Article

A methodology for mapping nativeand invasive vegetation coveragein archipelagos: An example fromthe Galapagos Islands

Gonzalo F Rivas-TorresUniversidad San Francisco de Quito, Ecuador; University of Florida, USA; Galapagos Science Center,

Galapagos

Fatima L BenıtezUniversidad San Francisco de Quito, Ecuador

Danny RuedaGalapagos National Park Directorate, Ecuador

Christian SevillaGalapagos National Park Directorate, Ecuador

Carlos F MenaUniversidad San Francisco de Quito, Ecuador; Galapagos Science Center, Galapagos

AbstractThis study develops a mixed, systematic, low-cost methodology to define and map native vegetation and thespread of the most aggressive invasive species in islands biomes, focusing on the Galapagos National Park(GNP). Based on preliminary legends defined by experts, Landsat 8/OLI fusion imagery was used for object-oriented classification to obtain the vegetation map of this archipelago. This technique was later verified andvalidated using high-resolution images from unmanned aerial vehicles (UAVs, i.e., drones) and dedicatedsatellites, ground truthing, and visual confirmation around GNP coasts. This mixed methodology allowedmapping of nine native ecosystems, six invasive-dominated vegetation units, and two types of lavas. Around53.63% of GNP is covered by native ecosystems and *2.2% is “canopy” dominated by invasive species todate. Native-dominated deciduous forest types cover *40.8% of the GNP and only *12.8% of the pro-tected area is nowadays covered by humid and transitional type native ecosystems. Among humid nativeecosystems, those distributed in the highlands only cover 4.8% and are highly threatened by invasive species,which are mostly distributed in these summit areas. Of the five islands (out of 18) recording invasive-dominated units, Isabela and Santa Cruz were the most infested. Cedrela odorata, Pennisetum purpureum,and Psidium guajava were the main invasive plants dominating the GNP canopy. Highly noxious Rubus niveuswas the only invasive species dominating areas among the five infested islands. Methodology detailed here

Corresponding author:Gonzalo F Rivas-Torres, Colegio de Ciencias Biologicas y Ambientales, Universidad San Francisco de Quito, Diego deRobles s/n y Pampite, Cumbaya, Quito 170157, Ecuador.Email: grivast@usfq.edu.ec

Progress in Physical Geography2018, Vol. 42(1) 83–111ª The Author(s) 2018

Reprints and permission:sagepub.co.uk/journalsPermissions.nav

DOI: 10.1177/0309133317752278journals.sagepub.com/home/ppg

proved useful to provide accurate spatially-explicit islands vegetation data, potential for replication in time,and is expected to aid suitable management of highly endangered and unique biotas in this and other tropicalisland biomes.

KeywordsDrones, Galapagos National Park, invasive species, Landsat 8, UAVs, native and endemic species

I Introduction

Due to unique environmental conditions and

high species endemism, tropical islands are con-

sidered conservation priorities among biodiver-

sity hotspots (Gillespie et al., 2014; Myers et al.,

2000). Tropical islands experience relatively

large developmental pressures and impacts that

risk their biological uniqueness, such as rapid

habitat transformation and invasive species

(Spatz et al., 2017).

Despite increasing efforts by national author-

ities and related organizations to acquire infor-

mation that contributes to the protection of

tropical island biomes, some areas are still lack-

ing basic maps that at proper scales define dis-

tributional ranges of native-dominated

vegetation and the main invasive species (non-

native organisms introduced by humans that

outcompete native species) that often colonize

these ecological units (Asner et al., 2008; Hel-

mer et al., 2002). Potential explanations for this

knowledge gap vary from difficulty in obtaining

affordable, temporally-different, cloud-free sat-

ellite images, the high cost of flying aircrafts

(and producing high resolution images of the

many and scattered islands), the hazardous cli-

mate which makes landing aircrafts challen-

ging, or the rugged, difficult to transverse

terrain (Auken et al., 2009; Devoto et al.,

2012). These and many other logistical and

financial problems are common when gathering

island spatial information, and thus highlight

the need for methodologies that can create accu-

rate vegetation maps of tropical island biomes.

There are significant advantages to having

comprehensive and replicable vegetation maps

(in space and time) of tropical island ecosys-

tems. Baseline maps, in the short term, provide

spatial knowledge of the units’ distribution, and

the habitat characteristics explaining particular

arrangements. In the long term, through replica-

tion using similar methodologies, they allow for

comparison of surface area coverage and the

conditions (i.e., other species, climate change,

or human related threats) that cause them to

contract or expand through the years (Xie

et al., 2008). Further, the capacity to map

invasive-dominated units on island systems is

of utmost importance to deliver accurate infor-

mation on the zones these species occupy (in the

present and future) and, which native vegetation

units are being impacted by invasive colonizers

(Asner et al., 2008). Invasive species are con-

sidered one of the most prominent threats to

local biota on islands (Kueffer et al., 2010).

Thus, it is essential to provide decision-makers

with accurate and comprehensible spatial infor-

mation that will assist in pest control efforts in

islands around the world.

From a methodological perspective, satellite-

based remote sensing has facilitated advances in

modeling, mapping, and understanding of ter-

restrial ecosystem distribution (Lefsky et al.,

2002), and has also been used for basic ecolo-

gical research (Kerr and Ostrovsky, 2003).

Among available satellite remote sensing pro-

grams, the Landsat series of satellites are par-

ticularly useful tools for monitoring and

mapping land cover biophysical properties (Roy

et al., 2014). These sensors provide a continuous

record of 44 years of space-based surface

84 Progress in Physical Geography 42(1)

observations currently available for free down-

load (www.nasa.gov/mission_pages/landsat/

main).

The new Landsat mission, Landsat 8, pro-

vides near-global coverage and presents new

opportunities for vegetation classification

(Chaofan et al., 2016; Roy et al., 2014), mainly

because its 15 m panchromatic band improves

the spatial resolution of maps through image

fusion processes. Additionally, the use of free

images like Landsat in mapping campaigns may

ensure repeatability in time (and space by

allowing the comparison of vegetation maps

within and among islands), which can help effi-

cient biodiversity monitoring in remote and

developing countries recording tropical island

systems. One of the most common vegetation

indicators that can be obtained from multispec-

tral imagery such as Landsat, and a tool that

could be used to fill the gaps in vegetation clas-

sification on island systems are vegetation

indexes. These indicators, including the widely

known normalized difference vegetation

indexes (NDVI), can help in differentiating

vegetation types among ecological and abiotic

gradients (such as the ones that are characteris-

tic of islands). This makes them promising for

initial coarse-scale differentiation between

invasive and native-dominated flora (or differ-

entiation within each of these categories, i.e., at

the species level) that occur in isolated land

masses (Martinuzzi et al., 2008).

Recently and in a parallel fashion to the devel-

opment of vegetation indexes, many researchers

have engaged in the innovate concept of object-

oriented analysis to classify moderate resolution

images, like those from Landsat 8, to improve the

classification accuracy as compared to the tradi-

tional, pixel-based approach (Al Fugara et al.,

2009; Chaofan et al., 2016; Shimabukuro et al.,

2015; Weih and Riggan, 2010; Yoon et al., 2003;

Zhai et al., 2016). The use of object-oriented

approaches for mapping ecological units has

increased because they facilitate classification

of real world objects (i.e. vegetation) that can

be characterized by contextual and spectral

information, an outcome that is not easily

achieved through traditional methods (Baatz

and Schape, 1999).

In addition to improvements in satellite ima-

gery, the recent and rapid development of hi-

tech tools that collect hi-resolution images, such

as unnamed aerial vehicles (UAVs) or “drones,”

is expected to significantly improve biodiver-

sity mapping (Geller et al., 2017; Getzin et al.,

2014; Ivosevic et al., 2015; Koh and Wich,

2012; Marvin et al., 2016). The accessible,

low-cost, and high-resolution images produced

by these vehicles can help in particular to fill

gaps from Landsat or other satellite sensors

that cannot capture close-up images due to

the presence of clouds. In addition, these

high-resolution images can be used to verify

preliminary maps generated by satellite

images and related indicators through ground

truthing. As such, combining the use of

drones with other remote sensing techniques

like those previously described can produce

affordable maps, which in turn may contrib-

ute to the formulation of environmental pol-

icies and strategies that strengthen local

administrative and conservation-related pro-

cesses (Andrew et al., 2014; Cohen and

Goward, 2004; IDEAM et al., 2007).

