Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for...
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MDPI AG
2022-03-01
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| Online Access: | https://www.mdpi.com/2306-5729/7/3/36 |
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| author | Temesgen Alemayehu Abera Ilja Vuorinne Martha Munyao Petri K. E. Pellikka Janne Heiskanen |
| author_facet | Temesgen Alemayehu Abera Ilja Vuorinne Martha Munyao Petri K. E. Pellikka Janne Heiskanen |
| author_sort | Temesgen Alemayehu Abera |
| collection | DOAJ |
| description | Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers. |
| first_indexed | 2024-03-09T13:46:54Z |
| format | Article |
| id | doaj.art-2ab50ea26aa04f11a7d0ce1de8ccdcb2 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T13:46:54Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-2ab50ea26aa04f11a7d0ce1de8ccdcb22023-11-30T20:58:24ZengMDPI AGData2306-57292022-03-01733610.3390/data7030036Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic DataTemesgen Alemayehu Abera0Ilja Vuorinne1Martha Munyao2Petri K. E. Pellikka3Janne Heiskanen4Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, FinlandTaita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers.https://www.mdpi.com/2306-5729/7/3/36Taita Tavetaland coverreference databasemachine learningSentinel-1Sentinel-2 |
| spellingShingle | Temesgen Alemayehu Abera Ilja Vuorinne Martha Munyao Petri K. E. Pellikka Janne Heiskanen Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data Data Taita Taveta land cover reference database machine learning Sentinel-1 Sentinel-2 |
| title | Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data |
| title_full | Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data |
| title_fullStr | Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data |
| title_full_unstemmed | Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data |
| title_short | Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data |
| title_sort | land cover map for multifunctional landscapes of taita taveta county kenya based on sentinel 1 radar sentinel 2 optical and topoclimatic data |
| topic | Taita Taveta land cover reference database machine learning Sentinel-1 Sentinel-2 |
| url | https://www.mdpi.com/2306-5729/7/3/36 |
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