SWAMP Training Course
6 – 16 July 2015
Obrzycko-Rzecin (POLWET), Poland
Retrieval of biophysical parameter
(LAI) from Remote Sensed data
Violeta ANASTASE1), Dimitri DAUWE2), Rocio
HERNANDEZ – CLEMENTE3) , Joanna SULIGA4)
1)
INCAS Bucharest 2) Uni. of Hasselt 3) Uni. of Swansea 4) Vrije Uni. Brussel
SUMMARY
Objectives
Presentation of the research site
Ground measurements: methods & results
Airborne measurements: methods & results
Conclusions
Objectives
Retrieval of biophysical parameter (LAI) from
Remote Sensed data
Spectral Imaging with UAV (Ricola)
Hyperspectral measurements with SVC (SpectraVista)
LAI measurements with SunScan
FOCUS on LAI
• Simulations and validation
SUMMARY
Objectives
Presentation of the research site
Ground measurements: methods & results
Airborne measurements: methods & results
Conclusions
RESEARCH SITE
Rzecin site (POLWET)
Field campaign: 11.07.2015
8 plots
4 vegetation classes
Carex elata
B20
Typha latifolia
B21
Mosses and
small Carex
B25 - 27
Equisetum fluviatilis
Menyanthes trifoliate
B22 - 24
SUMMARY
Objectives
Presentation of the research site
Ground measurements: methods & results
Airborne measurements: methods & results
Conclusions
LAI / f APAR measurements
LAI
• One average value
• Calculated by model
fAPAR (mode „All”)
Instrument SunScan
• 64 values
• One sample = 3 measures (below, above, up-sidedown)
LAI / f APAR measurements
Radiation readings of SunScan
(mode „all”)
Radiation
2000
1500
1000
500
0
0
10
20
30
40
50
60
70
No. of sensors
Transmitted
Incident
B20 – Carex elata
Reflected
Radiation
2000
1500
1000
500
0
0
10
20
30
40
50
60
70
No. of sensors
Transmitted
Incident
Reflected
B23 – Carex elata
LAI / f APAR measurements
Plot
LAI
f APAR
Cor (LAI;f APAR)
I_dif
20
21
22
23
24
25
26
27
4,8
3,5
1,7
2,3
1,4
0,9
0,4
0,5
0,93
0,84
0,68
0,51
0,20
0,12
0,06
0,05
0,60
0,72
0,27
-0,89
-0,73
0,84
0,88
0,55
0,14
0,15
0,14
0,17
0,14
0,14
0,14
0,14
Ground Measurements
The spectral measurements were
achieved using the SpectraVista HR1024i spectrometer.
For each ground target were
achieved 9 samplings in three
different points, to highlight the
vegetation characteristics.
www.spectravista.com
Spectra Vista Results – Vegetation
Reflectances
Averaged reflectance - Speccio
SCOPE simulation
SpectraVista Results – Measurements
Vs. Simulations
Canopy Retrieval
SUMMARY
Objectives
Presentation of the research site
Ground measurements: methods & results
Airborne measurements: methods & results
Conclusions
Airborne Measurements
UAV imaging with Ricola
Flight pattern for the first flight:
•
•
•
•
•
Starting time: 09.21 am (UTC time)
Starting point: near the 26th target
Ending point: near the Eddy covariance tower
Number of target points: 19
GPS coordinates: N 52° 45’ 42,3’’, E 016° 18’
34,6’’
• Flight lines: 4
• Flight length: 180 m
• Equipment: Ricola camera, Hyperspectral camera
Flight pattern for the second flight:
•
•
•
•
•
•
•
•
Starting time: 01.08 pm (UTC time)
Starting point: near the 20th ground target
Ending point: near the 24th ground target
Number of target points: 18
GPS coordinates: N 52° 45’ 33,6’’, E 016° 18’35,6’’
Flight lines: 4
Flight length: 170 m
Equipment: Ricola camera, Hyperspectral camera
Flight trajectory
Covered Area Image
Spectral Information
Ricola Images
Modelling with ARTMO
LUT-based inversion of physically based
radiative transfer model (RTM)
Used model: 4sail (LAI)
Look-up tables (LUT)
10000 simulations
Results of mapping Leaf Area Index
(LAI) with ARTMO
LAI
Validation points
B25
LAI from 0,6 to 1,3
mean = 0,9
B26
LAI from 0,3 to 0,6
mean = 0,4
Validation points
B25
LAI from 0,6 to 1,3
mean = 0,9
B26
LAI from 0,3 to 0,6
mean = 0,4
Cross-Validation
Statistics
Pearson
RMSE
Shannon
Statistics
Noise
RMSE
Pearson chisquare
Shannon
entropy
#
% train
samples
ME
RMSE
RELRMSE
NRMSE MAE
R
R2
R2adj
NSE
Speed
[s]
5
5
500
500
5 -0,20
5 0,39
0,29
0,47
25,16
40,56
28,76
46,35
0,23
0,42
0,51 0,26
0,04 0,00
0,18
-0,11
-0,34
-2,47
0,05
0,05
5
500
5
0,52
44,71
51,09
0,48
0,18 0,03
-0,08
-3,22
0,08
0,46
SUMMARY
Objectives
Presentation of the research site
Ground measurements: methods & results
Airborne measurements: methods & results
Conclusions
Conclusions
1. Wetlands can be characterized with high
heterogenity therefore a high spatial variability of
reflectance or biophysical parameters (e.g. LAI) can
be observed
2. Biophysical parameters (e.g. LAI) can be sucessfuly
(error < 30%) mapped with LUT-based inversion of
RTM and airborne (UAV) hyperspectral data
3. Improvement of modelling can be achieved by
collecting more groundtruth data of plant
functional traits (PFT)
SWAMP Training Course
6 – 16 July 2015
Obrzycko-Rzecin (POLWET), Poland
A big thank you goes out to EUFAR, OPTIMISE
and the enthousiast team of trainers and trainees!