UAVs in hydrology: opportunities (and challenges) from a ...€¦ · UAVs in hydrology:...
Transcript of UAVs in hydrology: opportunities (and challenges) from a ...€¦ · UAVs in hydrology:...
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UAVs in hydrology: opportunities (and challenges) from a novices perspective
Prof. Matthew McCabe Biological and Environmental Sciences and Engineering Division
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Development of a New Observational Capacity
UAVs offer an unprecedented opportunity in Earth Observation
• Emergency and disaster management and response
• Engineering and project management
• Environmental monitoring and sampling
• Applications in the hydrological and related sciences
Unmanned Autonomous Vehicles
http://www.iuavs.com/en/pages/aplicaciones_y_usos http://research.unl.edu/annualreport/2014/
Fire mapping Water quality sampling
Agricultural monitoring
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Low-Altitude Unmanned Autonomous Vehicle Sensing
UAVs are poised to deliver an information revolution in E-O science
• Availability of off-the-shelf and custom-designed systems and software
• Sensor miniaturization allows purpose specific payloads
• Targeted acquisition for high-resolution near real-time data collection
• Retrieval across a range of Earth system processes
Earth Observation with UAVs
FalconVIZ Quad-Copter
QuestUAV Fixed Wing
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UAV based Earth Observing Instrumentation
Advancing traditional satellite based EO-analysis
• Options for both fixed wing and quad-copter UAV solutions
• Comprehensive sensor package with interchangeable pay-loads
Unmanned Autonomous Vehicles
SONY NEX-7 Digital Camera
- 24MP - interchangeable lens - 560 grams
Tetracam MiniMCA 6 bands
- 490,560,665,705,740,865 nm - Replicate Senintel 2/Landsat - 700 grams
HeadWall Nano-Hyperspec
- 270 spectral bands - 400-1000nm & 640 spatial - 600 grams
Optris Pi-450 Thermal Sensor
- 7.5-13 μm (broadband) - 382 x 288 pixel - 320 grams
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Development of high-resolution Digital Elevation Models
Digitizing the KAUST campus for engineering and utilities planning
UAVs in the Hydrological Sciences
• Structure from Motion
for DEM development
• Ground control point
for geo-rectification
• Ultra-fine resolution
(decimeter scale)
• 3D walk-throughs
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Development of high-resolution Digital Elevation Models
UAVs in the Hydrological Sciences
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Development of high-resolution Digital Elevation Models
Feature extraction and vegetation modeling using UAVs and LiDAR
UAVs in the Hydrological Sciences
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Flood mapping and monitoring - hydrological forecasting
XXX
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UAVs in the Hydrological Sciences
http://spectrum.ieee.org/automaton/robotics/aerial-robots/falcon-uav-provides-colorado-flooding-assistance-until-fema-freaks-out
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Flood mapping and monitoring - hydrological forecasting
XXX
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UAVs in the Hydrological Sciences
http://spectrum.ieee.org/automaton/robotics/aerial-robots/falcon-uav-provides-colorado-flooding-assistance-until-fema-freaks-out
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Flood mapping and erosion monitoring
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UAVs in the Hydrological Sciences
d’Oleire-Oltmanns et al. (2012) “Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco”, Remote Sensing, 4(11), 3390-3416
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Monitoring rainfall variability and spatial patterns
Research being led by Prof. Greg Mariethoz at Uni Lausanne
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UAVs in the Hydrological Sciences
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Atmospheric profile retrievals and aerosol monitoring
XXX
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UAVs in the Hydrological Sciences
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Agricultural monitoring and vegetation health assessment
• Capacity to retrieve plant health, stress and physiological response
• Incorporate metrics into modeling system to constrain coupled H2O & C
UAVs in the Hydrological Sciences
Leaf Chlorophyll
Vegetation parameters inform crop health + water use
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UAVs also allow for spatially and spectrally equivalent validation.
Transcending the Point-to-Pixel Divide
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Feature identification and field-scale heterogeneity
Maximizing the information content from multi-resolution systems
The Spectral and Spatial Advantage
LandSat 30 m, 17 days RapidEye 5 m, daily UAV 10 cm, on-demand
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Translating UAV-based reflectances to crop data
• Employ empirical line correction method using Fieldspec targets
(i.e. UAV based digital counts to spectral surface reflectances)
• Develop a multi-variate linear regression via CUBIST regression tree
• Regression based on range of RapidEye spectral indices and in-situ LAI
• Applied derived relationship to the same UAV based spectral indices
Vegetation Retrievals from UAVs
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Feature identification and field-scale heterogeneity
XXX
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The Spectral and Spatial Advantage
• Hyper-resolution (10 cm)
• 25-min flight time
• 500 foot flight ceiling
Impact of dust storms
Observed water logging and soil textural variability
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Vegetation Retrievals from UAVs
Assumes consistency between spectral bands on RapidEye and UAV ✓
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Estimation of Land Surface
Temperature from UAV systems
• Serves as a proxy of crop stress
• On-board thermal sensor
• Sensitivity and accuracy
(environmental constraints)
• Expected within-field variability
Retrieval of Crop Thermal Signatures
Optris Pi-450 Thermal Sensor
- 7.5-13 μm (broadband) - 382 x 288 pixel - 320 grams
Clockwise rotation of center-pivot
Cooler and more dense
Warmer and more sparse
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XXX
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Retrieval of Crop Thermal Signatures
Clockwise rotation of center-pivot
Cooler and more dense
Coupled with coincident mapping of vegetation information, the thermal data allow estimation of plant water use (i.e. evaporation)
Warmer and more sparse
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Retrieval of Crop Thermal Signatures
Calibration Targets
Center-pivot wheel lines
• Temp variability around 10K
• Bare soil +310K at 9am
• Crop variation in 1-5K range
• Strongly related to LAI
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High resolution retrievals present unique problems
XXX
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Local Challenges – Atmospheric
Impacts of dust on atmosphere and vegetation
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High resolution retrievals present unique problems
XXX
• XXX
Local Challenges – Atmospheric
Impacts of dust on atmosphere and vegetation
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UAVs represent an exciting frontier in Earth observation
However, they are not without their challenges:
– spatial geo-referencing and image stitching
– mapping different sensors into the same image space
– spectral calibration and validation (particularly for thermal)
– sensor sensitivities to environmental conditions (wind, temperature)
– flight time limitations (area of acquisition – satellite coincidence)
– too much information i.e. how do we use the data appropriately?
– steep learning curve with potential dangers
– replacement cost of mistakes (crashes are inevitable)
Challenges and Other Issues
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Unmanned Autonomous Vehicles offer great opportunity in EO studies
• Potential to drive a new era of information technology in agriculture
• Replacement to high-resolution satellite missions ($$$)??
• Need for streamlined processing to deliver products
i.e. potential constraint on user uptake
• The science is lagging the technology: community playing catch-up
• Sensors not always fit for purpose – more advances being made
• Present new modeling challenges that need to be addressed
Summary and Discussion Points
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UAVs in hydrology: opportunities (and challenges) from a novices perspective
Prof. Matthew McCabe Biological and Environmental Sciences and Engineering Division