Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides
Abstract
:1. Introduction
2. Thermal Remote Sensing Techniques
2.1. Thermal Infrared Domain
2.2. Interpreting Thermal Infrared Signals
2.3. Remotely Sensed TIR Application in Geological Domains
3. Thermal Data Acquisition by UAVs
3.1. Data Processing: Atmospheric Correction
3.2. Data Processing: Geometric Correction
3.3. Auxiliary Data and Coregistration Processing
4. IRT Capabilities in Landslide Hazards
5. IRT Approaches in Coastal Rocky Cliffs Hazards
6. Methodological Synthesis and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name | Wavelength Range (μm) | Radiation Source | Surface Property of Interest |
---|---|---|---|
Visible (V) | 0.4–0.7 | solar | reflectance |
Near infrared (NIR) | 0.7–1.1 | solar | reflectance |
Short wave infrared (SWIR) | 1.1–1.35 1.4–1.8 2–2.5 | solar | reflectance |
Mid wave infrared (MWIR) | 3–4 4.5–5 | solar, thermal | reflectance, temperature |
Thermal infrared (TIR) | 8–9.5 10–14 | thermal | reflectance, temperature |
Microwave, radar | 1 mm–1 m | thermal (passive), artificial (active) | temperature (passive), roughness (active) |
Band | Spectral Range |
---|---|
10 | 8.125–8.475 μm |
11 | 8.475–8.825 μm |
12 | 8.925–9.275 μm |
13 | 10.25–10.95 μm |
14 | 10.95–11.65 μm |
Thermal Cameras/Parameters | FLIR SC 3000 | Therm Tracer Th1101 | FLIR P640 | FLIR B360 | FLIR B335 | FLIR i7 | FLIR SC620 | TESTO 885 |
---|---|---|---|---|---|---|---|---|
Spectral Range (μm) | 8–9 | 8–13 | 7,5–13 | 7.5–13 | 7.5–13 | 7.5–13 | 7.5–13 | 7.5–14 |
Frame Rate (Hz) | 50/60 | – | 30 | 30 | 9–30 | – | 30 | 33 |
Accuracy (+/− °C) | 1 | – | 2 | 2 | 2 | 2 | 2 | 2 |
Data Format | IMG, BMP | – | JPEG | JPEG | JPEG | JPEG | JPEG | BMP, JPEG, PNG |
Sensor Resolution (dpi) | 320 × 240 | – | 640 × 480 | 320 × 240 | 320 × 240 | 120 × 120 | 640 × 480 | 640 × 480 |
Radiometric Resolution (bit) | 8–14 | – | 14 | – | 14 | – | 14 | – |
Thermal Sensitivity (Noise Equivalent Temperature Difference-NETD) (m K) | 20 | 50 | 60 | 60 | 50 | 100 | 40 | 30 |
Focus | – | – | – | – | auto | absent | – | – |
Focal Length (mm) | – | – | 8 | – | 18 | – | – | – |
Weight (g) | 3200 | – | 1700 | 880 | 880 | 340 | 1900 | 1570 |
IFOV (mrad) | 1.1 | 2.2 | 0.65 | 1.4 | 2.59 | 3.7 | 1.3 | 1.7 |
Publication | [105] | [106] | [107] | [108] | [109] | [97,110] | [111] |
PHASES | Constraints | Traditional Field Survey | TLS | Photogrammetry | IRT |
---|---|---|---|---|---|
Planning | Cost-Effectiveness | Low cost | High cost | Low cost | Low cost [102] |
Data Resolution | High resolution (linear data) | Very high resolution (3D-spatial data) | High resolution (2D/3D-spatial data) | Medium/high resolution depending on object-sensor distance [110] | |
Solar Illumination | No influence | No influence | Highly dependent | Strongly dependent | |
Portability | High portability | Medium/Low portability | High portability | High portability [106] | |
Monitoring | Quite impossible | Possible, but with high costs and time | Possible and fast | Possible and fast | |
Expected Results | Local fractures network | 3D-fractures network (Orientation) | 3D-fractures network (Orientation) | Behavior of the fractures [168] | |
Data Collection | Time-consuming for data acquisition | High time-consuming for wide areas | Very slow acquisition | Fast acquisition | Fast acquisition [106] |
Accessibility (Distance Object-Sensor) | Issues to reach inaccessible areas | A ground station is necessary | Capable of reaching inaccessible areas | Capable of reaching inaccessible areas | |
