4.0 Article Data Paper

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

期刊

DATA
卷 7, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/data7030036

关键词

Taita Taveta; land cover; reference database; machine learning; Sentinel-1; Sentinel-2

资金

  1. European Union DG International Partnerships under the DeSIRA (Development of Smart Innovation through Research in Agriculture) program through the ESSA (Earth observation and environmental sensing for climate-smart sustainable agropastoral ecosystem trans [FOOD/2020/418132]

向作者/读者索取更多资源

Taita Taveta County, known for its biodiversity, has been mapped using satellite observations and machine learning algorithms. The land cover map produced has an overall accuracy of 81% and provides valuable information for land use planning, conservation management, and research.
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.

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