Thirteen airborne LiDAR study blocks were used to map depositional-floodplain (DFP) substrates (68, 958 ha) and a variety of erosional <i>terra firme</i> (ETF) substrates (56,623 ha).

<p>The <i>terra firme</i> zones are described in terms of basic geologic, topographic and physiognomic composition.</p

One 13,883 ha Amazonian landscape (block 12;Figure 1) showing (a) the digital terrain model with additional processing to delineate depositional floodplain (DFP; red) and erosional <i>terra firme</i> (ETF; white) substrates; (b) forest canopy height derived from 3-D imaging; and zoom images to indicate differences in height and gap variation within (c) DFP and (d) ETF forests.

<p>Each zoom image is 50 ha in size. Individual crowns are visible in red colors; forest canopy gaps are indicated in blue.</p

Thirteen CAO LiDAR mapping blocks were acquired in the southern Peruvian Amazon.

<p>The upper inset shows location of the study region within Peru. The lower inset shows the LiDAR mapping blocks against a map of aboveground carbon density (ACD; Mg C ha⁻¹), which integrates regional variation in geology, topography and canopy physiognomy <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060875#pone.0060875-Asner4" target="_blank">[20]</a>.</p

Canopy structure and gap statistics for CAO mapping block 12 including: (a) the distribution of canopy height for erosional <i>terra firme</i> (ETF) and depositional floodplain (DFP) forests (see Figure 2); (b) the vertical distribution of power-law exponents (λ) for each forest type in block 12; and (c) the gap-size frequency distributions for ETF and DFP forests for canopy gaps at <1 m and <20 m thresholds.

<p>Power-law exponents (λ) and the number of mapped gaps (n) are also provided.</p

Graphical representation of size-frequency distributions of canopy gaps on erosional <i>terra</i> firme and depositional floodplain substrates in the southwestern Peruvian Amazon at two height thresholds.

<p>The slopes of these lines are power-law exponents from the Zeta distribution that we estimated using maximum likelihood. Additional details are in Appendices S1 and S2.</p

Mean canopy height (± standard deviation) of forests on depositional-floodplain (DFP) and erosional <i>terra firme</i> (ETF) substrates (see Table 1), along with Zeta distribution (power-law) exponents (<b>λ</b>) of the gap-size frequency distributions for each site.

<p>Values are provided for gaps reaching to the ground level (<b>λ1</b> or ≥1 m) and for gaps found only in the upper canopy (<b>λ</b>20 or ≥20 m). Values in parentheses indicate the number of gaps mapped in each landscape.</p

Performance of three modeling techniques as assessed in 36 validation cells.

<p>Left panels highlight model performance against LiDAR-observed aboveground carbon density from CAO aircraft data (Mg C ha⁻¹), while right panels highlight the model performance by increasing distance from CAO aircraft data. The color-scale reflects the two-dimensional density of observations, adjusted to one dimension using a square root transformation.</p

Bin ranges for input variables used to produce a stratified map of the region over which carbon modeling was performed (see methods).

<p>Twenty total bands were dispersed non-randomly according to the strength of each variable in predicting carbon stocks, which has been shown to be an effective stratification method in previous studies (e.g., <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085993#pone.0085993-Asner3" target="_blank">[17]</a>). Thereafter, the input variables were subset by quantiles to determine bin ranges for the bands. These class combinations were subsequently intersected with a 134-class habitat map as described in the methods, resulting in 8,035 unique classes within the focal area of the present study. A hard bracket indicates values “greater than or equal to”, while a parenthesis indicates values that are “less than”.</p

In addition to the fractional cover map shown in Figure 1, four additional maps were created as input to the stratification and Random Forest models.

<p>(a) SRTM elevation ranging from a low of 90 m a.s.l. (green) to a high of 3884 m a.s.l. (yellow), (b) SRTM slope ranging from level inundated areas (light purple) to steep cliffs and rock faces (yellow), (c) SRTM aspect ranging from a bearing of zero degrees (black) to just under 360 degrees (white), and (d) habitat type, with broad variation highlighted by kaleidoscopic color. In addition, the second of two Random Forest models included four axes of position information (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085993#s2" target="_blank">Methods</a>).</p