Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted

Digital Surface Model over Olive Groves

Fran Domazetović

, Ante Šiljeg

, Ivan Marić

and Mladen Jurišić

University of Zadar, Department of Geography, Trg kneza Višeslava 9, 23 000 Zadar, Croatia

University of Osijek, Faculty of Agriculture, Vladimira Preloga 1, 31000, Osijek, Croatia

Keywords: Worldview-3 Stereo Imagery, UAV Photogrammetry, VHR DSMs, Vertical Accuracy.

Abstract: Worldview-3 stereo-extracted DSMs represent state-of-the-art products in the domain of satellite-based

digital surface modelling. Main goal of our research was to evaluate the vertical accuracy of WV-3 derived

DSMs over olive groves. Creation of high-accuracy WV-3 derived DSMs would allow efficient large scale

management and protection of this valuable agricultural resource.

Vertical accuracy of WV-3 derived DSM was evaluated at two test sites within Olive Gardens of Lun (Pag

Island, Croatia), through the comparison with reference UAV photogrammetry derived VHR DSM. Two test

sites were selected by object-based approach, established on spectral (NDVI, VARI) and height information

(digital olive models (DOMs)). While first test site covers one single, individual oldest olive tree (45 m²),

second test site covers larger area (2500 m²) with dense, unattended olive trees.

Although vertical accuracy of individual olive trees still significantly deviates from reference model (RMSE

= 3.604 m; MAE = 3.203 m), accuracy within larger test was much better (RMSE = 1.462 m; MAE = 1.127

m). This demonstrated that WV-3 stereo imagery has great potential for application in creation of DSMs over

large scale forested areas, that would be hard to cover with field geospatial techniques (e.g. LiDAR or UAV

photogrammetry).

1 INTRODUCTION

In recent years very high resolution (VHR) optical

satellite stereo imagery allowed extensive extraction

of digital surface models (DSMs) with application in

broad range of scientific fields (Aguilar et al., 2019).

With the emergence of commercial satellites (e.g.

IKONOS, Pleiades, GeoEye, Worldview), stereo

satellite imagery has become known as cost and time

effective method for creation of DSMs over large

areas (Shean et al., 2016; Goldbergs et al., 2019).

Although such DSMs lack the detail and resolution of

models created with field geospatial methods like

LiDAR or UAV photogrammetry, they require

minimal field deployment (Wang et al. 2019), thus

shortening the overall modelling process. If spatial

extent of created DSMs is considered, satellite stereo

imagery represents relatively inexpensive data

collection method, where single collected stereo-pair

https://orcid.org/0000-0003-3920-6703

https://orcid.org/0000-0001-6332-174X

https://orcid.org/0000-0002-9723-6778

https://orcid.org/0000-0002-8105-6983

image covers large swaths of Earth’s surface

(Goldbergs et al., 2019).

Development of satellites from Worldview

constellation has significantly advanced the

capabilities of capturing multispectral and stereo

satellite imagery with sub-meter ground sampling

distance (GSD) (Aguilar et al., 2013; Aguilar et al.,

2014). Currently most advanced commercial satellite

is Worldview-3 (WV-3), launched in August, 2014.

WV-3 provides highest commercially available

spatial resolution (one 0.31 m panchromatic band and

eight 1.24 m multispectral bands) of collected

satellite images, along with very large daily collection

capacity (up to the 1 200 000 km²) (Maxar

Technologies, 2019A). Stereo imagery is collected by

WV-3 on the daily basis, where images of specific

locations of interest are being collected from different

angles, along the in-track orbit, within minimal time

interval (Maxar Technologies, 2019A). Short

246

Domazetovi

c, F., Šiljeg, A., Mari

c, I. and Juriši

c, M.

Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted Digital Surface Model over Olive Groves.

DOI: 10.5220/0009471002460253

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 246-253

ISBN: 978-989-758-425-1

collection interval between two stereo images ensures

that changes (e.g. atmospheric conditions, land-cover

change, moving targets, etc.) at target location are

minimal, thus minimalizing the potential image

matching error.

