RESEARCH ARTICLE

Geospatial modeling of land cover change in

the Chocó-Darien global ecoregion of South
America; One of most biodiverse and rainy
areas in the world
J. Camilo Fagua ID1,2,3☯*, R. Douglas Ramsey1☯

1 RS/GIS Laboratory, Department of Wildland Resources and the Ecology Center, Utah State University,
Logan, Utah United States of America, 2 CIAF, Instituto Geográfico Agustı́n Codazzi, Bogotá DC, Colombia,
3 School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona,
a1111111111 United States of America
a1111111111
a1111111111 ☯ These authors contributed equally to this work.
a1111111111 * Camilo.Fagua@nau.edu, camilo.fagua@aggiemail.usu.edu
a1111111111

Abstract
The tropical rain forests of northwest South America fall within the Chocó-Darien Global
OPEN ACCESS
Ecoregion (CGE). The CGE is one of 25 global biodiversity hotspots prioritized for conserva-
Citation: Fagua JC, Ramsey RD (2019) Geospatial tion due to its high biodiversity and endemism as well as threats due to deforestation. The
modeling of land cover change in the Chocó-Darien
global ecoregion of South America; One of most
analysis of land-use and land-cover (LULC) change within the CGE using remotely sensed
biodiverse and rainy areas in the world. PLoS ONE imagery is challenging because this area is considered to be one of the rainiest places on
14(2): e0211324. https://doi.org/10.1371/journal. the planet (hence high frequency of cloud cover). Furthermore, the availability of high-reso-
pone.0211324
lution remotely sensed data is low for developing countries before 2015. Using the Random
Editor: Julia A. Jones, Oregon State University, Forest ensemble learning classification tree system, we developed annual LULC maps in
UNITED STATES
the CGE from 2002 to 2015 using a time series of cloud-free MODIS vegetation index prod-
Received: May 1, 2018 ucts. The MODIS imagery was processed through a Gaussian weighted filter to further cor-
Accepted: January 11, 2019 rect for cloud pollution and matched to visual interpretations of land cover and land use from
Published: February 1, 2019
available high spatial resolution imagery (WorldView-2, Quick Bird, Ikonos and GeoEye-1).
Validation of LULC maps resulted in a Kappa of 0.87 (Sd = 0.008). We detected a gradual
Copyright: © 2019 Fagua, Ramsey. This is an open
access article distributed under the terms of the
replacement of forested areas with agriculture (mainly grassland planted to support live-
Creative Commons Attribution License, which stock grazing), and secondary vegetation (agriculture reverting to forest) across the CGE.
permits unrestricted use, distribution, and Forest loss was higher between 2010–2015 when compared to 2002–2010. LULC change
reproduction in any medium, provided the original
trends, deforestation drivers, and reforestation transitions varied according to administrative
author and source are credited.
organization (countries: Panamanian CGE, Colombian CGE, and Ecuadorian CGE).
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.

Funding: We acknowledged COLCIENCIAS-

Colombia (529-2011) and Fulbright-US to support
this research, Ecology Center and Remote Sensing/ Introduction
GIS lab of Utah State University-US to provide
additional financial and technical support,
Land-use and land-cover (LULC) change brought about by human development are con-
DigitalGlobe Foundation to provide the high spatial stantly reshaping natural regions at local, national to global scales [1–4]. Evaluating these land-
resolution imagery, IDEAM-Colombia and IGAC- scape level changes annually within regions where the natural condition is composed of

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 Land cover change in the Chocó-Darien global ecoregion of South America

Colombia to provide data and technical support, tropical rain forests is difficult due to the high amounts of cloud cover obscuring remote sens-
and IDEA WILD to provide equipment. ing instruments. Globally, these regions are suffering significant LULC change [2,5,6] causing
Competing interests: The authors have declared much concern due to its potential effect on climatic change, biodiversity loss, hydrologic alter-
that no competing interests exist. ation, soil degradation, and loss of ecosystem services [3,7]. Some national and global estimates
have found that deforestation due to LULC change was significantly higher than reforestation
in Central and South America [2,6,8,9]. Conversely, other LULC change studies in the same
region show a reforestation trend during similar time periods [5,10]. Although the methodolo-
gies were different, these contradictory results suggest that LULC change could be highly het-
erogeneous in time and space in the tropical rain forest domain. It also shows that consistent
and accurate information about the LULC dynamic is critical for the management and protec-
tion of these forests.
Tropical rain forests are currently the most biodiverse landscapes on our planet [11–13].
In South America, tropical rain forests form three well define natural regions; the Amazon
Basin, the Brazilian Atlantic Forest, and the Chocó-Darien Global Ecoregion (CGE; also
known as the Chocó Biogeographic Region) (Fig 1A). The CGE is a lowland area located
along the pacific coast of eastern Panamá, western Colombia, and northwestern Ecuador

Fig 1. The Chocó-Darien Global Ecoregion (CGE): (a) estimated historical extent of Tropical Rain Forest (TRF) in South America: TRF-CGE (estimated TRF in CGE),
TRF-Amz (estimated TRF in Amazon basin), and TRF-BrAt (estimated TRF in Brazilian Atlantic Forest). (b) The Chocó-Darien Global Ecoregion (CGE): This global
ecoregion is formed by three smaller ecoregions: Magdalena-Urabá Moist forests (MgU), Chocó-Darién Moist Forests (ChD), and Western Ecuador Moist Forests
(WEc). Other four ecoregions have small sections in the CGE: Eastern Panamanian Montane Forests (EPaM), South American Pacific (SAP) Mangrove, Amazon-
Orinoco-Southern Caribbean (AOSC) Mangrove, and Mesoamerican Gulf-Caribbean (MGC) Mangrove.
https://doi.org/10.1371/journal.pone.0211324.g001

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 Land cover change in the Chocó-Darien global ecoregion of South America

