ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

Contents lists available at ScienceDirect

ISPRS Journal of Photogrammetry and Remote Sensing
journal homepage: www.elsevier.com/locate/isprsjprs

Assessing the impacts of canopy openness and flight parameters on
detecting a sub-canopy tropical invasive plant using a small unmanned
aerial system
Ryan L. Perroy a,⇑, Timo Sullivan b, Nathan Stephenson aab

University of Hawaii at Hilo, 200 West Kawili Street, Hilo, HI 96720, United States
Big Island Invasive Species Committee, 23 East Kawili Street, Hilo, HI 96720, United Statesa r t i c l e

i n f oArticle history:
Received 5 September 2016
Received in revised form 22 January 2017
Accepted 27 January 2017

Keywords:
Miconia calvescens
Hawai’i
Oblique
Canopy openness
UAS
Plant detectiona b s t r a c t
Small unmanned aerial systems (sUAS) have great potential to facilitate the early detection and management of invasive plants. Here we show how very high-resolution optical imagery, collected from small
consumer-grade multirotor UAS platform at altitudes of 30–120 m above ground level (agl), can be used
to detect individual miconia (Miconia calvescens) plants in a highly invaded tropical rainforest environment on the island of Hawai‘i. The central aim of this research was to determine how overstory vegetation cover, imagery resolution, and camera look-angle impact the aerial detection of known individual
miconia plants. For our finest resolution imagery (1.37 cm ground sampling distance collected at 30 magl), we obtained a 100% detection rate for sub-canopy plants with above-crown openness values >40%
and a 69% detection rate for those with >20% openness. We were unable to detect any plants with<10% above crown openness. Detection rates progressively declined with coarser spatial resolution imagery, ending in a 0% detection rate for the 120 m agl flights (ground sampling distance of 5.31 cm). The
addition of forward-looking oblique imagery improved detection rates for plants below overstory vegetation, though this effect decreased with increasing flight altitude. While dense overstory canopy cover,limited flight times, and visual line of sight regulations present formidable obstacles for detecting miconia and other invasive plant species, we show that sUAS platforms carrying optical sensors can be aneffective component of an integrated management plan within challenging subcanopy forest
environments.
Ó 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier
B.V. All rights reserved.

1. Introduction
A key strategy in the fight against invasive weeds, early detection of nascent alien plant populations has long been a focus ofapplied remote sensing research (see Bradley, 2014; Huang andAsner, 2009; Lass et al., 2005 for reviews of this topic). One aspectthat has proven particularly challenging is the detection of subcanopy species of concern within forest environments. When areas
with dense canopy cover are observed from above, subcanopy species are often partially or fully obscured by the overstory vegetation layer, making their detection difficult (Anderson et al., 1996;
Bradley, 2014). Researchers have attempted a variety of solutions⇑ Corresponding author.
E-mail addresses: rperroy@hawaii.edu (R.L.
(T. Sullivan), nathanms@hawaii.edu (N. Stephenson).

Perroy),tsull@hawaii.edu

for detecting understory species, including phenological (Tuanmu
et al., 2010; Wilfong et al., 2009) and imaging spectroscopy(Asner et al., 2008; Barbosa et al., 2016; Peerbhay et al., 2016)
approaches, as well as using microwave and lidar to penetrate forest canopies and detect understory exotic plant species (Ghulam
et al., 2014; Singh et al., 2015). While these efforts have shown
great promise, phenological approaches developed for deciduous
temperate species are of limited value in the tropics, and mostresearchers and land managers working in this part of the world
do not have access to expensive and specialized instrumentation.
A different solution may lie in the use of very fine spatial resolution
visible imagery collected from inexpensive small unmanned aerial
systems (sUAS). sUAS, defined as low altitude, short-endurance
systems weighing <5 kg (Watts et al., 2012), can be used to capturevery high resolution spatial data on-demand from low flight altitudes (Crommelinck et al., 2016; Puliti et al., 2015; Salamí et al.,

http://dx.doi.org/10.1016/j.isprsjprs.2017.01.018
0924-2716/Ó 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

2014). Though limited in flight time and payload capacity in comparison to manned airplanes and helicopters, sUAS platforms havelower operational costs, carry much lower risks of loss of life andproperty, and can fly within previously inaccessible areas such as
sheer ravines or forest interiors (Chisholm et al., 2013; Ogden,2013). As a result, many of the constraints previously associatedwith generating high resolution aerial imagery over an area ofinterest (e.g., scheduling conflicts, fuel costs, platform availability,
accessibility) have been removed or simplified, making it easier toquickly collect imagery with a very fine ground sampling interval(<0.05 m). Specific regulations concerning sUAS flight operations,which may include maintaining visible line-of-sight and other airspace and licensing requirements, provide a new set of constraints
and vary by country (ICAO, 2015).sUAS are beginning to be used asa new means of surveying for invasive species, often incorporating
image segmentation and machine learning algorithms (Dvorák
et al., 2015; Michez et al., 2016; Müllerová et al., 2016; Peña
et al., 2015). Most of these efforts have been limited to nonforested and agricultural landscapes. In Hawai‘i and other locations
with native forests under threat from invasive species, work isneeded to determine how well these new sUAS platforms candetect subcanopy invasive species in forest settings with multiple
vegetation canopy layers. The purpose of this study was to quantify
the impacts of flight altitude, camera look angle, and overstory
vegetation canopy cover on the ability of a sUAS platform to detectknown individual miconia (Miconia calvescens DC.) plants within ahighly invaded tropical rainforest on eastern Hawai‘i Island. Ouroriginal hypotheses were that miconia plants <1 m would be undetectable from the air in this forested setting and that there would
be a clearly defined maximum degree of overstory canopy cover
beyond which miconia plants would be undetectable. We hypothesized that the addition of oblique photos, in combination withimages taken in the traditional nadir orientation, would improvedetection results as the angled look direction would better allow
us to detect plants beneath the overstory vegetation canopy layer.
We were also interested in determining an optimal flight altitude
for detecting miconia plants, balancing the need to survey large
areas against the minimum resolution required to identify individual plants. The results of this study are broadly applicable todetecting other understory species of interest via sUAS or otherplatforms.

