Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Figures & data

Table I. Characteristics of passive sources of remotely sensed data.

Sensor	Spatial resolution (pixel side dimension at nadir)	Repeat cycle	Spectral coverage
AVHRR	1.1 km	Daily	Visible, near-infrared, thermal infrared
MODIS	0.25–1.00 km	Near daily	Visible, near-infrared, shortwave infrared, thermal infrared
VEGETATION	1.15 km	Near daily	Visible, near-infrared, shortwave infrared
Landsat: MSS, TM, ETM +	15–79 m	16 days	Visible, near-infrared, shortwave infrared, thermal infrared
IKONOS	0.8 m, 3.2 m	On demand	Visible, near-infrared
QuickBird	1 m, 4 m	On demand	Visible, near-infrared
SPOT 5-XS	2.5–25.0 m	2–3 days	Visible, near-infrared, shortwave infrared
IRS-1D LISS-III	23–70 m	5 days	Visible, near-infrared, shortwave infrared
Notes: AVHRR = Advanced Very High Resolution Radiometer; MODIS = Moderate Resolution Imaging Spectroradiometer; MSS = Multispectral Scanner; TM = Thematic Mapper; ETM + = Enhanced Thematic Mapper+.

Table II. Population error matrix for four-class map of forest cover.

	Map classification
Reference classification	Deciduous	Coniferous	Mixed	Non-forest	Total	Omission error
Deciduous	p 11	p 12	P 13	p 41	P 1 +
Coniferous	P 21	P 22	P 23	P 24	P 2 +
Mixed	P 31	P 32	P 33	P 34	P 3 +
Non-forest	p 41	P 42	p 43	P 44	p 4 +
Total	p +1	p +2	p +3	p +4	1.00
Commission error
Notes: p denotes a proportion.

Figure 1.  Assessing model misspecification for model-based inference: simple random sample estimates versus model-based estimates.

Figure 2.  Data recorded from an individual laser pulse emitted by small-footprint full-waveform lidar (the entire backscatter is recorded) and a discrete return lidar (discrete echoes are recorded; in this particular illustration a first echo, an intermediate echo and a last echo).

Figure 3.  Height measurements from an airborne laser scanning (ALS) data set of a spruce forest with high pulse density.

Table III. Relationships between forest structural attributes and lidar metrics.

Reference	Structural attribute	Geographical area	Forest type	Lidar system
Coops et al. (2007)	Stand height, basal area, vertical foliage distribution	British Columbia, Canada	Coniferous forest	DR, S, SF
Drake et al. (2003)	Biomass	Panama Costa Rica	Neotropical forest	FW, S, LF
Hopkinson & Chasmer (2009)	Fractional cover	Canada	Seven sites across Canada	DR, S, SF
Lefsky et al. (1999a)	Basal areaBiomass	Maryland, USA	Deciduous forest	FW, S, LF
Lefsky et al. (2002)	Biomass	Oregon, USAMaryland, USAManitoba, Canada	Coniferous forestDeciduous forestBoreal forest	FW, S, LF
Magnussen & Boudewyn (1998)	Stand height	British Colombia, Canada	Coniferous forest	DR, S, SF
Maltamo et al. (2005)	Tree numbers and height of different tree layers	Finland	Boreal forest	DR, S, SF
Means et al. (1999)	Height, basal area, leaf biomass, total biomass	Oregon, USA	Coniferous forest	FW, S, LF
Miura & Jones (2010)	Opening above ground, opening above low vegetation, presence of understory, canopy cover, presence of midstory, presence of high trees	Tasmania, Australia	Native forests	FW, S, SF
Morsdorf et al. (2006)	Leaf area indexFractional cover	Switzerland	Inner alpine valley	DR, S, SF
Næsset (1997a)	Mean tree height	Norway	Boreal forest	DR, S, SF
Næsset (1997b)	Stand volume	Norway	Boreal forest	DR, S, SF
Næsset & Gobakken (2008)	Above-ground biomass, below-ground biomass	Norway	10 boreal sites	DR, S, SF
Nelson et al. (1984)	Canopy closure, tree height	Pennsylvania, USA	Coniferous forest	DR, P, SF
Sherrill et al. (2008)	Mean height, biomass, leaf area index	Rocky Mountains, USA	Subalpine temperate	DR, S, SF
Takahashi et al. (2005)	Tree height	Japan	Coniferous forest	DR, S, SF
Weller et al. (2003)	Tree height, foliage projected cover	Queensland, Australia	Native forests	DR, P, SF
Zimble et al. (2003)	Tree height variance	Idaho, USA	Coniferous forest	DR, S, SF
Notes: Lidar systems: DR = discrete return; S = scanning; SF = small footprint; FW = full waveform; LF = large footprint; P = profiling.

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