Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

Table 1

Variables used in this study for estimating biomass.

Categories	Variables	Algorithm	References
Raw spectral features	Blue	B1 (mean)
	Green	B2 (mean)
	Red	B3 (mean)
	NIR	B4 (mean)
Topography	DEM	6 meters
	Slope
	Aspect
Vegetation indices	NDVI		[45]
	RVI		[45]
	DVI		[45]
	RDVI		[46]
	MSR		[14]
	SAVI		[47]
	OSAVI		[48]
	GEMI		[49]
		EVI			[47]
	Texture (derived from each spectral band)	GLCM mean (mean)		[50, 51]
GLCM variance (Var)					[50, 51]
Homogeneity (Hom)			[50, 51]
Contrast (Con)			[50, 51]
Dissimilarity (Dis)			[50, 51]
Entropy (Ent)			[50, 51]
Angular second moment (ASM)			[50, 51]
Correlation (Cor)			[50, 51]
Inverse difference (InvD)			[50, 51]

Note. NIR: near infrared; DEM: digital elevation model; NDVI: normalized difference vegetation index; RVI: ratio vegetation index; DVI: difference vegetation index; RDVI: renormalized difference vegetation index; MSR: modified simple ratio; SAVI: soil-adjusted vegetation index; OSAVI: optimized soil-adjusted vegetation index; GEMI: global environmental monitoring index; EVI: enhanced vegetation index; GLCM: grey-level co-occurrence matrix; P_i,j is the probability of values i and j occurring in adjacent pixels in the original image within the window defining the neighborhood; i refers to the digital number (DN) value of a target pixel; j is the DN value of its immediate neighbor; and N is the number of grey levels.