A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia Article Swipe
Hejar Shahabi
,
Ben Jarihani
,
Sepideh Tavakkoli Piralilou
,
David J. Chittleborough
,
Mohammadtaghi Avand
,
Omid Ghorbanzadeh
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19224893
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19224893
Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.
Related Topics
Concepts
Digital elevation model
Normalized Difference Vegetation Index
Scale (ratio)
Remote sensing
Elevation (ballistics)
Topographic Wetness Index
Random forest
Artificial intelligence
Land cover
Computer science
Geology
Cartography
Geography
Mathematics
Land use
Engineering
Geometry
Civil engineering
Oceanography
Climate change
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s19224893
- https://www.mdpi.com/1424-8220/19/22/4893/pdf?version=1573288271
- OA Status
- gold
- Cited By
- 88
- References
- 86
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2986126070
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2986126070Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s19224893Digital Object Identifier
- Title
-
A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, AustraliaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-11-09Full publication date if available
- Authors
-
Hejar Shahabi, Ben Jarihani, Sepideh Tavakkoli Piralilou, David J. Chittleborough, Mohammadtaghi Avand, Omid GhorbanzadehList of authors in order
- Landing page
-
https://doi.org/10.3390/s19224893Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/19/22/4893/pdf?version=1573288271Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/19/22/4893/pdf?version=1573288271Direct OA link when available
- Concepts
-
Digital elevation model, Normalized Difference Vegetation Index, Scale (ratio), Remote sensing, Elevation (ballistics), Topographic Wetness Index, Random forest, Artificial intelligence, Land cover, Computer science, Geology, Cartography, Geography, Mathematics, Land use, Engineering, Geometry, Civil engineering, Oceanography, Climate changeTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
88Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 14, 2024: 15, 2023: 8, 2022: 12, 2021: 17Per-year citation counts (last 5 years)
- References (count)
-
86Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.98393137 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |