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Article . 2017 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
DBLP
Article . 2018
Data sources: DBLP
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Geomorphometry from unmanned aerial surveys

Authors: Igor V. Florinsky; Vladimir M. Kurkov; Dmitrii P. Bliakharskii;

Geomorphometry from unmanned aerial surveys

Abstract

AbstractUnmanned aerial systems (UASs) are widely used for remote sensing, including the production of high‐resolution digital elevation models (DEMs). We study the possibilities of UAS‐based aerial surveys to produce photogrammetrically sound, high‐resolution DEMs intended for geomorphometric modeling. The study was conducted at the Zaoksky testing ground (Russia). To carry out an aerial survey, we used a UAS Geoscan‐101 equipped with a Sony DSC‐RX1 camera and a Topcon GNSS receiver. Aerial photographs were processed using Agisoft PhotoScan Professional software. Applying dense point cloud generation and classification, we produced DEMs with resolutions of 6 cm, 20 cm, and 1 m. Using a universal spectral analytical method, we derived models of several morphometric variables (i.e., slope gradient, horizontal, vertical, minimal, and maximal curvatures) from DEMs with resolutions of 20 cm and 1 m. We found that it is possible to produce noiseless models and well‐readable maps of morphometric variables for grassy areas with separately standing groups of trees and shrubs. However, UAS‐based DEMs cannot be applied for modeling of forested areas: there occur pronounced unrecoverable artifacts due to errors of automated classification of the dense point cloud. Finally, we present recommendations for the production of UAS‐derived, photogrammetrically sound, high‐resolution DEMs intended for geomorphometry.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
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