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Revista de Biología Tropical, ISSN: 2215-2075, Vol. 72: e56364, enero-diciembre 2024 (Publicado Abr. 16, 2024)
Biomass and bioethanol production of the shrub Ulex europaeus (Fabaceae)
estimated with remote sensing imagery in the Andean paramos
Diego F. Osorio-Castiblanco1; https://orcid.org/0009-0000-3415-4962
1. Department of Geography, University of Minnesota – Minneapolis, MN, 55455; osori050@umn.edu,
df.osorio12@uniandes.edu.co
Received 28-VIII-2023. Corrected 16-II-2024. Accepted 10-IV-2024.
ABSTRACT
Introduction: Gorse (Ulex europaeus, family Fabaceae) is an evergreen shrub native to Europe and invasive in
the Andean high-mountain tropical paramos.
Objective: To quantify the extent of a biological invasion within a paramo near Los Nevados National Natural
Park in Tolima, Colombia, and evaluate bioeconomic solutions to encourage shrub eradication while promoting
the local economy and industry.
Methods: An object-based supervised classification approach was conducted using UAS (Uncrewed Aircraft
Systems)-based RGB imagery and a Planet-derived NDVI (Normalized Difference Vegetation Index) layer, both
from 2022, to quantify the area invaded. This value, the height obtained from a UAS-derived nDSM (Normalized
Digital Surface Model), and a pair of allometric equations found in the literature were employed to estimate the
gorse aboveground biomass (AGB) and aboveground available fuel, also known as fuel load. Then, documented
bioethanol production estimations were applied to calculate its potential extraction based on the AGB values.
Results: The invaded area was quantified to be 66 465 m2, with an overall accuracy rate of 85.3 %. Furthermore,
the fuel load was found to be approximately half of the AGB, which poses a high risk of fire in the ecosystem.
The findings also suggest that up to USD 88 933.7 could be generated if all gorse in the study area is exploited
to produce bioethanol.
Conclusions: This study underscores the urgency of managing the biological invasion of gorse in the Andean
paramo and suggests the potential for bioeconomic solutions to mitigate the impact of these invasions.
Key words: aboveground biomass; fuel load; gorse; Andean paramo; remote sensing; bioethanol; bioeconomy.
RESUMEN
Biomasa y producción de bioetanol del arbusto Ulex europaeus (Fabaceae)
estimados a partir de imágenes de detección remota en los páramos andinos
Introducción: El retamo espinoso (Ulex europaeus, familia Fabaceae) es un arbusto perenne originario de Europa
e invasor en los páramos andino-tropicales de alta montaña.
Objetivo: Cuantificar la extensión de una invasión biológica dentro de un páramo cerca del Parque Nacional
Natural Los Nevados en Tolima, Colombia, y evaluar soluciones bioeconómicas para fomentar la erradicación
del arbusto y, al mismo tiempo, promover la economía e industria locales.
Métodos: Se llevó a cabo una clasificación supervisada basada en objetos utilizando imágenes RGB tomadas con
UAS (sistemas de aeronaves no tripulados) y una capa NDVI (índice de vegetación de diferencia normalizada)
derivada de Planet, ambas de 2022, para cuantificar el área invadida. Este valor, así como la altura obtenida a
partir de un nDSM (modelo digital de superficie normalizado) derivado de las imágenes UAS y un par de ecua-
ciones alométricas halladas en la literatura, se emplearon para estimar la biomasa aérea (AGB) y el combustible
https://doi.org/10.15517/rev.biol.trop..v72i1.56364
OTHERS
2Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72: e56364, enero-diciembre 2024 (Publicado Abr. 16, 2024)
INTRODUCTION
Gorse (Ulex europaeus, family Fabaceae)
is a spiny, perennial, evergreen shrub native to
Central and Western Europe, where seasonal
weather changes and several pathogens and
herbivores limit its prevalence. This plant is
known for its highly competitive nature, char-
acterized by two main features. Firstly, it forms
dense structures effective at blocking out and
displacing other native and cultivated species.