2. Materials and methods
2.1. Miconia calvescens
Miconia, a highly invasive understory alien tree species fromCentral and South America, presents a well-documented threat tonative tropical ecosystems across the globe due to its fecundity,
long-lived seeds, and ability to grow in extreme low light conditions and shade out native species (Meyer, 1994, 1998). Miconia
trees can grow up to 15 m tall, with large characteristic leaves up
to 1 m in length that have deep purple undersides (inset photo,Fig. 1), and can produce dense monotypic stands in both disturbed
and intact rainforest habitat (Csurhes, 1998; Meyer, 2010). The
crown size of an individual Miconia plant can vary from <1 m2
for seedlings to >5 m2 for mature plants. Currently infested areas
include rainforests in Australia, French Polynesia, Hawai‘i, New
Caledonia, and Sri Lanka (Brooks and Jeffery, 2010; Lowe et al.,
2000; Medeiros and Loope, 1997; Meyer, 1996, 2010) with thepotential to spread well beyond these areas (González-Muñoz
et al., 2015). Decades of intensive miconia eradication efforts
across the Pacific have largely been unsuccessful, in part becauseof difficulties with detection (Leary et al., 2014; Meyer, 2014;
Meyer et al., 2011).

175

2.2. Study site
The study site for this work is located at 19.4719°, 154.9554°,
approximately one mile south by southwest of the town of Pāhoa
on the Island of Hawai‘i (Fig. 1). The 0.8 ha site is oriented as a
160  50 m rectangle, flanked by a service road on the southernedge. Situated at an elevation of 237 m on an early successionallava flow ranging between 400 and 750 years old (Sherrod et al.,2007), the area has a 3% slope to the northeast and receives anannual rainfall of 3261 ± 104 mm (Giambelluca et al., 2013). Formerly cleared of native forest and used to grow sugarcane between
1897 and 1984, the land has been fallow and colonized by fast
growing invasive grasses and tree species since the closure of Puna
Sugar LTD in the mid-1980s (HSPAPA, 1992).
The vegetation canopy is now comprised of invasive tree species including Albizia (Falcataria moluccana), Common and Strawberry Guava (Psidium cattleyanum and guajava), Strangling
Banyan (Ficus sp.), Trumpet Tree (Cecropia obtusifolia), Octopus
Tree (Schefflera actinophylla), Bingabing (Macaranga mappa), andPrincess Flower (Tibouchina heteromalla). Invasive liana species
are also present, including Stink Vine (Paederia foetida) and Passion
Flower (Passiflora spp.). Understory vegetation species are dominated by two fern species, Uluhe (Dicranopteris linearis) and Hāpuʻu (Cibotium glaucum). Miconia was first identified in the areain 2006 and a four-year control effort immediately followed (Big
Island Invasive Species Committee, unpublished data). During thisperiod, 566 miconia plants were removed within the study area
(172 mature, 394 immature) through mechanical and chemicalmeans. Organized control efforts ended in 2010.
2.3. Data collection
2.3.1. sUAS flights
A consumer-grade Inspire-1 multirotor sUAS platform (DJI Inc.,
Shenzhen, China) was used to carry a DJI FC350 camera (20 mm
lens with a f/2.8 focus, 94 degree FOV, Sony EXMOR 1/2.3-inch
CMOS), mounted on a Zenmuse X3 gimbal during a series of flights
over the study area on July 28 and August 5, 2016. sUAS flights
were conducted between 11 a.m. and 3 p.m. under environmental
lighting conditions ranging from full sun to partly cloudy. Flight
operations were conducted manually at a forward velocity of
3 m/s and between 30 and 120 m above ground level (agl) to produce imagery datasets with varying ground sampling distance(GSD) (Table 1). It was impossible to safely fly lower than 30 m
due to the tree canopy, which reached heights of 25 m within
the study area and >30 m outside. Each flight took two to sevenminutes and consisted of multiple flight paths (re-flown in opposite directions), parallel to the road bounding the study area
(Fig. 1) and spaced between 12 and 30 m apart, depending on altitude, to achieve the desired overlap. For each flight, photos wereautomatically triggered in transit from a nadir position every
10 m along the flight path, resulting in image sets with an averageoverlap of 85% or greater, as suggested by Dandois et al. (2015).
The exception to this was a portion of the 30 m agl flight which,out of prudence given the proximity of the surrounding tree
canopy, followed a slightly modified flight path that produced
15% fewer photos than recommended for 85% overlap, while still
covering the study area. For the 60 m agl flight, uncertainty regarding the operation of the camera during the initial flight (which was
indeed operating properly) caused us to re-fly the mission, resulting in more than twice as many photos for that flight altitude. For
each flight, following the collection of the nadir photos the platform returned to the starting position and the area was re-flown
with the camera in a forward oblique (45 degree) position. This
resulted in two sets of photos that were processed and analyzed
for each flight altitude - nadir alone and nadir plus oblique

176

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

Fig. 1. 0.8 ha study site (red rectangle) south of Pāhoa on the Big Island of Hawai‘i showing the 108 surveyed miconia plant locations, sUAS-derived orthomosaic of study area
overlain on 2016 WorldView 2 satellite image. Yellow dashed line shows example sUAS flight path. (upper left) Locator map for study site on Hawai‘i Island. (inset photo)
Example surveyed miconia plant showing characteristic leaf shape and purple underside. (For interpretation of the references to color in this figure legend, the reader isreferred to the web version of this article.)

