Abstract
The aim of the present study is to test ESA’s Sentinel-2 (S2) satellites (S2A and S2B) for an efficient quantification of land cover (LC) and forest compositions in a tropical environment southwest of Mount Kenya. Furthermore, outcome of the research is used to validate ESA’s S2 prototype LC 20 m map of Africa that was produced in 2016. A decision tree that is based on significant altitudinal ranges was used to discriminate four natural tree compositions that occur within the investigation area. In addition, the classification process was supported by Google Earth images, and land use (LU) data that were provided by the local Kenyan Forest Service (KFS). Final classification products include four LC classes and five subclasses of forest (four natural forest subclasses plus one non-natural forest class). Results of the Jeffries-Matusita (JM) distance test show significant differences in spectral separability between all classes. Furthermore, the study identifies spectral signatures and significant wavelengths for a classification of all LC classes and forest subclasses where wavelengths of SWIR and the red-edge domain show highest importance for the discrimination of tree compositions. Finally, considerable differences can be seen between the utilized multi-temporal classification set (total of 39 bands from three acquisition dates) and ESA’s S2 prototype LC 20 m map of Africa 2016. A visual comparison of ESA’s prototype map within the investigation area indicates an overrepresentation of tree cover areas (as confirmed in previous studies) and also an underrepresentation of water. Key words: Tropical tree composites, Mt. Kenya, Sentinel-2, ESA S2 LC 20 m map of Africa.
Highlights
Forests are subject to several policies of individual states and important agreements from the United Nations (UN) (FAO, 2017; IPCC, 2003; UN, 1992, 1997, 2015)
The classification process was supported by Google Earth images, and land use (LU) data that were provided by the local Kenyan Forest Service (KFS)
Driven by the motivation of the Karantina University and the Kenya Forest Service (KFS), this study aims to fill this research gap through combined use of LU data, Google Earth images, topographic data as well as historic field-based data to develop an efficient method for the creation of spectral signatures
Summary
Forests are subject to several policies of individual states and important agreements from the United Nations (UN) (FAO, 2017; IPCC, 2003; UN, 1992, 1997, 2015). The amount of people that directly profit from forests as a natural resource is vast (FAO, 2016b). This applies to the forests of Mount Kenya National Park, Lewa Wildlife Conservancy, and Ngare Ndare Reserve of the greater Mount Kenya ecoregion. These forests shape an extensive ecosystem that represents habitat to numerous endemic species and provide water and other.
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