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Small unmanned aerial vehicles have been seeing increased deployment in field surveys in recent years. Their portability, maneuverability, and high-resolution imaging are useful in mapping surface features that satellite- and plane-mounted imaging systems could not access. In this study, we develop and apply a workplan for implementing UAV surveys in post-disaster settings to optimize the flights for the needs of the scientific team and first responders. Three disasters caused by geophysical hazards and their associated surface deformation impacts were studied implementing this workplan and was optimized based on the target features and environmental conditions. An earthquake that caused lateral spreading and damaged houses and roads near riverine areas were observed in drone images to have lengths of up to 40 m and vertical displacements of 60 cm. Drone surveys captured 2D aerial raster images and 3D point clouds leading to the preservation of these features in soft-sedimentary ground which were found to be tilled over after only 3 months. The point cloud provided a stored 3D environment where further analysis of the mechanisms leading to these fissures is possible. In another earthquake-devastated locale, areas hypothesized to contain the suspected source fault zone necessitated low-altitude UAV imaging below the treeline capturing Riedel shears with centimetric accuracy that supported the existence of extensional surface deformation due to fault movement. In the aftermath of a phreatomagmatic eruption and the formation of sub-metric fissures in nearby towns, high-altitude flights allowed for the identification of the location and dominant NE–SW trend of these fissures suggesting horst-and-graben structures. The workplan implemented and refined during these deployments will prove useful in surveying other post-disaster settings around the world, optimizing data collection while minimizing risk to the drone and the drone operators.
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Ybañezetal. Geosci. Lett. (2021) 8:23
https://doi.org/10.1186/s40562-021-00194-8
RESEARCH LETTER
Imaging ground surface deformations
inpost-disaster settings viasmall UAVs
Richard L. Ybañez1* , Audrei Anne B. Ybañez2, Alfredo Mahar Francisco A. Lagmay1,2 and Mario A. Aurelio1
Abstract
Small unmanned aerial vehicles have been seeing increased deployment in field surveys in recent years. Their port-
ability, maneuverability, and high-resolution imaging are useful in mapping surface features that satellite- and plane-
mounted imaging systems could not access. In this study, we develop and apply a workplan for implementing UAV
surveys in post-disaster settings to optimize the flights for the needs of the scientific team and first responders. Three
disasters caused by geophysical hazards and their associated surface deformation impacts were studied implement-
ing this workplan and was optimized based on the target features and environmental conditions. An earthquake that
caused lateral spreading and damaged houses and roads near riverine areas were observed in drone images to have
lengths of up to 40 m and vertical displacements of 60 cm. Drone surveys captured 2D aerial raster images and 3D
point clouds leading to the preservation of these features in soft-sedimentary ground which were found to be tilled
over after only 3 months. The point cloud provided a stored 3D environment where further analysis of the mecha-
nisms leading to these fissures is possible. In another earthquake-devastated locale, areas hypothesized to contain
the suspected source fault zone necessitated low-altitude UAV imaging below the treeline capturing Riedel shears
with centimetric accuracy that supported the existence of extensional surface deformation due to fault movement.
In the aftermath of a phreatomagmatic eruption and the formation of sub-metric fissures in nearby towns, high-
altitude flights allowed for the identification of the location and dominant NE–SW trend of these fissures suggesting
horst-and-graben structures. The workplan implemented and refined during these deployments will prove useful in
surveying other post-disaster settings around the world, optimizing data collection while minimizing risk to the drone
and the drone operators.
Keywords: Natural hazards, Post-disaster, Small UAV
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Introduction
Disasters caused by earthquakes and volcanic eruptions
regularly impact large areas at significant cost to lives and
infrastructure. Furthermore, fissures, landslides, subsid-
ence, and lateral spreading are post-disaster ground sur-
face deformations that both continue to pose a hazard to
populated areas as well as serve as key surface data for
the understanding of the hazard processes that caused
the disaster in the first place. Apart from humanitarian
and medical first-responders, quick-response teams com-
posed of geoscientists must also be deployed to these
post-disaster sites to study the affected area for hazard
processes that have happened and may still happen. e
post-disaster setting is typically more difficult to carry
out surveys and other scientific activity in due to the
still-dangerous conditions of the area as well as having to
navigate the on-going humanitarian crisis on-site. Imple-
mentation of a prepared workplan for such an event may
help scientists and responders to be more prepared for
responding quickly for data collection and analysis.