and has been declared as one of the top 25 global hotspots for conservation priorities (Fig
1B) [4,12–14]. Historically, most of the effort to estimate forest cover and the LULC dynamic
have been focused on the Amazon Basin, the largest tropical rain forests in the world [1,15–
21]. Likewise, LULC dynamic in the Brazilian Atlantic Forest has been well studied [16,22–
24]. Despite the fact that the CGE is recognized as one of the world’s most biologically
diverse regions [4,25], it has not received the same level of study relative to its LULC
dynamic. The countries that share the CGE and their research organizations have conducted
studies of the CGE within their own boundaries [8,10,26], but these studies have used differ-
ent methodologies and/or sensors, and do not allow for valid comparisons to evaluate the
region as a whole. Furthermore, regional and global studies of LULC change have been done
for large areas that include the CGE [3,5,27,28]. However, these analyses have used general
LULC categories, restricting the identification of deforestation drivers occurring in the CGE
and giving only a general idea about the region’s LULC dynamics. A study of LULC dynam-
ics focused specifically on the CGE is a fundamental need to guide proper management and
conservation for this area.
Past LULC change studies in neotropical rain forest regions have focused on the gain and/
or loss of forest cover [2,5,8,10,26]. Local studies within the Amazon Basin and Brazilian
Atlantic Forest ecoregions have accurately identified direct drivers of deforestation and their
temporal and spatial variation [18,29–31]. However, within the CGE, much less is known
about the direct drivers of deforestation. The United Nations Framework Convention on Cli-
mate Change negotiations has encouraged developing countries to spatially map direct drivers
of deforestation [32,33]. While some studies have shown reforestation trends due to apparent
abandonment of agriculture lands, this reforestation process has only been slightly studied in
the Colombian CGE [34].
An analysis of LULC change across the CGE is a challenge when remote sensing imagery is
used because this area is considered one of the rainiest on the planet with an annual average
rainfall between 8000 to 13000 mm [35]. Furthermore, the availability of high spatial resolu-
tion remote sensing data before 2015 is low for Panamá, Colombia and Ecuador. Conse-
quently, the satellite images that are available (Landsat, Satellite Pour l’Observation de la Terre
(SPOT), and RapidEye, for example) usually have a high percentage of cloud cover, making it
difficult for the development of regional land cover maps.
Nevertheless, over the past decade, methodologies using MODIS (MODerate resolution
Imaging Spectroradiometer) data have generated standard periodic cloud-free products aimed
at monitoring vegetation across the globe [36,37]. Merging these MODIS products with avail-
able high spatial resolution (e.g. WorldView, Ikonos, QuickBird, GeoEye) imagery, used as a
reference data source, with learning algorithms (e.g. Random Forest) offers potential for study-
ing region-wide LULC in areas like the CGE. These standardized remote sensing products can
also support biodiversity monitoring by coupling with essential biodiversity variables (vari-
ables that quantify biodiversity changes over time and across space), opening new possibilities
for conservation efforts in tropical areas similar to the CGE [38–40].We have applied a combi-
nation of methodologies developed by various authors to multi-temporal MODIS imagery to
generate yearly LULC maps across the CGE from 2002 to 2015. Our aim was to analyze LULC
temporal dynamics across this ecoregion and address the following objectives: 1) Evaluate
LULC change trends in the CGE and determine its heterogeneity in time and space. 2) Spa-
tially identify deforestation drivers, reforestation transitions (cover types that represent sec-
ondary forest-like vegetation), and quantify their change in time and space. We discuss the
types of information that are useful for conservation of biodiversity in the CGE relative to its
administrative organization (countries).

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 Land cover change in the Chocó-Darien global ecoregion of South America

Materials and methods

Study area
The Chocó-Darien Global Ecoregion (CGE) is a lowland area located along the Pacific coast
from Panamá, through Colombia, and into northwestern Ecuador and includes the lowland of
the Magdalena river valley (Fig 1B) [4,13,14]. The CGE became separated from the Amazon
tropical rain forest by the uplift of the Andes beginning around 25 million years ago. As a con-
sequence, groups of endemic species emerged producing a significant impulse of diversifica-
tion. Another substantial array of new species arose because of the relatively recent formation
of the Isthmus of Panamá (three million years ago), an geological event that formed a land
bridge for plants and animals from North and South America [13,41]. Presently, it is estimated
that the CGE has about 11,000 species of vascular plants (2,250 endemics), 900 species of birds,
350 species of amphibians (210 endemics), and 210 species of reptiles (63 endemic) [12,14]. It
was estimated in the year 2000 that the remaining tropical rain forest within the CGE covers
approximately 24% of its original distribution [12]. Due to this level of deforestation and the
high number of endemic species, the CGE was declared as one of the 25 global hotspots for
conservation priorities [4]. According to the World Wildlife Fund (2016), the CGE is formed
by four smaller terrestrial ecoregions: Chocó-Darién Moist Forests (73028.6 km2), Magdalena-
Urabá Moist Forests (76396 km2), Western Ecuador Moist Forests (33861.1 km2), and smaller
sections of Eastern Panamanian Montane Forests (2632.4 km2). Also, sections of three man-
grove ecoregions are found along the CGE coast: South American Pacific Mangrove (6252.4
km2), Amazon-Orinoco-Southern Caribbean Mangrove (702.9 km2), and a small area of
Mesoamerican Gulf-Caribbean Mangrove (50.2 km2) (Fig 1B). We did not include mangrove
ecoregions in our LULC change analysis because they are small areas of marine wetlands (3%
of the CGE) and our study was focused on terrestrial tropical rain forest.

LULC maps
We generated a temporal set of LULC maps based on a Random Forest [42] classification in
which we modeled a categorical response variable that identified eight LULC classes. Random
Forest is an ensemble learning algorithm that constructs multiple classification trees (e.g. 500
individual trees) by bootstrapping samples from an input data set, and combines the predic-
tions from all the trees to identify a modal response [43]. Random Forest is one of the most
robust statistically-based classification techniques and presents two main advantages for our
analysis; it has low sensitivity to the overfit produced by collinearity among predictors [44]
and allows for use of different types of response and predictor variables (e.g. numerical, binary,
categorical) in the classification process [45].
The mapping of these LULC classes was accomplished by training MODIS cloud-free tem-
poral image mosaics using 22 sampling sub-regions covering 20,708.6 km2 of total land area
within the CGE. These 22 sampling sub-regions corresponded with locations of available high-
resolution imagery. The cloud-free MODIS vegetation index products MOD13Q1.V006 (tiles
h10v07, h10v08, h10v09, and h09v09) were downloaded from the NASA Distributed Active
Archive Center and processed to transform the standard sinusoidal projection to WGS84 geo-
graphic coordinate system. This transformation resulted in a calculated pixel size of 231.3 m2.

Response variable (land-cover/land-use classes)

Training samples for each LULC class were collected by visually interpreting four types of high
spatial resolution images: WorldView-2, Ikonos, Quick Bird, and GeoEye-1. To improve visual
interpretation of LULC classes, the multispectral bands from these sensors were fused to their

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 Land cover change in the Chocó-Darien global ecoregion of South America