This is bolstered by its mycorrhizal connections
that help support its growth even in nutrient-
poor soil by fixing nitrogen (Egunjobi, 1969;
Grubb & Suter, 1970; Invasive Species Special-
ist Group, 2022; Vargas, 2007) Secondly, gorse
has a high seed-production rate that leads to
a density ranging from 500 to 20 742 seeds/
m2 (Ivens, 1978; Popay & Adams, 1990). These
seeds boast an impressive viability rate of over
80 % and can remain viable in the soil seed bank
for up to 30 years (Lee et al., 1986; Hoshovsky,
1989). Barochory dispersal plays an important
role in taking seeds up to 6 m from the plant,
which can be greater due to wind (Moss, 1959).
Additionally, gorse quickly matures and grows
up to 0.5 m per year under ideal conditions
(Lee et al., 1986).
Out of its native range, the consistent
weather patterns allow gorse to flower year-
round, and the lack of predators places no
natural limit on its spread. This adaptabil-
ity has allowed gorse to successfully colonize
Andean high-mountain tropical ecosystems,
even at altitudes exceeding 2 000 meters above
sea level, where temperature and precipitation
closely resemble its native habitat during the
flowering season (Zabaleta-Bejarano, 2008).
However, gorse is a dangerous invasive species
for the Andean ecosystems since it i) contains
highly flammable oil in its foliage and seeds
and ii) accumulates standing necromass, which
together represents an extreme fire hazard
that yields hotter fires than other weeds (Mac-
Carter & Gaynor, 1981). For instance, gorse has
been included in the top 10 invasive species of
concern with a rating of 7.39 out of 10 in the
Risk Assessment of Introduced Plant Species in
Colombia report, which means a high risk of
invasion (Baptiste et al., 2010; Calderon, 2003).
Indeed, gorse represents a significant threat
to Colombia’s exceptional biological richness.
This country hosts approximately 10 % of the
world’s biodiversity and has around 2 % of its
land covered by paramos (Convention on Bio-
logical Diversity, n.d.), a vital ecosystem located
over 2 800 meters above sea level. The Andean
paramo is considered the most biodiverse high-
mountain tropical region globally and one of
the most important ecosystems for human
well-being. Nonetheless, this delicate ecosys-
tem is highly vulnerable to invasive species like
gorse, which can disrupt its natural balance and
have far-reaching impacts on millions of peo-
ple dependent on essential ecosystem services,
such as drinking water (Anthelme & Peyre,
2019; Buytaert et al., 2006; Sklenar et al., 2014).
To address this issue, it becomes imper-
ative to undertake thorough assessments of
the extent of gorse invasions in the region
and quantify its biomass to develop effective
eradication programs. Currently, some models
disponible superficial, también conocido como carga de combustible. Luego, se utilizaron estimaciones documen-
tadas de producción de bioetanol para calcular su potencial extracción con base en los valores de AGB.
Resultados: El área invadida se cuantificó en 66 465 m2, con una exactitud global del 85.3 %. Además, se encontró
que la carga de combustible era aproximadamente la mitad del AGB, lo que representa un alto riesgo de incendio
en el ecosistema. Los resultados también sugieren que se podrían generar hasta 88 933.7 dólares estadounidenses,
si se explotara todo el retamo espinoso de la zona de estudio para producir bioetanol.
Conclusiones: Este estudio resalta la urgencia de gestionar la invasión biológica del retamo espinoso en el páramo
andino y sugiere potenciales soluciones bioeconómicas para mitigar el impacto de estas invasiones.
Palabras clave: biomasa aérea; carga de combustible; retamo espinoso; páramo andino; sensores remotos; bioe-
tanol; bioeconomía.
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allow for biomass estimations based only on
the plant’s height, meaning no destructive
techniques (e.g., burning) are required. For
example, Pearce et al. (2010) developed linear
mixed-effect models to estimate aboveground
biomass (AGB) and aboveground available fuel
(hereafter fuel load) in gorse shrublands by col-
lecting samples from most of the climatic zones
in New Zealand.
With the rapid expansion of UAS
(Uncrewed Aircraft Systems) remote sensing
methods, estimating AGB at a low cost has
become possible. These methods accurately
estimate canopy height (Acorse et al., 2019)
and aboveground volume, which are essential
to derive AGB (Peng et al., 2021). In some
cases, only optical images (RGB) are necessary
to estimate AGB, as demonstrated by Niu et al.
(2019), who quantified AGB from vegetation
indices and plant height datasets derived from
UAS-based RGB imagery. The same methodol-
ogy can be applied to fuel load quantification
as the latter depends on the same variables.
