Accurate automated assessment of gully cross-section geometry using the photogrammetric interface FreeXSapp-Reference-Cited by-同舟云学术

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Accurate automated assessment of gully cross-section geometry using the photogrammetric interface FreeXSapp

Published:2018-02-05 Issue:8 Volume:43 Page:1726-1736
ISSN:0197-9337
Container-title:Earth Surface Processes and Landforms
language:en
Short-container-title:Earth Surf. Process. Landforms

Author:

Castillo C.¹^ORCID,Marín-Moreno V.J.¹,Pérez R.¹,Muñoz-Salinas R.²,Taguas E.V.¹^ORCID

Affiliation:

1. Department of Rural Engineering; University of Córdoba; Córdoba Spain

2. Department of Computing and Numerical Analysis; University of Córdoba; Córdoba Spain

Funder

Ministerio de Economía y Competitividad

European Regional Development Fund

Publisher

Wiley

Subject

Earth and Planetary Sciences (miscellaneous),Earth-Surface Processes,Geography, Planning and Development

Reference40 articles.

1. The role of sidewall orocesses in gully development - some new examples;Blong;Earth Surface Processes and Landforms,1982

2. Gully geometry: what are we measuring?;Casalí;The Soil,2015

3. A new automated method for the determination of cross-section limits in ephemeral gullies;Castillo;Geophysical Research Abstracts,2017

4. A century of gully erosion research: urgency, complexity and study approaches;Castillo;Earth-Science Reviews,2016

5. SF3M software: 3-D photo-reconstruction for non-expert users and its application to a gully network;Castillo;The Soil,2015

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1. Effects of lithology and soil horizons on gully morphology in the Mollisol region of China;Geoderma;2024-06

2. Advancing gully topographic threshold analysis using an automated algorithm and high-resolution topography;CATENA;2024-04

3. Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenarios;Environmental Science and Pollution Research;2023-02-03

4. Towards a Standardised Methodology for Gully Topographic Threshold Analysis Using High-Resolution Dems;2023

5. Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenarios;2022-10-17

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