Using Galapagos, one of the most iconic

islands systems globally as an example, this

paper seeks to fill knowledge gaps in the spatial

distribution of native and invasive-dominated

vegetation units on tropical islands by: (a)

detailing a methodology that identifies and

details the particular characteristics of different

vegetation types in island ecosystems and; (b)

generating an updated vegetation map for the

Galapagos National Park (GNP or “protected

zone”) that is expected to be used as a baseline

for future mapping exercises and conservation

planning. These maps not only provide the most

accurate spatial representation of Galapagos

vegetation units and related statistics to date,

but also include important biotic information

Rivas-Torres et al. 85

that defines these mapped elements and will

facilitate the continued gathering of information

for improving and maintaining proper vegetation

classification. Above all, the mixed methodology

presented here (i.e. expert participation, object

oriented classification, and validation using high

resolution images), is expected to aid related

investigations in island sites that have similar

logistical constraints to geographic data gather-

ing to those in the Galapagos, and lack basic but

important information on vegetation distribution.

II Materials and methods

1 Study area

Galapagos is under the political jurisdiction of

Ecuador and is located approximately 1000 km

to the west of the country’s Pacific coast. The

archipelago, which comprises a total land sur-

face of about 798,500 ha, is formed by 13 major

islands, five minor islands, and 216 islets and

minor rocks (DPNG, 2014). Of these rocky out-

crops, four, namely Isabela (south), Santa Cruz,

San Cristobal, and Floreana, are presently

inhabited by human settlers (Figure 1). This

study focuses on mapping native and invasive-

dominated vegetation units in the land protected

area of Galapagos, the GNP, which in total

includes 96.77% of the insular land surface and

excludes area defined as agricultural/develop-

ment lands.

Currently, more than 40% of the vascular

native plant species found in Galapagos are esti-

mated to be endemic (Bungartz et al., 2009), and

around 31% of these are considered rare, while

only 38% have stable populations (Adsersen,

Figure 1. Geographical location of Galapagos in relation to mainland Ecuador. Agricultural lands or notprotected areas are highlighted for the four islands presenting human settlements: Santa Cruz, San Cristobal,Isabela, and Floreana (from higher to lower number of inhabitants). Darwin and Wolf in the northwesternlimit of the archipelago are located closer in this map for illustration purposes.

86 Progress in Physical Geography 42(1)

1989). As in other parts of the world, the set of

unique species that define the Galapagos are

under constant threat mainly due to non-native

invasive plants (Guezou et al., 2010; Trueman

et al., 2014). Over time, non-native species have

transformed into plagues difficult to control

(i.e., invasive species), colonizing vast areas

and displacing the native and endemic biota

(Renterıa and Buddenhagen, 2006). The Gala-

pagos have more than 870 non-native plants of

which approximately 10% might be categorized

as invasive or as highly threatening because of

their high dispersal rates and colonization

capacity (Guezou et al., 2010; Williamson and

Fitter, 1996).

Ecuador, on the other hand, has been recog-

nized as one of the most biodiverse countries in

the world (Balslev et al., 1998; Myers, 1990;

Orme et al., 2005). The astonishingly high spe-

cies diversity is also seen on larger ecological

scales. The most recent initiative that mapped

vegetation and ecosystems in continental Ecua-

dor using a regional methodology (MAE,

2013a) recorded a stunning 91 continental vege-

tation units in only 248,360 km2. This number is

even more striking when contrasted with com-

parable mapping initiatives in other countries

(e.g., California in the USA has about 424,000

km2 and 111 vegetation units; Comer et al.,

2003). In spite of such a remarkable number,

larger ecological scale diversity in Ecuador is

underestimated, mainly because Galapagos (a

province of Ecuador) does not have a systematic

representation of its main vegetation units.

2 Definition of vegetation unitsand preliminary legends

In order to outline the vegetation units in the

Galapagos and that were identified, classified

and cartographically represented in the resulting

maps here, we followed two steps: first, defini-

tion of preliminary legends using bibliographic

sources and previous attempts to map vegeta-

tion in the archipelago; and second, verification

and ground truthing to validate the preliminary

legends. For both steps, experts’ opinions were

formally gathered in workshops to define pre-

liminary legends and to validate the final

legends. Here we define a vegetation unit

according to the Classification System of the

Vegetation Map for Continental Ecuador

(SCMVEC in Spanish, MAE 2013a) that fol-

lows national and regional criteria to classify

vegetation. According to the SCMVEC, a vege-

tation unit must be defined using “an array of

methodologies that allow to group and limit bio-

tic communities and their interactions with the

elements in the environment, in a logical and

ordered manner, to obtain different categories

in an inclusive and hierarchical fashion.

Because vegetation constitutes the most visible

element of an ecosystem (considered here as a

synonym to ‘vegetation unit’) this feature is

used to spatially differentiate and geographi-

cally classify such units.”

2.1 Preliminary legends definition. Preliminary

legends are defined as the initial descriptions

of each vegetation unit that were later assigned

a proper name (i.e., final legends) and used to

geographically classify the observed units from

satellite images (see Object-oriented classifica-

tion section). Preliminary legends consisted of a

name and a short description of the abiotic and

biotic environmental variables characterizing

the vegetation units. Preliminary legends were

assigned following the SCMVEC (MAE 2013a)

in order to maintain a classification that makes

the outcome presented here consistent with

those used at national and regional levels

(Comer et al., 2003; MAE, 2013a). This is why

the nomenclature for the native-dominated

vegetation units defined here include the

phenological characteristics of each unit (i.e.,

evergreen vs deciduous), habits of main domi-

nating species and physiognomy (forest vs

shrubland/tallgrass), and in some cases, location

above sea level (i.e., highland or lowlands). It is

important to highlight that the mentioned

Rivas-Torres et al. 87

classification system (MAE, 2013a) was only

used as a nomenclatural tool and did not affect

the definitions of the vegetation units; because

of the historical conditions of isolation, these

units are recognized here as unique to these

islands and not similar to other units existing

in continental Ecuador.

To define each preliminary legend for

native-dominated units, published biblio-

graphic descriptions of vegetation changes for

the Galapagos were used to systematically

classify different vegetation units depicting

floral and environmental changes within

the archipelago (Itow, 1992, 2003; Jackson,

1994; Trueman et al., 2013). Public and private

institutions working in the Galapagos, such as

the GNP, the World Wide Fund for Nature

(WWF), Charles Darwin Foundation (CDF),

and Universidad San Francisco de Quito

(USFQ), were also asked to provide relevant

unpublished information, such as shape files

and data bases, which were used as a comple-

ment to define the preliminary legends classi-

fication. From these sources, and using

SCMVEC as a nomenclatural and methodolo-

gical tool, six preliminary legends for native-

dominated units were first defined: Mangrove

forests, Coastal humid forest and shrubland

(both previously known as marine or coastal

zone-previous names according to above men-

tioned authors), Deciduous forest (p.k.a. dry or

arid zone), Evergreen seasonal forest and

shrubland (p.k.a. transition zone or mixed for-

est), Evergreen forest and shrubland (p.k.a.

Scalesia, brown, and Miconia zones), and

Highland humid tallgrass (p.k.a. pampa zone).

The SCMVEC does not include guidelines to

name and classify invasive-dominated vegeta-

tion. Therefore, in order to maintain classifica-

tion consistency, preliminary legends for these

units also followed this methodology, but with

one variation: the Latin name for genus was

combined with common names of the main

invasive plant dominating the vegetation unit

and used as nomenclatural descriptors. These

changes were adopted after concluding,

alongside GNP staff, that these names will

help mangers and decision makers recognize

exotic-dominated vegetation units. The pre-

liminary definition of invasive-dominated

vegetation units was created using previous

attempts to map this kind of exotic vegetation

(i.e., TNC-CLIRSEN, 2006; and unpublished

data gathered from Galapagos-related institu-

tions). Though these sources are unpublished,

they have been recognized by GNP staff as

an internal informal resource. From this exer-

cise, a further six invasive dominated prelim-

inary legends were defined: Cedrela cedar,

mixed (when more than one invasive species

dominate the unit), Rubus blackberry, Penni-

setum grass, Cinchona quinine, and Psidium

guava (see Results section for details about

final invasive-dominated units).