Despite these recent advancements, there are
no guidelines for gorse AGB estimation in
Colombia. The Institute of Hydrology, Meteo-
rology, and Environmental Studies (IDEAM
in Spanish) has released a general protocol
for AGB and carbon estimation for non-tree
vegetation, which includes shrubs, but through
destructive sampling (Yepes et al., 2011). The
literature about UAS-based imagery for AGB in
Colombia is also scarce or null. Only a few stud-
ies have quantified AGB nationally using low-
resolution satellite imagery, such as MODIS
(Anaya et al., 2009; Galindo et al., 2011).
It is crucial then to identify the places that
gorse has colonized, quantify the area affected
by the invasion, and estimate its biomass to
establish adequate, sustainable environmental
management strategies oriented to eradicat-
ing the species, as this is a threat to Colom-
bia’s native biological resources. Therefore, the
overarching goal of this study is to estimate
gorse AGB and fuel load within a paramo
neighboring Los Nevados National Natural
Park (Tolima, Colombia) using remote sens-
ing imagery. First, an object-based supervised
classification quantifies the total area invaded
using i) high-resolution UAS-based RGB imag-
ery and ii) a Normalized Difference Vegetation
Index (NDVI) derived from Planet imagery.
Then, the allometric equations Pearce et al.
(2010) reported are used to estimate gorse AGB
and fuel load. The methods applied here can be
extended to other areas invaded in Colombia
and beyond to propose large-scale ecological
management strategies of gorse.
Likewise, this study aims to provide pre-
liminary results and context which will be
valuable for further multidisciplinary research
projects to build on to propose a comprehen-
sive program for gorse management. This may
be a bioeconomic plan for the industrial use
of gorse biomass that also promotes the local
economy and industry. Bioeconomy, as defined
by the Organization for Economic Cooperation
and Development (OECD, 2009), encompasses
the use of biological resources for economic
activities oriented to boost industrial processes
and enhance environmental sustainability. For
instance, Miller & Murthy (2017) found that
30.2 ± 5.8 gallons of ethanol can be extracted
from one ton of gorse. This information can
be used to treat gorse as a biological source of
ethanol (bioethanol) and encourage eradica-
tion programs. Hence, this study uses the AGB
results to quantify the potential bioethanol
production generated from exploiting gorse. In
addition, bioethanol market prices are also con-
sulted to assess the potential economic benefits
for the local community and industry.
MATERIALS AND METHODS
Study area: Situated within the Lagunilla
River valley, the study area is positioned at
4°53’32” N & 75°15’20” W, with an elevation
exceeding 3 250 meters above sea level in the
department of Tolima, Colombia. The defor-
estation due to the expansion of the farming
frontier, the edge effect caused by a road and
the river, and the presence of poor soils have
favored gorse colonization (Fig. 1). This spe-
cies here is problematic as this area is within
the paramo zone neighboring Los Nevados
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National Natural Park: a protected area that
supports the economic and human develop-
ment of the country’s central region (Parques
Naturales Nacionales de Colombia, n.d.).
Supervised classification: The raw UAS-
based RGB imagery (hereafter UAS imagery)
was provided by the Regional Autonomous
Corporation of the Department of Tolima (Cor-
tolima). The dataset consisted of 439 images
taken on July 10, 2022, covering a total area of
0.664 km2. They used a camera model FC6520
lens DJI MFT 15mm F1.7 ASPH with a photo
resolution of 5280 x 2970. The imagery was
processed in Pix4Dmapper version 4.7.5. All
images’ horizontal and vertical accuracy was
set at 1 m, and the coordinate system WGS 84
UTM zone 18N (EGM96Geoid) was selected
as the spatial reference. An orthomosaic with
12.51 cm pixel resolution and an unsigned 8-bit
pixel depth was obtained.
Also, a Digital Surface Model (DSM) was
generated using the default method of inverse
distance weighting (IDW), and a Digital Ter-
rain Model (DTM) was produced to subtract
its values from the DSM to get a normalized
DSM (nDSM). Pix4D allows for deriving a
high-quality DTM using an algorithm based on
the semiautomatic method developed by Unger
et al. (2009) that smoothens the DSM using a
variational energy minimization approach. To
improve the DTM accuracy, manual polygons
were created over the point cloud to specify
roads, ground, and high-vegetation classes, so
that only the latter class was smoothened.