Unmanned aerial vehicles (UAV), or more com-
monly known as drones, have seen expanded use in the
last few years in numerous industries and fields such as
Open Access
*Correspondence: rich.ybanez@nigs.upd.edu.ph
1 National Institute of Geological Sciences, College of Science, University
of the Philippines, Diliman, 1101 Quezon City, Philippines
Full list of author information is available at the end of the article
Page 2 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
agriculture, infrastructure, media productions, leisure,
and scientific research (Greenwood et al. 2020). eir
ease-of-use, portability, and ever-improving imaging
capabilities offer a wide range of applications, including
mapping of surface features. eir high-resolution cam-
eras and flight altitude often result in 2D images and 3D
maps that are more detailed and accurate than satellite
imagery (Sharma 2016). is, combined with the pres-
ervation of perishable data in a post-disaster scenario,
makes the swift deployment of small UAVs essential in
assessing the effects of the disaster (Gomez & Purdie
2016). While the scope of geohazards is broad, the three
hazards that have seen the most application and deploy-
ment of UAV technology and research in recent years are
earthquakes, volcanic eruptions, and landslides.
In 2014, Xu etal. (2014) implemented an unmanned
aircraft system for the acquisition and processing of
drone data in the aftermath of a disaster. Satellite images
may not be available in the days after a disaster when
assessment of damage is most crucial in preventing more
human and economic loss. Existing technology and
equipment allow for acquisition of data independent of
ground control points and relying solely on Global Posi-
tioning System (GPS) satellites whereas processing of
acquired images can be done in the field with the perti-
nent software. Mission planning, acquisition, data valida-
tion, and data consolidation can be done entirely in the
field ensuring adaptability to the dynamic situation on
the ground in a post-disaster scenario.
In the aftermath of the 2015 Nepal Earthquake, fixed-
wing UAVs were deployed to assess structural damage
in the Kathmandu valley in tandem with traditional heli-
copter overflights (Inoue etal. 2016). While successfully
surveying in at least four damaged towns, restrictions
included time windows and months-long approval pro-
cess from the local government. ese considerations
must be studied when planning flights, even in a post-
disaster scenario. Local restrictions are not necessarily
loosened in the event of a disaster.
Acquired UAV imagery and the assessment of post-
disaster damages from the resulting orthomosaics are
products that can be used as input in other Geographic
Information System (GIS) analyses such as overlay analy-
sis and point density mapping. Drone-acquired imagery
from the 2017 Mw 6.3 earthquake in Lesvos, Greece, was
correlated with the area’s relief and underlying geology to
delineate the distribution of damaged structures (Mav-
roulis etal. 2019). Despite differences in the material and
design of different houses, clustering of damaged struc-
tures had a perceivable trend towards the half of the town
underlain by alluvial deposits as compared to the other
half underlain by fluvial sediments and sedimentary
rocks.
In this study, we outline a workplan for the conduct of
UAV-assisted assessment surveys in a post-disaster set-
ting in the aftermath of three disasters in the Philippines
in 2019 and 2020 caused by natural hazards to assess the
location, distribution, and trends of these surface defor-
mations that would provide data for the broader regional
analysis of the source event’s characteristics, processes,
and mechanics. We demonstrate how this workplan was
able to guide flight plans and data gathering in post-dis-
aster settings, the type and quality of data that was pro-
duced from these imaging flights, and the immediate
and medium-term advantages of using this highly port-
able imaging system in the context of hazards impact
analysis and assessment. Moreover, the deployment of
small UAVs to disaster-stricken areas by field teams out-
paces satellite-based remote sensing platforms which
are also being used to rapidly assess impacted regions
with drones supplying immediate imagery as opposed to
remotely sensed products that take days to download and
process (Xu etal. 2014).
e 22 April 2019 Central Luzon Earthquake was trig-
gered by a Mw 6.1 earthquake at 10 km depth with an
epicenter located 18 km east of Castillejos, Zambales
(PHIVOLCS 2019a). Nearby known active faults are
the Philippine Fault, Iba Fault, East Zambales Fault and
Manila Trench which are the main generators in the area.
