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| − | ==1 Title, abstract and keywords<!-- Your document should start with a concise and informative title. Titles are often used in information-retrieval systems. Avoid abbreviations and formulae where possible. Capitalize the first word of the title. | + | ==Abstract== |
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| − | Provide a maximum of 6 keywords, and avoiding general and plural terms and multiple concepts (avoid, for example, 'and', 'of'). Be sparing with abbreviations: only abbreviations firmly established in the field should be used. These keywords will be used for indexing purposes.
| + | Wetlands are among the most ecologically important ecosystems on Earth and their sustainability depends critically on the water resources. In a scenario of climate change and increased anthropogenic pressure, detailed monitoring of the water resources provides a fundamental tool to assess the ecosystem health and identify potential threats. |
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| − | An abstract is required for every document; it should succinctly summarize the reason for the work, the main findings, and the conclusions of the study. Abstract is often presented separately from the article, so it must be able to stand alone. For this reason, references and hyperlinks should be avoided. If references are essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself. -->==
| + | Doñana wetlands, in Southwest Spain, dry out every summer and progressively flood in fall and winter to a maximum extent of 30,000 ha. The wetland filling up process was monitored in detail during the 2006–2007 hydrologic cycle by means 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. |
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| | + | Filtering and clustering algorithms were developed for the automated generation of Doñana flood maps 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 neighborhoods were assumed of a single class and their intensities were averaged. As a result, intensity fluctuations due to speckle and texture over areas of the same cover type were smoothed remarkably. |
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| | + | 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 pixels with 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. |
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| − | | + | 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 from six other hydrologic cycles. |
| − | ==2 The main text<!-- You can enter and format the text of this document by selecting the ‘Edit’ option in the menu at the top of this frame or next to the title of every section of the document. This will give access to the visual editor. Alternatively, you can edit the source of this document (Wiki markup format) by selecting the ‘Edit source’ option.
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| − | Most of the documents in Scipedia are written in English (write your manuscript in American or British English, but not a mixture of these). Anyhow, specific publications in other languages can be published in Scipedia. In any case, the documents published in other languages must have an abstract written in English.
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| − | 2.1 Subsections
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| − | Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1, 1.2, etc. and then 1.1.1, 1.1.2, ... Use this numbering also for internal cross-referencing: do not just refer to 'the text'. Any subsection may be given a brief heading. Capitalize the first word of the headings.
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| − | 2.2 General guidelines
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| − | Some general guidelines that should be followed in your manuscripts are:
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| − | * Use decimal points (not commas); use a space for thousands (10 000 and above).
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| − | * Follow internationally accepted rules and conventions. In particular use the international system of units (SI). If other quantities are mentioned, give their equivalent in SI.
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| − | 2.3 Tables, figures, lists and equations
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| − | Please insert tables as editable text and not as images. Tables should be placed next to the relevant text in the article. Number tables consecutively in accordance with their appearance in the text and place any table notes below the table body. Be sparing in the use of tables and ensure that the data presented in them do not duplicate results described elsewhere in the article.
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| − | 1. The first entry in this list
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| − | 2.4 Supplementary material
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| − | Supplementary material can be inserted to support and enhance your article. This includes video material, animation sequences, background datasets, computational models, sound clips and more. In order to ensure that your material is directly usable, please provide the files with a preferred maximum size of 50 MB. Please supply a concise and descriptive caption for each file. -->==
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| − | ==3 Bibliography<!--
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| − | ==4 Acknowledgments<!-- Acknowledgments should be inserted at the end of the document, before the references section. -->==
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| − | ==5 References<!--[1] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Article code. Available: http://www.scipedia.com/ucode.
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| − | [2] Author, A. and Author, B. (Year) Title of the article. Title of the Publication. Volume number, first page-last page.
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| − | [3] Author, C. (Year). Title of work: Subtitle (edition.). Volume(s). Place of publication: Publisher.
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| − | [4] Author of Part, D. (Year). Title of chapter or part. In A. Editor & B. Editor (Eds.), Title: Subtitle of book (edition, inclusive page numbers). Place of publication: Publisher.
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| − | [6] Institution or author. Title of the document. Year. [Online] (Date consulted: day, month and year). Available: http://www.scipedia.com/document.pdf.
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Wetlands are among the most ecologically important ecosystems on Earth and their sustainability depends critically on the water resources. In a scenario of climate change and increased anthropogenic pressure, detailed monitoring 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. The wetland filling up process was monitored in detail during the 2006–2007 hydrologic cycle by means 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 flood maps 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 neighborhoods were assumed of a single class and their intensities were 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 pixels with 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 from six other hydrologic cycles.