Due to the lack of a near-infrared (NIR)
band in the UAS imagery, Planet imagery
including NIR was also obtained: ortho scene
with catalog ID 20220808_151753_40_2274
from strip 5842545 taken on August 8, 2022
(less than one month after the UAS imagery).
This image was radiometrically-, geometri-
cally-, and sensor-corrected by Planet before
downloading using the cubic convolution resa-
mpling algorithm. It also had a 3 m pixel size
(after orthorectification), an unsigned 16-bit
pixel depth, and a WGS 84 UTM zone 18N map
projection (Planet, 2017).
In ArcGIS Pro version 3.0.2, a polygon
shapefile was manually created to represent the
area of interest (AOI), defined as the zone where
gorse was visibly identified within the study
area (Fig. 2). The UAS and Planet orthophotos
were clipped to this area. To enhance gorse
identification, the Raster Calculator tool was
used to i) generate the normalized difference
Fig. 1. Invasion of Gorse in the Study Area (Source: Cortolima, personal communication, 2022). A. A close-up photo
depicting gorse, characterized by its standing necromass and yellow flowers, suffocating native paramo species (non-flowered
vegetation). B. A long shot showing the extensive invasion of gorse in the flat valley landscape, marked by a mixture of green
and yellow vegetation.
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vegetation index (NDVI) from Planet NIR and
red bands and ii) obtain the nDSM. Subse-
quently, the UAS orthophoto, the NDVI, and
the nDSM were incorporated into a new project
using eCognition version 10.2. This software
was chosen as it has been demonstrated to have
highly effective object-based image analysis
(OBIA) methods for producing accurate clas-
sification maps of invasive plants (Michez et al.,
2016; Lourenco et al., 2021).
All the parameters used in the eCognition
ruleset were determined through trial and error.
First, the ruleset consisted of a multi-threshold
segmentation using the nDSM. A lower thresh-
old of 0.5 m was set to exclude most of the
ground, while an upper threshold of 6.5 m was
determined through manual inspection, as no
gorse plants were found to exceed this height.
Any pixel values out of this range were classi-
fied as Other, a class of no interest, while those
within the range were left unclassified. Then, a
multi-resolution segmentation was applied to
the unclassified class using a scale parameter of
20, a shape of 0.1, and a compactness of 0.5. To
remove most of the non-gorse areas, everything
with an NDVI value less than 0.54 was classi-
fied as Non-vegetated. Similarly, the texture was
used to mask out other types of vegetation. The
GLCM Homogeneity (quick 8/11) All Directions
algorithm with the UAS red and green bands
was used to classify as Other all the objects
with values greater than 0.06 and 0.048, respec-
tively. For the UAS blue band, the GLCM Mean
(quick 8/11) All Directions algorithm was used
to remove objects with values greater than 130.
Training sample polygons were created
in ArcGIS Pro and added to the eCognition
project for supervised classification. To ensure
accurate classification, a temporal class was
created to classify one-pixel-size objects, which
are problematic for texture analysis, and merge
them into the surrounding classes. The Random
Forest machine learning algorithm was chosen
as it is one of the most accurate classifiers (Fer-
nandez-Delgado et al., 2014). The parameters
used were: 30 for depth, 0 for minimum sample
count, 16 for maximum categories, and 500 for
maximum tree number. This number of trees
was selected since it has a low influence on the
model execution (Granzig et al., 2021).
The objects with the classification and
the mean nDSM were exported to separate
shapefiles. The misclassified objects were then
reviewed and corrected by generating a 150 m
cell-size grid using the Create Fishnet tool in
ArcGIS Pro. Each cell was visually inspected
at a 1:300 scale, and the classification was
approved if the visible error was within the
acceptable range of 15-20 %, as suggested by
Peyre et al. (2021). These acceptable errors
referred to mismatches between non-flowered
gorse and non-gorse vegetation. Next, only the
gorse class was exported to a new shapefile.
To assess the classification accuracy, first,
the Non-vegetated and Other classes were
grouped into a single class called No-gorse.
