Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape
<p>Vigna Grospoli vineyard in Lamole. Photo by G. Castelli.</p> "> Figure 2
<p>Location of the study area. Red border—North area; blue border—South area of the Grospoli vineyard in Tuscany region (Italy).</p> "> Figure 3
<p>Each vineyard row has been labelled as: internal (blue) for the closest rows to the dry-stone wall (dotted line), external rows (red) for the ones at the edge of the terrace (black lines). All the rows comprised between internal and external have been identified as intermediate (green).</p> "> Figure 4
<p>The orthophoto of the study area produced by the photogrammetric processing of the images acquired from the RGB flight over the Grospoli vineyard.</p> "> Figure 5
<p>Thermal maps of the Grospoli vineyard in (<b>a</b>) morning (08.50 CET) and (<b>b</b>) afternoon (15:00 CET). The enlarged windows highlight internal and external rows, showing their respective thermal behaviours.</p> "> Figure 6
<p>Difference between the afternoon thermal map and the morning orthomosaics highlighting, in the enlarged windows, the behaviour of the internal and external rows.</p> "> Figure 7
<p>Scatter plot of morning and afternoon temperatures for external (circles) and internal (black squares) rows.</p> "> Figure 8
<p>Temperature average values along the hillside terraced Grospoli vineyard. (<b>a</b>) North area; (<b>b</b>) South area. White and grey timeseries represent morning and afternoon temperatures respectively. Black dots represent internal rows temperature mean values.</p> "> Figure 9
<p>Sun position over the Grospoli vineyard on 8 September 2017, at (<b>a</b>) 08:00 CET (solar rays perpendicular to the rows) and (<b>b</b>) 15:00 CET (solar rays irradiating all the rows).</p> ">
Abstract
:1. Introduction
- Investigate strengths and weaknesses of the use of TIR sensor mounted on UAV for thermal analysis of terraced crops;
- conduct a preliminary test on the possible thermal effect that dry-stone walls can have on the vineyard microclimate, testing the hypothesis that stones have an influence on the temperature patterns of the field which can influence grape ripening and quality.
2. Study Area
3. Materials and Methods
3.1. VIS Data Capture and Processing
3.2. TIR Data Capture and Processing
- automatic extraction of single frames from the thermal video, by setting the proper time of acquisition to maintain a suitable overlapping (Auto Key Presser was used as software). The result is a series of .csv datasheet files for each screenshot;
- conversion of the .csv files into 16-bit TIF images, through a point-to-point conversion (software ThermoVision_JoeC v. 1.0.6.0), to obtain three kinds of files: i) temperature 16-bit raster images (thermograms) with black and white values scaled according to the min/max temperature values of the overall set of images, ii) pictures with color palette set according to their own min/max temperature values, iii) an overview .txt file which reports the overall temperature range;
- evaluation of thermal outliers for each frame: a temperature range too large results in an insufficient contrast for the identification of homologous points by the photogrammetric software. In this case, the frames containing people were removed, because body temperature (~36 °C) increased the temperature maximum overall value. On the contrary, the aluminium targets, which reflect sky temperature, gave outliers of about −30 °C. In fact, the solar rays enter the thermal camera as reflected instead of emitted radiation, thus compromising the measures. For this reason, a default threshold of 0 °C was assigned, with an automatic script made with MathWorks MATLAB, to all values < 0 °C;
- after this normalization, the extracted thermograms were finally processed with the photogrammetric software Agisoft Photoscan (version 1.2.6 build 2834). The photogrammetric workflow, to obtain the DEM and the final thermal orthomosaics, is the same as reported in Section 3.1 for VIS processing, with the difference of having grey-scaled thermograms instead of RGB pictures;
- finally, a linear transformation was applied to the thermal orthomosaics, with a GIS software (ESRI ArcGIS) to re-calibrate the 16-bit raster values as a function of the min/max values of temperatures, by considering the overall data set.