2.2 Preliminary legends and ground truthingvalidation. The 12 native and invasive-

dominated preliminary legends were later dis-

cussed with 14 experts currently working in the

Galapagos from the Ministry of Environment,

Galapagos Government Council, WWF, CDF,

GNP, and academia. The experts were pre-

sented with the 12 preliminary legends and the

methodology used to obtain them (SCMVEC,

MAE, 2013a, 2013b). In a half-day workshop

(1 of 2 for this stage), the three criteria for vege-

tation classification, physiognomic, environ-

mental, and floristic classifiers, were discussed

in relation to the Galapagos vegetation units

(MAE, 2013a, 2013b). All expert comments and

responses were systematized into a final docu-

ment which outlined two main observations on

the 12 preliminary legends: while all the defined

invasive-dominated units were recognized to

occur in the Galapagos, two changes were said

to be necessary for the native-dominated

legends: first, a vegetation unit corresponding

to dry conditions and existing only in the Isabela

mountaintops (here defined as Highland decid-

uous tallgrass) must be defined and mapped;

88 Progress in Physical Geography 42(1)

and second, the deciduous forest defined at this

stage might be differentiated in later geographic

representations in order to map potential phy-

siognomic differences (i.e., dry lands that apart

from tress are dominated by shrubs or herbs).

Using this expert input, 15 preliminary

legends (six invasive-dominated and nine

native-dominated including the Highland decid-

uous tallgrass and three different deciduous

lowland vegetation types) were used to perform

ground truthing exercises over six islands (Bal-

tra, Floreana, Isabela, San Cristobal, Santa

Cruz, and Pinzon). Ground truthing was later

followed by a second validation workshop with

GNP staff and more experienced park rangers,

to verify the distribution in the ground of the

proposed units, including different vegetation

occurrence in remote areas. More details for this

stage are in given in the Appendix.

After definition and validation of the prelim-

inary legends were complete, these 15 prelimi-

nary vegetation units were used to outline the

hypothetical vegetation distribution of Galapa-

gos. The next step was to test this hypothesis,

using the cartographic methodology described

below (Figure 2), and to assemble a vegetation

map for the islands.

3 Remote sensing and object orientedclassification

3.1 Data acquisition. Two types of sensors were

used to obtain satellite images to define the pre-

liminary legends and to observe vegetation units

in the Galapagos: the Operational Land Imagery

(OLI) sensor from satellite Landsat 8, and the

radar system from Shuttle Radar Topographic

Mission (SRTM). The OLI sensor generates

Figure 2. The cartographic methodology workflow of the present project: (a) image preprocessing;(b) object oriented classification; and (c) verification and validation of ground data.

Rivas-Torres et al. 89

multispectral images with a 30 m moderate spa-

tial resolution. Landsat 8, in contrast to its pre-

decessors, is more capable of detecting land

surface change and can more accurately display

the spectral properties of vegetation (Ke et al.,

2015). The SRTM uses a single-pass radar inter-

ferometry to generate the most complete near-

global, high-resolution digital elevation models

(DEMs) of the Earth’s land surface above sea

level with a spatial resolution of 1 arc/s equiv-

alent to 30 m resolution.

Satellite images from the warm-wet season

of the archipelago, typically running from

December to June (Collins and Bush, 2011),

were selected for this study as during this time

of the year vegetation unit “boundaries” are eas-

ier to define, particularly between dry and tran-

sitional areas of the region (i.e., lowland

deciduous and seasonal forests). Additionally,

by reviewing the Landsat archives since the

1980’s, it can be observed that the lowest level

of cloud cover over the GNP typically runs

between February and March. Lastly, the El

Nino event of 2015–2016 was among the stron-

gest observed since 1950, which reduced rain-

fall during the wet season (and changed rainfall

patterns around the world, L’Heureux et al.,

2016; NASA Earth Observatory, 2016), making

the images from that year mostly cloud-free and

easier to interpret.

Five OLI sensor scenes (path/row 17/60,

17/61, 18/60, 18/61, and 19/59) were needed

to cover the study area. The reference year for

the distribution mapping of the vegetation units

is 2016. However, some images from 2015 were

used to fill fairly small gaps (by performing

cloud and shadow masking ¼ 16.5% of the

whole archipelago) when clouds were present.

Table 1 provides an overview of the Landsat

8/OLI imagery used in this study.

Because the present study focuses on the land

protected area of the Galapagos archipelago, a

thematic layer that precisely divides this area

from the agricultural land was inserted in the

maps (DPNG, 2014).

3.2 Image pre-processing. Radiometric calibra-

tion, clouds and shadow masking, atmospheric

correction, and image fusion were used to trans-

form raw sensor data (i.e., satellite generated

images) into an appropriate format for posterior

spectral analysis. For the radiometric calibration,

digital number (DN) values of each image were

converted to spectral radiance values.

Table 1. Landsat8/OLI data.

Path/Row Acquisition Date Type IslandsStudy area cloud

coverage (%)

17/60 23 Feb 2016 Principal Genovesa 017/61 23 Feb 2016 Principal San Cristobal, Santa Fe, Espanola 4.3717/61 8 May 2015 Fill gaps San Cristobal 4.0917/61 24 Mar 2015 Fill gaps San Cristobal 9.5618/60 17 Mar 2016 Principal Isabela, Fernandina, San Cristobal, Santiago 6.2018/60 18 Apr 2016 Fill gaps Isabela, Fernandina, San Cristobal, Santiago 4.1318/60 27 Feb 2015 Fill gaps Isabela, Fernandina, San Cristobal, Santiago 6.7618/60 13 Jan 2016 Principal Marchena 0.518/60 31 Mar 2015 Principal Pinta 018/60 17 Feb 2015 Fill gaps Isabela, Santa Cruz, Floreana 6.7618/61 17 Mar 2016 Principal Isabela, Santa Cruz, Fernandina 318/61 17 Feb 2015 Fill gaps Isabela, Santa Cruz, Floreana 6.4418/61 18 Apr 2016 Principal Floreana 2.419/59 6 May 2016 Principal Darwin, Wolf 0

90 Progress in Physical Geography 42(1)

Subsequently, cloud masks were created

through automated cloud detection using a prin-

cipal component analysis (PCA) from blue, red,

and SWIR2 bands (Ahmad and Quegan, 2012;

Lavanant and Lee, 2005). The cloud threshold

and cloud masking were then determined by

analyzing PC1 histogram (PC1 brightness �8). Lastly for shadow masking, the cloud mask

was projected on the ground surface, consider-

ing the effects of clouds elevation and sun illu-

mination angle (Martinuzzi et al., 2007). More

details on cloud masking are described in the

Appendix.

3.3 Object-oriented classification. Object-oriented

classification (OOC) is defined as an essential

technique that can be used in remote sensing

exercises that seek to analyze high spatial reso-

lution images (Doxani et al., 2008; Jawak et al.,

2015). It also has been used in the analysis of

moderate spatial resolution images, providing

significant results (Blaschke, 2010; Dorren

et al., 2003; Shimabukuro et al., 2015; Yoon

et al., 2003). OOC uses satellite generated

images to perform spatial and spectral analyses

that allow the integration of relevant properties

such as shape, texture, and information from the

spectrum with contextual information from ana-

lyzed entities from the earth’s surface (in this

case vegetation units).

The OOC process is divided into two steps:

segmentation and classification.

Segmentation algorithms group single-pixel

objects with their neighbors to form a signifi-

cant object that is defined by scale parameters

and homogeneity criterion (spectral and shape

criteria; Trimble, 2011). Here a multiresolution

segmentation, which is an optimization proce-

dure that minimizes the average heterogeneity

and maximizes the respective homogeneity of

the above-mentioned units, was used for this

purpose (Trimble, 2011). Each mosaic image

was segmented with a constant “composition

of homogeneity criterion” and the scale para-

meter was set at two levels. For the first level,

the scale parameter 200 (i.e., 200 pixels, one

pixel corresponding to 225 m2) was selected to

differentiate water from the land surface. The

second segmentation level used a scale para-

meter of 25 and was only applied to the land

surface, which allowed the differentiation of

uniform vegetation units (see Appendix, Figure

6). The default settings for the “composition of

homogeneity criterion” were: color factor of

0.9, shape factor of 0.1, compactness factor of

0.2, and smoothness factor of 0.8. These para-

meters were selected using a trial and error

empirical analysis (Moeller and Stefanoy,

2004; Zoleikani et al., 2017), which choose the

most suitable parameters to detect small rem-

nants of vegetation units such as the mangroves

(fine scale) and discriminate spread and irregu-

lar vegetation units with different seasonal char-

acteristic (high color and smoothness factors).

