Use of photogrammetry for the study of riparian vegetation dynamics

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Submitted on 9 Jul 2015

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Use of photogrammetry for the study of riparian vegetation dynamics

Borbála Hortobágyi, Franck Vautier, Andreas Burkart, Thomas Jan Wrobel, Jean-Luc Peiry, Johannes Steiger, Dov Jean-François Corenblit

To cite this version:

Borbála Hortobágyi, Franck Vautier, Andreas Burkart, Thomas Jan Wrobel, Jean-Luc Peiry, et al..

Use of photogrammetry for the study of riparian vegetation dynamics. I.S.Rivers - 2e Conférence Internationale ”Recherches et Actions au service des fleuves et grandes rivières”, Jun 2015, Lyon, France. Use of photogrammetry for the study of riparian vegetation dynamics, 2015. �hal-01170149�

I.S.RIVERS

LYON 2015

2

y = x

0 5 10 15 20 25

0 5 10 15 20 25

Vegetationheight–Pointcloud(m)

Vegetation height - Field (m) Individual Plot

Vegetation height measurement in the field and from the point cloud

y = x

0 5 10 15 20 25 30

0 5 10 15 20 25 30

Vegetationheight-Pointcloud(m)

Vegeation height - Field (m)

Vegetation height measurement in the field and from the point cloud Individual Plot

Airplane flight:

corridor

UAV flight:

point bar

Isolated Margin Plot

-4 -3 -2 -1 0 1 2 3

0 5 10 15 20 25 30 35 40

[-1,5;-1] ]-1;-0,5] ]-0,5;0] ]0;0,5] ]0,5;1] ]1;1,5] ]1,5;2] ]2;2,5]

Number of measurement

Error of 3D model (m)

Individual Plot

0 5 10 15 20 25 30

[0;2,5 m] ]2,5;5 m] ]5;10 m] ]10;15 m] ]15;20 m] 20 m<

Number of measurment

Vegetation height classes Outliers Absent Measured

Background

 Riparian vegetation responds to hydrogeomorphic disturbances and also controls sediment erosion and deposition. However, there are still gaps in understanding and quantifying reciprocal biogeomorphic processes within river systems. Here, we focus on the quantification of riparian vegetation dynamics. Photogrammetry can currently be used to derive two dimensional spatiotemporal variations in riparian vegetation cover within fluvial corridors as well as vegetation height models for the quantification of vegetation vertical growth rates.

 The heterogeneity of riparian vegetation height and spatial distribution including isolated trees within fluvial corridors represents a difficulty for building 3D models.

Objectives

 Building high resolution 3D photogrammetric models of riparian vegetation height based on aerial photographs at two complementary scales.

 Evaluating the quality of both models using vegetation height field measurements.

USE OF PHOTOGRAMMETRY FOR THE STUDY OF RIPARIAN VEGETATION DYNAMICS

Utilisation de la photogrammétrie pour l’étude de la végétation riveraine

Borbála Hortobágyi ¹ , Franck Vautier ² , Andreas Burkart ³ , Thomas Jan Wrobel ⁴ , Jean-Luc Peiry ¹ , Johannes Steiger ¹ , Dov Corenblit ¹

1

Laboratoire de Géographie physique et environnementale (GEOLAB) - Université Blaise Pascal - Clermont-Ferrand II, CNRS : UMR6042 - Maison des Sciences de l'Homme UBP-CNRS 4, rue Ledru 63057 Clermont-Ferrand cedex 1 - France

(corresponding author: Borbala.HORTOBAGYI@univ-bpclermont.fr).

²

Maison des Sciences de l'Homme de Clermont-Ferrand (MSH Clermont) - Université Blaise Pascal - Clermont-Ferrand II, CNRS : USR3550 - Maison des Sciences de l'Homme 4, rue Ledru 63057 Clermont-Ferrand cedex 1 - France.

³

Institute für Bio- und Geowissenschaften, IBG-2: Pflanzenwissenschaften, Forschungszentrum Jülich, 52425 Jülich, Germany.

⁴

Institute of Plant Biochemistry, Heinrich-Heine-Universität Düsseldorf,

Universitätsstraße 1, 40225 Düsseldorf, Germany

Study site

 The study reach is located on the Allier river, France. It is a dynamic wandering river characterised by lateral erosion in the outer bends of meanders and gravel point bar formation in the inner bends.