corresponding panchromatic band (S1 Table). We reviewed previous regional LULC studies
within the CGE to help define our LULC classes [8,26,28,34]. From these studies, we estab-
lished eight general LULC classes.
1) Woody vegetation: This type of vegetation included tropical rain forest with trees taller
than 30 m, secondary vegetation (shrubs and smaller trees) as well as mosaics of both. This is
the primary natural cover type that occurs within the CGE [13,46,47]. Initially, forest and sec-
ondary vegetation were established as two different LULC classes; however, the Random For-
est classification could not adequately separate them. Likewise, we created a mixed woody
class (pixels with 20%–80% of woody and the rest the pixel cover by agricultural land), but the
Random Forest classification could not separate this cover type either. Consequently, after
doing a Fuzzy accuracy analysis [48] of a preliminary classification, forest and shrub were
merged into a woody vegetation class. 2) Wetland: The CGE has a complex of river basins with
swamps and shallow lakes ("ciénagas") covering large areas along the rivers. Wetland areas
were absent in previous LULC work performed within portions of the CGE [5,8,10,49] and as
a result have been markedly underestimated in global maps [2,28]. 3) Grassland: Introduced
grass species which are used primarily for cattle grazing [50]. Within the CGE, large areas of
native grasses do not occur as natural vegetation [46,47]. 4) Crops: Agriculture consisting of
annual or semiannual crops (corn, sugar canna, plantain, mainly). 5) Palm plantations: Exten-
sive areas of the CGE have been cultivated with African palm (Elaeis Guineensis Jacq) [51].
These palms take about three years to mature and produce oil. The useful life of a palm planta-
tion is about 25 years at which point plantations are replanted with younger palms [52,53]. In
terms of remote sensing, this relatively stable structure of palm plantations allowed its identifi-
cation as a LULC class using our imagery resources. 6) Settlements and infrastructure. 7) Con-
tinental water including rivers and lakes. 8) Bare areas: This class was not taken into account
in the final analysis due to its low representation.
The 192,924 km2 of land corresponding to the CGE was divided into square sample areas of
231.3 m x 231.3 m to match the MODIS pixel size. Based on this grid, a stratified sampling was
applied to the area intersecting the aforementioned high spatial resolution images as follows:
we visually identified sample squares with 100% of any of the eight LULC categories. We then
superimposed a second grid of 1 km2 as spatial filter to select one square of 231.3 m2 for every
1 km2 square. This spatial filter ensured that sample sites were separated by 693 m or more. By
doing this, we identified 18,559 sample sites classified as one of the eight LULC classes. To esti-
mate the error rate for the visual interpretation, we compared our visual interpretation with
the visual interpretations of the Corine Land cover project for Colombia [54], which used
many resources (high spatial resolution imagery, aerial photos, Landsat, and field visits) to
reach the best possible visual interpretation of land cover. We coupled 375 of our MODIS sam-
pling sites to the corresponding interpretations from the Corine Land Cover effort for the
years 2002, 2003, and 2007. The agreement between both interpretations resulted in a kappa of
0.93 (Accuracy = 0.95), showing a high level of consistency between both interpretations.

Predictor variables
Five MODIS-based predictor variables were generated from the MOD13Q1 product (16-Day
L3 Global 250 m Vegetation Indices). The MOD13Q1 product provides the highest quality
pixels from 16 daily images for four spectral bands: blue (459 nm -479 nm), red (620 nm –670
nm), near infrared (NIR: 841 nm– 876 nm), and mid-infrared (MIR: 2105 nm –2155 nm); as
well as two indices: Enhanced Vegetation Index (EVI) and the Normalized Difference Vegeta-
tion Index (NDVI). EVI reduces atmospheric influences on vegetation detection and improves
identification of vegetation with dense canopies, such as tropical forest, where NDVI tended to

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 Land cover change in the Chocó-Darien global ecoregion of South America

saturate [55]. However, we used both EVI and NDVI as predictors because NDVI has been
equivalent or better than EVI detecting vegetation covers with low biomass and canopies, such
as grassland, shrub, crop, and subtropical deciduous forests [56–59]. The MOD13Q1 product
also provides layers that estimate vegetation index quality, sensor view zenith, solar zenith
angles, individual pixel Julian day, and a pixel reliability ranking. For our analysis we did not
use the blue spectral band due to its lower spatial resolution, 462.7 m2. The yearly collection of
MOD13Q1 data consists of 23 temporally sequential periods (365 days and 16 days per period)
for every year from 2001 to the present. We utilized the entire range of data from 2001 through
the end of 2015, for a total of 345 individual measurements of red, NIR, MIR, NDVI and EVI
for every 231.3 meter pixel in the CGE. Although the MOD13Q1 product attempts to evaluate
pixel quality as a function of radiometric and atmospheric conditions (cloud interference),
these data can still contain anomalies that are caused by factors not relevant to the amount of
photosynthetically active surface cover, namely atmospheric conditions. To account for these
anomalies and therefore the uncertainty within the vegetation index products, we applied a
Gaussian weighted filter to the 23 temporal periods for each year and for each of the five spec-
tral variables. This filter reduced the variation of the MODIS bands and indices and replaced
outlier values with estimates calculated by the Gaussian weighted series (Fig 2). We used the
output of the Gaussian weighted filter to estimate an annual mean for each band and index,
and we used these means as predictor variables [37,60]. This analysis was performed using
TerrSet Geospatial Monitoring and Modeling Software from Clark Labs [61], with each year
from 2001 to 2015 representing a time series cycle (a total of 15 time series cycles) with a tem-
poral filter length or “window” of 5. In addition to the MODIS-based predictor variables, we
included the SRTM90 (NASA Shuttle Radar Topographic Mission) elevation data and its cor-
responding slope values as ancillary data in support of the image classification process [62,63].
Elevation and slope have been found to affect the type of land cover that occurs in a specific
area; forest tends to be preserved in places with higher altitude and slope (due to a more diffi-
cult access) while crops and palm plantations occur in places with low slope [52,64]. As well,
the wetlands in the CGE are located in areas with an altitude near or under the sea level
[47,65]. Additionally, the elevation data of SRTM90 could be affected by densely vegetated
areas [66–69] and wetlands [70]. SRTM has a spatial resolution of 90 m2 and was, therefore,
resampled to 231.35 m2 to match the MOD13Q1 pixels using a bilinear interpolation. From
this resampled digital elevation model, SRTM elevation and topographic slope were extracted
for each training side.

Random forest classification

A total of 18,559 training sites located within the 22 sampling areas were classified visually into
our eight LULC classes using high resolution imagery. The training site database, therefore,
contained the interpreted LULC class as well as the predictor variables of temporal spectral
and vegetation index values (NDVI and EVI) for each year along with SRTM values and topo-
graphic slope where columns represented the response and predictor variables and rows con-
sisted of the 18,559 observations (S2 Table). The Random Forest algorithm operates by
constructing a large number of decision trees from random subsets of predictor variables and
the resulting classification consists of the modal response of all trees for a particular outcome
[45,71]. We used the R statistical packages ‘‘randomForest’ [43] and “ModelMap” [71] to gen-
erate our yearly LULC maps (the R code to build a map is available in S1 Codes). The random-
Forest utility in R generated a default number of trees (500), using a 80% subset of the samples
(training subset) for every bootstrap iteration and the square root of the number of predictors
as the number of predictors used to identify a split at each node. This RF model can provide

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 Land cover change in the Chocó-Darien global ecoregion of South America