As WV-3 stereo-extracted DSMs represent state-

of-the-art products in the domain of satellite-based

digital surface modelling, main goal of our research

was to evaluate the vertical accuracy of such DSMs

over olive groves. Assessment of vertical accuracy

for DSM produced from WV-3 stereo-pair image

(DSM



) was based on comparison with reference

VHR DSM (DSM



) produced with the unmanned

aerial vehicle (UAV) photogrammetry. UAV

photogrammetry was chosen for creation of reference

DSM, as practical and cost effective geospatial

method that allows creation of accurate and reliable,

high-quality VHR DSMs over terrains with

noticeable vegetation presence (Mohan et al., 2017;

Tomaštík et al., 2017; Krause et al., 2019).

Although overall quality of DSMs derived from

Worldview stereo imagery was already evaluated in

some previous researches, their main focus was

mostly on comparison with reference LiDAR data

(Shean et al., 2016; Aguilar et al., 2019; Goldbergs et

al., 2019; Nemmaoui et al., 2019; Rizeei & Pradhan,

2019) and on accuracy of extraction of various man-

made structures (e.g. buildings (Qin, R., 2014),

plastic greenhouses (Aguilar et al., 2019; Nemmaoui

et al., 2019), etc.).

In our research we have decided to concentrate on

assessment of vertical accuracy of DSM over olive

groves, as important specific land cover type, that

covers large areas of Mediterranean and serves as one

of the main agricultural sources of income and

development (Orlandi et al., 2017). Detailed DSMs of

olive groves are the basis for efficient management

and protection of this valuable agricultural resource,

as they can provide unprecedented insight into all

spatio-temporal changes that are occurring within the

groves (Jiménez-Brenes et al., 2017). Therefore,

possibility of application of WV-3 stereo imagery for

creation of high-quality DSMs would significantly

improve large scale management and protection

efforts.

2 STUDY AREA

Quality of Worldview-3 extracted DSM was assessed

within Olive Gardens of Lun (OGL), located on Lun

peninsula at most northern part of Pag Island, Croatia

(Fig. 1A).

Figure 1: Location of study site within Croatia (A), location

of GCPs, CPs and test sites (TA1 and TA2) within the study

area (B).

OGL represents a protected olive grove that

contains some of the oldest millennial olive trees in

the World. As such it is protected as Sites of

Community Importance (SCI), under the Natura 2000

network of European nature protection areas

(European Environment Agency, 2019). Primary

quality assessment (Test site 1 (TA1)) covered one

individual olive tree, that represents oldest and largest

single tree within the study area. Secondary quality

assessment was performed within larger test site (Test

site 2 (TA2)), covered by dense, unattended olive

trees. Through primary assessment we wanted to

evaluate DSM vertical accuracy for representation of

individual trees, where we expected model quality to

be less good. In secondary assessment we wanted to

evaluate vertical accuracy of created DSM over

thicker vegetation cover with larger tree crown

surface, where we expected higher overall vertical

accuracy. Location of test sites within the study area

can be seen on Fig. 1B.

Acquisition of both WV-3 imagery and UAV

aerial imagery was conducted before the seasonal

pruning of the olive trees (Gucci, R. & Cantini, C.,

2000), so that possible human-induced changes in

height of olive trees are eliminated.

3 MATERIALS AND METHODS

3.1 Data Acquisition and Specifications

Acquisition of Worldview-3 Imagery

Worldview stereo imagery covering our study area

was collected by WV-3 satellite on December 4

Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted Digital Surface Model over Olive Groves

247

2018, within the time span of 8 minutes. Stereo

images were collected at ideal conditions, with 0%

cloud cover and with optimal off-NADIR angles (<

30º) (Nemmaoui et al., 2019), thus achieving claimed

5 m CE90

/LE90

absolute horizontal accuracy

specification with 2.3 m Root Mean Square Error

(RMSE) (Maxar Technologies, 2019B).

WV-3 stereo images of study area were provided

to the Authors as OrthoReady Stereo imagery

(OR2A), through the funding of DigitalGlobe

Foundation. OR2A is radiometrically and sensor

corrected imagery, that has no terrain corrections

applied and is suitable for further analysis,

orthorectification and elevation extraction (Maxar

Technologies, 2019B). OR2A contains metadata

(.STE file) required for orientation of stereo pairs in

various photogrammetric software packages and

creation of DSMs (Maxar Technologies, 2019B).