Table 1
Imagery datasets included in the study.
Flight altitude(agl) (m)Number of photos collected
Total (Nadir &Oblique)

Nadir photos (min. # requiredfor 85% overlap)

30
40
50
60
80
100
120

287
248
179
386
172
151
124

156 (184)
144 (114)
90 (73)254 (46)
105 (35)
84 (18)72 (16)(Table 1). Maximum slant line distances within the study area forthe oblique imagery ranged from 100 m (30 m agl imagery) to>235 m (120 m agl imagery). All flights were conducted following
Federal Aviation Administration (FAA) operating rules under the
University of Hawaii at Hilo’s Blanket Area Public Agency Certificate of Authorization 2015-WSA-172-COA.
2.3.2. Ground control points
Prior to the sUAS flights, six ground control points (GCPs) in theform of white crosses were deployed across the 0.8 ha study area
and their three dimensional coordinates (WGS84, UTM Zone 5N)
determined with a Trimble GeoXH 6000 handheld differential
GPS connected to a Zephyr 2 external antenna mounted on a tripod. GCPs were occupied for 20 min each (1 s interval) and theresulting GPS data post-processed with the nearest operationalbase station. Based on the differential correction report, 94.6% of
the corrected logged positions had an estimated accuracy of 5–
15 cm. Five of the GCPs were used for image registration, with
one held out as a check point to independently assess positional
error.
2.3.3. Miconia plant field measurements
In order to quantify our effective detection rates, 247 individual
miconia plants were located and tagged across the study site via afield campaign prior to the sUAS flights. Of these, a subset of 108

Nadir Mean
GSD (cm)

Nadir photo Mean
footprint (m2)

RMS error (m)NadirNadir &
Oblique

1.37
1.69
2.17
2.6
3.54
4.37
5.31

2524
3728
5867
8490
15,461
23,96833,496

0.027
0.031
0.04
0.029
0.03
0.041
0.045

0.047
0.04
0.043
0.046
0.036
0.064
0.049

plants were surveyed with the differential GPS system describedabove (3 min occupation, average positional accuracy of <1.0 m
for 92% of the corrected logged positions) and their crown heightmeasured using an extendable survey pole. Plants <0.5 m in height
were not included in the study. To determine the degree ofobstruction above each miconia plant from overstory vegetation,a Canon EOS 5D camera with a Canon EF 15 mm 1:2.8 fisheye lens(180 degree diagonal angle of view) was mounted to an extendable
pole and raised 10 cm above the top of the miconia plant crown(Fig. 2). Photos were captured using an infrared remote trigger andbrought into WinSCANOPY software package (Regent Instruments
Inc., Canada) for analysis to determine canopy openness (Jarčuška
et al., 2010).

2.4. Data processing and analysis
An overview of the data collection and analysis workflow isshown in Fig. 3. The sUAS-derived photos and GCP coordinates
were brought into Pix4Dmapper Pro software (Lausanne, Switzerland) for processing via Structure-from-Motion (SfM) to generate
an orthomosaic, digital surface model, and three-dimensional georeferenced point cloud for each flight (Westoby et al., 2012).
Default settings were used for all processing steps. Based on thesoftware-generated report, root mean squared positional errors
for the ground control points were <7 cm for all flights. The mean

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

177

Fig. 2. (Left) Collecting photos of the vegetation canopy cover directly above the miconia plant crown using a fisheye lens camera mounted on an adjustable height survey
pole. (Center) Example raw photos from miconia plants #60, 36, and #58, (Right) resulting sky cover classified images following processing in WinSCANOPY software(calculated openness values of 42%, 45%, and 6% from top to bottom).

Fig. 3. Workflow for data collection, processing, and analysis.

footprint of nadir imagery (Table 1) was determined by georeferencing three raw images collected at each flight altitude(image-to-image registration with the orthomosaic) and calculating their average area.
Following the generation of the three-dimensional canopy
models, the individual crown coordinates for the surveyed miconia
plants were brought into the ‘raycloud’ environment of Pix4dmapper Pro for assessment of their detectability for each of the different flights (Fig. 4). The raycloud environment (a linked
visualization of the generated point cloud and contributing raw
photos) was chosen for the detection analysis over a more traditional orthomosaic due to challenges in generating very high resolution orthomosaics in natural forest environments and the loss of

pertinent 3D information. For each known miconia crown, all possible photos that contained that 3D position were visually assessedfor positive detection of the plant. Positive detections wererecorded when the three-dimensional position ‘bulls-eye’ of thetargeted plant fell directly on a visible miconia leaf within at least
one raw image containing that position (Fig. 4, right side). In thecase of dense infestations where many individual plants of varying
heights were located in close proximity to one another, positive
detections were only marked for the tallest plants in order to minimize double-counting. In all cases where the positive identification of an individual miconia plant was in doubt, it was marked
as ‘not detected’. The known miconia detections for each flight
were independently assessed by 2–3 different analysts familiar