Methods

UAV Airplane

Material Falcon-8 (Asctec GmbH) Cessna172

Scale Point bar (18 ha) Floodplain (2300 ha)

Camera Sony NEX-5n Canon EOS 6D

Flight altitude 80 m 535 m

Resolution 25 mm/pixel 10 cm/pixel

Results – evaluation of the 3D photogrammetric models of riparian vegetation height UAV

 The model based on UAV photographs has a very good accuracy. The results show a significant overestimation for higher vegetation classes.

Airplane

 The model based on airplane photographs shows a lower accuracy than the model from UAV photos and a significant underestimation for isolated trees.

 The difference between vegetation height measured in the field and on the point cloud showed that 83,33% of the 3D model’s error is between -0,5 and 0,5 m. The highest errors and variability are related to poplar trees.

Isolated Margin Plot

Herbs Poplar Shrub

-1,5 -1 -0,5 0 0,5 1 1,5 2 2,5

0 5 10 15 20 25

0 5 10 15 20 25

Vegetationheight-Point cloud (m)

Vegeation height - Field (m) 0

5 10 15 20 25

0 5 10 15 20

Vegetationheight-Point cloud (m)

Vegeation height - Field (m) R²=0,992

a>1 [0,985; 1,016]

(α=0,05, p<0,0001)

R²=0,995

a>1 [0,999; 1,056]

(α=0,05, p<0,0001)

Overestimation max.: 2,199 m Underestimation max.: -1,279 m

0 5 10 15 20 25

0 5 10 15 20 25

Vegetationheight-Point cloud (m)

Vegeation height - Field (m)

0 5 10 15 20 25 30

0 5 10 15 20 25 30

Vegetation height -Point cloud (m)

Vegeation height - Field (m)

Shrub Poplar

Overestimation max.: 1,217 m Underestimation max.: -3,42 m

Num. of measurement:

17 11 58

 Difficulties: 1) some of the vegetation measured in the field did not appear within the point cloud; 2) the impossibility to determine vegetation height on certain parts of the point cloud because of outliers (noise). The highest errors and variability are related to poplar trees.

R²=0,964

a<1 [0,893; 0,983]

(α=0,05, p<0,0001)

R²=0,980

a>1 [0,966; 1,048]

(α=0,05, p<0,0001)

Conclusions

 ^UAV

• High precision representation of heterogeneous riparian vegetation.

• Easier logistics and flight organisation.

• Restrictions: 1) can be applied only on a smaller spatial scale (ex. point bar); 2) not suitable to represent small (15-40 cm), isolated poplars.

 ^Airplane

• Medium precision representation of heterogeneous riparian vegetation.

• Method is adequate to study riparian vegetation at a larger scale (ex. 10 km reach) and for vegetation >3 m.

• Restrictions: 1) flight requires strict application of the flight plan; 2) logistics (target installation, availability of the pilot, the airplane etc.) and flight organization more time-consuming.

Linear regression between vegetation height measurement on the point cloud and in the field Linear regression between vegetation height measurement on the point cloud and in the field

for individuals for plots

for individuals for plots

Measurementdifference(Point cloud –Field) (m) Measurementdifference(Point cloud –Field) (m)

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3

Field measurement Cross section drawing on the point cloud

Profile view applied for measurement

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Vegetation types Tree (poplar, salix a.), shrub, herb Vegetation height 0,1 m – 23 m

Measurement methods

1. Isolated vegetation (individual)

2. Dominant vegetation height in circular sampling plots with a radius of 5 m

Measurement tools 5 m measuring rod, laser telemeter Comparison of the

measurement UAV and airplane flight →

aerial photographs

Point clouds creation by photogrammetry

ERDAS LPS

Problems with photographs quality

Agisoft PhotoScan

Measurement on the point clouds (.las) in ArcMap

Vegetation height

measurement on the field

Measurement by sampling plot →

dominant height

Measurement by individual

1

3

4

2

 This river section is also characterised by a complex landscape mosaic with a heterogeneous spatial distribution of vegetation patches of different sizes and ages.

 Peaky shape of poplar trees are more difficult to represent accurately within the point cloud.

 Overestimation of higher vegetation classes by the photogrammetric model compared to field measurements: related to field measurement errors?

 The extracted growth rates are a fundamental component to be considered when studying feedbacks between fluvial landform construction and vegetation establishment and succession.

3D photogrammetric model

Aerial photograph - UAV