Detailed specifications of acquired WV-3 stereo

imagery are given in table 1.

Table 1: Specifications of acquired WV-3 stereo imagery.

Image ID WV-3A WV-3B

Image type Stereo OR2A Stereo OR2A

Acquisition date 04.12.2018. 04.12.2018.

Acquisition time 14:20:46 14:28:40

Off-NADIR (º) 12.1 27.1

Cloud cover (%) 0 0

GSD (m) 0.30 0.30

Scan direction Forward Backward

Sun azimuth (º) 157.1 157.6

Sun elevation (º) 62 62.1

Product pixel

size (PAN)

0.3 m 0.3 m

Product pixel

size (MS)

1.2 m 1.2 m

Aerial Survey with UAV Photogrammetry

UAV photogrammetric survey was carried out over

study area on March 10

, 2019, with our newly-

developed repeat aerophotogrammetric system

(RAPS). RAPS represents advanced

aerophotogrammetric system, composed of

professional-grade DJI Matrice 600 PRO drone,

Gremsy T3 gimbal, Sony Alpha A7RII (42 MP)

DSLR camera equipped with 20 mm lens and Reach

M+ GNSS module for UAV mapping. Due to the

advanced flight capabilities and camera

characteristics RAPS allows acquisition of aerial

CE90 - circular error at the 90th percentile, where

minimum of 90 percent of the points measured has a

horizontal error less than the stated CE90 value.

images with very-high resolution and positioning

precision.

Parameters of carried aerial surveys (double-grid

flight profiles, GSD (cm), flight speed (m/s), side and

forward overlap (%), etc.) were planned and

automated in Universal Ground Control Software

(UgCS) according to the suggestions given in Pepe et

al., 2018. Flight height was set to 165 m above

ground, side and forward overlap were set to 80% and

GSD was 2.6 cm. Overall accuracy of DSM



created from collected aerial images was improved

with 6 fixed ground control points (GCPs) and 3

check points (CPs) distributed uniformly within the

study area. GCPs and CPs were marked before the

aerial survey with red paint and their precise

coordinates were collected with Stonex S10 Real

Time Kinematic (RTK) GPS. Survey was carried in

static mode.

3.2 Data Processing and DSM

Production

Creation of DSM from WV-3 Stereo Imagery

DSM of study area was created from WV-3 stereo

imagery in OrthoEngine 2018 suite of Geomatica

2018 software. Workflow for DSM creation within

OrthoEngine can be divided into following substeps:

math model selection (1), introduction of ground

control points (GCPs), check points (CPs) and tie

points (TPs) required for image orientation (2),

bundle adjustment (3), epipolar image creation (4)

and automatic DSM extraction (5).

First step of DSM



extraction includes the

selection of corresponding math model (1) that serves

as mathematical relationship used for correlation of

two-dimensional (2D) image pixels with correct

three-dimensional (3D) locations on the ground (X,

Y, Z) (Barazzetti et al., 2016). Optical Satellite

modeling based on provided rational polynomial

coefficients (RPC) and zero-order polynomial

adjustment was selected as one of the most commonly

used math models for DSM extraction from WV

stereo imagery (Aguilar et al., 2013; Aguilar et al.,

2019; Goldbergs et al., 2019). In order to produce

highly accurate DSM, introduction of GCPs (2) is

required for systematic compensation of RPC

induced errors and improve overall image geo-

referencing accuracy (Aguilar et al., 2013; Goldbergs

et al., 2019). Therefore, seven GCPs and five CPs

LE90 - 90th percentile linear error, where minimum of 90

percent of vertical errors fall within the stated LE90

value.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

248

scattered throughout the study area and surveyed with

the Stonex S10 RTK-GPS were introduced, along

with 187 TPs automatically detected from WV-3

stereo-pair. Reported RMSE for used GCPs, CPs and

TPs is given in table 2.

Table 2: RMSE for GCPs, CPs and TPs used for creation of

WV-3 stereo-derived DSM of study area.