178

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

Fig. 4. (Left) Three-dimensional ‘raycloud’ of study area. sUAS photo locations (colored spheres-blue for initial, green for calibrated, red for uncalibrated) taken from 50 m
flight altitude. Green ‘rays’ connecting a targeted miconia plant crown position with raw photos that include that location (a subset of the contributing raw photos, with small‘bulls-eye’ targets in the center of each image, is shown on right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version ofthis article.)with sUAS imagery and invasive species. Analyst assessment time
for the 108 known miconia plants took between 60 and 90 min per
set of flight images. Other miconia plants within the study areathat were not included in the ground survey, though present andidentifiable in our imagery, were not evaluated as we were solelyinterested in determining detection rates using known plants.
Fisheye photos of the sky directly above each surveyed miconia
plant were processed within WinSCANOPY software to estimate
the visibility of miconia crowns when seen from the air (Fig. 2).
We chose to use openness, defined as the fraction of open sky
unobstructed by vegetation above the lens in three dimensional
space and the complement of canopy closure (openness = 1 –canopy closure), as the canopy parameter for this study because
openness is a three-dimensional measure of the open sky cover(Jennings et al., 1999) and we were interested in the impacts ofincluding oblique imagery on detection rates. Ground-based,wide-angle digital photography has been shown to be a simple
and accurate technique for estimating canopy openness/closurein forest environments (Paletto and Tosi, 2009). Gap fraction, ameasure of canopy cover, was not chosen for this analysis as it only
considers two-dimensional space. Optimal thresholds for partitioning image pixels between sky and canopy cover were determined manually for each of the fisheye photos, and openness
results were reproducible to within 3%.

3. Results
Within our study area and using the protocols described above,
the highest overall successful detection rate we obtained for
known miconia plants was 40.7% (44 out of 108 surveyed miconia
plants positively identified) for the 30 m agl flight that included
both nadir and oblique images (Fig. 5). The addition of oblique photos improved detection rates at each flight altitude, although thiseffect generally decreased as flight altitude increased. Detection
rates were inversely related to flight altitude and the corresponding GSD of the imagery. Beyond a flight altitude of 100 m (GSD of
4.37 cm), it was difficult to positively identify and discriminate
individual miconia from other plants with any degree of confidence without a priori knowledge of their existence (Fig. 6).

We also examined the relationship between detection rates andcanopy openness and plant crown height for each of the different
flight altitudes (Fig. 7). Miconia plants beneath very dense overstory vegetation (openness between 0 and 10%, n = 32) were undetectable at all flight altitudes. For plants with higher openness
values, detection rates were negatively related to imagery GSD
and flight altitude. For the 30 m and 40 m image datasets, all
known plants with openness >40% were detected. For the 50 m
imagery, >50% openness was required to achieve 100% detection
success, and this threshold increased to >70% openness for the
100 m flight imagery (grey shading in Fig. 7 depicts detection success for targeted plants, binned in 10% openness increments). The
minimum plant crown height detected was also related to imagery
GSD and flight altitude, rising from 0.7 m at 30 m agl to 2.2 m at
100 m agl.

4. Discussion
For ecosystems under threat from invasive species, effective
management requires early detection of nascent populations andcontinual monitoring. These are resource-intensive endeavors,
requiring both aerial and ground-based operations, and make up
a significant portion of the $2B annual U.S. federal budget dedicated to invasive species activities (US NISC, 2013). New tools that
can improve detection rates and reduce costs are therefore of greatinterest. Our results show that an inexpensive consumer-gradesUAS platform can be used to successfully detect individual invasive miconia plants, including plants <1 m tall and with >10%
above-crown canopy openness values. Similar to other studies
using high-resolution visible imagery for the detection of subcanopy targets, we found that the amount of above-crown openness is a key determinant for success (Van Andel et al., 2015;
Wilson and Ference, 2001). We also found that plant detection
rates decreased with increasing flight altitudes and GSD, corroborating previous work from precision agriculture (Peña et al.,2015; Torres-Sánchez et al., 2013). The addition of oblique images
improved detection rates by allowing us to ‘‘see under” the canopy(Fig. 8), though that effect decreases with increasing flight altitude.
Other studies have also shown oblique sUAS imagery to be usefulR.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

179

Fig. 5. Relationship between flight altitude, image GSD, and detection success for known miconia plants. Error bars depict standard deviation from multiple analyst detection
assessments.

Fig. 6. Representative imagery of miconia plant #23, collected from the ground and at the different flight altitudes used in this study. Image GSD (in cm) corresponding toeach flight altitude is also reported.

for detecting individual trees, although in an urban context (Linet al., 2015).
Miconia’s characteristic leaf shape, deep central vein, and purple underside provide a strong contrast with other vegetation species, allowing it to be readily identified under semi-open sky
conditions in the highest resolution imagery. Our highest resolution imagery (30 m agl, 1.37 cm GSD) was extremely effective
when above-crown openness was high, achieving a 100% detection
rate for plants with above-crown openness values >40%, and a 69%
detection rate for plants with above-crown openness values >20%.
These numbers may be slightly conservative, as the 30 m dataset
did not reach the recommended minimum image overlap(Dandois et al., 2015) across the entire study area due to the proximity of the vegetation canopy at that flight altitude. For the specific case of miconia, which takes four or more years of growth toreach a fruiting stage and develops most rapidly under partial
and full sun conditions (Meyer, 1998), these initial results may
be adequate to support a proposed ‘‘juvenilization” management
approach targeting mature trees to prevent the distribution of fruit(Meyer et al., 2011). For example, an accelerated miconia intervention strategy in the East Maui Watershed has employed manned

helicopter sweeps with herbicide ballistic technology (HBT) on a
60–90 day repeat interval (Leary et al., 2014). Despite the high frequency of intervention efforts, new mature targets continue tointermittently appear based on visual observations from repeat
helicopter surveys. Low-cost repeated sUAS operations could economically complement and inform these types of intervention
efforts by generating spatial imagery and target coordinates prior
to manned operations, though plants under very dense canopy closure will invariably be missed.
While problematic, our present inability to detect plants undervery dense overstory vegetation does not mean that this technique
is without value. Miconia can serve as the dominant canopy (particularly as monotypic cohort patches) in Hawaiian and other rainforest settings (Meyer, 2010), and gap-phase regeneration is aknown driver of seed germination for pioneer species including
Melastomataceae (Ellison et al., 1993; Pearson et al., 2002). The
ability to generate very high-resolution imagery over areas atrisk of invasion by alien species holds great potential for assistingwith their control, but effectively integrating sUAS into invasive
species management programs will require overcoming a numberof existing limitations.