Fig 2. An example of the time series filtering procedure from 2009 to 2013 using the Gaussian weighted filter (GWF). GWF
improved the identification of land cover using the MODIS bands and indices. (a) A time series EVI pixel of woody vegetation
before and after filtering; outliers are replaced with estimates calculated by the Gaussian weighted filter. (b) Temporal variation of
120 pixels corresponding to woody vegetation before filtering and (c) the same 120 pixels after filtering; the variance of these
120-time series is reduced. (d) Post filtering results–simplified to means, for 120 woody vegetation pixels, 116 grassland pixels, 542
palm plantation pixels, 99 settlement pixels, 101 water pixels, and 454 wetland pixels. GWF increased the differentiation among
these land covers. The GWFs were applied using the Terrset software [61] with a temporal filter length of 5.
https://doi.org/10.1371/journal.pone.0211324.g002

accuracy estimates using OOB (or Out-of-Bag, a first independent subset from the training
data) [45] or using a second independent group formed by the 20% of samples that were not
used as training subset (testing subset). We reported the kappa from the second independent
group to reduce a possible overestimation in the accuracy [72]. Accuracy estimates included
Kappa (K), which was categorized into the following ranges of agreement: poor K < 0.4, good
K 0.4 < K< 0.75, excellent K > 0.7575 [73], as well as percent omission and commission errors
for each LULC class.
A Random Forest classification is accomplished using available training data and therefore
is subject to training data distribution amongst the different response classes. The dominant
land cover category in our study area consisted of the woody vegetation class. Of our 18,559
sample sites, 14,228 samples (76%) consisted of the woody vegetation class. In order to detect
the potential impact of this large sample size relative to other land cover categories on the accu-
racy of minority classes, we randomly reduced the samples representing the woody vegetation
class from 14,228 to 1,144 to match the sample size of the second most prevalent class, Grass-
land [74] (S3 Table). Random Forest classifications were run on both sample sets and the vali-
dation results (using a second independent group) showed that K for the original data was
0.872 and K for the reduced samples of woody vegetation class was 0.876, commission and
omission errors were similar for both sample distributions (S4 and S5 Tables). Consequently,
we decided to use the original sample set of 18,559 training samples. Using this methodology,
we developed a RF-based LULC classification for each of the 15 years using SRTM values,
topographic slope, combined with MOD13Q1 MODIS data for each year as predictors for that
year. We used the R package ‘ModelMap’ which uses the ‘RGDAL’ libraries to generate LULC
maps using the RF model outputs. For all of the 15 individual years, our LULC maps reached a
high accuracy of K = 0.872, with a standard deviation of 0.008.

Woody vegetation split

As we describe before, woody vegetation could not be separated into forest and shrub (second-
ary vegetation) classes. These two LULC classes had similar spectral and NDVI—EVI signa-
tures provided by the MODIS data and consequently, Random Forest classification could not
separate them. This spectral similarity is common in tropical rain forests when using other
multi-spectral sensors such as Landsat [75]. For that reason, a final refinement after the RF
classifications was applied to the annual LULC maps generated from the annual sequence of
MODIS imagery. Pixels classified as woody vegetation were converted to forest when that
pixel was classified as woody vegetation for every year of our sequence (2001–2015). On the
other hand, if a pixel was classified as woody vegetation on the last year of our sequence
(2015), but in previous years that pixel was classified as another type such as grassland, crop,
or palm plantation, it was recoded as secondary vegetation. A similar methodology had been
applied previously in Amazonian forests [1]; this seemed like a logical method of splitting the
woody vegetation class into forest and secondary vegetation since the year-to-year accuracy of
each LULC map was high and the time required for succession to reach the canopy structure
of a mature forest in other neotropical rain forest ranges between 190–217 years [76,77].

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 Land cover change in the Chocó-Darien global ecoregion of South America

Likewise, shrubby vegetation typically takes over 20 years in TRF areas to develop arborescent
structures [78–80]. In other words, it is improbable that forests converted to a farm-like land-
use will reach a forest-like stage in 15 years or less. Consequently, these pixels were considered
secondary vegetation (landscapes converting from a farm-based land-use to natural vegeta-
tion). Based on this logic, we developed 14 final LULC maps (2002 to 2015) that included eight
LULC classes: forest, secondary vegetation, wetland, grassland, crops, palm plantations, settle-
ment, and continental water. The time series maps started in 2002 due to our methodology for
splitting woody vegetation needs an initial sequence of annual maps, 2001–2002. To test accu-
racy of the secondary vegetation class, 191 pixels mapped as secondary vegetation were ran-
domly selected and independently classified using visual interpretation of the available high
resolution images. The accuracy of our secondary vegetation classification averaged 84.2%
with a standard deviation across the years of 10.4.

Analysis of LULC change

LULC change trends. To determine LULC change trends, we estimated non-parametric
Pearson correlations between the area occupied for every LULC class and the corresponding
year in the annual sequence. A significant positive Pearson’s correlation coefficient indicates a
significant increase in the trend of that specific LULC class while a negative correlation coeffi-
cient indicates a significant reduction in area as years progress. This methodology has been
applied previously to other LULC analyses [10,81]. Pearson correlations were estimated at two
spatial levels: 1) the entire CGE, and 2) the areas of the CGE corresponding to the countries
that share this global ecoregion (Panamá, Colombia and Ecuador). Pearson correlations also
were calculated in two time periods for each of the two spatial levels: from 2002 to 2010 and
from 2010 to 2015. We chose these two time periods because we found that woody vegetation,
forest, and secondary vegetation trends (main objectives of our analysis) significantly changed
around 2010. Additionally, other studies that include the CGE analyzed LULC change from
2001 to 2010 [5,8–10]: therefore, an analysis of temporal change between 2002–2010 provided
an opportunity to compare our results with other studies.
Drivers of deforestation and farm conversion to secondary vegetation. To quantify the
direct drivers of deforestation (direct drivers refer to the land cover that replaces forest cover)
[82,83], we identified the following transitions. (1) Deforestation due to cattle grazing opera-
tions as indicated by areas of forest or secondary vegetation replaced by grassland. (2) Defores-
tation by annual or semiannual crops as indicated by areas of forest and secondary vegetation
replaced by crops. (3) Deforestation by extensive palm plantations as indicated by areas of for-
est and secondary vegetation replaced by palm. (4) Deforestation by infrastructure and urban
expansion as indicated by areas of forest and secondary vegetation replaced by human devel-
opment. Conversely, we quantified the conversion from every farming land-use (grassland,
crop, and palm plantations) to secondary vegetation as reforestation transitions. These defor-
estation and reforestation transitions were estimated for two temporal sequences 2002–2010
and 2010 2015 (using the first year of every sequence as the base year).