Point

type

𝑁



RMS

E X

(m)

RMS

E Y

(m)

RMS

E Z

(m)

MEAN

RMSE (m)

GCP

7 0.369 0.197 0.504 0.356

5 0.320 0.517 0.737 0.525

0.071 0.017 0.001 0.029

Introduced GCPs and TPs are then used for bundle

adjustment (3) which in combination with RPC-

derived sensor geometry calculates the exact position

of satellite at the time of image collection. Next step

covers creation of epipolar image (4), that represents

stereo-pair image, where left and right images are

reprojected to have common orientation and

matching features distributed along the common x-

axis (PCI Geomatics Enterprises, 2018).

Final step in creation of DSM



was automated

DSM extraction (5). Recent research conducted by

Goldbergs et al. (2019) demonstrated that frequently

used semi-global matching (SGM) is not suitable for

creation of DSMs over forested areas, since it

significantly underestimates tree presence and height.

Therefore, in order to produce best possible quality

DSM of our research area, we have tested both SGM

and normalized cross-correlation (NCC) technique

(both implemented within Geomatica Orthoengine

2018) for automated DSM extraction. Pixel sampling

interval was set to 1 for both NCC and SGM derived

DSMs, meaning that image correlation was

performed at full image resolution, thus enabling

extraction of fine details (e.g. bushes, trees, buildings,

etc.) in created DSMs (PCI Geomatics Enterprises,

2018).

Final created DSM



was used for

orthorectification of pansharpened 8-ban

multispectral WV-3 image of the study area with 0.3

m spatial resolution that was later used for extraction

of vegetation cover, through OBIA approach.

Creation of DSM from UAV Photogrammetry

Aerial imagery acquired by RAPS was used for

creation of VHR reference DSM of the study area in

Agisoft Metashape 1.5.1. software. This software is

currently one of the most advanced and precise

image-based 3D modelling software that uses

structure-from-motion (SfM) algorithm and multi-

view 3D reconstruction technology for creation of

high-quality models (Mancini et al., 2013). Workflow

for extraction of VHR DSM from aerial images

collected by RAPS followed the recommendations

given in James et al., 2019. Based on processing of

collected aerial imagery VHR reference DSM



with 10 cm spatial resolution and digital ortophoto

image (DOF) with 3 cm spatial resolution were

created.

3.3 Object-based Extraction of Olive

Groves

Extraction and mapping of olive groves within OGL

was performed by object-based image analysis

(OBIA) in eCognition Developer 9 software. OBIA

extraction of olive trees was based on spectral

(multispectral WV-3 image, DOF) and height

information (digital olive tree models (DOMs)).

DOMs were generated as difference between created

DSM and corresponding digital elevation model

(DEM). DEMs were created from DSM



and

DSM



with DSM2DTM algorithm in Geomatica

2018 software.

In the first step of OBIA all olive trees within

study area were extracted from the multispectral 8-

band WV-3 image and created DSM



through the

use of multiresolution segmentation algorithm (MRS)

(scale = 25, compactness = 0.9, shape = 0.5) and

threshold-based classification (meanNDVI ≥ 0.18,

meanDOMheight ≥ 0.5 m).

In second step olive trees were extracted from

DOF through the same OBIA approach. While

approach and MRS parameters (scale = 25,

compactness = 0.9, shape = 0.5) were identical,

threshold-classification had to be adjusted to different

spatial and spectral resolution of UAV

photogrammetry derived DSM and DOF. Since DOF

is composed of only visual RGB bands, threshold

classification had to be based on Visible

Atmospherically Resistant Index (VARI)

(meanVARI ≥ 0, meanDOMheight ≥ 0.5 m) instead

of NDVI.

Two datasets of extracted olive trees were then

intersected in order to derive overlapping area.

Overlapping area represents the area which is covered

by olive trees on both reference and WV-3 derived

DSMs (Fig. 2.). As such it was taken as test area for

validation of DSM



vertical accuracy.

Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted Digital Surface Model over Olive Groves

249

Figure 2: Test area (TA1) derived as overlap between area

representing single olive tree on WV-3 and reference data.

3.4 Model Vertical Accuracy

Assessment

Overall vertical accuracy of produced WV-3 DSM

was assessed over olive groves with VERTICAL tool,

that allows regular sampling of height values from

two defined DSMs (Domazetović, 2018).