180

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

Fig. 7. Percent openness and miconia plant crown height detection plots for the different sUAS flights. Open blue circles are positive detection ‘hits’ within the nadir images,
filled blue circles are additional positive ‘hits’ from the oblique photos at that same altitude, and open red circles are misses. Grey shading represents binned detection rates
based on 10% openness increments (0–10%, 10–20%, etc.). Pie charts indicate overall known plant detection rate using all images at that altitude. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version of this article.)

Two of the most important of these are limited flight times andregulatory visual line-of-sight requirements. For the DJI Inspire-1
multirotor platform used in this study, maximum flight times are
16–18 min. Depending on the altitude, airspeed, and other flight
conditions, this translates into nadir coverage areas of roughly
20–30 ha per battery, which is sufficient for small areas but inadequate for monitoring large forest parcels. Oblique imagery collection requires additional flight time, at least for UAS platforms witha single camera. Advances in UAS battery and fuel technology areprogressing rapidly (Bole et al., 2014; Kaya et al., 2016; Lee et al.,2015), but short flight times will likely constrain the use of inexpensive sUAS platforms for the near future.
Another operational aspect that must be considered, at least in
the United States and other countries with strict UAS airspace regulations, is the visual line-of-sight requirement. Currently, the United States requires an exemption from the Federal Aviation

Administration for flight plans that include ‘extended’ or beyond
line-of-sight operations (USDOT, 2016). In dense forest environments without access to adequate vantage points, this presents agreater limitation on acquiring imagery than any technologicalobstacle. Fortunately, beyond line-of-sight UAS operations are
becoming more common and easier to obtain (Atkins, 2014;
Naftel, 2009; Stevenson et al., 2015).
For this work we focused on determining the environmental
and operational parameters that impact our ability to detect individual miconia plants, using a set of known plant locations within aheavily invaded tropical forest environment. To our knowledge,
this is the first attempt to quantify these parameters for a subcanopy invasive species using a sUAS platform carrying a RGB camera. Similar trials could be conducted for other invasive plant
species and environments, and the results would vary based on
the phenological and morphological characteristics of the target

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183

181

Fig. 8. Improved detection ability using oblique imagery. (A) Field survey to mark 1.1 m tall miconia plant #85. (B) Fisheye sky cover photos above plant #85 (top, classified;bottom, raw) showing 13.14% openness. (C) Nadir photo over plant #85 (30 m agl), inset close-up of #85 location shows overstory canopy and no visible miconia plant. (D)Oblique photo of same (30 m agl), inset shows clearly visible plant.

species and the contrast with surrounding vegetation types (Huang
and Asner, 2009; Hung and Sukkarieh, 2015; Torres-Sánchez et al.,2013). While we endeavored to ensure the work reported here isaccurate, possible sources of error that may influence our results
include positional error in the surveyed plant locations, misidentification of plants in locations with multiple miconia in close proximity, mislabeling of plants during the fieldwork component, andthe problem of a priori knowledge for analysts repeatedly evaluating the same area with different sets of imagery. While importantto acknowledge, we believe that the combined errors likely affectonly a small number of plants in our study and should not affectthe overall findings.
We believe the true value of this work will lie in combining our
findings with robust plant detection algorithms and more capable
UAS platforms and instrumentation, to produce automated detection systems that can continually survey and monitor meaningful
expanses (100 s–10,000 s ha) of threatened forest ecosystems.
Computer vision algorithms, including machine learning andobject-based image analysis, are making it possible to rapidly
and autonomously detect targets within imagery (Hu et al., 2015;
Krizhevsky et al., 2012; Penatti et al., 2015; Reyes et al., 2015),
although the high noise environments of tropical forests present
distinct challenges that still need to be overcome. With continued
improvements in sensor resolution and miniaturization, battery
capacity, and detection algorithms, sUAS deployed over areas ofinterest could greatly improve detection rates while reducing operational costs by decreasing reliance on manned helicopter andground-based surveys. Incorporating lidar or other instrumentation able to penetrate the canopy layer to these platforms, alongwith aerially-deployed herbicide applicators such as HBT (Learyet al., 2013; Rodriguez et al., 2015), would further improve theirefficacy.

Along with the sUAS-derived imagery itself, which has been thefocus of this study, the ability to generate three dimensional digital
aerial photogrammetric point clouds via SfM provides additional
capabilities that can improve our ability to characterize forest
structure and detect sub-canopy plants. While sUAS-derived photogrammetric point clouds are not as comprehensive as those generated from lidar instruments, they have been used to measure avariety of forestry metrics, such as vegetation height, stem volume,and canopy cover (Fraser et al., 2016; Puliti et al., 2015; Wallace
et al., 2016). sUAS-derived estimates of canopy closure, in particular, could be used to provide a means of attaching a confidence
measure to the positive and negative detections across a forested
environment (i.e., more confidence should be assigned to a ‘nodetections’ result within an area of high canopy openness than
within an area of moderate or low canopy openness). This typeof analysis and supporting information, while not explored here,
could allow managers to determine if certain areas may require
additional resources and monitoring and allow them to more efficiently allocate their limited resources.