Results
In 2002, 63.9% of the CGE (120,246 km2) was classified as woody vegetation (forest and sec-
ondary vegetation combined). In 2010, woody vegetation increased to 68.5% (128,801.8 km2),
and in 2015, 65.5% (123,320.6 km2). In other words, woody vegetation increased 4.6% between
2002–2010 and reduced 3% between 2010–2015. For woody vegetation, 90.4% was identified
as forest in 2002, 72.1% in 2010 and 67.6% in 2015. LULC trends for the entire CGE shows
that secondary vegetation increased significantly from 2002 to 2010 (R = 0.94, p< 0.01)

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 Land cover change in the Chocó-Darien global ecoregion of South America

whereas forest (R = -0.96, p< 0.001) and agriculture (R = -0.64, p< 0.05 for grassland; R =
-0.64, p<0.06 for crop; R = -0.89, p< 0.02 for palm) decreased showing a progressive replace-
ment of forest and agriculture with secondary vegetation (Fig 3). Some of these trends changed
between 2010 to 2015; woody vegetation declined but not significantly, forest maintained its
decreasing trend (R = -0.98, p< 0.001), and grassland increased (R = 0.85, p< 0.02) while the
other agricultural land use trends did not show significant trends (Fig 3). These results show
that deforestation transitions (changes from forest or secondary vegetation to farm covers)
was higher between 2010–2015 than 2002–2010 and indicate that grassland was the main land
cover that replaced woody vegetation (forest and secondary vegetation) between 2010–2015.
When LULC trends are compared between political divisions during 2002–2010, we found
that woody vegetation increased in the Colombian and Ecuadorian CGE during 2002–2010
(R = 0.78, p< 0.01; R = 0.64, p< 0.05) but did not show a significant trend in the Panamanian
CGE. Secondary vegetation increased significantly in every national territory (R = 0.95, p<
0.01 for Panamá; R = 0.91 p< 0.01 for Colombia, and R = 0.85, p< 0.01 for Ecuador) while for-
est decreased (R = -0.89, p< 0.01 for Panamá; R = -0.95 p< 0.001 for Colombia, and R = -0.94,
p< 0.01 for Ecuador). Grassland decreased in the Colombian CGE (R = -0.68 p< 0.04) but it
did not show a significantly trend in Panamá and Ecuador. Crops decreased in the Ecuadorian
CGE (R = -0.63 p< 0.05) and palm plantation decreased in the Colombian CGE (R = -0.86 p<
0.01). Between 2010–2015, some of the previous trends changed. Woody vegetation decreased
significantly in Panamanian and Colombian CGE (R = -0.94, p< 0.01; R = -0.89, p< 0.02), for-
est maintained its decreasing trend in all three countries (R = -0.85, p< 0.02 for Panamá; R =
-0.91 p< 0.01 for Colombia, and R = -0.96 p< 0.02 for Ecuador), grassland increased in Pan-
amá and Colombia (R = 0.86, p< 0.03 for Panamá; R = 0.89 p< 0.02 for Colombia, and
R = 0.63 p< 0.03 for Ecuador), and crops tended to decrease non significantly in the three
countries (Table 1).
The analysis of LULC transition showed that grassland was the most frequent deforestation
driver between 2002 to 2010 for the entire CGE (63%) and for each country (73% in Panamá,
65% in Colombia, and 58% in Ecuador) (Fig 4A; S6 Table). Grassland was also the most fre-
quent land cover that change to secondary forest (reforestation) across the entire CGE (50%),
in Panamá (65%), and Colombia (58%), but it was different in Ecuador where crops were the
most frequent type to convert to secondary vegetation (55%) (Fig 4B; S6 Table). Subsequently,
from 2010 to 2015, LULC transitions also showed that grassland was most frequent deforesta-
tion driver across the CGE (73%) as well as in every country (94% in Panamá, 76% in Colom-
bia, and 59% in Ecuador) (Fig 5A; S7 Table). Grassland was also the most frequent land cover
that converted to secondary vegetation during 2010–2015 for the CGE (47%) and in two coun-
tries (68% to Panamá and 53% to Colombia). In Ecuador, crops to secondary vegetation was
the highest reforestation transition (55%) again (Fig 5B, S7 Table). The net deforestation was
almost two times higher during 2010–2015 (15,145 km2) than 2002–2010 (7,228 km2) in the
CGE; this pattern was similar in every country (Figs 4A and 5A). Conversely, net reforestation
was higher between 2002–2010 (17783 km2) than 2010–2015 (9120 km2) in the CGE. As well,
reforestation tended to be higher in every country during 2002–2010 compared to 2010–2015
(Figs 4B and 5B).

Discussion
Heterogeneity of LULC temporal dynamics
LULC change trends have been temporally heterogeneous across the CGE. We identified an
overall increase in woody vegetation driven mainly by an increase in secondary vegetation
between 2002–2010, this increase, however, ceased between 2010–2015. Conversely, grassland

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 Land cover change in the Chocó-Darien global ecoregion of South America

Fig 3. Land-use and land-cover (LULC) change trends in The Chocó-Darien Global Ecoregion (CGE). Significant correlation coefficients (R) are shown for the
two temporal periods 2002–2010 and 2010–2015 (a). Significant P range values; P<0.001(��� ), P<0.01(�� ), and P<0.05(� ). LULC maps for 2002, 2010 and 2015 are
showed (b, c, d).
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 Land cover change in the Chocó-Darien global ecoregion of South America

Table 1. Correlations of land-use and land-cover (LULC) change trends among administrative divisions; The Chocó-Darien Global Ecoregion (CGE), Colombian
CGE (Col CGE), Ecuadorian CGE (Ecu CGE), and Panamanian CGE (Pan CGE). Pearson’s correlation coefficient (R) are shown for two time periods 2002–2010 and
2010–2015. Significant codes: 0 ‘��� ’ 0.001 ‘�� ’ 0.01 ‘� ’ 0.05.
Region Time Woody Veg. R(p) Forest R(p) Second. Veg. R Grass-land R(p) Crops R(p) Palm-Plan R(p) Wet-land R(p)
(p)
CGE 2002–2010 0.81 (0.02)� -0.69 -0.96 (0.001)��� -0.98 0.94 (0.01)�� -0.64 (0.05)� 0.85 -0.65 (0.06) -0.75 -0.89 (0.02)� 0.89 0.51 (0.16) -0.56
2010–2015 (0.13) (0.001)��� 0.17 (0.75) (0.03)� (0.07) (0.07) (0.25)
Col 2002–2010 0.78 (0.01)�� -0.95 (0.001)��� -0.91 0.91 (0.01)�� -0.68 (0.04)� -0.33.(0.39) -0.86.(0.01)�� 0.08 (0.86) -0.54.
CGE 2010–2015 -0.89(0.02)� (0.01)�� -0.14 (0.8) 0.89.(0.02)� -0.57.(0.24) -0.01.(0.99) (0.27)
Ecu 2002–2010 0.64 (0.05)� -0.11 -0.94 (0.01)� � -0.96 0.85 (0.01)�� 0.14 (0.74) 0.03 -0.63 (0.05)� -0.56 (0.15) 0.31 0.96 (0.01)��
CGE 2010–2015 (0.84) (0.02)� 0.22 (0.68) (0.63) -0.72 (0.11) (0.54) -0.14 (0.8)
Pan 2002–2010 -0.57 (0.11) -0.94 -0.89 (0.01)�� -0.85 0.95 (0.01)�� 0.39 (0.3) 0.86 0.06 (0.88) -0.78 0.1 (0.8) 0.36 0.36 (0.34) 0.75
CGE 2010–2015 (0.01)�� (0.02)� 0.73 (0.1) (0.03)� (0.07) (0.22) (0.09)
https://doi.org/10.1371/journal.pone.0211324.t001