The classification process includes: (a) the

construction of a hierarchical scheme, which

allows a semantic organization of classes and

reduces complexity (Doxani et al., 2008); and

(b) class descriptions that can be carried out

either by the nearest neighbor classifier or by

fuzzy functions (Al Fugara et al., 2009; Kressler

et al., 2005; Yan, 2003). Figure 7 in the Appen-

dix shows different levels within a hierarchical

classification scheme used in this research,

which was built on the basis of the preliminary

legends described above. In this study, the fuzzy

membership function classifier was used to allo-

cate each segment to a vegetation unit class.

Inputs by experts and ground truthing data col-

lected to verify the proposed preliminary

legends (60 points collected in the 15 prelimi-

nary vegetation units, see Appendix) were used

to define the membership functions (features

and threshold values) that describe each vegeta-

tion unit. For each segment the following fea-

tures were calculated based on vegetation

spectral behavior criteria: (a) object features:

mean layer values (blue, green, red, NIR,

SWIR1, SWIR 2, elevation) and ratio layer val-

ues (blue, green); (b) class-related features:

Rivas-Torres et al. 91

relative border to neighbor objects and exis-

tence of sub-objects (super-objects); and,

(c) vegetation indexes derived from satellite

images. Table 7 in the Appendix shows the

vegetation indices used in this process. A PCA

was also performed to evaluate the relevance

of each feature to describe each vegetation

unit using the 60 ground truthing data (in situ

field) points.

4 Verification and validation groundclassification data

4.1 Verification. After the final maps for native

and invasive-dominated units were assembled,

this research used high resolution images

obtained from drones, data gathered by ground

control points from in situ plots located at each

vegetation unit, and trips around the islands to

perform a final verification of the recognized

vegetation units on the Galapagos’ surface

(including remote areas; Figure 1).

First for this final stage, 20 drone flights were

carried out in difficult to access sites within the

GNP. A DJI Phantom 3 Professional aircraft

equipped with a 4 K Gimbal Camera (12MP;

Da-Jiang Innovations, China) was used to

obtain high resolution images of sites of inter-

est. Between 150 and 200 images were obtained

per flight (with a spatial resolution of 3.5–5.5

cm/pixel) covering on average 15 ha each

(approximately 300 ha covered in total). An

ortho-mosaic image was created for each flight

(using Agisoft Photoscan software V 1.2.6) in

order to identify invasive-dominated vegetation

units occurring within a native-dominated

matrix and that were difficult to define using

only Landsat images (see Figure 8 in the Appen-

dix). For each of the 20 mosaics created after

drone flights, two verification points (one for

native and one for invasive-dominated vegeta-

tion present in the image) were chosen within

the images to visually verify that invasive and

native-dominated coverage identified in the

high-resolution mosaics corresponded to one

of the final mapped vegetation units.

Second, 148 in situ 4� 4 m2 plots were estab-

lished and recorded in Fernandina, Genovesa,

Isabela, Santa Cruz, San Cristobal, and Santiago

islands. These plots were established principally

in sites covered by invasive plants, transition-

type vegetation boundaries, and native domi-

nated units comprising of small patches or forest

remnants in good conservation state. In each of

these plots, vegetation units and geographic loca-

tion were assigned and recorded.

Third, 35 verification points recorded from

boats surrounding the coast of nine islands were

also used to confirm the preliminary vegetation

units (Figure 1). Collection of these points con-

sisted of recording the spatial location in the

ground (projected from the boat) of the verified

coastal site along with the description of a spe-

cialist (i.e., authors) that confirms the observed

vegetation distribution.

In total, 217 verification points (40 for drone

images, 148 from in situ plots, and 35 from boat

reference points) were collected and used to

improve the PCA used in the classification stage

and to better define the preliminary classifica-

tion of the vegetation units (this PCA is reported

in the Results).

4.2 Validation. Validation of the final vegetation

map was performed using 490 random sample

points distributed throughout the study area that

were systematically validated by visual inter-

pretation of high resolution images. Images

used in this process were obtained from Google

Earth, specifically from the dates March 27

2014, February 14 2016, February 19 2016,

March 15 2016, May 5 2016, October 8 2016,

and October 26 2016, and between 2010 and

2013 from the RapidEye sensor. A stratified

random sampling procedure was used to distri-

bute the sample points in proportion to each

land cover stratum. For accuracy, a confusion

matrix was generated and related statistics were

calculated to assess the overall accuracy and the

92 Progress in Physical Geography 42(1)

kappa coefficient (ranging from 0 ¼ no accu-

racy to 1 ¼ total accuracy), which are common

techniques for assessing the alignment between

map classes against classes on the ground

(Naesset, 1996; Ponce-Hernandez et al., 2004;

Stehman, 1996).

Finally, a biotic approach was used to vali-

date native vegetation units, which consisted of

testing the similarity of units when presence and

abundance of native and endemic plant species

(grouped by genera) are compared to each other

(using a multivariate clustering classical

method—UPGMA algorithm—and a Jaccard

similarity matrix, both calculated using PAST

v 3.14, Hammer et al., 2001). Occurrence data

per unit was obtained after crossing our native

vegetation map with all the native species

occurrence records obtained from the most

updated plant lists available for this region (Jar-

amillo and Guezou, 2013). Only accurate

records presenting correct geo references were

used for these analyses. In total, 5829 native and

endemic plant records belonging to 205 indi-

genous genera were used for this validation.

Jaccard index values below 0.8 were considered

not significant, meaning pairwise analyzed units

recording index values above this number

should be considered similar in their biotic

characteristics.

III Results

1 Features selection and object orientedclassification

The OOC methodology used here allowed a

selection of the most appropriate features to

classify each vegetation unit. As a result of this

analysis (performed by a PCA using 217 collec-

tion points), the most significant features, used

to characterize the fuzzy membership function

for each vegetation unit, are for the first time

being used and defined in the Galapagos and

shown in detail in Table 2.

Additionally, the OOC methodology defined

the ranges of vegetation units recorded by

NDVIs and similar indexes, and helped to dis-

criminate and map these classes. These analyses

showed that NDVI mean values of higher than

0.6 denote sites of dense vegetation cover, such

as humid and evergreen native-dominated units.

Meanwhile, NDVI mean values between 0.2

and *0.59 typify deciduous vegetation, and

values from 0 to *0.2 are characteristic of

rocky outcrops or areas where deciduous vege-

tation is very sparse.

Likewise, NDWI2 and NGRDI indexes,

which have strong responses to water content

and vegetation greenery, contributed signifi-

cantly to the identification and discrimination

of deciduous and transition type vegetation

units (i.e., Evergreen seasonal forest and shrub-

land) (Table 2), as they had values consistently

close to zero (both negative and positive) when

defining deciduous cover types (Table 3).

The use of these kind of features in classify-

ing invasive-dominated units allowed for a

ranking based on water content (from higher

to lower as shown by NDWI2 index) in this

order: Cedrela cedar, Pennisetum grass, mixed

forest, Rubus blackberry, Chinchona quinine,

and Psidium guava (Table 3).

2 Vegetation unit definition and distributionin the Galapagos

As a result of the mixed methodology presented

here, twenty land cover types were identified

and later mapped: nine native vegetation units

dominated by native and endemic vegetation,

six invasive species vegetation units dominated

by different alien plants, and two types of lava

dominated coverage (Figures 3 and 4). Three

other classes, water, agricultural lands, and

urban settings, are also presented in the results

for comparative purposes.

The 15 resulting native and invasive-

dominated vegetation units were the same as the

preliminary legends defined and verified by

experts in the two workshops designed for this

purpose. The nine native-dominated vegetation

Rivas-Torres et al. 93

units defined by our methodology, and verified

and validated by the mixed-methods approach,

if sorted by elevation from low- to highlands

are: Mangrove forest, Coastal humid forest and

shrubland, Deciduous tallgrass, Deciduous

shrubland, Deciduous forest, Evergreen seaso-

nal forest and shrubland, Evergreen forest and

shrubland, Humid tallgrass, and Highland

deciduous tallgrass.