Very strong to strong ground shaking was experienced in
the adjacent and nearby provinces of Bataan, Pampanga,
Bulacan, and Metro Manila which resulted in 16 casual-
ties, 86 injured, and 14 missing persons as well as at least
80 damaged structures in the affected areas (National
Disaster Risk Reduction and Management Council 2019;
PHIVOLCS 2019a). Landslides and liquefaction, mani-
fested by sand boils and lateral spreading, were observed,
and reported in several areas, particularly in the hilly and
deltaic areas, respectively, of Zambales and Pampanga.
e October 2019 Cotabato Earthquakes was a
series of four strong earthquakes of Mw 6.1 to 6.6 that
occurred within the vicinity of Makilala, Cotabato from
16 to 31 October 2019 (PHIVOLCS 2019b). e Mw
6.3 earthquake was located 22 km southeast of Tulu-
nan, Cotabato with a depth of 8km whereas the Mw 6.1
and 6.3 earthquakes on 29 October have an epicenter
located 25 km southeast of Tulunan and a depth of
7km. Lastly, the 31 October earthquake had a shallow
depth of 2km and an epicenter near Tulunan, Cotabato
(PHIVOLCS 2019b). Landslides, tension cracks, ground
fissures, and liquefaction were reported in numerous
towns surrounding the epicenters of the earthquakes.
Li etal. (2020) consider this earthquake sequence as the
result of the rupturing of conjugate faults reacting to an
E–W directed regional compressional stress in south-
ern Philippines. ere were 23 reported casualties, 563
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Ybañezetal. Geosci. Lett. (2021) 8:23
injured, 11 missing, and nearly 50,000 structures dam-
aged as a result of these earthquakes (National Disaster
Risk Reduction and Management Council 2020a).
e 12 January 2020 Taal Volcano phreatomagmatic
eruption resulted in a 15-km-high ash column that
rained heavy ashfall on the surrounding towns (Laurel
2020; PHIVOLCS 2020a). e Taal eruption occurred in
the afternoon following a series of phreatic explosions
which progressed to a large phreatomagmatic eruption
late in the afternoon. Nearly 750,000 people were dis-
placed or affected by the resulting ashfall (National Dis-
aster Risk Reduction and Management Council 2020b).
Several fissures were reported in towns surrounding
Taal Lake southwest of Taal Volcano dealing damage to
houses and other infrastructure (PHIVOLCS 2020b).
Using InSAR analysis, Bato etal. (2020) associates this
volcanic activity that involved magma expulsion and
fissure formation to lateral dike emplacement.
In all three disaster events, small UAVs were deployed
to facilitate rapid assessment of ground deformation pro-
cesses on nearby relief and built-up areas to preserve the
orientation, appearance, and distribution of these defor-
mation features, as well as to obtain actionable data on
evacuation, resettlement, and mitigation (Fig. 1). Fur-
thermore, the UAV images obtained were and will be
used as reference for more detailed studies such as analy-
sis of lateral spreading phenomena in the riverine areas in
Pampanga, Shallow Seismic Reflection surveys of poten-
tial fault zones in Cotabato, and mapping and analysis of
fissures in communities around Taal Volcano Island and
Taal Lake.
UAV deployment andworkplan
Two quadcopter UAVs were used during these three
events: the DJI Mavic 2 Pro and the DJI Mavic Air. Both
UAVs feature a compact foldable design making trans-
port and deployment relatively easy. e Mavic 2 Pro
Fig. 1 Location map of the three disaster events: the 2019 Central Luzon earthquake in pink boxes, the 2019 Cotabato earthquakes in green boxes,
and the 2020 Taal eruption in blue boxes. The earthquake epicenters are symbolized by red stars and labeled with their dates and magnitudes.
Taal Volcano’s main crater is indicated by a red triangle. Points of observation for the effects of these hazards are indicated by orange markers in
their maps with corresponding images at the right-most column of the figure. The Focal Mechanism Solution for the last earthquake in the 2019
Cotabato Earthquakes is also included
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Ybañezetal. Geosci. Lett. (2021) 8:23
was primarily used due to its superior camera and flight
time. is model is equipped with the L1D-2c Hasselblad
camera with a 20-megapixel 1 sensor and with a field of
view of about 77°, aperture of f/2.8–f/11 and shooting
range. Flight time is rated at 25min of normal use (DJI
2020). e Mavic Air served as a backup UAV and fea-
tures a less-powerful 12-megapixel camera with a 1/2.3
sensor and a flight time of 20min (DJI 2018). e drone
controllers were attached to an iPad mini or an Android
phone, depending on the situation, and loaded with the
DJI controller app, the Pix4D app, and the Ctrl+DJI app
which ties the two apps together on an Android device.
e Pix4D app on iOS, while being more stable than its
Android counterpart, does not include a feature to load
shapefiles or project-based mission planning where mul-
tiple flights can be found on one window.
e option to load shapefiles and plan multiple flight
missions on a single map is crucial when surveying a tar-
get area efficiently and quickly. In the search for a fault
rupture in the 2019 Cotabato Earthquakes, shapefiles
containing early and initial interpretations from deforma-
tion maps from satellite imagery were used as guides for
locating areas of interest for drone imaging missions. Fig-
ure2 shows the inferred fault trace from remotely sensed
data represented as a red shapefile loaded unto the Pix4D
flight planning Android app providing guidance for opti-
mized multi-mission flight plans that can cover the larg-
est possible are of interest in the least number of flights.
