Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series
Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series w...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7402 |
| _version_ | 1789406646581067776 |
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| author | Aguilar Torres, Manuel Ángel Nemmaoui, Abderrahim Novelli, Antonio Aguilar Torres, Fernando José García Lorca, Andrés Miguel |
| author_facet | Aguilar Torres, Manuel Ángel Nemmaoui, Abderrahim Novelli, Antonio Aguilar Torres, Fernando José García Lorca, Andrés Miguel |
| author_sort | Aguilar Torres, Manuel Ángel |
| collection | DSpace |
| description | Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7402 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-74022023-10-10T11:07:37Z Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series Aguilar Torres, Manuel Ángel Nemmaoui, Abderrahim Novelli, Antonio Aguilar Torres, Fernando José García Lorca, Andrés Miguel Landsat 8 WorldView-2 time series object-based classification greenhouse mapping decision tree Moment Distance Index Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively. 2020-01-16T12:11:29Z 2020-01-16T12:11:29Z 2016-06-18 info:eu-repo/semantics/article 2072-4292 http://hdl.handle.net/10835/7402 en https://www.mdpi.com/2072-4292/8/6/513 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | Landsat 8 WorldView-2 time series object-based classification greenhouse mapping decision tree Moment Distance Index Aguilar Torres, Manuel Ángel Nemmaoui, Abderrahim Novelli, Antonio Aguilar Torres, Fernando José García Lorca, Andrés Miguel Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title | Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title_full | Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title_fullStr | Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title_full_unstemmed | Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title_short | Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series |
| title_sort | object-based greenhouse mapping using very high resolution satellite data and landsat 8 time series |
| topic | Landsat 8 WorldView-2 time series object-based classification greenhouse mapping decision tree Moment Distance Index |
| url | http://hdl.handle.net/10835/7402 |
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