Then, a stratified random sampling approach
was employed using the Create Accuracy
Assessment Points tool in ArcGIS Pro. A total
of 245 samples were selected according to the
equation developed by Cochran (1977):
where n is the number of samples, Z is the con-
fidence interval (set at 95 % or 1.96), P is the
expected overall accuracy (set at 0.8), and E is
the allowed error (set at 0.05). Later, a new field
was created in the attribute table of the points to
add ground reference values. To avoid bias, the
“Classified” field in the attribute table was hid-
den during the ground reference data collection
process. As no other data were available, the
ground reference values were derived from the
same UAS orthophoto used for the classifica-
tion. Finally, a confusion matrix was generated
to calculate various accuracy metrics, including
overall accuracy and the Kappa coefficient.
AGB, fuel load, and bioethanol quanti-
fication: First, Pearce et al. (2010) developed a
pair of linear equations to estimate gorse AGB
and fuel load:
ln(AGB) = 1.44 + 0.872 * ln(OsHt) + ln(1.02)
ln(fuel load) = 0.848 + 0.552 * ln(OsHt) + ln(1.04)
6Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72: e56364, enero-diciembre 2024 (Publicado Abr. 16, 2024)
where OsHt is overstorey log height (m), and
the ln() at the end is a correction factor for the
bias introduced when exponentiating the func-
tions to obtain AGB and fuel load (kg/m2) in
dry weight basis (db). According to the authors,
the equations yield an average precision of 30 %
for AGB and 40 % for fuel load. For this study,
the equations were inserted in the ArcGIS Pro
Calculate Field tool, taking the objects’ mean
nDSM values as the overstorey log height,
which generated AGB and fuel load density
values for each object. Next, the densities were
multiplied by the area of each object to obtain
the mass values (kg), and the total AGB and fuel
load over the whole AOI were obtained by sum-
ming the values for all the objects.
Second, Miller & Murthy (2017) found that
30.2 ± 5.8 gallons of ethanol can be extracted
from one ton of gorse (db), increasing to
30-40 % more if C5-sugar fermenting micro-
organisms are utilized besides yeast. These
values were used with the AGB calculated
above to estimate the potential gorse bioethanol
content in the AOI. In addition, the potential
income from bioethanol production was cal-
culated based on information provided by the
National Federation of Biofuels of Colombia
about the ethanol market price during the sec-
ond semester of 2022, which corresponded to
COP 13 048/gal. To convert this price to USD,
the average annual exchange rate for 2022 was
used, which was USD 1 = COP 4 257.68. Con-
sequently, the calculations’ unit price was USD
3.06/gal of bioethanol.
RESULTS
The total area invaded is estimated to be
66 465 m2, representing around 10 % of the
study area (as illustrated in Fig. 2), where the
average gorse height is 2.05 m.
The findings of the accuracy assessment
demonstrate an overall accuracy rate of 85.3 %.
Notably, the producer’s accuracy for the gorse
class was observed to be relatively low when
contrasted with that of the no-gorse class.
This discrepancy suggests a greater incidence
of omission error in the classification of the
gorse class. Specifically, the analysis identified
that 33 points, which were associated with the
gorse class, were misclassified as not being
gorse, whereas, in the case of the no-gorse
class, only three points were inaccurately clas-
sified as gorse. Conversely, the user’s accuracy
for the gorse class demonstrated a high value,
indicating a low commission error, as 83 out of
86 points were correctly predicted as gorse. In
addition, a Kappa coefficient of 70.1 % shows
that the classification performed significantly
better than what could be achieved by random
chance, i.e., there is a substantial agreement
between the two classes. Table 1 shows the
results of the accuracy assessment.
The results obtained for biomass consider-
ing the allometric equations average precision
are i) AGB ± 30 % = 527 978.9 ± 158 393.7 kg,
which yields an overall density of 7.94 ± 2.38 kg/
m2 (when divided by the total area occupied by
gorse); and ii) fuel load ± 40 % = 233 805.7 ±
Fig. 2. Predicted distribution of gorse across the AOI within the study area. Imagery from Cortolima (personal
communication, 2022).
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93 522.3 kg, with a density of 3.52 ± 1.41 kg/m2.
Likewise, Fig. 3 shows the estimation of the total
potential bioethanol production and income
generated based on the AGB ± 30 % results.
Overall, the estimated bioethanol production
ranges from 11 161.5 to 29 019.8 gals, depending
on whether C5-sugar fermenting microorgan-
isms are used and the percentage of the produc-
tion increase. Thus, the generated income may
vary from USD 34 205.3 up to USD 88 933.7.
Table 1
Confusion matrix of the supervised classification.
Mapped class Reference
Gorse No-gorse Row total User’s Accuracy
Gorse 83 3 86 0.965
No-gorse 33 126 159 0.792
Column total 116 129 245
Producer’s Accuracy 0.716 0.977
Overall accuracy = 85.3 %. Kappa = 70.1 %.
Fig. 3. Potential bioethanol production (A) and income (B) generated, with and without C5-sugar fermenting microorganisms,
based on the AGB estimations. The error bars represent the ± 30 % range of precision.
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DISCUSSION
Gorse is a dangerous invasive species
affecting Colombian paramos; therefore, it is
crucial to seek a bioeconomic model encom-
passing environmental management strategies
that lead to i) the eradication or species control
processes and ii) generating economic ben-
efits for local communities that are the most
affected. Unfortunately, in Colombia, the quan-
tification of AGB and fuel load is done through
destructive practices that are labor-intensive
and costly (Pearce et al., 2010; Yepes et al.,
2011). Moreover, there is no easily accessible
literature regarding UAS-based imagery for
AGB quantification in the country, nor specific
guides for gorse AGB and fuel load estimation.
Hence, this study proposes a first approach to
estimating gorse AGB and fuel load within a
paramo zone neighboring Los Nevados Natural
National Park using high-quality UAS-based
RGB imagery and an NDVI layer derived from
Planet imagery. Likewise, the study uses the
AGB results to obtain initial estimations of the
potential production of bioethanol, and income
generated if all this gorse is exploited.
This project is novel in estimating gorse
AGB and fuel load in Colombia using only
remote sensing techniques. Both values
obtained here align with the results from the
studies cited by Pearce et al. (2010) to calibrate
their models. Regarding gorse total biomass,
Egunjobi (1969) reported a range between
0.68 and 10.86 kg/m2, including roots. As for
fuel load, Anderson & Anderson (2009) found
fuel load density ranging from 2.6 to 7.4 kg/
m2. Basanta et al. (1988) and Soto et al. (1997)
estimated similar values (2.5-6.0 kg/m2) for
communities in the Atlantic. In Spain, Vega et
al. (2005) obtained values from 4.6 to 5.2 kg/m2,
which are slightly higher due to their focus on
gorse shrubs measuring 2.3-2.4 m tall. Like-
wise, other research studies using the models
by Pearce et al. (2010) and measuring gorse
overstorey log height with UAS remote sensing
methods have obtained similar results. Valencia
et al. (2023a), Valencia et al. (2023b) found i)
AGB and fuel load value ranges of 1.74-7.91 kg/
m2 and 0.36-1.91 kg/m2, respectively, for popu-
lations whose average height was 1.1 m; and ii)
a fuel load value ranging from 2.5 to 3 kg/m2
for populations around 1.5 m tall. Overall, the
fuel load here represents an extreme fire hazard
for the paramo ecosystem as nearly half of the
gorse AGB is flammable.
As for the bioethanol production, the mean
quantity obtained from gorse, 30.2 gal/ton, is
higher than that extracted from conventional
crops, such as sugarcane and sugar beets, which
yield 19.5 gal/ton and 24.8 gal/ton, respec-
tively (Shapouri & Salassi, 2006). This is because
gorse has a high cellulosic content (Osorio-
Castiblanco et al., 2019). Moreover, it is worth
remembering that bioethanol extraction from
gorse can increase by up to 40 % if C5-sugar fer-
menting microorganisms are used together with
yeast. Under this scenario, the income gener-
ated ranges from around USD 48 000 to 89 000,
which would be the first approach to attenuate
the enormous economic cost caused by invasive
species. Within Central and South America
and the Caribbean Island region, Colombia
ranks as the country with the second-highest
average annual cost due to biological invasions:
USD 191.77 million (Heringer et al., 2021).
Furthermore, this money would benefit the
local, low-income communities affected by the
degradation of the ecosystem services they rely
on. According to the National Administrative
Department of Statistics (DANE, 2023) Toli-
ma’s annual GDP per capita in 2022 was COP
22 253 352, or USD 5 226.64, when using the
2022 average annual exchange rate.
