Object-based mapping and classification features for tropical highlands using Sentinel-1, Sentinel-2, and GEDI canopy height data - A case study of the Muringato catchment, Kenya

Abstract: Tropical highlands remain a challenging target for remote sensing due to their high heterogeneity of the landscape and frequent cloud cover, causing a shortage of high-quality and reliable comprehensive data on land use and land cover on a local or regional scale. These, however, are urgently needed by local stakeholders and decisionmakers. This applies for example to the Muringato sub-catchment in Nyeri County, Kenya, where acute water problems have been identified to be usually directly related to specific land use and land cover. This article contributes to the understanding of tropical highlands from a remote sensing perspective by examining Sentinel-1, Sentinel-2 and Global Forest Canopy Height Model data from the Global Ecosystem Dynamics Investigation, all provided by the Google Earth Engine. To do so, we assess classifiers derived from these datasets for different land cover types, analyzing the performance of promising candidates identified in the literature, using 2,800 s.... https://www.erdkunde.uni-bonn.de/article/view/2949

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Object-based mapping and classification features for tropical highlands using Sentinel-1, Sentinel-2, and GEDI canopy height data - A case study of the Muringato catchment, Kenya ; volume:77 ; number:1 ; year:2023 ; Erdkunde ; 77, Heft 1 (2023)

Beteiligte, Orts- und Zeitangaben

• Urheber: Goebel, Marcus ; Thiong’o, Kuria ; Rienow, Andreas

Weitere Informationen

• DOI: 10.3112/erdkunde.2023.01.03
• URN: urn:nbn:de:101:1-2410281720504.932902746430
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:31 MESZ