Flight parameters must be adjusted on-the-fly depend-
ing on environmental conditions and the size and extent
of surface features to be mapped and captured in the
field. A default flight altitude is set by Pix4D at 50 m
above the surface: a reasonable elevation for avoiding tall
trees and structures in relatively flat terrain but still low
enough to capture centimetric surface features such as
cracks and fractures caused by lateral spreading. Where
obstacles are absent, flight elevation can be reduced to 10
to 20m to obtain very high-resolution imagery of the sur-
face features. Where surface deformations cover a larger
area which need to be surveyed, elevations of 100m can
be reached with surface features still observable.
Two flight modes can be used in conducting surveys:
manual and automatic. e automatic flight mode is
generated by the flight planning feature of the Pix4D
app where a specified target area for image acquisition is
selected for the map and the software automatically sets
the flight path covering the entire area in consideration of
the set flight altitude and image overlap percentage. Once
the flight is started, the Pix4D app forwards the flight
plan to the DJI controller app which carries out the flight
automatically. e manual flight mode may be necessary
where obstacles are present in the drone’s flight path such
as trees and structures, particularly when the flight ele-
vation is lower than Pix4D’s minimum automated flight
altitude of 10m. e drone operator must avoid possible
obstacles while manually activating the drone’s camera
and ensuring that an adequate overlap is attained for the
orthomosaic processing.
While the Pix4D manual and other UAV flight guide-
lines state that line-of-sight and UAV visibility must be
always maintained, it may be necessary to fly the drone
beyond these parameters in a post-disaster setting if
impacted or deformed areas must be assessed while
keeping the drone pilot and field team in relative safety.
As such, distances between the take-off point and the
survey grid start point could be as much as 1km, and the
end point, or farthest point at 3km. e DJI manual indi-
cates signal connection between the controller and the
drone could be maintained at distances of up to 7km.
However, based on the experience of the researchers in
the three deployments in this study, distances of beyond
3km result in rapid signal degradation and potential loss
of the drone in semi-urban and rural settings. Interfer-
ence from houses, electric lines, and other urban struc-
tures are the likely reason for this diminished signal range
in such locations and must be taken into account when
planning survey grids (DJI 2020).
ere are two considerations in a post-disaster scenario
when finding the optimal setting for the image overlap:
the need for an accurate 3D point cloud and the amount
of time it will take for the flight and by extension the
battery consumption. A lower image overlap of around
20–30% will result in fewer flyovers, faster flight time,
and less battery consumption. In a post-disaster scenario
where the ability to recharge batteries is dictated by the
availability of power and time is a crucial resource for
Fig. 2 Shapefiles of possible fault trace (red) derived from initially
generated interferograms and projected flight plans (green and gray
boxes) loaded on Pix4Dcapture flight planning app
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Ybañezetal. Geosci. Lett. (2021) 8:23
surveying as large an area as possible, a low image over-
lap setting is ideal. is low overlap, however, is just
enough for generating a 2D orthomosaic of the study area
with tie-points too few for generating a 3D point cloud.
Where a 3D point cloud is necessary, such as when there
is a need to capture and preserve ground deformation for
subsequent analysis, an image overlap of 60–80% is pre-
ferred to capture the necessary number of tie-points for
the software to generate a point cloud. However, further
emphasis is placed on the increased battery consumption
of high-overlap flights. As such, the objectives of each
flight must be pre-planned to maximize flight time and
data acquisition for the desired products.
e acquired images from automated flights can be
processed into an orthomosaic, digital surface model
(DSM), and 3D point cloud through the Pix4D cloud
service, Pix4D Desktop interface, or similar third-party
drone processing software (Fig. 3). e manual flight
from this study’s post-disaster assessment was processed
through ESRI ArcGIS Pro’s Ortho Mapping module.
Pre-deployment and on-site phases of surveys are
highly dependent on several conditions and parameters
(Fig. 4). e data requirement and workflow exhibits
the necessary strategic planning for disasters. Remotely
sensed post-disaster data, where available, provides spa-
tial constrains and priority areas for assessing the effects
of the natural hazard. For large-scale, high-magnitude
hazards such as earthquakes, this is useful in determin-
ing the highest priority areas for surveying within a very
large disaster-stricken area. Terrain and hazard maps
provide additional guidance for accessibility and possi-
ble locations of mappable ground deformation features.
