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...

Cijeli opis

Bibliografski detalji

Glavni autori:	Médard Mpanda Mukenza, John Kikuni Tchowa, Felana Nantenaina Ramalason, Heritier Khoji Muteya, Jan Bogaert, Yannick Useni Sikuzani, Jean-François Bastin
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-06-01
Serija:	Remote Sensing
Teme:	compositional mapping subpixel heterogeneity carbon stock estimation bias continuous land-cover representation tropical dry forests
Online pristup:	https://www.mdpi.com/2072-4292/18/12/1979
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!