The resulting vegetation units were grouped

in six main supra (i.e., “parents”) categories

defining land cover according to the class hier-

archical analysis in this study (Table 3). Overall,

the object-oriented method used in this investi-

gation proved to be a highly accurate method to

classify land cover (kappa¼ 0.86, OCA¼ 87.35;

see Table 4). Additionally, when this type of

vegetation was analyzed for similarities in

Table 2. Selected features used to define each vegetation unit in the classification process and the percent-age of the total variation these explained in each principal component (PC).

ClassFeaturesPC1 %

FeaturesPC2 %

FeaturesPC3 %

Total explainedin PCA (%)

Evergreen forest and shrubland NDVI 65% 18%

-

83%NDWI2 Elevation

B/GaNGRDIBand 7

Evergreen seasonal forest and shrubland NDVI 47%Band 6

Band 3 21% 91%NGRDI 23% Band 2NDWI1

Coastal humid forest and shrubland NDVI 57% Band 4 27%-

84%NDWI1 Elevation

Deciduous forest NDVI 61% 20%

-

81%NDWI1 Band 3

Band 6NGRDIB/G

Deciduous shrubland Band 3 46%NDWI1

22% NDWI2 15% 83%Band 5 NDVI

Humid tallgrass DVI 65% Band 3 15%-

80%NDVI Band 1Band 7 Elevation

Highland deciduous tallgrass NDVI 59% Band 3 22%-

81%NDWI1 Band 4DVI Elevation

Deciduous tallgrass Band 5 56% NDWI1 25%-

81%Band 3 NDVI

NGRDIMangrove forest Band 6 60% 18% 11% 89%

DVI Band 2 ElevationNDWI1

Invasive species NDVI 49%Band 2Band 3Band 5

20%B/GElevation

17% 86%NDWI1NGRDIBand 7

aB/G: Band2/Band3

94 Progress in Physical Geography 42(1)

abundance and presence of native and endemic

species by genera, there were no significant

similarities among them (i.e., Jaccard index val-

ues < 0.8; correlation ¼ 0.95; see Figure 5),

which supports the proposed classification for

this group on biotic grounds.

In terms of spatial coverage and distribution

of native-dominated units for the archipelago,

results indicate that of the 771,600.81 ha pre-

sently defined as protected land by the GNP,

34% of this (or 262,527 ha) is Deciduous forest.

This is followed by Evergreen seasonal forest

and shrubland (7.89%), Evergreen forest and

shrubland (3.99%), and Deciduous shrubland

(3.66%). Other native-dominated vegetation

units are presented in Table 3. Presently,

native-dominated dry forest vegetation types—

including Deciduous forest, Deciduous shrub-

land, Deciduous tallgrass, and Highland decid-

uous tallgrass—dominate the archipelago,

covering *40.8% of terrestrial protected area.

This analysis also shows that only 12.8% of the

National Park is now covered by humid and

transitional type native vegetation. Thus,

native-dominated vegetation units cover

53.6% of the GNP in total. The main islands

with lower to higher coverage of transitional

and humid native vegetation types in the Gala-

pagos highlands (*200 m a.s.l.) are: Fernan-

dina with 2.7% (percentage combined from

Evergreen seasonal forest and shrubland, the

Evergreen forest and shrubland, and Humid

Table 3. Total area occupied by each of the categories and “child” classes defined in this study, and thecorrespondent vegetation indexes. Percentages are calculated in relation to the total GNP protected area¼771,600.8 ha – out of the 797,325.6 total ha – encompassing the Galapagos province.

Category Land cover classes

Total areaMeanNDVI

MeanNDWI1

MeanNDWI2

MeanNGRDI

MeanDVIha %

Forest andshrub

Evergreen forest andshrubland

30,788.9 3.99 0.82 �0.74 0.33 0.23 0.31

Evergreen seasonal forestand shrubland

60,887.7 7.89 0.78 �0.69 0.29 0.20 0.27

Coastal humid forest andshrubland

1,377.5 0.18 0.73 �0.67 0.39 0.20 0.28

Deciduous forest 262,527.0 34.02 0.55 �0.53 0.05 0.03 0.15Deciduous shrubland 28,258.0 3.66 0.31 �0.37 �0.12 �0.07 0.06

Herbaceousvegetation

Humid tallgrass 4477.5 0.58 0.81 �0.72 0.32 0.21 0.34Highland deciduous tallgrass 6922.6 0.90 0.49 �0.50 0.03 �0.01 0.15Deciduous tallgrass 17,137.4 2.22 0.21 �0.19 �0.09 �0.09 0.04

Mangroves Mangrove forest 1470.4 0.19 0.63 �0.56 0.45 0.18 0.17Invasive Species Cedrela cedar 1977.4 0.26 0.91 �0.83 0.48 0.33 0.42

Mixed 1142.1 0.15 0.85 �0.76 0.37 0.28 0.35Rubus blackberry 495.1 0.06 0.82 �0.74 0.31 0.22 0.36Pennisetum grass 2871.7 0.37 0.82 �0.72 0.39 0.24 0.36Cinchona quinine 61.0 0.01 0.81 �0.73 0.27 0.19 0.26Psidium guava 10,312.6 1.34 0.79 �0.71 0.24 0.20 0.27

Rocky outcrop Recent lavas 252,273.3 32.69 0.13 �0.18 0.01 �0.05 0.01Old lavas 87,914.8 11.39 0.10 �0.17 0.05 �0.07 0.01

Other areas Agricultural lands 25,174.1 3.26 0.78 �0.72 0.28 0.17 0.29Urban settings 550.7 0.07 0.32 �0.35 0.01 �0.03 0.09Water 705.7 0.09 �0.39 0.58 0.44 0.32 �0.01

Rivas-Torres et al. 95

tallgrass, Table 2), San Cristobal with 4.2%,

Pinzon with 8.6%, Santiago with 10.7%, Isabela

with 14.2%, Floreana with 15.8%, and Santa

Cruz with 18.3%. Lava accounted for 44.09%of total GNP land cover when grouped as the

supra category rocky outcrops. Distribution,

coverage, and altitudinal ranges per native-

dominated unit and island are presented in

Table 5.

The six invasive-dominated vegetation units

identified in this study were (listed from lower

to higher coverage): Cinchona quinine (domi-

nated by the tree Cinchona pubescens), cover-

ing 0.01% of all the protected area of

Galapagos; Rubus blackberry (by the shrub

Rubus niveus) covering 0.06%; “mixed” that

represent a forest where the invasive species

co-dominate with native species, covering

0.15%; Cedrela cedar (by the tree Cedrela

odorata) covering 0.26%; Pennisetum grass

(by the grass Pennisetum purpureum) covering

0.37%; and Psidium guava (by the tree Psidium

guajava) covering 1.34% of all the GNP (Table

3).

The resulting map depicting invasive-

dominated unit distribution shows that five

islands have such vegetation units and only one

of them, Santiago, is not inhabited by humans

(Figure 4). Of these five islands, Isabela and

Santa Cruz have the highest percentage of

invasive-dominated vegetation units, 2.88% and

3.09% respectively, in relation to the protected

area on each island (Table 6). Of the six invasive

dominated units, only Rubus blackberry is pres-

ent in all the five islands. When all islands with

these units are grouped together, on average

invasive-dominated vegetation is distributed

around 440 m a.s.l. (max 750, min 130 m

Figure 3. Resulting map presenting Galapagos native units distribution for all the islands of this archipelago.For the entire vegetation map of the Galapagos (including invasive-dominated units) and to display moredetails, please visit www.institutodegeografia.org/vega.

96 Progress in Physical Geography 42(1)

Figure 4. Map presenting the distribution of invasive-dominated vegetation units for the five islands thatrecorded this type of alien flora. Please note for (b) Santa Cruz, the complex matrix invasive plants’ distributionscreate in the surroundings (mainly northern part) of the agricultural land of this island. Zoom squares highlightthe total area where invasive-dominated units are present in the space, and below are shown in detail thedistributions of those units in the protected area of each island. For an online visual version of the mapspresented here please visit www.institutodegeografia.org/vega.

Rivas-Torres et al. 97

Tab

le4.C

onfu

sion

mat

rix

and

the

num

ber

ofs

ample

dpoin

ts(s

um

colu

mns)

for

each

vege

tation

and

map

ped

unit

(full

nam

esbel

ow

)re

sultin

gfr

om

obje

ct-

ori

ente

dim

age

clas

sific

atio

n.