VERTICAL tool samples height values along the

cross-sections (Cs), in intervals that can be adjusted

by the user in regard to the spatial resolution of

evaluated models. For the purpose of this research

both Cs and height sampling interval were set to 1 m.

Sampled height values are then used for automated

calculation of vertical difference (∆ℎ between two

given DSMs, according to the following formula:

∆ℎ  ℎ



 ℎ



(1)

Positive values of ∆ℎ indicate that DSM



exaggerates the height of certain point, in comparison

to the reference DSM



, while negative values are

indicating underestimated height values. Neutral

values (∆ℎ = 0) indicate that at that point DSM



does not deviates vertically from DSM



. Vertical

difference ( ∆ℎ ) was also used for calculation of

RMSE and Mean absolute error (MAE), as

representative measures of DSM



vertical accuracy

within test sites.

4 RESULTS AND DISCUSSION

4.1 Created WV-3 DSMs of Study Area

As spatial resolution of initial PAN WV-3 stereo

imagery was 0.3 m and pixel sampling interval for

NCC and SGM was set to 1, spatial resolution of final

created DSM



was 0.6 m.

Although NCC and SGM approaches were

based on identical input WV-3 stereo imagery and

GCPs, resulting DSMs were very dissimilar.

Significant differences between DSMs of study area

created by NCC and SGM approaches are obvious

even from basic visual comparison of created models

(Fig. 3.). DSM created by SGM approach was much

smoother and it lacks most of single, individual trees,

thus confirming what was stated by Goldbergs et al.

(2019.). On the other hand, NCC also failed to

represent all individual olive trees, but representation

was much better than with SGM approach.

Figure 3: Visual comparison of DSMs created by NCC

(right) and SGM (middle) approaches with DSM created

from UAV photogrammetry (left); red ellipse – area

covered by individual, dispersed olive trees.

While SGM was very straightforward and easy-

to-use, NCC allowed higher autonomy for adjustment

of user-defined parameters for DSM extraction to the

local characteristics of our study area. Namely, high

individual olive trees rise several meters above the

surrounding terrain, significantly rising overall

surface roughness. SGM technique neglected the high

surface roughness and created rather smooth DSM,

with very poor representation of individual olive

trees. To solve this problem, we set the smoothing

filter and terrain type parameters of NCC technique

to fill holes only and mountainous, respectively. Fill

holes only parameter interpolates all holes in created

DSM, but does not apply any additional filtering and

smoothing, which is important for preservation and

representation of individual olive trees in the created

model. Although terrain within our study area is

represented by gentle hills, we decided to set terrain

type parameter to mountainous, in order to preserve

individual trees, that would be filtered with other two

terrain type parameters (flat, hilly). As a result, DSM

produced by NCC had much better representation of

individual olive trees than DSM produced by SGM,

and thus this DSM was chosen as final DSM



GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

250

4.2 Vertical Accuracy of WV-3 DSM

DS𝐌

𝐖𝐕𝟑

Vertical Accuracy within TA1

Within TA1 VERTICAL tool determined the vertical

difference between DSM



and reference DSM



in 140 height points, distributed within 8 cross-

sections. It can be noted that DSM



underestimates

the height of individual olive tree crown, with

negative vertical difference present in 92.857% of all

sampled points. Presence of significant height

underestimation is uniform within whole TA1 area.

This demonstrated that although produced DSM



managed to reproduce individual olive trees, vertical

accuracy of such representation is relatively low, as it

is further confirmed with RMSE and MAE values for

whole TA1 (Table 3.).

DS𝐌

𝐖𝐕𝟑

Vertical Accuracy within TA2

Within TA2 VERTICAL tool sampled 5202 height

points in total, distributed within 61 cross-sections.

Spatial distribution of sampled vertical transects and

two exemplary vertical profiles can be seen in Fig. 4.

Figure 4: Spatial distribution of 61 vertical transects

sampled within dense olive trees of TA2 (left); profiles of

two selected vertical transects (Cs 48 & Cs 74)

demonstrating visual comparison between DSM



and

reference DSM



(right).

