Author
Listed:
- Azamat Suleymanov
(Laboratory of Artificial Intelligence in Environmental Research, Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia)
- Mikhail Komissarov
(Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia)
- Mikhail Aivazyan
(Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia)
- Ruslan Suleymanov
(Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Department of Geodesy, Cartography and Geographic Information Systems, Ufa University of Science and Technology, 450076 Ufa, Russia)
- Ilnur Bikbaev
(Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia
Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia)
- Arseniy Garipov
(Laboratory of Artificial Intelligence in Environmental Research, Decarbonisation Technologies Center, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia)
- Raphak Giniyatullin
(Ufa Institute of Biology, Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia)
- Olesia Ishkinina
(Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia)
- Iren Tuktarova
(Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia)
- Larisa Belan
(Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
Department of Geology, Hydrometeorology and Geoecology, Ufa University of Science and Technology, 450076 Ufa, Russia)
Abstract
Unmanned aerial vehicles (UAVs) are rapidly becoming a popular tool for digital soil mapping at a large-scale. However, their applicability in areas with homogeneous vegetation (i.e., not bare soil) has not been fully investigated. In this study, we aimed to predict soil organic carbon, soil texture at several depths, as well as the thickness of the AB soil horizon and penetration resistance using a machine learning algorithm in combination with UAV images. We used an area in the Eurasian steppe zone (Republic of Bashkortostan, Russia) covered with the Stipa vegetation type as a test plot, and collected 192 soil samples from it. We estimated the models using a cross-validation approach and spatial prediction uncertainties. To improve the prediction performance, we also tested the inclusion of oblique geographic coordinates (OGCs) as covariates that reflect spatial position. The following results were achieved: (i) the predictive models demonstrated poor performance using only UAV images as predictors; (ii) the incorporation of OGCs slightly improved the predictions, whereas their uncertainties remained high. We conclude that the inability to accurately predict soil properties using these predictor variables (UAV and OGC) is likely due to the limited access to soil spectral signatures and the high variability of soil properties within what appears to be a homogeneous site, particularly in relation to soil-forming factors. Our results demonstrated the limitations of UAVs’ application for modeling soil properties on a site with homogeneous vegetation, whereas including spatial autocorrelation information can benefit and should be not ignored in further studies.
Suggested Citation
Azamat Suleymanov & Mikhail Komissarov & Mikhail Aivazyan & Ruslan Suleymanov & Ilnur Bikbaev & Arseniy Garipov & Raphak Giniyatullin & Olesia Ishkinina & Iren Tuktarova & Larisa Belan, 2025.
"Unmanned Aerial Vehicles Applicability to Mapping Soil Properties Under Homogeneous Steppe Vegetation,"
Land, MDPI, vol. 14(5), pages 1-16, April.
Handle:
RePEc:gam:jlands:v:14:y:2025:i:5:p:931-:d:1642156
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:931-:d:1642156. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.