Weather Conditions | Partially limited | Completely limited | Partially or completely limited | Partially or completely limited [114] | |
Visibility | Daytime | Daytime | Daytime | Daytime and nighttime [107,114] | |
Field of View | Limited | Potential occlusions | Complete | Complete | |
Data analysis and Results | Geomechanical Features | Expert judgment, maps [194] | Fracture network (roughness, orientation, opening). Point Cloud | Fracture network (roughness, direction, opening). Ortophotos and Point cloud | Classified fracture network, RQD correlation [168] Thermal images |
Physical Properties | Limited areas of sampling | None | None | Relationships between thermal data and porosity and bulk density [109] |
Holding Device | Topic | |
---|---|---|
Quattrochi et al., 1998 [195] | Airborne | Thermal energy fluxes of different vegetation types in urban environment |
Ninomiya et al., 2005 [196] | Spaceborne | Detecting mineralogic or chemical composition of rocks |
Wu et al., 2005 [106] | Terrestrial | Eroded caves inside a shotcreted slope |
Sheng et al., 2010 | Airborne | Agriculture field coverage, black marker detection |
Teza et al., 2012 [107] | Terrestrial (120–150 m) | Shallow inhomogeneities, weathered rock cliff areas |
Martino and Mazzanti, 2014 [193] | Terrestrial | Main joints, recent collapsed areas/detachments in a rock coastal cliff |
Baroň et al., 2014 [108] | Terrestrial and UAV | Open cracks and zones of tension within rock slope instability, loosened rock zones, pseudo-karst caverns |
Mineo et al., 2015a; Pappalardo et al., 2016 [154,165] | Terrestrial (3 m) | Geostructural features, fracturing degree, daytime temperature exchange of a rock slope |
Mineo et al., 2015b [160] | Terrestrial | Thermal contrast between vegetated portion, weathered and bare rock along an unstable slope |
Pappalardo et al., 2017 [197] | Terrestrial | Discontinuity system and fracture sectors of a rock wedge |
Frodella et al., 2017 [102] | Terrestrial and airborne | Wedge fractures, erosional channels, scarps, earth flow ponds, seepage sectors, debris cones |
Fiorucci et al., 2018 [111] | Terrestrial | Surficial temperature distribution on rock masses—thermal response of jointed rock-block—seasonal behavior differences |
Pappalardo et al., 2018 [161] | Terrestrial | Morphologic features, lithological differences, Landslides bodies |
Grechi and Martino 2019 [198] | Terrestrial (20 m) | Surficial temperature distribution of rock mass arch in terms of temporal and spatial evolution |
Frodella et al., 2020 [97] | Terrestrial (600 m) | Weathering rock areas: Moisture content connected to the ephemeral drainage network |
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Melis, M.T.; Da Pelo, S.; Erbì, I.; Loche, M.; Deiana, G.; Demurtas, V.; Meloni, M.A.; Dessì, F.; Funedda, A.; Scaioni, M.; et al. Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides. Remote Sens. 2020, 12, 1971. https://doi.org/10.3390/rs12121971
Melis MT, Da Pelo S, Erbì I, Loche M, Deiana G, Demurtas V, Meloni MA, Dessì F, Funedda A, Scaioni M, et al. Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides. Remote Sensing. 2020; 12(12):1971. https://doi.org/10.3390/rs12121971
Chicago/Turabian StyleMelis, Maria Teresa, Stefania Da Pelo, Ivan Erbì, Marco Loche, Giacomo Deiana, Valentino Demurtas, Mattia Alessio Meloni, Francesco Dessì, Antonio Funedda, Marco Scaioni, and et al. 2020. "Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides" Remote Sensing 12, no. 12: 1971. https://doi.org/10.3390/rs12121971