Fig 4. Quantification of deforestation (deforestation drivers) and reforestation transitions from 2002 to 2010. (a)
Percentage of deforested area and net area deforested by every deforestation driver, and (b) percentage of reforested
areas and net area reforested by every reforestation transition. The Chocó-Darien Global Ecoregion (CGE), Colombian
CGE (Col CGE), Ecuadorian CGE (Ecu CGE), and Panamanian CGE (Pan CGE).
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 Land cover change in the Chocó-Darien global ecoregion of South America

Fig 5. Quantification of deforestation (deforestation drivers) and reforestation transitions from 2010 to 2015. (a) Percentage of deforested area and net area
deforested by every deforestation driver, and (b) percentage of reforested areas and net area reforested by every reforestation transition. The Chocó-Darien Global
Ecoregion (CGE), Colombian CGE (Col CGE), Ecuadorian CGE (Ecu CGE), and Panamanian CGE (Pan CGE).
https://doi.org/10.1371/journal.pone.0211324.g005

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 Land cover change in the Chocó-Darien global ecoregion of South America

showed an overall decrease between 2002–2010 and an overall increase between 2010–2015.
These trend shifts were similar between the Colombian and Ecuadorian portions (92% of CGE
land) and suggest that external drivers could affected LULC change across the CGE. During
the first decade of this century (2000–2010), the Colombian and Ecuadorian agricultural sec-
tors declined, thus reducing cultivated (grassland, crops, palm) area and allowing for the
growth of secondary vegetation. The Colombian agriculture sector decreased 1.1% during this
period [84–86] while the Ecuadorian agricultural sector decreased by 1.8%. This was remark-
able in Ecuador because its agricultural sector had grown by 6.1% between 1990–2000 [87].
Increases in secondary vegetation were also found in several developing countries within Latin
America during the first ten years of the present century [5]. Some scholars have claimed that
the globalization of markets negatively impacted the agriculture sectors of these countries
resulting in abandonment of farm land and eventual reforestation [88,89]. Subsequently, from
2010 to 2015, Colombia and Ecuador showed a remarkable acceleration in their economic
growth due to the global increase in the price of mining products (specially, oil, coal, energy,
and gold). This acceleration could have a positive impact on all sectors of their economies
(improving transportation routes, infrastructure in general, market for farming products)
intensifying the use of farming areas. In Colombia, gross domestic agricultural product grew
from negative values in 2009 to 5.5% in 2014 [90] and two important routes that cross large
areas of the Colombian CGE were built (the route Tumaco- Tuquerres in Nariño department
and the route Virginia-Quibdó in Risaralda and Choco departments). These routes correspond
to some of the deforestation that we identified in our maps. In Ecuador, gross domestic agri-
cultural product grew 6% from 2009 to 2013 [91]. This increase in agricultural production
should have had a negative effect on the regeneration of secondary vegetation thus increasing
deforestation as our results indicate. Some authors have claimed that reforestation in the
Colombian CGE territory during 2002–2010 occurred principally due to land abandonment
caused by internal armed conflicts in Colombia [34]. However, we found the same pattern in
Ecuador during the same period (2002–2010), a country with no armed conflict. The regrowth
of secondary vegetation across farming areas was proportionally higher in the Ecuadorian
CGE compared to the Colombian CGE. Additionally, we found that reforestation has
decreased significantly between 2010–2015 in the Colombian CGE while the armed conflict
was still occurring and this area had a strong presence of the two main guerrilla groups in
Colombia. This evidence suggests that economic growth could have a greater influence on the
balance of deforestation and reforestation compared to local phenomenon such as armed con-
flicts. The Panamanian economy is not based on agriculture (main sectors in Panamá are
transportation, communication, market, services and banking) [92]. This could explain the flat
trend for woody vegetation in the Panamanian CGE through 2002–2010; however, reductions
in woody vegetation, secondary vegetation and forest also occurred in the Panamanian CGE
during 2010–2015 indicating increased human land use driven by economic growth during
this time period. Panamá had the highest economic growth in Latin America between 2000–
2013 (7.2% on average) [92,93]. Only forest had an overall consistent temporal trend cross the
CGE, and tended to decline during both time periods across the three countries. Our split of
woody vegetation into secondary vegetation and forest allowed us identify this progressive
replacement of well-preserved forest primarily by grassland and secondary vegetation. Forest
reduction has been documented in the Colombian CGE [8] and in the Ecuadorian CGE [26]
between 2001–2010 using Landsat data and discriminations between forest and secondary
vegetation.
Agricultural expansion was the most frequent deforestation driver during both time periods
across the CGE; 98% of deforestation due to agricultural conversion and 1% by the establish-
ment of settlement and infrastructure. Our results agree with other reports showing

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 Land cover change in the Chocó-Darien global ecoregion of South America

agricultural expansion as the main deforestation driver in the tropics [94,95]. In addition, we
analyzed sub-categories of agricultural deforestation drivers (grassland, crops, and palm plan-
tations) and found that grassland conversion was the main cause of deforestation across the
CGE during both time periods. Extensive cattle grazing is a main agricultural activity for the
areas corresponding to Magdalena-Urabá Ecoregion (46% of the CGE land and this entire
sub-ecoregion is in Colombia) and to Western Ecuador Ecoregion (17% of the CGE land and
the entire sub-ecoregion is in Ecuador) (Fig 1B) [96–99]. Other causes that explain the gradual
replacement of forest by grassland and secondary vegetation cross the CGE during both time
periods (2002–2010 and 2010–15) are the colonial process and the land possession policies of
Colombia, Ecuador and Panamá. Basically, colonists are required to prove they are using land
in order to become landowners. The cheapest and fastest method to prove land use is to con-
vert forest to grassland. However, many of these deforested areas are underutilized and they
consequently revert to secondary vegetation. Evidence supporting this hypothesis has been
documented by other scholars; Davalos et al. (2014) found that forest conversion to grassland
in several areas of the Amazon within Colombia were not related to beef production. They
concluded that colonists were removing forest to prove active land use, gain ownership of the
property, and wait for land values to increase [30]. IGAC (2015) found that deforestation after
colonization in areas with fragile soils, such as Choco-Darien ecoregion of the CGE (Fig 1B),
resulted in 38% of soils becoming unproductive in Colombia [100]. Historically, land posses-
sion has been a main source of economic and political power in Colombia and Ecuador result-
ing in land conflicts [9,101]. Consequently, future pressure on forest areas across the CGE
could increase since this area hosts the largest population of colonist in Panamá and Ecuador
[9,102].
Reforestation transitions were also heterogeneous cross the CGE. Grassland to secondary
vegetation was the highest reforestation transition cross the CGE; however, it was different in
the Ecuadorian CGE (16% of the CGE land) where crop conversion to secondary vegetation
was the highest reforestation transition during both time periods (2002–2010 and 2010–2015).
Agriculture consisting of annual or semiannual crops (corn, plantain, coffee, rice) was the
principal driver of reforestation in the Ecuadorian CGE during 1990 and 2000 [9]. Manabı́,
Esmeraldas (the south side), and Santo Domingo (the largest Ecuadorian provinces in the
CGE) are provinces considered to specialized in crop production, but cattle has increased
since 2000 in this region while crops have decreased; presently, about 50% of the land consists
of cultivated grassland and 18% by crops [103,104] and are consistent with our results.
Some scholars have claimed that palm plantations were one of the main drivers of defores-
tation in the CGE [53,105–107]. Our results showed that palm plantation was the third most
significant deforestation driver across the CGE and its effect on forest and woody vegetation
was different in every country; palm was the second deforestation driver in Colombia and the
third in Ecuador. Panamá did not have palm plantations and thus it was not a factor in that
country. Also, the reduction of forest as a result of palm plantations is substantial lower than
the reduction produced by grassland cross the CGE. The zones that we identified as areas with
palm plantation in Colombia coincide with the municipalities identified as areas with palm
plantations by the Colombian Federation of Palm Farmers [108]. Specifically, we found that
palm plantations were concentrated in three areas: Near the Colombia-Ecuador border,
around the Urabá gulf, and cross Magdalena valley. As well, we found that palm plantations
are partially spread cross the Ecuadorian CRB, which agrees with Ecuadorian studies about
palm distribution; the Ecuadorian CGE is the region with the most palm plantations in this
country and these cultivated areas have doubled between 2000 and 2010 [109].
Mining for mineral resources has been a primary historical economic activity along the
Pacific coast of Colombia within the CGE. Due to the increasing price of gold, silver and

