Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series
The main objective of this work is to assess and use Landsat TM5 for the inventory and study of the evolution of water quality in gravel pit ponds within the natural park, Parque Regional del Sureste (PRSE), from 1984 to 2011. First, the normalized difference water index (NDWI) was applied to 230 La...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-09-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2019.1674136 |
| _version_ | 1797661129341665280 |
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| author | Carolina Echavarría-Caballero José Antonio Domínguez-Gómez Concepción González-García María Jesús García-García |
| author_facet | Carolina Echavarría-Caballero José Antonio Domínguez-Gómez Concepción González-García María Jesús García-García |
| author_sort | Carolina Echavarría-Caballero |
| collection | DOAJ |
| description | The main objective of this work is to assess and use Landsat TM5 for the inventory and study of the evolution of water quality in gravel pit ponds within the natural park, Parque Regional del Sureste (PRSE), from 1984 to 2011. First, the normalized difference water index (NDWI) was applied to 230 Landsat TM5 images to distinguish water and non-water information. Next, the surface reflectance derived from Landsat TM5 images atmospherically corrected with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) was compared to water-leaving reflectance measured in the field, showing a significant overestimation of surface reflectance derived from Landsat data. Thus, a new regression equation was proposed linking the in situ Secchi depth (SD) measurements to surface reflectance measured by Landsat 5. This regression was validated and subsequently applied to every image, providing an SD value for each pixel in the time series. Finally, the retrieved SD values were used to compute the trophic state of water bodies according to the Organization for Economic Co-operation and Development (OECD) classification. The results show an increase in the surface area of water bodies in the PRSE from 1984 to 2006, as well as an improvement in their water quality. |
| first_indexed | 2024-03-11T18:40:36Z |
| format | Article |
| id | doaj.art-9b310dbc2e3c488f9bc7a946dbdc89c9 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:36Z |
| publishDate | 2019-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-9b310dbc2e3c488f9bc7a946dbdc89c92023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712019-09-0145569170610.1080/07038992.2019.16741361674136Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time SeriesCarolina Echavarría-Caballero0José Antonio Domínguez-Gómez1Concepción González-García2María Jesús García-García3Universidad Politécnica de Madrid, E.T.S. Ingeniería de Montes, Forestal y del Medio NaturalCrop Research InstituteUniversidad Politécnica de Madrid, E.T.S. Ingeniería de Montes, Forestal y del Medio NaturalUniversidad Politécnica de Madrid, E.T.S. Ingeniería de Montes, Forestal y del Medio NaturalThe main objective of this work is to assess and use Landsat TM5 for the inventory and study of the evolution of water quality in gravel pit ponds within the natural park, Parque Regional del Sureste (PRSE), from 1984 to 2011. First, the normalized difference water index (NDWI) was applied to 230 Landsat TM5 images to distinguish water and non-water information. Next, the surface reflectance derived from Landsat TM5 images atmospherically corrected with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) was compared to water-leaving reflectance measured in the field, showing a significant overestimation of surface reflectance derived from Landsat data. Thus, a new regression equation was proposed linking the in situ Secchi depth (SD) measurements to surface reflectance measured by Landsat 5. This regression was validated and subsequently applied to every image, providing an SD value for each pixel in the time series. Finally, the retrieved SD values were used to compute the trophic state of water bodies according to the Organization for Economic Co-operation and Development (OECD) classification. The results show an increase in the surface area of water bodies in the PRSE from 1984 to 2006, as well as an improvement in their water quality.http://dx.doi.org/10.1080/07038992.2019.1674136 |
| spellingShingle | Carolina Echavarría-Caballero José Antonio Domínguez-Gómez Concepción González-García María Jesús García-García Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series Canadian Journal of Remote Sensing |
| title | Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series |
| title_full | Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series |
| title_fullStr | Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series |
| title_full_unstemmed | Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series |
| title_short | Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series |
| title_sort | assessment of landsat 5 images atmospherically corrected with ledaps in water quality time series |
| url | http://dx.doi.org/10.1080/07038992.2019.1674136 |
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