5. Conclusions
Early detection of invasive species via remote sensing is ofmajor interest for ecological and economic reasons, but even withspecialized instrumentation, detecting subcanopy vegetation species has proven difficult. Our results demonstrate that an inexpensive, consumer-grade small UAS platform can detect an invasive
species -- miconia -- in a complex tropical forest subcanopy setting
under certain threshold conditions. The ability to generate imagery
on-demand with a ground GSD <2 cm made it possible to detect
individual miconia plants under heavy overstory vegetation cover

182

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183(>10% openness), and the inclusion of oblique images improved
detection rates by >10% for the highest resolution imagery dataset.
Overall detection rates were negatively related to flight altitude,
and no miconia plants were confidently detected using imagery
with a GSD >5 cm or under canopy cover with <10% openness.
We believe that sUAS technology can provide a safer and more economical alternative to manned aerial surveillance (Ogden, 2013),
especially in remote and challenging tropical environments. To
our knowledge, this is the first study that has systematicallyassessed the impacts of flight altitude, camera look angle, andcanopy openness above targeted plant crown on invasive plant
detection rates using a sUAS platform.
Acknowledgments
Funding: this work was supported in part from a grant provided
by the Hawaii Invasive Species Council. The authors would like toacknowledge the following for their field assistance and help withthis project: Arthur Cunningham, Jamison Domingsil, Rose Hart,
Heather Kimball, Keely Roth, the Fall 2015 GEOG385 students,
and the BIISC Early Detection Team. The authors would also liketo thank two anonymous reviewers and Dr. James Leary, Roberto
Rodriguez, and Sharon Dansereau for their editorial comments
and input.
References
Atkins, E.M., 2014. Autonomy as an Enabler of Economically-viable, Beyond-line-ofsight, Low-altitude UAS Applications with Acceptable Risk. In AUVSI Unmanned
Systems.
Anderson, G.L., Everitt, J.H., Escobar, D.E., Spencer, N.R., Andrascik, R.J., 1996.
Mapping leafy spurge (Euphorbia esula) infestations using aerial photography
and geographic information systems. Geocarto Int. 11 (1), 81–89.
Asner, G.P., Jones, M.O., Martin, R.E., Knapp, D.E., Hughes, R.F., 2008. Remote sensingof native and invasive species in Hawaiian forests. Remote Sens. Environ. 112(5), 1912–1926.
Barbosa, J.M., Asner, G.P., Martin, R.E., Baldeck, C.A., Hughes, F., Johnson, T., 2016.
Determining subcanopy Psidium cattleianum invasion in Hawaiian forests
using imaging spectroscopy. Remote Sens. 8 (1), 33.
Bole, B., Daigle, M., Gorospe, G., 2014. Online Prediction of Battery Discharge andEstimation of Parasitic Loads for an Electric Aircraft. ESC, 2, 5S2P.
Bradley, B.A., 2014. Remote detection of invasive plants: a review of spectral,
textural and phenological approaches. Biol. Invasions 16 (7), 1411–1425.
Brooks, S., Jeffery, M., 2010. Status of Miconia calvescens and the Eradication
Program in Australia. In: Loope, L.L., Meyer, J.-Y., Hardesty, B.D., Smith, C.W.
(Eds). Proceedings of the International Miconia Conference. Maui Invasive
Species Committee and Pacific Cooperative Studies Unit, University of Hawaii at
Manoa, Keanae, Maui, Hawaii.
Chisholm, R.A., Cui, J., Lum, S.K., Chen, B.M., 2013. UAV LiDAR for below-canopy
forest surveys. J. Unmanned Vehicle Syst. 1 (01), 61–68.
Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M.Y., Vosselman, G., 2016.
Review of automatic feature extraction from high-resolution optical sensor data
for UAV-based cadastral mapping. Remote Sens. 8 (8), 689.
Csurhes, S.M., 1998. Miconia calvescens, a potentially invasive plant in Australia’s
tropical and sub-tropical rainforests. In: Proceedings of the First Regional
Conference on M. calvescens Control. Papeete, Tahiti, French Polynesia.
Gouvernement de Polynésie française/University of Hawai’i at Manoa/Centre
ORSTOM de Tahiti, vol. 90.
Dandois, J.P., Olano, M., Ellis, E.C., 2015. Optimal altitude, overlap, and weather
conditions for computer vision UAV estimates of forest structure. Remote Sens.
7 (10), 13895–13920.
Dvorák, P., Müllerová, J., Bartalos, T., Bruna, J., 2015. Unmanned aerial vehicles foralien plant species detection and monitoring. Int. Arch. Photogram., Remote
Sens. Spat. Inform. Sci. 40 (1), 83.
Ellison, A.M., Denslow, J.S., Loiselle, B.A., 1993. Seed and seedling ecology ofneotropical Melastomataceae. Ecology 74 (6), 1733–1749.
Fraser, R.H., Olthof, I., Lantz, T.C., Schmitt, C., 2016. UAV photogrammetry formapping vegetation in the low-Arctic. Arct. Sci. 2 (3), 79–102.
Ghulam, A., Porton, I., Freeman, K., 2014. Detecting subcanopy invasive plant
species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm. ISPRS J.
Photogram. Remote Sens. 88, 174–192.
Giambelluca, T.W., Chen, Q., Frazier, A.G., Price, J.P., Chen, Y.L., Chu, P.S., Eischeid, J.
K., Delparte, D.M., 2013. Online rainfall atlas of Hawai’i. Bull. Am. Meteorol. Soc.
94 (3), 313–316.
González-Muñoz, N., Bellard, C., Leclerc, C., Meyer, J., Courchamp, F., 2015. Assessing
current and future risks of invasion by the ‘‘green cancer” Miconia calvescens.
Biol. Invasions 17 (11), 3337–3350.

