UAV-Based Thermal Reconnaissance Across Five Volcanic Landscapes: Aspect-Related Surface Temperature Patterns in the Kula Volcanic Field

UAV thermal imaging provides high-resolution observations of surface-temperature organization across topographically complex volcanic terrain. This study examines UAV-derived apparent surface temperature across five contrasting volcanic landscapes in the Kula Volcanic Field, western Türkiye. RGB, thermal, and elevation data collected during a two-day UAV campaign were analyzed to compare north-, east-, south-, and west-facing surfaces within common 5-m elevation intervals and along representative south–north swath profiles. Apparent surface temperature showed no consistent relationship with elevation. South-facing surfaces were warmer than north-facing surfaces in 80 of 82 elevation-bin comparisons, while median within-site south–north differences ranged from 5.0 to 30.1 °C. The clearest thermal–topographic organization occurred at Sandal Divlit, where opposing outer and inner slopes formed an alternating warm–cool–warm–cool sequence. More complex patterns occurred at sites characterized by reservoir proximity, fragmented volcanic terrain, agricultural modification, vegetation, and heterogeneous surface materials. Aspect–elevation comparisons provided a consistent basis for interpreting thermal patterns, although aspect did not account for all local variation. UAV thermal imagery can complement RGB imagery and terrain models in volcanic-landform reconnaissance, field targeting, and geoconservation-oriented documentation. Because acquisition timing and solar geometry varied among surveys, the observations represent flight-specific spatial contrasts rather than persistent thermal regimes.

Authors

Institutions

Publication Details

Journal
Drones
Published
2026-10-26
DOI
https://doi.org/10.3390/drones10110802
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

UAV-Based Thermal Reconnaissance Across Five Volcanic Landscapes: Aspect-Related Surface Temperature Patterns in the Kula Volcanic Field

Ömer Yetemen
Drones
Geochemistry and Geologic Mapping
article

UAV-Based Thermal Reconnaissance Across Five Volcanic Landscapes: Aspect-Related Surface Temperature Patterns in the Kula Volcanic Field

Ömer Yetemen
article en

Abstract

UAV thermal imaging provides high-resolution observations of surface-temperature organization across topographically complex volcanic terrain. This study examines UAV-derived apparent surface temperature across five contrasting volcanic landscapes in the Kula Volcanic Field, western Türkiye. RGB, thermal, and elevation data collected during a two-day UAV campaign were analyzed to compare north-, east-, south-, and west-facing surfaces within common 5-m elevation intervals and along representative south–north swath profiles. Apparent surface temperature showed no consistent relationship with elevation. South-facing surfaces were warmer than north-facing surfaces in 80 of 82 elevation-bin comparisons, while median within-site south–north differences ranged from 5.0 to 30.1 °C. The clearest thermal–topographic organization occurred at Sandal Divlit, where opposing outer and inner slopes formed an alternating warm–cool–warm–cool sequence. More complex patterns occurred at sites characterized by reservoir proximity, fragmented volcanic terrain, agricultural modification, vegetation, and heterogeneous surface materials. Aspect–elevation comparisons provided a consistent basis for interpreting thermal patterns, although aspect did not account for all local variation. UAV thermal imagery can complement RGB imagery and terrain models in volcanic-landform reconnaissance, field targeting, and geoconservation-oriented documentation. Because acquisition timing and solar geometry varied among surveys, the observations represent flight-specific spatial contrasts rather than persistent thermal regimes.

DronesVol. 10(11)
Istanbul Technical University (TR)
Openalex Percentile: Top 13%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon