hyperspatial mapping of water, energy and carbon...

Sheng Wang1, Jakob Jakobsen2, Andreas Ibrom1, Peter Bauer-Gottwein1, Monica Garcia1

Hyperspatial mapping of water, energy and carbon fluxes with an Unmanned Aerial System

23/02/2018

[email protected]

1. Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs.

Lyngby, Denmark

2. National Space Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark

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Outline

• 1. Introduction

• 2. Objective and method

• 3. Study region

• 4. Results

• 5. Conclusion & Future work

2 Hyperspatial mapping of water, energy and carbon fluxes with Unmanned Aerial Systems


UAS: important remote sensing tool

Hyperspatial mapping of water, energy and carbon fluxes with Unmanned Aerial Systems 3

High spatial resolution (<1m)

Flexible time and location

Cloudy and overcast is OK

Low cost

Combine sensors

Bridge satellite and tower based remote sensing

Reality Aircraft and satellite UAS

<1 m 30-60 m 1-3 km

1. Introduction

incre

ase s

patial

resolu

tion


Ecosystems: natural and agricultural

• UAS integrated ecosystem monitoring system: adaptive management

– Snapshot estimation of soil moisture (SM), gross primary productivity

(GPP) and evapotranspiration (ET) (spatial)

– Continuous estimation of SM, GPP and ET (temporal)



2. Objective

Operational framework to continuously estimate SM, GPP and ET from UAS

optical and thermal imagery for various weather conditions.


High quality UAS Image:

Irradiance changing due to cloud moving, especially for high latitudes

Low irradiance for overcast and cloudy days

Snapshot estimation:

Operational and robust (physical based) methods to estimate SM, GPP and ET

SM, GPP and ET could be estimated by one flight campaign

Parsimonious model (few ground observations, data-scarcity regions)

Continuous estimation:

Temporal interpolation between UAS image acquisitions


2. Method


Platform

Payload

Models

+

+

ET and GPP

GPP:CO2 assimilation

ET: water consumption

UAS monitoring system:

<1m resolution, various weather conditions


Platform

Smart UAV

A collaboration between

• Sky watch A/S (in charge of designing the vehicle)

• DTU Space and Environment (in charge of payload and sensor integration)

Apprentice S


Hexacopter DJI s900

Requirement: 2kg payload


Payload

2. Thermal infrared camera: Flir Tau 324

3. Multi-spectral camera: TetraMini MCA 4. Cosine receptor + Ocean Optics FLAME-VIS-NIR

1. Sony DSC-RX100


350-1000nm

7.5–13.5μm

Total weight: 2kg



Models

Workflow for data processing and modeling:


UAS flight campaigns


Number Date Time weather condition

1 11-04-2016 9:33-10:15 cloudy

2 02-05-2016 14:20-14:57 cloudy

3 12-05-2016 10:14-11:05 sunny

4 25-05-2016 10:01-10:33 sunny

5 01-08-2016 11:06-12:50 overcast

6 15-08-2016 14:00-14:30 cloudy

7 20-09-2016 10:45-12:15 cloudy

8 07-10-2016 11:01-12:05 sunny

9 19-05-2017 11:52-15:39 sunny

10 22-05-2017 10:19-10:38 cloudy

11 26-05-2017 11:13-14:46 sunny

12 18-06-2017 12:27-13:01 cloudy

13 28-06-2017 13:12-15:45 cloudy


3. Study site

• DTU Risø campus willow-field EC flux site (DK-RCW)

– 11 ha willow bioenergy plantation

– mean annual temperature 8.5 ℃ and precipitation 600 mm

– Loamy soil



4. Results

4.1 UAS image quality

Weather in high latitudes: high fraction of overcast, cloudy and low irradiance

Overcast Clear days

26.46%

Low irradiance: radiometric multispectral camera cal/val

Camera LED

ASD

Cloudy issues: Tensor decomposition

2m diameter

STSM Dr. Pablo J. Zarco-Tejada

Wang S. …, Garcia M. Optimizing Unmanned Aerial System optical mapping for low irradiance and cloudy conditions. 2018. in preparation.


4.2 UAS snapshot estimation

𝜆𝐸𝑖 = 𝑓𝑤𝑒𝑡𝜆𝐸𝑝𝑐

𝜆𝐸𝑐 = 1 − 𝑓𝑤𝑒𝑡 𝑓𝑔𝑓𝑇𝑓𝑀𝜆𝐸𝑝𝑐

𝜆𝐸𝑆 = 𝑓𝑆𝑀𝜆𝐸𝑝𝑠

GPP: Light use Efficiency model (Monteith et al., 1977) ET: Priestley-Taylor Jet Propulsion Laboratory model (Fisher et al., 2008) unify two models with the same constraints on canopy carbon assimilation

and transpiration

𝐺𝑃𝑃 = 𝜀𝑚𝑎𝑥𝑓𝑔𝑓𝑇𝑓𝑀𝑓𝑉𝑃𝐷𝐴𝑃𝐴𝑅

Biophysical constraints limiting transpiration

and assimilation

Absorbed PAR radiation

Max light use efficiency

Wang S. Ibrom A, Bauer-Gottwein P, Garcia M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agricultural and Forest Meteorology 248 (2018): 479-493.


‘Top-down’: treat canopy as a whole, down-regulate the potential to the actual values operational, less parameterization and fewer inputs

VI: NDVI Radiation (Swin, Swout,

Lwin, LWout) Ta, RH, P, WS

Inputs:

Navigation Tetra MCA Cosine receptor +radiometer

Flir Tau2 Weather station



Volumetric soil moisture estimation from Unmanned Aerial System thermal and optical imagery

Temperature-vegetation dryness index (TVDI, Sandholt et al., 2002; Garcia et al., 2014)

Surface energy balance + PM

TVDI =𝑇𝑠 𝑚𝑎𝑥,𝑖 − 𝑇𝑠𝑚𝑖𝑛

𝑇𝑠𝑚𝑎𝑥 − 𝑇𝑠𝑚𝑖𝑛= 1 −

𝜃𝑣 − 𝜃𝑚𝑖𝑛

𝜃𝑚𝑎𝑥 − 𝜃𝑚𝑖𝑛


Zhu et al., 2017

Green vegetation fraction

Cor

rect

ed L

ST (℃

)

LST NDVI TVDI



(a) (b)

(a) 5 cm (b) 15 cm (c) 30 cm

Temperal validation

(a) No buffer (b) 0.5m buffer (c) 1.0m buffer

(d) 1.5m buffer (e) 2.0m buffer (f) 2.5m buffer

Spatial validation

Wang S. …, Garcia M. Estimation of volumetric soil moisture from Unmanned Aerial System based thermal and optical imagery. 2018. To be submitted.


soil moisture




Eddy covariance footprint model (Kljun et al., 2015)

GPP: RMSE = 2.04 μmol∙C∙m-2∙s-1

ET: RMSE = 17.82 W∙m-2

Wang S. …, Garcia M. Hyperspatial mapping evapotranspiration and gross primary productivity from UAS optical and thermal imagery. 2018. In preparation.



Continuous estimation of evapotranspiration and gross primary productivity from an Unmanned Aerial System

An operational and simple SVAT model: Soil-Vegetation Energy, water and CO2 traNsfer model (SVEN): Rn, G, LE, H, ET, SWC, GPP and Rs

The joint ET and GPP model (Wang et al., 2018) with ‘force-restore’ and ‘Green-Ampt’ infitration methods

4.3 Continuous estimation



Ensemble Kalman filter data assimilation: LST->SVEN

4.2 Continuous estimation

Continuous estimation of evapotranspiration and gross primary productivity from an Unmanned Aerial System

Wang S. …, Garcia M. Continuous estimation of evapotranspiration and gross primary productivity from an Unmanned Aerial System. In preparation.


Conclusion

19 Hyperspatial mapping of water, energy and carbon fluxes with Unmanned Aerial Systems

We designed an operational UAS based continuous monitoring

system, including payload and models, to estimate SM, GPP and

ET for various weather conditions.

To achieve continuous modeling the system merges a ‘top-

down’ remote sensing model for GPP and ET with time-

dynamic modules for ground heat flux and infiltration.

Good accuracy of SM, GPP and ET is obtained over a willow forest

flux site.


Future work

Future work will focus on SCOPE model to incorporate optical

reflectance and thermal information to retrieval biophysical and

biochemical parameters to improve GPP and ET estimation from

UAS imagery with a ‘bottom-up’ approach.

Hyperspatial mapping of water, energy and carbon fluxes with Unmanned Aerial System 20

STSM

Dr. Christiaan van der Tol


Acknowledgement

Monica Garcia DTU-Environment

Andreas Ibrom DTU-Environment

Peter Bauer-Gottwein DTU-Environment

Jakob Jakobsen DTU-Space

Veronica Sobejano Paz DTU-Environment

Filippo Bandini DTU-Environment

Christian Josef Köppl DTU-Environment

Xin Liu Sino-Danish Center

Daniel Olesen DTU-Space

Carsten Dam-Hansen DTU-Photonics

Andreas Baum DTU-Compute

Majken Caroline Looms Zibar Copenhagen University

STSM

Dr. Christiaan van der Tol

STSM

Dr. Pablo J. Zarco-Tejada


Thanks for your attentions!

hyperspatial mapping of water, energy and carbon...

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