Hawaiian Sugar Planters’ Association Plantation Archives, 1992. University of
Hawaii at Manoa Library Hawaiian Collection. Register of the Puna Sugar
Company/Olaa Sugar Company. <http://www2.hawaii.edu/~speccoll/p_puna.
html> (accessed on August 31, 2016).
Hu, F., Xia, G.S., Hu, J., Zhang, L., 2015. Transferring deep convolutional neuralnetworks for the scene classification of high-resolution remote sensing
imagery. Remote Sens. 7 (11), 14680–14707.
Hung, C., Sukkarieh, S., 2015. Using robotic aircraft and intelligent surveillancesystems for orange hawkweed detection. Plant Prot. Quart. 30 (3), 100.
Huang, C.Y., Asner, G.P., 2009. Applications of remote sensing to alien invasive plantstudies. Sensors 9 (6), 4869–4889.
International Civil Aviation Organization, 2015. Manual on Remotely Piloted
Aircraft Systems. <http://www4.icao.int/demo/pdf/rpas/10019_cons_en%20-%
20Secured.pdf> (accessed on Aug 31, 2016).
Jarčuška, B., Kucbel, S., Jaloviar, P., 2010. Comparison of output results from two
programmes for hemispherical image analysis: Gap Light Analyser andWinScanopy. J. For. Sci. 56 (4), 147–153.
Jennings, S.B., Brown, N.D., Sheil, D., 1999. Assessing forest canopies andunderstorey illumination: canopy closure, canopy cover and other measures.
Forestry 72 (1), 59–74.
Kaya, N., Turan, Ö., Midilli, A., Karakoç, T.H., 2016. Exergetic sustainability
improvement potentials of a hydrogen fuelled turbofan engine UAV byheating its fuel with exhaust gasses. Int. J. Hydrogen Energy 41 (19), 8307–
8322.
Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep
convolutional neural networks. In: Advances in Neural Information Processing
Systems, pp. 1097–1105.
Lass, L.W., Prather, T.S., Glenn, N.F., Weber, K.T., Mundt, J.T., Pettingill, J., 2005. A
review of remote sensing of invasive weeds and example of the early detectionof spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila
paniculata) with a hyperspectral sensor. Weed Sci. 53 (2), 242–251.
Leary, J.J., Gooding, J., Chapman, J., Radford, A., Mahnken, B., Cox, L.J., 2013.
Calibration of an Herbicide Ballistic Technology (HBT) helicopter platformtargeting Miconia calvescens in Hawaii. Invasive Plant Sci. Manage. 6 (2), 292–
303.
Leary, J., Mahnken, B.V., Cox, L.J., Radford, A., Yanagida, J., Penniman, T., Duffy, D.C.,
Gooding, J., 2014. Reducing nascent miconia (Miconia calvescens) patches withan accelerated intervention strategy utilizing herbicide ballistic technology.
Invasive Plant Sci. Manage. 7 (1), 164–175.
Lee, B., Park, P., Kim, C., 2015. Power Managements of a Hybrid Electric Propulsion
System Powered by Solar Cells, Fuel Cells, and Batteries for UAVs. In: Handbookof Unmanned Aerial Vehicles. Springer, Netherlands, pp. 495–524.
Lin, Y., Jiang, M., Yao, Y., Zhang, L., Lin, J., 2015. Use of UAV oblique imaging for thedetection of individual trees in residential environments. Urban For. Urban
Gree. 14 (2), 404–412.
Lowe, S., Browne, M., Boudjelas, S., de Poorter, M., 2000. 100 of the world’s worst
invasive alien species: a selection from the global invasive species database. The
Invasive Species Specialist Group (ISSG), 12 pp.
Medeiros, A.C., Loope, L.L., 1997. Status, ecology, and management of the invasiveplant, M. calvescens DC (Melastomataceae) in the Hawaiian Islands. Records ofthe Hawaii Biological Survey for 1996. Bishop Museum Occasional Papers, vol.
48, pp. 23–36.
Meyer, J.Y., 1994. Mécanismes d’invasion de Miconia calvescens DC en Polynésie
française. PhD. thesis. Université Montpellier II Sciences et Techniques duLanguedoc, Montpellier.
Meyer, J.Y., 1996. Status of M. calvescens (Melastomataceae), a dominant invasive
tree in the Society Islands (French Polynesia). Pac. Sci. 50 (1), 66–76.
Meyer, J.Y., 1998. Observations on the reproductive biology of M. calvescens DC(Melastomataceae), an alien invasive tree in the island of Tahiti (South Pacific
Ocean). Biotropica 30 (4), 609–624.
Meyer, J.Y., 2010. The M. calvescens saga: 20 years of study and control in French
Polynesia (1988–2008). In: Loope, L.L., Meyer, J.Y., Hardesty, B.D., Smith, C.W.
(Eds.), Proceedings of the International M. Calvescens Conference, Keanae, Maui,
Hawaii. Maui Invasive Species Committee and Pacific Cooperative Studies Unit.
University of Hawaii, Manoa, pp. 1–19.
Meyer, J.Y., 2014. Critical issues and new challenges for research and managementof invasive plants in the Pacific Islands. Pacific Conservat. Biol. 20 (2), 146–164.
Meyer, J.Y., Loope, L.L., Goarant, A.C., 2011. Strategy to control the invasive alien tree
Miconia calvescens in Pacific islands: eradication, containment or somethingelse? Pp. 91–96 in Island Invasives: Eradication and Management. In: Veitch, C.
R., Clout, M.N., Towns, D.R. (Eds.), Proceedings of the International Conference
on Island Invasives. IUCN and CBB, Gland and Auckland.
Michez, A., Piégay, H., Jonathan, L., Claessens, H., Lejeune, P., 2016. Mapping ofriparian invasive species with supervised classification of Unmanned Aerial
System (UAS) imagery. Int. J. Appl. Earth Obs. Geoinf. 44, 88–94.
Müllerová, J., Bruna, J., Dvorák, P., Bartalos, T., Vítková, M., 2016. Does the Data
Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant
Invasions. ISPRS-International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, pp. 903–908.
Naftel, J.C., 2009. NASA Global Hawk: A New Tool for Earth Science Research.
Ogden, L.E., 2013. Drone ecology. Bioscience 63 (9), 776.
Paletto, A., Tosi, V., 2009. Forest canopy cover and canopy closure: comparison ofassessment techniques. Eur. J. Forest Res. 128 (3), 265–272.
Pearson, T.R., Burslem, D.F., Goeriz, R.E., Dalling, J.W., 2003. Interactions of gap sizeand herbivory on establishment, growth and survival of three species ofneotropical pioneer trees. J. Ecol. 91 (5), 785–796.

