The use of sun elevation angle for stereogrammetric boreal forest height in open canopies

Abstract

Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; < 5 m spatial resolution) yields fine-scaled digital surface models (DSMs) of elevation. These DSMs may represent elevations that range from the ground to the vegetation canopy surface, are produced from stereoscopic image pairs (stereopairs) that have a variety of acquisition characteristics, and have been coupled with lidar data of forest structure and ground surface elevation to examine forest height. This work explores surface elevations from HRSI DSMs derived from two types of acquisitions in open canopy forests. We (1) apply an automated mass-production stereogrammetry workflow to along-track HRSI stereopairs, (2) identify multiple spatially coincident DSMs whose stereopairs were acquired under different solar geometry, (3) vertically co-register these DSMs using coincident spaceborne lidar footprints (from ICESat-GLAS) as reference, and (4) examine differences in surface elevations between the reference lidar and the co-registered HRSI DSMs associated with two general types of acquisitions (DSM types) from different sun elevation angles. We find that these DSM types, distinguished by sun elevation angle at the time of stereopair acquisition, are associated with different surface elevations estimated from automated stereogrammetry in open canopy forests. For DSM values with corresponding reference ground surface elevation from spaceborne lidar footprints in open canopy northern Siberian Larix forests with slopes < 10°, our results show that HRSI DSMs acquired with sun elevation angles > 35° and < 25° (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors < 0.68 m relative to lidar. The latter, particularly those with angles < 10°, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (μ = 6.0 m, σ = 1.4 m) was consistent with the distribution of plot-level mean tree heights (μ = 6.5 m, σ = 1.2 m). We conclude that the variation in sun elevation angle at time of stereopair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register and difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.

Keywords: WorldView; biome boundary; digital surface model; ecotone; forest structure; photogrammetry; stereogrammetry; sun elevation angle; taiga; tundra.

Figure 1.

The study sites in open canopy forests in northern Siberia. The light grey regions indicate extents of HRSI DSM coverage, and the 5 dark grey regions indicate the sites (Kheta, Khatanga, Kotuy-north, Kotuy, and Kotuykan) for which overlap of multiple HRSI DSMs provided areas for further surface elevation analysis with ICESat-GLAS lidar footprint transects (white lines) and locations of reference field plots (white circles).

Figure 2.

DSMs of the same location from stereopair acquisitions with different sun elevation angles. A subset of the Khatanga study site for which stereopairs were collected on each of two successive days in July 2012. The top row shows the panchromatic image for July 11 and 10th, and the bottom row shows their corresponding color-shaded relief image of the DSM. HRSI acquisition geometry describing the relative position of the sun and the sensor to the image (target) associated with one image of each stereopair is shown in the corresponding polar plots. The corresponding images (not shown) for 7/11/2012 and 7/10/2012 have satellite elevation and azimuth angles of 64°& 80° and 59° & 308°, respectively.

Figure 3.

Comparison of DSM elevation errors with reference lidar elevations. The RMSE values were derived on a per-DSM basis (each dot corresponds to one DSM) from linear models built from the relationship of original mean DSM values with reference surface elevations from lidar footprints. Vertical dashed lines indicate the upper and lower bounds of the DSM_{sun_low} and DSM_{sun_high} types, respectively.

Figure 4.

Lidar footprints provided reference ground surface elevation estimates to examine the influence of terrain aspect on DSM errors. The magnitude of the RMSE from the linear model of each DSM’s elevations to those of corresponding lidar ground surface are summarized. The error bars (black lines) indicating the bootstrapped 95% confidence interval of the RMSE and number of lidar footprints on which each DSM’s model was built are shown for each DSM. Individual DSMs are grouped according to DSM type.

Figure 5.

Comparison of co-registered elevations from DSMs and lidar footprints, by study site. These relative frequency distributions, grouped according to DSM type, show the difference of co-registered DSM elevation values from those of reference lidar, for each of the 4 study sites.

Figure 6.

Comparison of co-registered elevations from DSMs and lidar footprints, by land cover class. These relative frequency distributions, grouped according to DSM type, show the difference of co-registered DSM elevation values from those of reference lidar, for the set of footprints that were classified into one of two general land cover classes, ‘forest’ or ‘non-forest’. In the ‘forest’ plot, the gray bars represent plot-level mean tree heights.

Figure 7.

The relative frequency distributions of two types of height observations at lidar footprints. The left facet shows the height distributions from spaceborne DSM-differencing at unclassified, ‘forest’ and ‘non-forest’ footprints. The right facet shows field-derived plot-level mean tree heights in open canopy forest plots in northern Siberia. The portion of the lidar footprints were classified as ‘forest’ and ‘non-forest’ to underscore the difference in heights from these two groups, and the spaceborne heights are shown adjacent to those from field measurements to illustrate the similarity in forest heights between the two.

Figure 8.

Mapped examples, each ~ 7 km², of DSM-differenced heights in open canopy forests in northern Siberia. Forest height estimates are presented at 2 m spatial resolution from image-level DSM differencing applied after vertical co-registration of DSM pairs at each study site. Shown at the spatial scale of large groups of trees, each example shows the pattern of forest structure that is revealed with mapped forest height across sites.