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Remote Sensing and Wetland Ecology: a South African Case Study

Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standa...

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Autores principales: De Roeck, Els R., Verhoest, Niko E.C., Miya, Mtemi H., Lievens, Hans, Batelaan, Okke, Thomas, Abraham, Brendonck, Luc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675558/
https://www.ncbi.nlm.nih.gov/pubmed/27879892
http://dx.doi.org/10.3390/s8053542
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author De Roeck, Els R.
Verhoest, Niko E.C.
Miya, Mtemi H.
Lievens, Hans
Batelaan, Okke
Thomas, Abraham
Brendonck, Luc
author_facet De Roeck, Els R.
Verhoest, Niko E.C.
Miya, Mtemi H.
Lievens, Hans
Batelaan, Okke
Thomas, Abraham
Brendonck, Luc
author_sort De Roeck, Els R.
collection PubMed
description Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 – 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands.
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spelling pubmed-36755582013-06-19 Remote Sensing and Wetland Ecology: a South African Case Study De Roeck, Els R. Verhoest, Niko E.C. Miya, Mtemi H. Lievens, Hans Batelaan, Okke Thomas, Abraham Brendonck, Luc Sensors (Basel) Article Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 – 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands. Molecular Diversity Preservation International (MDPI) 2008-05-26 /pmc/articles/PMC3675558/ /pubmed/27879892 http://dx.doi.org/10.3390/s8053542 Text en © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
De Roeck, Els R.
Verhoest, Niko E.C.
Miya, Mtemi H.
Lievens, Hans
Batelaan, Okke
Thomas, Abraham
Brendonck, Luc
Remote Sensing and Wetland Ecology: a South African Case Study
title Remote Sensing and Wetland Ecology: a South African Case Study
title_full Remote Sensing and Wetland Ecology: a South African Case Study
title_fullStr Remote Sensing and Wetland Ecology: a South African Case Study
title_full_unstemmed Remote Sensing and Wetland Ecology: a South African Case Study
title_short Remote Sensing and Wetland Ecology: a South African Case Study
title_sort remote sensing and wetland ecology: a south african case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675558/
https://www.ncbi.nlm.nih.gov/pubmed/27879892
http://dx.doi.org/10.3390/s8053542
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