The accuracy assessment reveals that the
model accurately predicts locations invaded
by gorse. However, challenges arise in identify-
ing all invaded locations, primarily due to the
natural variability, and the different spectral
signatures and textures of mature gorse plants.
This issue is further compounded when dealing
with green, non-flowered gorse plants, often
leading to confusion with other vegetation. In
parallel, a notable factor contributing to this
complexity is the relatively large cell size of
the NDVI layer (in comparison with the UAS
orthophoto) which excluded some invaded
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areas along the river, road, and barren soil.
Hence, the values obtained in this study may be
slightly underestimated, meaning more gorse
biomass may be present in the AOI, posing a
higher risk to the paramo and Los Nevados
National Natural Park.
The methodology developed for this
study could be improved in several ways. For
instance, LiDAR data would be beneficial to get
ground returns where high, dense vegetation is
predominant to improve the DTM and, thus,
yield a more accurate nDSM, which is crucial
to estimating log height for the allometric equa-
tions. A UAS-based NIR band is necessary to
create a high-resolution NDVI layer to mask
out the non-vegetated areas more accurately.
Similarly, a deeper analysis of the ethanol con-
tent in gorse in the Andean region is recom-
mended as the reference data used in the study
is sourced from Oregon, US, and said content
may vary geographically. Also, since gorse has a
high lignocellulosic content, other bioeconomic
projects may also be considered, such as car-
bon fibers from lignin, cellulose whiskers, etc.
(Osorio-Castiblanco et al., 2019).
Finally, it is important to note that this
paper only provides preliminary results about a
bioeconomic model and, consequently, further
research from multiple disciplines is needed to
develop a successful, robust bioeconomic pro-
gram leading to gorse eradication. Specifically,
the program leaders must also consider the
problems, challenges, and possible unintended
consequences derived from the exploitation
activities. These may include failing to affect
gorse population growth; creating a highly
economic, dependent market for locals who
will be affected once the program is over;
or promoting new invasions, among others
(Nuñez et al., 2012).
In conclusion: Gorse (Ulex europaeus) has
severely invaded an ecosystem within the par-
amo zone neighboring Los Nevados National
Natural Park (Tolima, Colombia) and poses a
high risk of further invasion and fire. This study
is novel in Colombia as it represents the first
attempt to identify the species and estimate its
total aboveground biomass and fuel load over
the affected ecosystem solely based on remote
sensing data, primarily UAS-based imagery.
Furthermore, the methodology outlined here
can be extrapolated to other Andean ecosys-
tems that have been invaded by gorse. Overall,
the results showed AGB and fuel load densities
of 7.94 ± 2.38 kg/m2 and 3.52 ± 1.41 kg/m2,
respectively, which align with values reported
in the literature. Since nearly half of the AGB
corresponds to fuel load, urgent environmental
management strategies are needed to control
the exponentially growing expansion of the
species and reduce the risk of fire. Addition-
ally, this article proposes preliminary results
on bioethanol extraction from gorse, which
could be valuable for developing a comprehen-
sive bioeconomic plan to encourage large-scale
sustainable eradication programs and gener-
ate benefits for the surrounding low-income
communities heavily reliant on the ecosystem
services this paramo provides.
Ethical statement: the authors declare that
they all agree with this publication and made
significant contributions; that there is no con-
flict of interest of any kind; and that we fol-
lowed all pertinent ethical and legal procedures
and requirements. All financial sources are fully
and clearly stated in the acknowledgments sec-
tion. A signed document has been filed in the
journal archives.
ACKNOWLEDGMENTS
The author acknowledges the Regional
Autonomous Corporation of the Department
of Tolima (Cortolima) for the UAS-based data
provided under the document recorded with
the number 420 of August 4, 2022; the Remote
Sensing Lab at the University of Minnesota,
Twin Cities, for the geospatial support and GIS
software provided; and U-Spatial, University of
Minnesota, for providing the Planet imagery.
This project was also conducted thanks to the
Fulbright Colombia Científica-Pasaporte a la
Ciencia scholarship under the Foco-Reto Pais
Bioeconomia branch.
10 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 72: e56364, enero-diciembre 2024 (Publicado Abr. 16, 2024)
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