Ref

eren

cedat

a

Map

ped

Unit

Eg.

f.sEg.

s.f.s

Dec

.fD

ec.s

Cs.

hm

.f.s

Hum

.tg

H.d

ec.tg

Dec

.tg

Mgv

Cq

Cc

Pgv

Mx

Rb

Png

RL

OL

WU

sA

lSu

mA

ccura

cy

Classificationdata

Eg.

f.s20

32

25

0.8

0Eg.

s.f.s

37

41

11

44

0.8

4D

ec.f

392

21

11

100

0.9

2D

ec.s

324

21

30

0.8

0C

s.hm

.f.s

28

10

0.8

0H

um

.tg

11

11

11

15

0.7

3H

.dec

.tg

11

911

0.8

2D

ec.tg

117

119

0.8

9M

gv1

910

0.9

0C

Q1

78

0.8

8C

C3

12

15

0.8

0PG

v1

31

15

20

0.7

5M

x1

18

10

0.8

0R

B1

16

80.7

5PnG

10

10

1.0

0N

L2

64

470

0.9

1O

L1

31

40

45

0.8

9W

19

10

0.9

0U

s10

10

1.0

0A

L20

20

1.0

0Su

m26

50

108

30

10

12

10

19

10

712

19

86

11

66

47

910

20

490

Acc

ura

cy0.7

70.7

40.8

50.8

00.8

00.9

20.9

00.8

90.9

01.0

01.0

00.7

91.0

01.0

00.9

10.9

70.8

51.0

01.0

01.0

0O

vera

llcl

assi

ficat

ion

accu

racy¼

87%

Kap

pa

coef

ficie

nt¼

0.8

6

Eg.

f.s:Eve

rgre

enfo

rest

and

shru

bla

nd,Eg.

s.f.s

:Eve

rgre

ense

asonal

fore

stan

dsh

rubla

nd,D

ec.f:

Dec

iduous

fore

st,D

ec.s

:D

ecid

uous

shru

bla

nd,C

s.hm

.f.s:

Coas

talhum

idfo

rest

and

shru

bla

nd,H

um

.tg:

Hum

idta

llgra

ss,H

.dec

.tg:

Hig

hla

nd

dec

iduous

tallg

rass

,D

ec.tg:

Dec

iduous

tallg

rass

,M

gv:M

angr

ove

,C

Q:C

inch

ona

quin

ine,

CC

:C

edre

lace

dar

,PG

v:Pis

idiu

mgu

ava,

Mx:M

ixed

,RB:R

ubus

bla

ckber

ry,P

nG

:Pen

nis

etum

gras

s,N

L:N

ewla

vas,

OL:

Old

lava

s,W

:Wat

er,U

s:U

rban

sett

lem

ents

,AL:

Agr

icultura

llan

ds.

98

a.s.l.), or mostly in the highlands and humid

regions of the archipelago. Denominated mixed

forest, followed by Psidium guava and Cedrela

cedar alien-dominated vegetation types had the

widest altitudinal ranges among mapped

invasive-dominated units (Table 6).

IV Discussion

Despite the urgent need to map biodiversity in

unique protected sites, such as island biomes,

there is a lack of systematic, replicable, and

affordable methodologies to describe the distri-

bution of native and invasive-dominated vege-

tation on isolated islands. Here, we present a

mixed, affordable, and replicable methodology

to provide information, at a proper scale, regard-

ing vegetation distribution in the protected zone

of the most iconic tropical archipelago on the

globe. We expect that the map presented here,

along with the resulting native vegetation dis-

tribution, invasive-dominated vegetation units,

methodology, and secondary information such

as vegetation features generated for each unit,

might be of significant aid for scientists, man-

agers, and the public locally and globally.

Overall, the methodology used here to under-

stand an archipelago’s general vegetation

distribution using a multi-scale object-based

classification scheme showed relatively high

accuracy when compared to specialized litera-

ture (*87%; Foody, 2002). This allows us to

suggest such mixed methodology can be imple-

mented in other island biomes to map vegeta-

tion distribution with a significant degree of

accuracy. However, it is important to note that

the information generated in this study consti-

tutes a reference framework to identify vegeta-

tion units at the “ecosystem scale” (with a

nominal minimum mapping unit (MMU) of

1/4 ha, and a representation scale of 1:75.000)

and not at the level of plant or vegetation

associations (MAE, 2013a). Therefore, if the

objective in future or similar mapping initia-

tives is to define finer scale vegetation aspects,

like vegetation associations or particular species

distributions for particular islands, other meth-

odologies should be incorporated and explored.

Additionally, and for a general comparison

(more details can be found in the Appendix)

we visually evaluated how the outcomes of the

present study differ from previous similar

attempts like the TNC-CLIRSEN 2006 map

(at a representation scale of 1:50.000).

As a result of the OOC methodology used

here, the primary features that can aid in proper

Figure 5. Combined multivariate clustering classical method (left) and Jaccard similarity matrix showingnative ecosystems discrimination. Notice the lack of values � 0.8, meaning pairwise analyzed units may bedefined as significantly different in their biotic characteristics (number of species per genus).

Rivas-Torres et al. 99

Tab

le5.A

ltitudin

alra

nge

(in

ital

ics

and

ma.

s.l.)

and

area

(per

centa

gefr

om

tota

lare

a/ha)

reco

rded

per

isla

nd

by

each

nat

ive

ecosy

stem

sdes

crib

edby

this

study.

The

altitu

din

alra

nge

does

not

nec

essa

rily

repre

sent

aco

ntinuous

dis

trib

ution

ofea

chunit,a

sso

me

vege

tation

types

may

pre

sent

isola

ted

pat

ches

with

adis

cret

edis

trib

ution.D

arw

inIs

land

does

not

pre

sent

altitu

din

alra

nge

sas

its

size

isto

osm

allto

calc

ula

teth

isva

riab

le.

Nat

ive

ecosy

stem

Isla

nd

Eve

rgre

enfo

rest

and

shru

bla

nd

Eve

rgre

ense

asonal

fore

stan

dsh

rubla

nd

Coas

talhum

idfo

rest

and

shru

bla

nd

Dec

iduous

fore

stD

ecid

uous

shru

bla

nd

Hum

idta

llgra

ssH

ighla

nd

dec

iduous

tallg

rass

Dec

iduous

tallg

rass

Man

grove

Isab

ela

200–900

(4.5

%/2

1,0

99.2

)100–700

(8.9

%/4

1,2

47.6

)4–20

(0.2

%/9

08.5

)40–800

(23.9

%/1

10,9

51.8

)40–300

(1.2

%/5

733.7

)700–1000

(0.8

%/3

860.7

)900–1500

(1.3

%/6

166.0

)5–100

(1.5

%/6

848.0

)0–15

(0.3

%/1

223.3

)Sa

nta

Cru

z150–700

(5.3

%/4

719.6

)50–400

(12.9

%/1

1,5

41.8

)5–15

(0.5

%/4

44.7

)5–300

(64.4

%/5

7,5

67.5

)10–100

(9.6

%/8

597.0

)650–800

(0.1

%/1

20.4

)5–70

(3.6

%/3

202.3

)0–10

(0.1

%/1

18.1

)Sa

nC

rist

obal

350–500

(0.2

%/1

06.2

)100–450

(3.9

%/1

861.9

)0–7

(0.0

02%

/0.9

)0–200

(67.9

%/3

2,1

20.2

)1–100

(16.4

%/7

768.8

)1–150

(4.1

%/1

927.2

)0–5

(0.0

2%

/10.5

)Fe

rnan

din

a500–1400

(0.1

%/9

0.6

)500–1300

(2.4

%/1

521.4

)5–10

(0.0

01%

/0.9

)100–900

(12%

/7674.4

)40–300

(2.9

%/1

831.9

)800–1400

(0.2

%/1

06.4

)950–1400

(1.2

%/7

56.7

)15–75

(0.9

%/5

46.2

)0–15

(0.1

%/6

8.1

)Sa

ntiag

o400–700

(5.8

%/3

357.1

)200–600

(4.2

%/2

435.5

)0–15

(0.0

3%

/15.0

)15–300

(47.3

%/2

7,2

06.3

)5–200

(5.9

%/3

371.2

)650–750

(0.6

%/3

59.7

)5–100

(2.8

%/1

590.5

)0–15

(0.1

%/5

0.5

)Fl

ore

ana

300–400

(6.8

%/1

160.6

)200–350

(9%

/1528.9

)10

-250

(77.3

%/1

3,1

26.9

)5–150

(2.1

%/3

62.0

)0–100

(1.8

%/3

12.7

)Pin

ta450–600

(3.9

%/2

33.1

)350–500

(6.1

%/3

58.1

)10–350

(43.3

%/2

560.9

)500–650

(0.5

%/3

0.3

)5–80

(1.6

%/9

1.9

)M

arch

ena

50–250

(1.3

%/1

75.2

)5–10

(0.0

3%

/3.3

)5–150

(19.8

%/2

598.2

)G

enove

sa5–20

(0.3

%/4

.0)