Finally, local reports provide the best data for the loca-
tions of target areas as well as where assessment is most
needed in response to evacuation and resettlement
requirements.
Fig. 3 Three types of output from a 2-dimensional mapping mission with a high measure of image overlap at 60%
Fig. 4 Data requirements and workplan for conducting UAV surveys in a post-disaster setting
Page 6 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
Results anddiscussion
2019 Central Luzon earthquake
In the aftermath of the 2019 Central Luzon Earthquake
last 22 April 2019, the mayor of Floridablanca, Pam-
panga requested for geological assessment of several
fissures that damaged houses in fear that these fissures
mark the trace of an active fault and would therefore
be earthquake-generating. Northeast-trending fissures
with individual lengths of approximately 5 to 10 m
affecting a total length of approximately 170m were
found to be consistently dropping downslope to the
southeast by 30cm towards a topographic low identi-
fied to be dried-out riverbed being used as cropland.
ese fissures appear as linear while some are arcuate
in shape in Quaternary alluvium. During earthquakes,
the ground lowers or subsides due to ground shaking.
Lateral spreading or rock spread is the lateral displace-
ment of sloping ground because of pressure build-up
in the surface deposits during occurrences of ground
shaking (Hungr etal. 2014).
One segment of these fissures passing through
approximately 15 houses is shown in Fig.5. is ortho-
mosaic was generated from images captured from 50m
above the ground with adequate resolution for record-
ing the location and extent of the fissures. e trend of
the fissures is parallel to the ephemeral stream to the
southeast, indicating a lateral spreading process along
the riverbank. e vertical displacements of these fis-
sures are noticeably more prominent towards the
stream, starting at sub-cm measurements at the far-
thest point and measuring up to 30 cm nearest the
stream (Fig.5, inset).
In another part of the river downstream, more occur-
rences of lateral spreading were found to affect farm-
land (Fig.6). e fissures trend to the north-northeast,
following the more northward trend of the dried-out
stream which is, in this location, still used as farmland.
e surface deformation in this area is characterized by
two different occurrences: the first being along the gen-
tly sloping riverbank leading into the dried-out river and
the second being on the dried-out river itself. Along and
above the riverbanks, fissures extended to about 40m in
length, deformed an area 30m measured perpendicularly
from the riverside, and displaced the surface vertically by
at the most 60cm.
ese images were acquired from a flight elevation of
50m and an overlap of 80% resulting in a spatial reso-
lution of 1.17 cm. Six months later, the area has been
re-tilled and new crops have been planted removing
all evidence of the widespread lateral spreading that
occurred there in the months prior (Fig.6C). e imme-
diate collection of UAV imagery in the days after the dis-
aster allowed for the digital preservation of these features
for subsequent study and analysis of formation patterns
and mechanisms of fissures in sedimentary basins.
A road in a different area of Pampanga was also found
to have collapsed rendering it impassable. Figure 7A
shows the orthomosaic of the north-trending road with
Fig. 5 Northwest-trending fissures as a result of lateral spreading (pointed by red arrows) induced by ground shaking. Inset shows on-ground
image of fissure and a vertical drop of approximately 30 cm
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Ybañezetal. Geosci. Lett. (2021) 8:23
its western half collapsing into dried-out wetlands while
the eastern half remains considerably intact despite also
being bound by a stream. e scale of the collapse of this
road highlights the need for improved construction to
account for the wetland terrain surrounding it. Figure7B
shows the same area in a 3D point cloud with the west-
ward orientation of the collapsed blocks preserved by the
model. Based on the point cloud and DSM, block sec-
tions of the road as high as 4m high collapsed into the
dried-out wetlands to the west with up to 150m of road
being affected. Fissures observed in Pampanga after the
Central Luzon earthquake are related to sedimentologi-
cal and hydrological changes in the basin. e formation
of fissures in the area poses a geologic hazard that must
be acknowledged and mapped since these may cause fur-
ther damage in the future along the riverside communi-
ties of Pampanga.
2019 Cotabato earthquakes
During the October 2019 Cotabato Earthquakes, UAV
flights were used as one of the methods in searching for
an undiscovered fault trace suspected to be the source
of the last of the 4 earthquakes, a M 6.5 occurring on 31
October. e UAV provided information that otherwise
would not have been obtained from images gathered by
satellite missions or from conventional airborne sur-
vey techniques. One such occurrence on-site for UAV
flights was searching for landslides and a potential fault
scarp. Towards the middle of the inferred fault seg-
ment, a huge landslide which flowed west-southwest
was found atop a north–south trending ridge (Fig.8).