Unlike TA1 vertical difference between

DSM



and reference DSM



is less pronounced

within TA2. Dense, unattended crowns of olive trees

within TA2 are forming relatively homogenous

surface, which is much better represented within

created DSM



, than crowns of individual olive

trees. Underestimation (31.311% of all sampled

points) and overestimation (65.569% of all sampled

points) of heights are both present in points sampled

within TA2 (Fig. 5.). However, calculated RMSE and

MAE values are demonstrating that overall vertical

accuracy within this test site is much higher than

within TA1 (Table 3.).

Table 3: RMSE and MAE as measures of DSM



vertical

accuracy within two test areas.

Test

Area

𝑁



height

samples

Total

area

(m²)

Root

Mean

Square

Error

(m)

Mean

absolute

error (m)

TA1 140 45 3.604 3.203

TA2 5202 2500 1.462 1.127

Better vertical accuracy within TA2 can be

attributed to the characteristic of olive trees within

this area, thus confirming our expectation that DSM

vertical accuracy will be better for olive trees with

larger and denser concentration of crowns.

Figure 5: Vertical difference between DSM



and

DSM



measured in 5202 height points sampled within

TA2 with VERTICAL tool.

5 CONCLUSION

Main aim of our study was to evaluate the vertical

accuracy and potential of WV-3 derived DSMs for

application over olive groves.

Comparison of NCC and SGM approaches has

demonstrated how image matching technique and

user-defined parameters influence the representation

of individual olive tree in produced DSMs. In overall,

NCC approach with user-defined parameters adjusted

to local characteristics of study area provided DSM

with significantly higher representation of individual

olive trees.

Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted Digital Surface Model over Olive Groves

251

Although WV-3 stereo-extracted DSMs represent

state-of-the-art products for satellite-based digital

surface modelling, through our research we have

concluded that vertical accuracy and representation of

individual olive trees still significantly deviates from

our reference VHR model created with UAV

photogrammetry. However, if we consider that our

both test sites covered very small areas and that

calculated RMSE and MAE values are relatively low

(in respect to spatial resolution of DSM



and initial

WV-3 stereo imagery), we can conclude that vertical

accuracy of produced DSM



is more than

satisfactory.

As demonstrated by RMSE and MAE values

vertical accuracy was especially good over larger test

area (TA2), covered by dense, unattended olive trees.

This demonstrated that WV-3 stereo imagery has

great potential for application in creation of DSMs

over large scale forested areas, that would be (due to

high costs and terrain inaccessibility) hard to cover

with field geospatial techniques (e.g. LiDAR or UAV

photogrammetry).

ACKNOWLEDGEMENTS

This research was performed within the project UIP-

2017-05-2694 financially supported by the Croatian

Science Foundation.

Authors would like to thank DigitalGlobe

Foundation (Maxar Technologies), Hexagon

Geospatial and SPH Engineering for provided

necessary VHR Worldview satellite imagery and

software (UgCS, Erdas Imagine 2018.).

REFERENCES

Aguilar, M. A., Bianconi, F., Aguilar, F. J., & Fernández, I.

(2014). Object-based greenhouse classification from

GeoEye-1 and WorldView-2 stereo imagery. Remote

sensing, 6(5), 3554-3582.

Aguilar, M. Á., del Mar Saldaña, M., & Aguilar, F. J.

(2013). Generation and quality assessment of stereo-

extracted DSM from GeoEye-1 and WorldView-2

imagery. IEEE Transactions on Geoscience and

Remote Sensing, 52(2), 1259-1271.

Aguilar, M. A., Nemmaoui, A., Aguilar, F. J., & Qin, R.

(2019). Quality assessment of digital surface models

extracted from WorldView-2 and WorldView-3 stereo

pairs over different land covers. GIScience & remote

sensing, 56(1), 109-129.

Barazzetti, L., Roncoroni, F., Brumana, R., & Previtali, M.

(2016). Georeferencing accuracy analysis of a single

worldview-3 image collected over Milan. The

International Archives of the Photogrammetry, Remote

Sensing and Spatial Information Sciences, Volume XLI-

B1.

Domazetović, F. (2018). Quantitative analysis of gullies of

Pag island through the use of high-resolution models

(Master thesis, University of Zadar. Department of

Geography.).

European Environment Agency (2019.). Natura 2000 End

2018 – Shapefile. Retrived from https://www.eea.

europa.eu/data-and-maps/data/natura-10/natura-2000-

spatial-data/natura-2000-shapefile-1 (accessed 12

December 2019).