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 Land cover change in the Chocó-Darien global ecoregion of South America

platinum in international markets between 2010 and 2015, mining has increased with little
governmental control in the Colombian Choco-Darien. Miners cut down forest, turn the soil,
and separate minerals from soil material using mercury with water from nearby rivers. Addi-
tionally, areas are deforested to build roads to transport machinery [110,111]. Frequently, this
mining activity occurs in smaller areas than our MODIS pixels size (231.3 m2); consequently,
the spatial scale of our analysis did not allow us to study this driver of deforestation. Further-
more, up-to-date maps of mining activities do not exist and high resolution imagery for this
portion of the study area are consistently cloud covered. Recently, the Colombian government
has been using aerial cameras to document illegal mining in specific areas of the CGE, how-
ever, these methodologies are not applicable for an analysis of the entire region. Illegal farming
activity, predominantly coca (Erythroxylum coca), is commonly found in the Colombian side
(Nariño Department) near the border with Ecuador [112]. These areas were coincident with
one of the deforestation areas that we identify in our maps. Although, we cannot discriminate
coca crops from other farming activities, the documented distribution of this crop is evidence
of its significant influence as a deforestation driver within the CGE.

On the conservation of biodiversity

Considering the high diversity and endemism of the CGE, the rapid reduction of forest is a pri-
mary concern for conservation activities. Currently the CGE still has significant reserves of
original forest. FAO (2010) estimated that 64% of the global woody vegetation corresponded
to forest regeneration following anthropogenic disturbances [113]. We estimate that 34% of
woody vegetation in the CGE in 2015 corresponded to secondary vegetation, suggesting that
the CGE has a higher proportion of well conserved forest (42% by our estimate) than other
areas across the world. These areas support high levels of biodiversity making them important
for conservation. Tropical rain forest areas across the CGE occupied 83312 km2 in 2015; there-
fore, the CGE contains the second largest mass of tropical rain forest in South America, after
the Amazon Basin. However, the fast and gradual replacement of forest areas by secondary
vegetation points to another main concern. The high levels diversity and endemism prior to
deforestation in these forests cannot be recovered after reforestations. That is, secondary for-
ests evolving from secondary vegetation will have decreased biodiversity and different species
assemblages [114].
The most conserved forests in the CGE are located in Panamá and along the pacific coast of
Colombia. Human colonization has been restricted in these areas by two main geographic bar-
riers, the Andes Mountains in the east and the Pacific Ocean to the west. However, the defores-
tation line has moved forward in two primary locations: to the east of the Colombia-Panamá
border (in the northeast of these well-preserved forests) and on the Colombia-Ecuadorian bor-
der (to the south of these well-preserved forests). Mitigating deforestation in these two areas is
critical to the conservation of the CGE. The integration of our time series of maps (built using
standard remote sensing products) and field data on essential biodiversity variables [38,40]
could be used to estimate biodiversity change in the context of LULC change.

Map-production methodology
The methodology applied in this work created accurate LULC maps in one of the cloudiest
areas of the planet. This methodology can be also used to create LULC maps with higher spa-
tial resolution in cloudy areas using other sensors with relatively high temporal resolution.
During the first 15 years of the 21 century, MODIS was the only sensor with moderate spatial
resolution that offered enough temporal resolution to apply our methodology to build LULC
maps in the CGE. After 2015, other sensors with higher spatial resolution, such as Landsat-7,

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 Land cover change in the Chocó-Darien global ecoregion of South America

Landsat-8 and Sentinel-2, have started to produce data with high temporal grain (when cou-
pled together) for every part of the Earth. Merging the data produced by these sensors effec-
tively increases their individual temporal grain and could be used to generate yearly LULC
maps that allow for the identification of forest successions in greater detail.
Developing annual maps of LULC across the CGE using satellite-based remote sensing
instruments with higher spatial resolution than MODIS had not been successful. The United
Nations Collaborative Program on reducing emissions from deforestation and forest degrada-
tion (REDD) in Colombia used available Landsat imagery to develop four forest/non-forest
land cover maps for the years 2000, 2005, 2010 and 2012 [115,116]. Each of these maps were
developed using Landsat mosaics consisting of 3–4 contiguous years of imagery resulting in
13% of the area with no-information due to cloud cover. Our approach, using MODIS, allowed
us to develop annual maps from 2002 to 2015 and identify LULC trends with a finer temporal
grain. However, the MODIS pixel size cannot detect land cover change smaller than the 250
m2 nominal pixel size which could affect our results. We therefore compared the published
trends of the four Landsat forest/no-forest maps from the Colombian REDD project with our
MODIS maps for the same time periods. This analysis showed similar forest change trends
between the Landsat and MODIS products; forest cover change trends were negatively corre-
lated in similar proportions in the Landsat and MODIS maps (Landsat: R = -0.99, p = 0.003;
MODIS: R = -0.97, p = 0.02). We also compared the woody vegetation change (forest and sec-
ondary vegetation) of our 2002 and 2014 MODIS land cover maps with the global forest
change (GFC) maps of Hansen et al. (2013), which estimated loss and gain of tree cover
between 2000 and 2014. To make an accurate comparison, we clipped the area classified as for-
est in our initial 2002 LULC map along with the LULC change between 2002 and 2014. We
extracted the corresponding area of tree cover, tree loss and tree gain between 2000–2014 from
the GFC database. The GFC product did not distinguish between forest (old forest) and sec-
ondary vegetation (young forests) as we did. Therefore, we combined these two classes into
simply “woody vegetation” for the comparison. Our MODIS-based maps detected 6.35%
woody vegetation loss between 2002 and 2014 compared to 3.9% for the GFC product. This
level of non-agreement can be explained by the differences in spatial and temporal resolution
as well as the definition of map classes between the GFC Landsat-based maps and our
MODIS-based maps. The GFC database consists of two global maps of tree cover percentage
for 2000 and 2014. The GFC database does not record the dynamics of tree cover between
these two dates; consequently, the GFC does not discriminate between younger and older tree
cover. Further, the increased spatial resolution of the GFC product compared to MODIS
allows forest transitions to be identified at a finer scale. Small areas of non-forest within a
matrix of forest tended to be classified as secondary forest using MODIS whereas the GFC
product seemed to identify these areas as non-forest. Consequently, our MODIS-based prod-
uct seemed to overestimate deforestation as compared to the GFC database. However, this dif-
ference is mitigated by the inclusion of widespread palm plantations and wetlands as tree
cover in the GFC product where we were able exclude them from our classification of forest. A
direct comparison, therefore is difficult.
We used the GFW processed MODIS MOD13Q1 to build the LULC annual maps. The
MOD13Q1 dataset is a 250m resolution 16-day composite product calibrated to reflectance
using an atmospheric correction for aerosol gases, and a BRDF (Bidirectional Reflectance Dis-
tribution Function) adjustment [117,118]. MOD13Q1 adopts two cloud filters [36,119] and an
aerosol quality filter. Recently, other MODIS products, such as MOD09 (MOD09Q1 and
MOD09A1), have been developed with improved cloud filtering using the MAIAC algorithm
(Multi-Angle Implementation of Atmospheric Correction) [120]. We chose to use the
MOD13Q1 product over the MOD09 products after comparing annual time series of NDVIs