15–70

(90.4

%/1

261.7

)5–25

(5%

/69.7

)Esp

anola

0–5

(0.0

05%

/0.3

)10–200

(82.5

%/5

107.5

)10–100

(6.4

%/3

94.4

)5–100

(9.8

%/6

06.2

)Sa

nta

Fe50–200

(31.9

%/7

91.0

)25–150

(64.6

%/1

599.2

)R

abid

a50–350

(8.1

%/4

1.2

)20–200

(30.5

%/1

55.5

)10–250

(50.7

%/2

58.5

)Pin

zon

300–450

(8.6

%/1

54.0

)50–300

(80.9

%/1

447.0

)50–150

(2.4

%/4

3.4

)15–150

(4%

/71.7

)W

olf

150–260

(18.3

%/2

2.5

)100–200

(35.1

%/4

3.0

)20–100

(31.5

%/3

8.6

)0–20

(5%

/6.1

)D

arw

in______

(31.3

%/2

0.4

)______

(51.7

%/3

3.7

)______

(7%

/10.8

)

100

Tab

le6.

Are

a(h

a)an

dal

titu

din

alra

nge

(ma.

s.l.)

reco

rded

per

isla

nd

by

each

inva

sive

-dom

inat

eve

geta

tion

unit

des

crib

edby

this

study.

The

altitu

din

alra

nge

does

notnec

essa

rily

repre

senta

continuous

dis

trib

ution

ofe

ach

unit,a

sso

me

vege

tation

types

may

pre

sentis

ola

ted

pat

ches

with

adis

cret

edis

trib

ution.

Isab

ela

Santa

Cru

zFl

ore

ana

San

Cri

stobal

Santiag

o

Are

aN

PG¼

464,8

19.4

Are

aN

PG¼

89,3

48.2

Are

aN

PG¼

16,9

84.6

Are

aN

PG¼

47,3

39.8

Are

aN

PG¼

57,4

73.1

Spec

ies

Are

a%

Alt.

range

Are

a%

Alt.

range

Are

a%

Alt.

range

Are

a%

Alt.

range

Are

a%

Alt.

range

Ced

rela

ceda

r251.6

0.0

5200–450

1,6

87.7

1.8

9150–650

12.3

0.0

7300–400

25.8

0.0

5350–450

Mix

ed864.3

0.1

9100–600

273.4

0.3

1550–700

3.2

0.0

1300–400

1.2

20.0

02

700–800

Rub

usbl

ackb

erry

224.1

0.0

5300–600

129.8

0.1

5550–700

27.6

0.1

6300–400

1.1

0.0

0400–500

112.4

30.1

96

500–700

Penn

iste

umgr

ass

2,7

75.8

0.6

0200–500

64.2

0.0

7600–750

31.7

0.1

9300–400

Cin

chon

aqu

inin

e61.0

0.0

7550

-800

Psid

ium

guav

a9,2

83.5

2.0

0100–300

549.0

0.6

1500–750

307.9

1.8

1250–350

172.2

0.3

6350–500

Tota

l13,3

99.3

2.8

82765.0

3.0

9379.6

2.2

3202.3

0.4

3113.6

50.2

0

101

classification of vegetation units usually occur-

ring in islands such as the Galapagos (i.e., decid-

uous type forest, and humid highlands) were

identified (Table 2). These features provided

information about key vegetation properties such

as plant vigor, productivity, and temporal pat-

terns that, as noted by other investigations (e.g.,

Geller et al., 2017), help to define vegetation

units in different mapping conditions.

Invasive-dominated units, particularly those

dominated by Psidium guava, Rubus black-

berry, and the mixed invasive forest, which

mainly invade the natural humid highlands of

the islands, were difficult to distinguish in the

satellite imagery because they exhibit similar

spectral response in the visible and NIR regions

as the recipient native communities. The two

bands of short-wave infrared (SWIR) used in

this assessment played an important role in

improving the identification and discrimination

of invasive species (see Skowronek et al., 2017

for similar examples), because these bands are

sensitive to vegetation cover and leaf moisture

content. They also had a better ability to pene-

trate canopy in relation to visible bands, which

in turn improved the discrimination of this kind

of vegetation (Chen et al., 2005; Xiao et al.,

2002). It is also important to highlight that some

invasive species colonize the understory of

native dominated dense canopies (like the case

of R. niveus). Consequently, the number of real

hectares covered by such plants is underesti-

mated by methodologies like this one, as they

mainly classify and interpret information about

the uppermost vegetation layer. R. niveus is one

of the most invasive plant species recorded in

the Galapagos, and the capacity to colonize

open and closed canopy areas might be one of

the mechanisms allowing its widespread pres-

ence on the islands it invades (Tye et al., 2008).

Despite these methodological limitations, the

present mapping technique determined R.

niveus (when dominating “the canopy” of our

vegetation units) is the only invasive species

occurring in all the invaded islands (Table 6),

supporting the fact that it is widely distributed in

areas infested by non-native plants.

Other investigations assessing the accuracy

of methods to map biodiversity or produce envi-

ronmental models highlight the importance of

incorporating experts’ opinions to streamline

outcomes such as vegetation maps (Krueger

et al., 2012; Store and Kangas, 2001). The pre-

liminary legends derived from experts, proved

to be a good initial hypothesis to test plant dis-

tributions in the Galapagos. All of the nine

native-dominated units outlined in workshops

with experienced staff working in Galapagos

were confirmed by this project’s methodology.

Three of these nine units, specifically the Decid-

uous tallgrass, Deciduous shrubland, and the

Highland deciduous tallgrass, can be considered

new descriptions for the Galapagos vegetation

classes. The Highland deciduous tallgrass has

been mentioned in the few studies assessing

vegetation zonation in Galapagos, but only to

provide brief, general descriptions of this dry

vegetation type. Thus, this unit, located mainly

on the volcano tops in Isabela, has never been

systematically mapped (Itow, 2003; Trueman

and d’Ozouville, 2010). In addition to the accu-

rate classification methodology that helped map

native-dominated vegetation in the Galapagos,

the ecological existence of each unit was also

confirmed by analyses showing distinctive bio-

tic characteristics associated with each class

(i.e., pairwise differences in genus and species

presence and abundances).

Also, it is widely recognized that attempts to

map vegetation distribution using remote sen-

sing techniques need appropriate verification

and validation (Strahler et al., 2006). According

to our experience after this mapping exercise,

and despite the relatively long distances

between islands forming the archipelago, meth-

ods such as point plots and direct observations

were important tools to perform in situ verifica-

tion and validation of the final maps and their

vegetation units. For instance, boat trips around

the coasts of virtually all the islands in the

102 Progress in Physical Geography 42(1)

Galapagos, were in our opinion, very helpful to

define and identify native vegetation distribu-

tions in the slopes of islands with some degree

of elevational gradients. In general, the vegeta-

tion on islands with mountain chains (or volca-

noes) exhibit a clear zonation pattern that change

along the altitudinal gradient (Trueman and

d’Ozouville, 2010) that can be visually identifi-

able from boats that are able to approach the

coast. Additionally, as was the case for this inves-

tigation, recent studies recognize the fundamen-

tal role of drones in the verification steps of

different mapping exercises because of their abil-

ity to survey relatively large areas with high def-

inition cameras (500 m height, 20 cm/pixel

definition) or other specialized sensors that

can capture spectral signatures useful to distin-

guish different species or vegetation changes

(Ivosevic et al., 2015; Koh and Wich, 2012).

Drones were also particularly important in this

study to obtain high resolution images that

helped to verify plant coverage in sites that were

difficult to reach by foot or to interpret from the

moderate resolution images.