Dense vegetation throughout the study area hampered
data collection of high-altitude UAV flights. In the pro-
cessed orthomosaic, the landslide is barely perceptible
only evidenced by the lightly colored freshly exposed
Fig. 6 Fissures from lateral spreading located within farmland and beside an ephemeral stream, also being used as farmland. A Post-earthquake
imagery. B High-contrast imagery showing fissures. Deformation structures contained by white boxes. C Rehabilitated farmland 6 months after
earthquake without evidence of lateral spreading. D Cross-section from drone-generated DSM showing the largest vertical offset due to the
subsidence. Transect is indicated by the red line
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Ybañezetal. Geosci. Lett. (2021) 8:23
soil as well as trees near the head scarp leaning towards
the direction of the landslide. Figure8B shows histori-
cal imagery available on Google Earth taken in Sep-
tember of 2016 where there is no evidence of exposed
soil or a landslide scarp. e landslide scarp’s curvature
towards the southern end is seen to follow the tree line
shadow that curves in the same way indicating a ridge
or change in relief that the landslide followed.
Preliminary and initial analysis of pre- and post-earth-
quake Interferometric Synthetic Aperture Radar (InSAR)
time series images suggested the presence of NE–SW
trending deformation zone centered at the epicenter of
Fig. 7 Collapsed road in Central Luzon as a result of ground shaking. A Plan-view based on orthomosaic imagery. B Oblique view of 3-dimensional
point cloud
Fig. 8 A Landslide obscured by thick vegetation (red dashed line). Extent validated on the ground and characterized in drone imagery by
light-colored freshly exposed soil and slanting trees. B Google Earth imagery taken Sept. 2016 of the same area showing the lack of a landslide
scarp. The southern edge of the landslide scarp follows the curved elevated tree line
Page 9 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
the Mw 6.5 tremor of 31 October 2019 in the village of
Biangan, in the northern sector of Makilala town. e
east–west trending Riedel shears suggesting dextral fault-
ing, consistent with the focal mechanism solution con-
sistently indicating a dextral solution along the NE–SW
trending nodal plane (Ekström etal. 2012) of the asso-
ciated event, and the interference pattern inferred from
InSAR data (Li etal. 2020).
Individual shears were observed at lengths of about 1 to
2m stretching across a zone of about 50m in flat farm-
land. e dense and tall vegetation, composed of coconut
trees, as well as the sub-meter scale of the Riedel shears
necessitated a manual low-altitude flight below the tree-
line while avoiding collision with coconut trees. Relying
on its capability to fly at low altitudes and hence ability to
obtain high-resolution images with centimetric accuracy,
a UAV survey over the suspected trace of a NE–SW fault
confirmed the presence of E–W-oriented, metric scale,
en echelon cracks arranged along a NE–SW trending
zone about a kilometer long.
e result is a patchwork of manually shot images at
a flight altitude of 4m above ground level following a
roughly northeast–southwest trending flight path (Fig.9).
e resulting orthomosaic formed from the low-altitude
flight reveals the high-resolution preservation of the
east–west en echelon fissures from Riedel shears, which
support the hypothesis of a NE-trending dextral fault in
the volcaniclastic region which were missed out in earlier
attempts to find a ground surface rupture.
ese E–W en echelon fissures in the surface captured
by the UAV were used as an impetus for investigating the
subsurface fault and surface field mapping. A high-reso-
lution shallow seismic reflection survey was conducted
one month after with the imaged Riedel shears serving
as the primary surface guides for the planning of seismic
profile acquisition.
Fig. 9 Orthomosaic generated from low-altitude manual flight. Red arrows point to east–west trending Riedel shears
Page 10 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
2020 Taal eruption
e 2020 Taal eruption deposited several centimeters of
ashfall in the surrounding provinces of Batangas, Laguna,
and Cavite with fissures being reported in the Batangas
towns located to the southwest of Taal Volcano. Numer-
ous volcanic earthquakes produced fissures reported
in the Batangas towns located to the southwest of Taal
Volcano such as Lemery, Agoncillo, Calaca and San
Nicholas where UAV surveys were conducted in these
towns to image the fissures. Figure 10 shows one sec-
tion of San Nicolas town with its ash-covered houses and
roads flown from an altitude of 100m above ground level.
e individual average length of fissures captured by the
UAV from San Nicolas town ranged from 2 to 5m, with
Fig. 10 Orthomosaic of ash-covered town crossed by northeast-trending fissures delineated in red. Insets A, B, and C show some of the most
prominent fissures (red arrows). The longest measured fissure in this zone, as visible from the drone imagery, is approximately 19 m (partially
contained in inset A)
Page 11 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
the longest being 19m. e large area being investigated
necessitated high-altitude flights to capture as large an
area as possible while remaining within the danger zone
and under the threat of an imminent eruption. However,
despite the high-altitude flights, northeast-trending fis-
sures were still captured by the drone flights, visible in
insets A, B, and C of Fig.10.
