistic of Doñana’s covers backscattering. A digital terrain model and a vegetation map were then utilized to complement the ASAR data. The use of irregular filtering neighborhoods adapted to the terrain elevation contours drastically improved the ASAR images filtering. Pixels highly likely to belong to the different cover classes were selected by combining the vegetation map, the DTM-based sub-basin segmentation and previous knowledge on the covers backscattering. The regions were grown based on the Mahalanobis distance, yielding accurate classification maps of flooded and emerged cover types

On the synergistic use of Envisat/ASAR imagery and ancillary spatial data for monitoring Doñana wetlands

A time series of Envisat/ASAR images was acquired for monitoring the inundation evolution in Doñana National Park wetlands. Flood mapping from the ASAR data alone was unfeasible due to the complex casuistic of Doñana’s covers backscattering. A digital terrain model and a vegetation map were then utilized to complement the ASAR data. The use of irregular filtering neighborhoods adapted to the terrain elevation contours drastically improved the ASAR images filtering. Pixels highly likely to belong to the different cover classes were selected by combining the vegetation map, the DTM-based sub-basin segmentation and previous knowledge on the covers backscattering. The regions were grown based on the Mahalanobis distance, yielding accurate classification maps of flooded and emerged cover types

Enhancing the retrieval of stream surface temperature from Landsat data

International audienceThermal images of water bodies often show a radiance gradient perpendicular to the banks. This effect is frequently due to mixed land and water thermal pixels. In the case of the Landsat images, radiance mixing can also affect pure water pixels due the cubic convolution resampling of the native thermal measurements. Some authors recommended a general-purpose margin of two thermal pixels to the banks or a minimum river width of three pixels, to avoid near bank effects in water temperature retrievals. Given the relatively course spatial resolution of satellite thermal sensors, the three pixel margin severely restricts their application to temperature mapping in many rivers. This study proposes a new algorithm to enhance the retrieval of stream surface temperature using Landsat 8 thermal data, although it is also applicable to Landsat 7 and Landsat 5. The aim is not to perform a subpixel radiance unmixing but to refine the selection of unmixed, reliable pixels for temperature mapping. For this purpose, the spatial arrangement of native Landsat thermal pixels is approximated, and pure water pixels in the downscaled thermal band are selected accordingly. The least-favourable cubic convolution near-bank radiance mixing is simulated on image basis. Only pure thermal water pixels unaffected by the simulated worst-case resampling are selected. The algorithm allowed retrieving water surface temperature in reaches down to 120 m wide, clearly improving the existing three pixel, i.e. 300 m for Landsat 8, recommendation. The enhancing algorithm was applied to a reach in the Ebro River reach, Spain. It provided spatially distributed temperatures in narrow parts, upstream and downstream of a wide reservoir, offering new insight of the overall impact of the reservoir over the river thermal regime

Wetland inundation monitoring by the synergistic use of ENVISAT/ASAR imagery and ancilliary spatial data

on the water resources. In a scenario of climate change and increased anthropogenic pressure, detailedmonitoring of the water resources provides a fundamental tool to assess the ecosystem health and identify potential threats. Doñana wetlands, in Southwest Spain, dry out every summer and progressively flood in fall and winter to a maximum extent of 30,000 ha. Thewetland filling up processwasmonitored in detail during the 2006–2007 hydrologic cycle bymeans of twenty-one Envisat/ASAR scenes, acquired at different incidence angles in order to maximize the observation frequency. Flood mapping from the two uncorrelated ASAR channel data alone was proved unfeasible due to the complex casuistic of Doñana cover backscattering. This study addresses the synergistic utilization of the ASAR data together with Doñana's digital elevation model and vegetation map in order to achieve flood mapping. Filtering and clustering algorithms were developed for the automated generation of Doñana floodmaps from the ASAR images. The use of irregular filtering neighborhoods adapted to the elevation contours drastically improved the ASAR image filtering. Edge preservation was excellent, since natural edges closely follow terrain contours. Isotropic neighborhoodswere assumed of a single class and their intensitieswere averaged. As a result, intensity fluctuations due to speckle and texture over areas of the same cover type were smoothed remarkably. The clustering and classification algorithm operate on individual sub-basins, as the pixel elevation is more accurately related to the cover classes within them. Vegetation and elevation maps plus knowledge of Doñana backscattering characteristics from preceding studies were initially used to select seed pixelswith high confidence on their class membership. Next, a region growing algorithm extends the seed regions with new pixels based on their planimmetric adjacency and backscattering Mahalanobis distance to the seeds. During the seed region growth, new pixels' possible classes are not constrained to their cover type according to the vegetation map, so the algorithm is able to capture temporal changes in the vegetation spatial distribution. Comparison of the resultant classification and concurrent ground truth yielded 92% of flood mapping accuracy. The flood mapping method is applicable to the available ASAR images of Doñana fromsix other hydrologic cycles.Postprint (published version

Analysis of ASAR/Envisat polarimetric backscattering characteristics of Doñana National Park wetlands

Doñana National Park wetlands, in southwest Spain, undergo yearly cycles of inundation and drying out. These cycles, together with great extensions of annual helophytes, make of Doñana a rapidly changing environment. 43 ASAR/Envisat images of Doñana in HH, VV and HV polarizations were acquired throughout the hydrological year 2006/07 with the aim to monitor in detail an entire flooding cycle. The images were ordered in the seven ASAR swaths to achieve high observation frequency. Backscattering temporal signatures of two main land cover types were obtained in the three polarization configurations and six ASAR swaths. Polarimetric behavior of the signatures is analyzed with the aid of extensive site data, such as a precise digital elevation model and continuous records of water level and meteorological parameters. Conclusions on the feasibility to discriminate emerged versus flooded land are derived for the different incidence angles, land cover types and phenological stages.Peer Reviewe