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 Land cover change in the Chocó-Darien global ecoregion of South America

of both products. We found that, overall, the pre-GFW NDVI temporal sequence of
MOD13Q1 (original data) time series are less variable than the NDVI temporal sequence of
MOD09Q1 within the CGE, and where pixels coincided temporally between the two products
on the 16-day cycle, the calculated NDVI values were often identical between the two products.
Consequently, the MOD13Q1 time series after GWF had significantly less variation (t = 5.54;
p = 0.02), allowing for a better discrimination between land cover types. Additionally,
MOD09Q1 consists of only the first two spectral MODIS bands (red and NIR) which would
not provide an EVI calculation and the MOD09A1 product, which allows for a calculation of
EVI, has a spatial resolution of 500m reducing our ability to discriminate between spatially
adjacent land cover types. Therefore, for our purposes, we found the MOD13Q1 product supe-
rior to the MOD09 products.

Conclusions
By analyzing annual land-use and land-cover (LULC) change dynamics in the Chocó-Darien
Global Ecoregion (CGE), we found that LULC change varied temporally. Deforestation and
reforestation occurred across the CGE; however, deforestation increased after 2010 showing
an increased risk for CGE conservation. We detected a gradual replacement of forest areas by
secondary vegetation and agriculture, mainly grassland, which would then transition to sec-
ondary vegetation. The increased loss of forest after 2010 should be an important concern for
the preservation of CGE biodiversity because forests in this ecoregion have high levels of spe-
cies richness and endemism which are difficult to recover through reforestation. In other
words, secondary forests evolving from secondary vegetation would have decreased biodiver-
sity and different species assemblages [114].
We also found spatial variations that need to be considered when developing CGE-wide
management plans aimed at preserving biodiversity and ecosystem services. Across national
boundaries, the Ecuadorian section had the smallest proportion of forest (11%; 3578.6 km2;
mostly located in the north near the border with Colombia), for that reason, restoration pro-
grams are urgently needed in the Ecuadorian CGE. The Colombian CGE had the largest area
of forest (66160 km2; mostly located in the east along the pacific coast from the Panamanian
border, south to the northern border of the Cauca Department) but also the largest deforested
area. The Panamanian CGE contains the largest proportion of forest within their boundaries
(88%; 13569 km2) but this forested area is only 8% of the CGE. However, the forest in the Pan-
amanian CGE are fundamental to the connection of fauna and flora between Central and
South America because these forests span the Isthmus of Panamá a land bridge for the biodi-
versity for the American continent. Regions with high deforestation transitions, such as the
southern Colombian CGE, show areas where forest protection strategies should be imple-
mented. Whereas regions with high reforestation transitions can identify areas in which forest
restoration programs might be established, as the north of the Ecuadorian CGE, for example.
Our methodological approach for producing accurate LULC maps can be applied in other
cloudy regions using open source software and imagery available at no cost.

Supporting information
S1 Table. High spatial resolution imagery used to the visually interpreting of the land-use
and land-cover (LULC) classes.
(DOCX)
S2 Table. Table of response and predictor variables used in the Random Forest classifica-
tion. S2 Table is in CSV format. This table can be download in the next link https://zenodo.

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 Land cover change in the Chocó-Darien global ecoregion of South America

org/record/2543865#.XEI9llxKiM8.
(DOCX)
S3 Table. Original distribution of the land-use and land-cover (LULC) classes and sam-
pling reduction.
(DOCX)
S4 Table. Confusion matrix of the second independent group from the original data.
Kappa, commissions and omissions are in the matrix.
(DOCX)
S5 Table. Confusion matrix of the second independent group from the data when woody
vegetation class is reduced as the grassland class number. Kappa, commission and omission
are in the matrix.
(DOCX)
S6 Table. Deforestation (deforestation drivers) and reforestation transitions from 2002 to
2010.
(DOCX)
S7 Table. Deforestation (deforestation drivers) and reforestation transitions from 2010 to
2015.
(DOCX)
S1 Codes. R codes used for the Random Forest classification (run with S2 Table).
(DOCX)

Acknowledgments
We acknowledged COLCIENCIAS-Colombia (529–2011) and Fulbright-US to support this
research, Ecology Center and Remote Sensing/GIS lab of Utah State University-US to provide
additional financial and technical support, DigitalGlobe Foundation to provide the high spatial
resolution imagery, IDEAM-Colombia and IGAC-Colombia to provide data and technical
support, and IDEA WILD to provide equipment.

Author Contributions
Conceptualization: J. Camilo Fagua, R. Douglas Ramsey.
Data curation: J. Camilo Fagua.
Formal analysis: J. Camilo Fagua, R. Douglas Ramsey.
Funding acquisition: J. Camilo Fagua, R. Douglas Ramsey.
Investigation: J. Camilo Fagua, R. Douglas Ramsey.
Methodology: J. Camilo Fagua, R. Douglas Ramsey.
Resources: J. Camilo Fagua, R. Douglas Ramsey.
Supervision: R. Douglas Ramsey.
Validation: J. Camilo Fagua, R. Douglas Ramsey.
Visualization: J. Camilo Fagua.
Writing – original draft: J. Camilo Fagua, R. Douglas Ramsey.

PLOS ONE | https://doi.org/10.1371/journal.pone.0211324 February 1, 2019 19 / 25

 Land cover change in the Chocó-Darien global ecoregion of South America

Writing – review & editing: J. Camilo Fagua, R. Douglas Ramsey.

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