Gully-erosion estimation and terrain reconstruction using analyses of microtopographic roughness and LiDAR
Abstract
Gully mapping techniques successfully identify gullies over a large range of breadths
and depths in complex landscapes but practices for estimating gully volumes need further
development. Gully gap-interpolation for estimation of gully volume does not often
factor in landscape microtopography in the generation of the new surface. These approaches
can thus overestimate large classical gully volumes, averaging over depressions, or
underestimate volumes by creating overly-smooth highly curved surfaces. Microtopographic
methodology was developed to estimate the pre-gully surface and gully volume across
the Calhoun Critical Zone Observatory (CCZO) in South Carolina, USA. The CCZO is a
Southern Piedmont landscape severely gullied by historic agriculture with upland Ultisols
many meters deep. Our gully-mapping and gully-filling approaches used 1 m LiDAR elevation
data and is based on the premise that gullies are local depressions on uplands which
are deeply incised with high microtopographic roughness. Our smoothing-via-filling-rough-depressions
(SvFRD) algorithm iteratively fills gullies until landscape microtopographic roughness
is reduced and unchanging after a subsequent iteration. Results were evaluated in
the context of prior landscape bulk erosion estimates ranging from 1483 to 3708 m
/ha as well as field surveys of gullies. Minimally eroded reference and highly-eroded
post-agricultural terrain were compared to test gully-mapping and volume accuracy.
Comparing gully-volume estimation techniques, inverse-distance-weighting (IDW) yielded
the highest volume (1072 m /ha) followed by ANUDEM (638 m /ha) while spline-interpolation
yielded the lowest estimate (555 m /ha). SvFRD landscape gully volume estimates (615.5
m /ha) were most similar to ANUDEM interpolation with roughness and gully extent results
most similar to spline interpolation. Spline interpolation is effective and easily
implemented but if microtopographic accuracy and mapping of fine-scale erosions features
is desired to hindcast pre-gully terrain conditions, our depression-filling approach,
implemented using free GIS and statistical software, is an effective method to estimate
reasonable erosion volumes. 2 3 3 3 3 3
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https://hdl.handle.net/10161/22565Published Version (Please cite this version)
10.1016/j.catena.2021.105264Publication Info
Brecheisen, ZS; & Richter, DDB (2021). Gully-erosion estimation and terrain reconstruction using analyses of microtopographic
roughness and LiDAR. Catena, 202. pp. 105264-105264. 10.1016/j.catena.2021.105264. Retrieved from https://hdl.handle.net/10161/22565.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Zach Brecheisen
Associate In Research
Daniel D. Richter
Professor in the Division of Earth and Climate Science
Richter’s research and teaching links soils with ecosystems and the wider environment,
most recently Earth scientists’ Critical Zone. He focuses on how humanity is transforming
Earth’s soils from natural to human-natural systems, specifically how land-uses alter
soil processes and properties on time scales of decades, centuries, and millennia.
Richter's book, Understanding Soil Change (Cambridge University Press), co-authored
with his former PhD
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