R.L. Perroy et al. / ISPRS Journal of Photogrammetry and Remote Sensing 125 (2017) 174–183
Peerbhay, K., Mutanga, O., Lottering, R., Bangamwabo, V., Ismail, R., 2016. Detecting
bugweed (Solanum mauritianum) abundance in plantation forestry using
multisource remote sensing. ISPRS J. Photogram. Remote Sens. 121, 167–176.
Peña, J.M., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A.I., López-Granados, F.,
2015. Quantifying efficacy and limits of unmanned aerial vehicle (UAV)
technology for weed seedling detection as affected by sensor resolution.
Sensors 15 (3), 5609–5626.
Penatti, O.A., Nogueira, K., dos Santos, J.A., 2015. Do deep features generalize fromeveryday objects to remote sensing and aerial scenes domains? In: Proceedingsof the IEEE Conference on Computer Vision and Pattern Recognition Workshops,
pp. 44–51.
Puliti, S., Ørka, H.O., Gobakken, T., Næsset, E., 2015. Inventory of small forest areasusing an unmanned aerial system. Remote Sens. 7 (8), 9632–9654.
Reyes, A.K., Caicedo, J.C., Camargo, J.E., 2015. Fine-tuning deep convolutional
networks for plant recognition. In: Working Notes of CLEF 2015 Conference.
Rodriguez, R., Jenkins, D.M., Leary, J.J., 2015. Design and validation of a GPS logger
system for recording aerially deployed herbicide ballistic technology
operations. IEEE Sens. J. 15 (4), 2078–2086.
Salamí, E., Barrado, C., Pastor, E., 2014. UAV flight experiments applied to theremote sensing of vegetated areas. Remote Sens. 6 (11), 11051–11081.
Singh, K.K., Davis, A.J., Meentemeyer, R.K., 2015. Detecting understory plantinvasion in urban forests using LiDAR. Int. J. Appl. Earth Obs. Geoinf. 38, 267–
279.
Sherrod, D.R., Sinton, J.M., Watkins, S.E., Brunt, K.M., 2007. Geologic map of the Stateof Hawai‘i. US Geological Survey Open-File Report, 1089, pp. 83.
Stevenson, J.D., O’Young, S., Rolland, L., 2015. Beyond line of sight control of small
unmanned aerial vehicles using a synthetic environment to augment first
person video. Procedia Manuf. 3, 960–967.
Torres-Sánchez, J., López-Granados, F., De Castro, A.I., Peña-Barragán, J.M., 2013.
Configuration and specifications of an unmanned aerial vehicle (UAV) for earlysite specific weed management. PLoS ONE 8 (3), e58210.

183

Tuanmu, M.N., Viña, A., Bearer, S., Xu, W., Ouyang, Z., Zhang, H., Liu, J., 2010.
Mapping understory vegetation using phenological characteristics derived fromremotely sensed data. Remote Sens. Environ. 114 (8), 1833–1844.
United States Department of Transportation, 2016. BILLING CODE 4910–13-P<http://www.faa.gov/uas/media/RIN_2120-AJ60_Clean_Signed.pdf> (accessed
21 August, 2016).
United States National Invasive Species Council (NISC), 2013. Fiscal Year 2013
Interagency Invasive Species Performance-Based Crosscut Budget <https://www.doi.gov/sites/doi.gov/files/migrated/invasivespecies/org_collab_budget/
upload/NISC_2013_Crosscut_Budget_Summary.pdf> (accessed 19 August2016).
Van Andel, A.C., Wich, S.A., Boesch, C., Koh, L.P., Robbins, M.M., Kelly, J., Kuehl, H.S.,
2015. Locating chimpanzee nests and identifying fruiting trees with anunmanned aerial vehicle. Am. J. Primatol. 77 (10), 1122–1134.
Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., Vopěnka, P., 2016. Assessment offorest structure using two UAV techniques: a comparison of airborne laser
scanning and structure from motion (SfM) point clouds. Forests 7 (3), 62.
Watts, A.C., Ambrosia, V.G., Hinkley, E.A., 2012. Unmanned aircraft systems inremote sensing and scientific research: classification and considerations of use.
Remote Sens. 4 (6), 1671–1692.
Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M., 2012.
‘Structure-from-Motion’photogrammetry: a low-cost, effective tool forgeoscience applications. Geomorphology 179, 300–314.
Wilfong, B.N., Gorchov, D.L., Henry, M.C., 2009. Detecting an invasive shrub indeciduous forest understories using remote sensing. Weed Sci. 57 (5), 512–520.
Wilson, B.A., Ference, C.G., 2001. The influence of canopy closure on the detection ofunderstory indicator plants in Kananaskis Country. Canad. J. Remote Sens. 27(3), 207–215.