Light detection aperture radar (LiDAR) images from
the DOST-UP DREAM and Phil-LiDAR Program (2019)
show that the regional location of the fissures is charac-
terized by morphological features suggesting horst-gra-
ben structures (Fig.11). e fissures trended dominantly
to the NE which could be related to the suspected vol-
canic rift zone straddling the Pansipit River trending NE
(Aurelio etal. 2020).
Drone-aided mapping produced images of deforma-
tion related to these fissure zones indicating a NE–SW
preferred orientation (Fig.10). In other field-observation
sites, very steep dips either directed to the NW or SE
were found. e relative movement of blocks separated
by the fissures is alternating upward and downward on
adjacent blocks, consistent with a horst-and-graben
structure. Measurements of as large as 2 m, averaging
about 0.3m of vertical displacement were recorded.
Table 1 summarizes the different input cited in the
workplan in Fig.4 as applied in the three different disas-
ter areas and as their corresponding products have been
presented.
Apart from the immediate availability of data from
drone deployments in post-disaster settings compared
to other commonly used tools such as satellite-based
remote sensing and plane-mounted radar such as LiDAR,
the sub-centimetric resolution of the products and the
ability to fly under trees, clouds, and other obstructions
as shown in Fig. 9 makes drone surveying superior to
these higher-altitude acquisition platforms. Furthermore,
the portability of these tools and the relatively lower cost
to the commonly used alternatives make drone surveys
more accessible to research and response groups that do
not have the financial resources of aerospace agencies or
aerial survey companies.
Conclusion
UAVs have proven to be useful, if not indispensable,
equipment in the post-disaster assessment toolkit to
gather data quickly and safely within the disaster-stricken
zone. e investigative approach using UAVs for the
three disasters successfully mapped lateral spreading on
sloping ground such as old sedimentary rivers and dried
wetlands, surface ruptures with the help of remotely
sensed data over difficult terrain, and horst-and-graben
structures in volcanic terrains. UAV-estimated displace-
ments are in the same order of magnitude with centimet-
ric accuracy. is strongly justifies use of drone surveys
in areas that are not accessible, especially immediately
after a geologic hazard hits a populated area and rapid
science-based decisions are crucial in saving lives and
property. Data collection for both scientific study as well
as humanitarian response can be fulfilled by drones and
a well-implemented and well-informed series of flight
plans. ese flight plans need to be adjusted on-site given
constrains on time, accessibility, terrain and vegetation,
and the size of the area of investigation. However, flight
plan optimization for time and safety in a post-disaster
setting must also ensure that the quality and resolution of
surface data being captured meets the needs of scientists
as well as disaster-response teams.
e varied formats of output that drone-collected
imagery provide multiple possibilities for post-disaster
analysis and preservation of ground surface deforma-
tions that have been observed in these post-disaster sites.
ese ground deformation features are often removed by
subsequent human activity necessitating preservation in
a digital format.
We presented a disaster-response UAV-deployment
workplan in our study to guide researchers and first
responders in the implementation of their own deploy-
ments in the event of disasters caused by geophysical
hazards. Examples of implementation of this workplan
in three different disaster events in the Philippines were
discussed and their products and results consolidated for
reference of others who would need to assess other post-
disaster areas around the world. If followed, this work-
plan and the indicated drone settings may prove useful
in optimizing data collection, ensuring the safety of the
drone team and the drone itself, and generating data use-
ful for both scientific analysis as well as humanitarian
response and aid.
Fig. 11 LiDAR DTM of Pansipit Rift Valley. The rift valley is
characterized by horst-and-graben structures upon which the town
of San Nicolas sits. The coverage of Fig. 10 is shown
Page 12 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
Table 1 Summary of workplan information cited in Fig. 4 as applied to the different disaster events and their corresponding products
*The number of days after the event that the survey team was deployed and data was acquired
Event Lag
days* Pre-deployment On-site considerations Flight parameters Products
Remotely-
sensed
data
Terrain
and
hazard
maps
Local
reports Shapeles Accessibility
and
situational
safety
Target
features Size of
aected
site
(km2)
Size of
deformed
surface
features
Dim. of
ight
plan
(m2)
Elev.
