Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data
The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human-made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, aut...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
2008
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/40722 |
| _version_ | 1855537750746333184 |
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| author | Islam, Aminul Thenkabail, Prasad S. Kulawardhana, Wasantha Alankara, Ranjith Gunasinghe, Sarath Edussuriya, C. Gunawardana, A. |
| author_browse | Alankara, Ranjith Edussuriya, C. Gunasinghe, Sarath Gunawardana, A. Islam, Aminul Kulawardhana, Wasantha Thenkabail, Prasad S. |
| author_facet | Islam, Aminul Thenkabail, Prasad S. Kulawardhana, Wasantha Alankara, Ranjith Gunasinghe, Sarath Edussuriya, C. Gunawardana, A. |
| author_sort | Islam, Aminul |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human-made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, automated methods were investigated in order to rapidly delineate wetlands; this involved using: (a) algorithms on SRTM DEM data, (b) thresholds of SRTM-derived slopes, (c) thresholds of ETM+ spectral indices and wavebands and (d) automated classification techniques using ETM+ data. These algorithms and thresholds using SRTM DEM data either over-estimated or under-estimated stream densities (S d) and stream frequencies (S f), often generating spurious (non-existent) streams and/or, at many times, providing glaring inconsistencies in the precise physical location of the streams. The best of the ETM+-derived indices and wavebands either had low overall mapping accuracies and/or high levels of errors of omissions and/or errors of commissions. Second, given the failure of automated approaches, semi-automated approaches were investigated; this involved the: (a) enhancement of images through ratios to highlight wetlands from non-wetlands, (b) display of enhanced images in red, green, blue (RGB) false colour composites (FCCs) to highlight wetland boundaries, (c) digitizing the enhanced and displayed images to delineate wetlands from non-wetlands and (d) classification of the delineated wetland areas into various wetland classes. The best FCC RGB displays of ETM+ bands for separating wetlands from other land units were: (a) ETM+4/ETM+7, ETM+4/ETM+3, ETM+4/ETM+2, (b) ETM+4, ETM+3, ETM+5 and (c) ETM+3, ETM+2, ETM+1. In addition, the SRTM slope threshold of less than 1% was very useful in delineating higher-order wetland boundaries. The wetlands were delineated using the semi-automated methods with an accuracy of 96% as determined using field-plot data. The methodology was evaluated for the Ruhuna river basin in Sri Lanka, which has a diverse landscape ranging from sea shore to hilly areas, low to very steep slopes (0 to 50 ), arid to semi-arid zones and rain fed to irrigated lands. Twenty-four per cent (145 733 ha) of the total basin area was wetlands as a result of a high proportion of human-made irrigated areas, mainly under rice cropping. The wetland classes consisted of irrigated areas, lagoons, mangroves, natural vegetation, permanent marshes, salt pans, lagoons, seasonal wetlands and water bodies. The overall accuracies of wetland classes varied between 87% and 94% (K hat = 0.83 to 0.92) with errors of omission less than 13% and errors of commission less than 1%. |
| format | Journal Article |
| id | CGSpace40722 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2008 |
| publishDateRange | 2008 |
| publishDateSort | 2008 |
| record_format | dspace |
| spelling | CGSpace407222025-03-11T09:50:20Z Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data Islam, Aminul Thenkabail, Prasad S. Kulawardhana, Wasantha Alankara, Ranjith Gunasinghe, Sarath Edussuriya, C. Gunawardana, A. wetlands mapping satellite surveys remote sensing The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human-made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, automated methods were investigated in order to rapidly delineate wetlands; this involved using: (a) algorithms on SRTM DEM data, (b) thresholds of SRTM-derived slopes, (c) thresholds of ETM+ spectral indices and wavebands and (d) automated classification techniques using ETM+ data. These algorithms and thresholds using SRTM DEM data either over-estimated or under-estimated stream densities (S d) and stream frequencies (S f), often generating spurious (non-existent) streams and/or, at many times, providing glaring inconsistencies in the precise physical location of the streams. The best of the ETM+-derived indices and wavebands either had low overall mapping accuracies and/or high levels of errors of omissions and/or errors of commissions. Second, given the failure of automated approaches, semi-automated approaches were investigated; this involved the: (a) enhancement of images through ratios to highlight wetlands from non-wetlands, (b) display of enhanced images in red, green, blue (RGB) false colour composites (FCCs) to highlight wetland boundaries, (c) digitizing the enhanced and displayed images to delineate wetlands from non-wetlands and (d) classification of the delineated wetland areas into various wetland classes. The best FCC RGB displays of ETM+ bands for separating wetlands from other land units were: (a) ETM+4/ETM+7, ETM+4/ETM+3, ETM+4/ETM+2, (b) ETM+4, ETM+3, ETM+5 and (c) ETM+3, ETM+2, ETM+1. In addition, the SRTM slope threshold of less than 1% was very useful in delineating higher-order wetland boundaries. The wetlands were delineated using the semi-automated methods with an accuracy of 96% as determined using field-plot data. The methodology was evaluated for the Ruhuna river basin in Sri Lanka, which has a diverse landscape ranging from sea shore to hilly areas, low to very steep slopes (0 to 50 ), arid to semi-arid zones and rain fed to irrigated lands. Twenty-four per cent (145 733 ha) of the total basin area was wetlands as a result of a high proportion of human-made irrigated areas, mainly under rice cropping. The wetland classes consisted of irrigated areas, lagoons, mangroves, natural vegetation, permanent marshes, salt pans, lagoons, seasonal wetlands and water bodies. The overall accuracies of wetland classes varied between 87% and 94% (K hat = 0.83 to 0.92) with errors of omission less than 13% and errors of commission less than 1%. 2008 2014-06-13T14:48:15Z 2014-06-13T14:48:15Z Journal Article https://hdl.handle.net/10568/40722 en Limited Access Islam, Aminul; Thenkabail, Prasad S.; Kulawardhana, Wasantha; Alankara, Ranjith; Gunasinghe, Sarath; Edussuriya, C.; Gunawardana, A. 2008. Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data. International Journal of Remote Sensing, 29:(24):7077-7106. |
| spellingShingle | wetlands mapping satellite surveys remote sensing Islam, Aminul Thenkabail, Prasad S. Kulawardhana, Wasantha Alankara, Ranjith Gunasinghe, Sarath Edussuriya, C. Gunawardana, A. Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title | Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title_full | Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title_fullStr | Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title_full_unstemmed | Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title_short | Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data |
| title_sort | semi automated methods for mapping wetlands using landsat etm and srtm data |
| topic | wetlands mapping satellite surveys remote sensing |
| url | https://hdl.handle.net/10568/40722 |
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