3.3. GIS Processing
3.4. Statistical Analysis
4. Results
4.1. Results of VIS Image Analysis
4.2. Results of TIR Image Analysis
4.3. Results of Thermal Behaviour Analysis
5. Discussion
6. Conclusions
- obtain sufficient radiometric accuracy to reveal daily thermal variations induced by both natural sources (sun) as by anthropic artifacts (dry-stone walls)
- contain the operative costs and have higher spatial resolution compared to traditional remote sensing platforms for TIR sensors (aircrafts and satellites)
- cover a wider area in respect to ground-based TIR surveys
- georeferencing spatial (RGB) and radiometric (TIR) information in a GIS software
- repeat the survey multiple times during the day, thanks to reduced time needed for data acquisition
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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DJI Phantom 4 Pro Specifications | |
---|---|
Typology | Quadricopter |
Weight | 1388 g |
Diagonal size | 350 mm |
Max flight time | Approx. 30 min |
Max speed | 72 kph (S-mode) |
Power source | LiPo 4S |
Satellite positioning system | GPS-GLONASS |
Gimbal stabilization | 3-axis (pitch, roll, yaw) |
Range pitch | 120° (−90° to +30°) |
Operating frequencies | 2.4 GHz and 5.7 GHz |
Max Transmission distance | 3.5 km (CE) |
Operating temperature | 0–40 °C |
Obstacle Sensory Range | 0.2–7 m |
Camera | DJI FC6310 | OPTRIS PI450 |
---|---|---|
Spectral range | RGB 2 | TIR 4 (7.5–13 μm) |
Sensor | 1″ CMOS | FPA 5, uncooled |
Sensor size | 13.1 × 8.7 mm | 25 × 25 μm |
Resolution | 20 MP (5472 × 3648 px) | 382 × 288 px |
Focal length | 8.8 mm (f/2.8–f/11) | 8 mm |
FOV 1 | 84° | 62° × 49° |
Output | JPEG 3 image | .RAVI 6 video |
Weight | 300 g | 320 g |
Temperature resolution | - | ± 2 °C |
Operating temp. range | 0–40 °C | −20–250 °C |
Flight Plans | VIS Range | TIR Range |
---|---|---|
Time of acquisition | 13:30 | 08:50 1/15:00 2 |
Flight altitude AGL | 70 m | 40 m |
Forward overlap | 80% | 80% |
Side overlap | 70% | 60% |
GSD | 2 cm/pix | 12 1,2 cm/pix |
Number of pictures | 206 | 578 1/603 2 |
Speed | 5 m/s | 3 m/s |
Control Points | Check Points | |||
---|---|---|---|---|
RMSE [cm] | RMSE [pix] | RMSE [cm] | RMSE [pix] | |
RGB | 1.59 | 0.13 | 2.52 | 0.15 |
TIR morning | 0.34 | 0.05 | 5.43 | 0.05 |
TIR afternoon | 0.45 | 0.06 | 11.4 | 0.06 |
Morning | Afternoon | |
---|---|---|
External | 17.6 (0.9) | 23 (0.4) |
Internal | 15.4 (0.4) | 23.1 (0.5) |
P-value | 7E-09 | 0.919 |
Statistical significance | >99% | NO |
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Tucci, G.; Parisi, E.I.; Castelli, G.; Errico, A.; Corongiu, M.; Sona, G.; Viviani, E.; Bresci, E.; Preti, F. Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape. ISPRS Int. J. Geo-Inf. 2019, 8, 87. https://doi.org/10.3390/ijgi8020087
Tucci G, Parisi EI, Castelli G, Errico A, Corongiu M, Sona G, Viviani E, Bresci E, Preti F. Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape. ISPRS International Journal of Geo-Information. 2019; 8(2):87. https://doi.org/10.3390/ijgi8020087
Chicago/Turabian StyleTucci, Grazia, Erica Isabella Parisi, Giulio Castelli, Alessandro Errico, Manuela Corongiu, Giovanna Sona, Enea Viviani, Elena Bresci, and Federico Preti. 2019. "Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape" ISPRS International Journal of Geo-Information 8, no. 2: 87. https://doi.org/10.3390/ijgi8020087
APA StyleTucci, G., Parisi, E. I., Castelli, G., Errico, A., Corongiu, M., Sona, G., Viviani, E., Bresci, E., & Preti, F. (2019). Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape. ISPRS International Journal of Geo-Information, 8(2), 87. https://doi.org/10.3390/ijgi8020087