The results from the present methodology

showed that of the *771,600.81 ha comprising

the GNP, around 54% is covered by native vege-

tation and around 2.2% is dominated by inva-

sive species. The vegetation units recorded in

the mountain tops, above 300 m a.s.l., on aver-

age, show more humid conditions throughout

the year because they benefit from higher water

availability resulting from air currents loaded

with moisture arriving from the lowland’s

coasts (Trueman and d’Ozouville, 2010). It is

important to highlight that humid native vegeta-

tion in the highlands occupy only *4.57% (the

sum of Evergreen forest and shrubland and

Humid tallgrass) of the total protected land. The

humid and more productive native ecosystems

in the Galapagos have experienced extensive

habitat conversion due to direct and indirect

human activities, resulting in a significant reduc-

tion of area formerly occupied by these unique

environments (Renterıa and Buddenhagen,

2006). In addition to human related activities,

climate change, and pressures like invasive spe-

cies—which according to the present analysis

are more prevalent in the highlands—represent

a direct threat to these small patches of native

vegetation in the mountain tops of the Galapa-

gos. Tropical montane forests, in particular those

located on isolated islands, are threatened by

climate change as local assemblages are

expected to be highly affected by even small

abiotic-driven shifts (Loope and Giambelluca,

1998). This study strongly suggests to prioritize

highland vegetation conservation in Galapagos,

including permanent monitoring of invasive spe-

cies expansion and native vegetation responses

to changing climatic conditions, in order to facil-

itate the survival of endemic plant populations

that exist only in these unique habitats. It is

expected that replication of the present metho-

dology in the next 2–5 years, together with an

on-going project assessing historical distribu-

tions of the resulting vegetation units in these

islands, will help to monitor colonization by

most invasive species on this archipelago and

thus, aid in important managerial and prioritiza-

tion processes implemented by the GNP.

This study generated necessary knowledge

on the vegetation distribution of an iconic island

ecosystem that can inform managerial actions.

Further, we expect the research, especially its

methodology, to result in more replicable and

low-cost information from other tropical islands

that face threats to their unique biota.

Acknowledgements

Authors want to thank all the Galapagos National

Park rangers and staff for their valuable contribution

to this investigation and logistic support in main

stages of this study. Special thanks to all the experts

and colleagues who participated in the workshops

implemented at the first stages of the present

research, and all those who contributed with signif-

icant comments of the legends before and after their

completion. This investigation was performed under

GNP permit No. PC-35-16, and according to all reg-

ulations specified by the local authority. This project

Rivas-Torres et al. 103

is a result of an official scientific and collaboration

agreement between the GNP Directorate, and GSC,

USFQ and the first author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest

with respect to the research, authorship, and/or pub-

lication of this article.

Funding

The authors disclosed receipt of the following finan-

cial support for the research, authorship, and/or pub-

lication of this article: Field work was possible due

to the Ministry of Environment of Ecuador and a

Galapagos Science Center-GSC scholarship granted

to first author and the logistic and financial support

of the Galapagos Research Cruise 2016 organized by

the GSC, Universidad San Francisco de Quito, GNP

and University of North Carolina at Chappell Hill.

ORCID iD

Gonzalo F. Rivas-Torres http://orcid.org/0000-

0002-2704-8288

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Appendix

Materials and methods

Ground truthing and second validation workshop.Baltra, Floreana, Isabela, San Cristobal,

Santa Cruz, and Pinzon islands were chosen

because virtually all of them present low-

lands and highlands and were expected to

record all of the preliminary defined units.

In total, 60 ground truthing points were

spread over these islands to perform valida-

tion of the preliminary legends obtained after

first workshop with experts. In each point,

validation surveys (different from verification

and validation ones described in Section 3 in

Methods), including 12 abiotic and biotic

predictors (as defined by MAE, 2013a) were

used to assign each point to one of the pre-

liminary legends.

After ground truthing was performed and it

was verified all the preliminary legends were

recognized to be occurring in the field (i.e.,

all validation points in all 6 islands were

systematically assigned to a preliminary

vegetation unit), a second workshop was

assembled, this time with the participation

of GNP staff that is literally walking every

remote site in this archipelago. In this final

one-day workshop, 12 GNP park rangers par-

ticipated and were asked to recognize the 15

preliminary vegetation units defined from

previously described efforts, focusing mainly

in remote areas that were not covered in the

ground truthing campaign. In this exercise,

park rangers on one hand verified the exis-

tence in the space of the proposed prelimi-

nary vegetation units; and on the other,

successfully assigned (in a directed exercise

using preliminary maps) remote areas in San-

tiago, Fernandina, and Isabela for example, to

one of the 15 preliminary legends. Having

GNP rangers and staff as participants of this

section and this project as a whole, ensures

the maps presented here and the vegetation

units they describe, to be accepted and used

for the different managerial activities this

dependency performs in this protected area.

Gaps after cloud masking. In order to fill the gaps

generated by the cloud mask, image mosaick-

ing was performed. To eliminate the radio-

metric difference between images (variability

caused by atmospheric conditions, sensor-

target illumination geometry and acquisition

dates), a radiometric correction was applied

using an Automatic Radiometric Normaliza-

tion with an iteratively re-weighted MAD

transformation (Canty and Nielsen, 2008).

Lastly for preprocessing, to enhance land use

and vegetation cover classification accuracy,

atmospheric correction and image fusion tech-

niques (Lin, 2015) were performed. Specifi-

cally, the Gram-Schmidt pansharpening

algorithm was used to integrate geometric

details of the high spatial resolution (15 m)

panchromatic image with the spectral informa-

tion of the low spatial resolution multispectral

image (Lourenco et al., 2011; Muhsin and

Mashee, 2012; Solanky et al., 2015).

Discussion

Here we present a short analysis depicting

eight maps, from the four inhabited islands

of the Galapagos (figure below), detailing the

vegetation cover obtained by TNC-CLIRSEN

2006 project (left) and this investigation

Figure 6. Hierarchical network showing differentlevels of image segmentation and the correspondingscale.

108 Progress in Physical Geography 42(1)

(right). As the main objective of the present

paper was not a direct comparison of differ-

ent methodologies, and due to the difficulty

of acquiring the actual methodology used by

TNC-CLIRSEN 2006 to generate their map,

we were not able to have a strong analysis

contrasting both resulting maps. However, we

did some visual contrasts that are shown

below. In general, we are discussing how the

definition of our maps (even when the refer-

ence scale is 1:75.000 and TNC-CLIRSEN

2006 is 1:50.000) have better resolution to

classify (see the insert for an example) the

evergreen seasonal forest and shrubland

Figure 7. Class hierarchies identified in this study.

Figure 8. Ortho-mosaics derived from drone images, used in verification and validation processes. Thisortho-mosaics allowed us to identify the invasive-dominated extensions in some accessible points anddeterminate the spectral behavior of that species in Landsat imagery.

Rivas-Torres et al. 109

(known before as transition zone). On the

other hand, it is also important to highlight

how the present investigation and the metho-

dology used here, allow to better define in

the space the deciduous forest and the shrub-

land, and the invasive-dominated units

(“invasive species”) surrounding the agricul-

tural lands (gray). Is also important to clarify

that our map is now detailing which invasive

species are covering the protected area of the

GNP and is presenting a separate map for

that purpose.

Table 7. Vegetation indexes used in this study to characterize each vegetation unit.

Index Utility Equation Source

Normalized differencevegetation index

Provides a measure of vegetationgreenness related with thephotosynthetic activity of the plant.NDVI is also sensitive for the sparsevegetation

NDVI ¼ NIR�RedNIRþRed Rouse et al. (1973)

Difference vegetationindex

Distinguish vegetation from soil DVI ¼ NIR� Red Richardson andWiegand (1977)

Normalized green–red differencevegetation index

Vegetation phenology NGRDI ¼ Green�RedGreenþRed Tucker (1979)

Normalized differencewater index

NDWI1 enhance the presence ofwater in remotely imagery.

NDWI1 ¼ Green�NIRGreenþNIR NDWI1, McFeeters

(1996)NDWI2 is sensitive to changes in liquid

water content of vegetationcanopies.

NDWI2 ¼ NIR�SWIR1NIRþSWIR1 NDWI2, Gao (1996)

110 Progress in Physical Geography 42(1)

Rivas-Torres et al. 111