Quantifying forest cover at Mount Kenya: Use of Sentinel-2 for a discrimination of tropical tree composites

Abstract

The aim of the present study is totest 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 20m map of Africa that was produced in 2016. A decision tree that is based on significant altitudinal ranges was used to discriminatefour natural tree compositions that occur within the investigation area. In addition, the classification process was supported by Google Earthimages, and land use (LU) data that wereprovided by the local Kenyan Forest Service(KFS). Final classification products include four LC classes and five subclasses of forest (four natural forestsubclasses plus one non-natural forest class). Results of theJeffries-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 subclasseswherewavelengths of SWIR and the red-edge domain show highestimportancefor the discrimination of tree compositions.Finally, considerable differences can be seen between the utilized multi-temporal classification set (total of 39 bands fromthree acquisition dates) and ESA’s S2 prototype LC 20m map of Africa 2016. A visual comparison of ESA’s prototypemapwithin the investigation area indicates an overrepresentation of tree cover areas (as confirmed in previous studies) andalso an underrepresentation of water.