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Quantifying Categorical Information Loss in Forest Compositional Mapping: Implications for the Accuracy of Forest Assessment in Lualaba Province (DR Congo)

Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional inf...

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Principais autores: Médard Mpanda Mukenza, John Kikuni Tchowa, Felana Nantenaina Ramalason, Heritier Khoji Muteya, Jan Bogaert, Yannick Useni Sikuzani, Jean-François Bastin
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI AG 2026-06-01
coleção:Remote Sensing
Assuntos:
compositional mapping
subpixel heterogeneity
carbon stock estimation bias
continuous land-cover representation
tropical dry forests
Acesso em linha:https://www.mdpi.com/2072-4292/18/12/1979
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