(m) Overlap
size (%) Orien-
tation Flight
time
(min)
Reso-
lution
(cm)
Types
2019
Central
Luzon
earth-
quake
1 IfSAR
DTM Escorted
by
local
offi-
cials
Earthquake
epicenter Highly acces-
sible and
safe
Lateral
spread-
ing
(Fig. 6)
3.6 Length:
40 m 8000 50 80 NE 5 1.17 Ortho-
mosaic
Point
cloud
DSM
Affected
resi-
dents
Vertical
offset:
60 cm
Road
collapse
(Fig. 7)
0.1 Length:
150 m 40,000 50 80 N 25 1.18
Vertical
collapse:
4 m
2019
Cota-
bato
earth-
quakes
7 Initially-
processed
InSAR
products
IfSAR
DTM Escorted
by
local
offi-
cials
Interpreted
line from
InSAR
products
Damaged
roads Landslide
(Fig. 8)0.5 Area:
6000 m2300,000 100 70 NE 18 4.78 Ortho-
mosaic
Raster
images Land-
slide
hazard
maps
Affected
resi-
dents
Earthquake
epicent-
ers
Militant threat Riedel
shears
(Fig. 9)
0.002 Lengths:
1–2 m N/A
(man-
ual
flight)
4 10–30 NE 10 0.11
Landslide
threat Area:
900 m2
2020 Taal
erup-
tion
12 LiDAR
DTM NDRRMC
reports Points from
reports of
reported
fissures
Within 14 km
danger
zone
Fissures
(Fig. 10)1.8 Lengths:
1–19 m 135,000 100 60 NW 18 3.38 Ortho-
mosaic
Volcanic
haz-
ards
maps
PHI-
VOLCS
reports
Damaged
roads
Social
media
posts
Danger of
eruption
Page 13 of 14
Ybañezetal. Geosci. Lett. (2021) 8:23
Acknowledgements
This research study was supported by University of the Philippines National
Institute of Geological Sciences, University of the Philippines Resilience
Institute, and Energy Development Corporation. The authors would like
to acknowledge the local government units of Floridablanca, Pampanga,
Makilala, Cotabato and Batangas province who provided logistical assistance
as well as critical information on the location of the observed deformation
features and phenomena. The authors would like to express their apprecia-
tion to the following laboratories/groups: UP RI NOAH Hazard Team, UP
NIGS Volcano-Tectonics Laboratory, and the UP NIGS Structural Geology and
Tectonics Laboratory. The authors would also like to acknowledge John Dale
Dianala of Oxford University and UP NIGS for providing the initial interpreta-
tion of interferograms that guided the flight plans in Cotabato.
Authors’ contributions
RLY is the main drone operator, image processor, and contributor to the
manuscript. AABY is the secondary drone operator, provided supplementary
field data and notes, and contributed to the manuscript. AMFAL provided
overall direction in the mobilization and conduct of the fieldworks, provided
specific targets for data capture in the field, and shaped the cohesive flow and
thought of the manuscript. MAA provided critical technical input both in the
observation and capture of features in the field as well as in the manuscript
itself. All authors were present in the three post-disaster sites included in
this study and observed together and individually the different features and
phenomena discussed in this manuscript. All authors read and approved the
final manuscript.
Funding
The field deployments to the disaster areas studied in this article were made
possible by funding from the National Institute of Geological Sciences of the
University of the Philippines, Diliman and with donations from UP Alumni
through the UP Foundation Inc. Additional funding was provided by the Uni-
versity of the Philippines System through the UP Resilience Institute project
“Pandemics, Compound Disasters, and Other Complex Emergencies”.
Availability of data and materials
The datasets generated and analyzed during the current study are available
on Google Drive: https:// drive. google. com/ drive/ folde rs/ 1P71w a6BCs bYoeS
uh0hk XcLd8 CVT2d 5UK. Use of these datasets can be cited as follows: “Ybanez
et al., 2021 (this paper)”.
Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1 National Institute of Geological Sciences, College of Science, University
of the Philippines, Diliman, 1101 Quezon City, Philippines. 2 University
of the Philippines Resilience Institute, University of the Philippines, Diliman,
1101 Quezon City, Philippines.
Received: 18 December 2020 Accepted: 8 June 2021
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Mavic 2 pro/zoom user manual
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DJI (2020) Mavic 2 pro/zoom user manual. https:// www. dji. com/ mavic-2/ info# specs. Accessed 16 July 2020