Goldbergs, G., Maier, S. W., Levick, S. R., & Edwards, A.

(2019). Limitations of high resolution satellite stereo

imagery for estimating canopy height in Australian

tropical savannas. International Journal of Applied

Earth Observation and Geoinformation, 75, 83-95.

Gucci, R., & Cantini, C. (2000). Pruning and training

systems for modern olive growing. Csiro Publishing.

Jiménez-Brenes, F. M., López-Granados, F., de Castro, A.

I., Torres-Sánchez, J., Serrano, N., & Peña, J. M.

(2017). Quantifying pruning impacts on olive tree

architecture and annual canopy growth by using UAV-

based 3D modelling. Plant Methods, 13(1), 55.

Krause, S., Sanders, T. G., Mund, J. P., & Greve, K. (2019).

UAV-based photogrammetric tree height measurement

for intensive forest monitoring. Remote Sensing, 11(7),

758.

Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri,

S., & Gabbianelli, G. (2013). Using unmanned aerial

vehicles (UAV) for high-resolution reconstruction of

topography: The structure from motion approach on

coastal environments. Remote Sensing, 5(12), 6880-

6898.

Maxar Technologies (2019A.). Worldview-3 datasheet.

Retrieved from https://www.digitalglobe.com/

company/about-us (accessed 03 December 2019).

Maxar Technologies (2019A.).Stereo Imagery datasheet.

Retrieved from https://www.digitalglobe.com/

resources (accessed 03 December 2019).

Mohan, M., Silva, C. A., Klauberg, C., Jat, P., Catts, G.,

Cardil, A., ... & Dia, M. (2017). Individual tree

detection from unmanned aerial vehicle (UAV) derived

canopy height model in an open canopy mixed conifer

forest. Forests, 8(9), 340.

Nemmaoui, A., Aguilar, F. J., Aguilar, M. A., & Qin, R.

(2019). DSM and DTM generation from VHR satellite

stereo imagery over plastic covered greenhouse areas.

Computers and Electronics in Agriculture, 164,

104903.

Orlandi, F., Aguilera, F., Galan, C., Msallem, M., &

Fornaciari, M. (2017). Olive yields forecasts and oil

price trends in Mediterranean areas: a comprehensive

analysis of the last two decades. Experimental

Agriculture, 53(1), 71-83.

PCI Geomatics Enterprises (2018.). Geomatica

OrthoEngine Course Exercises. Retrieved from

https://www.pcigeomatics.com/pdf/TrainingGuide-

Geomatica-OrthoEngine.pdf (accessed 06 Jenuary

2020).

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

252

Pepe, M., Fregonese, L., & Scaioni, M. (2018). Planning

airborne photogrammetry and remote-sensing missions

with modern platforms and sensors. European Journal

of Remote Sensing, 51(1), 412-436.

Qin, R. (2014). Change detection on LOD 2 building

models with very high resolution spaceborne stereo

imagery. ISPRS journal of photogrammetry and remote

sensing, 96, 179-192.

Rizeei, H. M., & Pradhan, B. (2019). Urban mapping

accuracy enhancement in high-rise built-up areas

deployed by 3D-orthorectification correction from

WorldView-3 and LiDAR Imageries. Remote Sensing,

11(6), 692.

Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E.,

Joughin, I. R., Porter, C., & Morin, P. (2016). An

automated, open-source pipeline for mass production of

digital elevation models (DEMs) from very-high-

resolution commercial stereo satellite imagery. ISPRS

Journal of Photogrammetry and Remote Sensing, 116,

101-117.

Tomaštík, J., Mokroš, M., Saloň, Š., Chudý, F., & Tunák,

D. (2017). Accuracy of photogrammetric UAV-based

point clouds under conditions of partially-open forest

canopy. Forests, 8(5), 151.

Wang, S., Ren, Z., Wu, C., Lei, Q., Gong, W., Ou, Q., ... &

Li, C. (2019). DEM generation from Worldview-2

stereo imagery and vertical accuracy assessment for its

application in active tectonics. Geomorphology, 336,

107-118.

Assessing the Vertical Accuracy of Worldview-3 Stereo-extracted Digital Surface Model over Olive Groves

253