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...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Médard Mpanda Mukenza, John Kikuni Tchowa, Felana Nantenaina Ramalason, Heritier Khoji Muteya, Jan Bogaert, Yannick Useni Sikuzani, Jean-François Bastin
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2026-06-01
Schriftenreihe:	Remote Sensing
Schlagworte:	compositional mapping subpixel heterogeneity carbon stock estimation bias continuous land-cover representation tropical dry forests
Online-Zugang:	https://www.mdpi.com/2072-4292/18/12/1979
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