This data package contains four LiDAR-derived topographic metrics at 5
m spatial resolution covering the Hubbard Brook Experimental Forest,
within the White Mountain National Forest in central New Hampshire,
USA.
The LiDAR was collected during leaf-off and snow-free conditions by
Photo Science, Inc. in April 2012 for the White Mountain National
Forest (WMNF) using an Optech GEMINI Airborne Laser Terrain Mapper
using an Optech GEMINI Airborne Laser Terrain Mapper (ALTM) at 1,158
meters AGL with a 30% overlap. The scan frequency was 49.3 Hz and a
total scan angle of 27 degrees (+13.5 and -13.5 degrees from NADIR)
resulting in a resolution of 0.548 per meter across and along track
for average point spacing of 3 points per square meter. A 1 m raster
bare earth DEM was interpolated from returns classified as terrain
points by Photo Science, Inc. using automatic classification methods
available in TerraScan software.
The 1 m DEM was aggregated to a coarser resolution of 5 m using mean
cell aggregation as this resolution was shown to best predict soil
distribution and groundwater dynamics (Gillin et al., 2015a; b). The
DEM was then treated with a low-pass smoothing filter over a nine-cell
square neighborhood moving window to remove high-frequency data.
Maximum slope (%) (Travis et al., 1975) and topographic position index
(100 m circular window) (Guisan et al., 1999) were calculated using
the 5 m low-pass filtered DEM.
Upslope accumulated area (UAA) and topographic wetness index (TWI)
were calculated by applying a sink-filling algorithm (Wang and Liu,
2006) to the DEM to maintain the downslope gradient and facilitate the
computation of topographic metrics related to subsurface flow (Gillin
et al., 2015a). TWI (Beven and Kirkby, 1979) was computed with the UAA
using multiple triangular flow direction algorithm (Seibert and
McGlynn, 2007) as the numerator and a 5 m horizontal distance for
downslope index (Hjerdt et al., 2004) as the denominator. All digital
terrain analyses were conducted using System for Automated
Geoscientific Analyses (SAGA, version 2.1.4) (Conrad et al., 2015) and
ArcGIS© (ArcMap, version 10.5) software.
Beven, K.J., and M.J. Kirkby. 1979. A physically based, variable
contributing area model of basin hydrology / Un modèle à base physique
de zone d’appel variable de l’hydrologie du bassin versant. Hydrol.
Sci. Bull. 24(1): 43–69. doi: 10.1080/02626667909491834.
Conrad, O., B. Bechtel, M. Bock, H. Dietrich, E. Fischer, et al. 2015.
System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci.
Model Dev. 8(7): 1991–2007. doi:
https://doi.org/10.5194/gmd-8-1991-2015.
Gillin, C.P., S.W. Bailey, K.J. McGuire, and J.P. Gannon. 2015a.
Mapping of Hydropedologic Spatial Patterns in a Steep Headwater
Catchment. Soil Sci. Soc. Am. J. 79(2): 440–453. doi:
10.2136/sssaj2014.05.0189.
Gillin, C.P., S.W. Bailey, K.J. McGuire, and S.P. Prisley. 2015b.
Evaluation of Lidar-derived DEMs through Terrain Analysis and Field
Comparison. doi: info:doi/10.14358/PERS.81.5.387.
Guisan, A., S.B. Weiss, and A.D. Weiss. 1999. GLM versus CCA spatial
modeling of plant species distribution. Plant Ecol. 143(1): 107–122.
doi: 10.1023/A:1009841519580.
Hjerdt, K.N., J.J. McDonnell, J. Seibert, and A. Rodhe. 2004. A new
topographic index to quantify downslope controls on local drainage.
Water Resour. Res. 40(5): W05602. doi: 10.1029/2004WR003130.
Seibert, J., and B.L. McGlynn. 2007. A new triangular multiple flow
direction algorithm for computing upslope areas from gridded digital
elevation models. Water Resour. Res. 43(4). doi: 10.1029/2006WR005128.
Travis, M.R., G.H. Elsner, W.D. Iverson, and C.G. Johnson. 1975.
VIEWIT: computation of seen areas, slope, and aspect for land-use
planning. Gen Tech Rep PSW-GTR-11 Berkeley CA Pac. Southwest Res. Stn.
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http://www.fs.usda.gov/treesearch/pubs/27276 (accessed 26 October
2018).
Wang, L., and H. Liu. 2006. An efficient method for identifying and
filling surface depressions in digital elevation models for hydrologic
analysis and modelling. Int. J. Geogr. Inf. Sci. 20(2): 193–213. doi:
10.1080/13658810500433453.
