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Distinguising between natural forests from exotic tree plantations is essential to get an accurate picture of the world’s state of forests. Most exotic tree plantations support lower levels of biodiversity and have less potential for ecosystem services supply than natural forests, and differencing them is still a challenge using standard tools. We use a novel approach in south-central of Chile to differentiate tree cover dynamics among natural forests and exotic tree plantations. Chile has one of the world's most competitive forestry industry and the region is a global biodiversity hotspot. Our collaborative visual interpretation method combined a global database of tree cover change, remote sensing from high-resolution satellite images and expert knowledge. By distinguishing exotic tree plantation and natural forest loss, we fit spatially explicit models to estimate tree-cover loss across 40 millions of ha between 2000-2016. We were able to distinguish natural forests from exotic tree plantations with an overall accuracy of 99% and predicted forest loss. Total tree cover loss was continuous over time, and the disaggregation revealed that 1,549,909 ha of tree plantations were lost (mean = 96,869 ha/year), while 206,142 ha corresponded to natural forest loss (mean = 12,884 ha/year). Mostly of tree plantations lost returned to be plantation (51%). Natural forests were converted mainly (75%) to transitional land covers (e.g. shrubland, bare land, grassland), and an important proportion of these may finish as tree plantation. This replacement may undermine objectives of increased carbon storage and biodiversity. Tree planting as a solution has gained increased attention in recent years with ambitious commitments to mitigate the effects of climate change. However, negative outcomes for the environment could result if strategies incentivize the replacement of natural forests into other land covers. Initiatives to reduce carbon emissions should encourage differentiating natural forests from exotic tree plantations and pay more attention on protecting and managing sustainably the former.
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Environ. Res. Lett. 15 (2020) 124055 https://doi.org/10.1088/1748-9326/abca64
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LETTER
Natural forests loss and tree plantations: large-scale tree cover
loss differentiation in a threatened biodiversity hotspot
Adison Altamirano1,2, Alejandro Miranda1,3, Paul Aplin4, Jaime Carrasco5,6, Germ´
an Catal´
an1,7,
Luis Cayuela8, Taryn Fuentes-Castillo9, Angela Hern´
andez10, María J Martínez-Harms11,
Franco Peluso12, Marco Prado1, Rosa Reyes-Riveros1,7, Tracy Van Holt13, Cristian Vergara14,
Carlos Zamorano-Elgueta15 and Carlos Di Bella16,17
1Landscape Ecology and Conservation Lab, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera, Temuco, Chile
2Butamallin Research Center for Global Change, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera, Temuco,
Chile
3Center for Climate and Resilience Research (CR2), Universidad de Chile, Santiago, Chile
4Department of Geography and Geology, Edge Hill University, Ormskirk, United Kingdom
5Industrial Engineering Department, Universidad de Chile, Santiago, Chile
6Complex Engineering System Institute—ISCI, Santiago, Chile
7Doctorado en Ciencias Agroalimentarias y Medioambiente, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La
Frontera, Temuco, Chile
8Department of Biology, Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Madrid, Spain
9Instituto de Geografía, Facultad de Historia, Geografía y Ciencia Política, Pontificia Universidad Cat´
olica de Chile, Santiago, Chile
10 Centro de Investigaci´
on en Ecosistemas de la Patagonia (CIEP), Coyhaique, Chile
11 Center for Applied Ecology and Sustainability (CAPES), Pontificia Universidad Catolica de Chile, Santiago, Chile
12 Instituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (CIRN-INTA Castelar), Hurlingham, Buenos Aires,
Argentina
13 Center for Sustainable Business, Leonard N. Stern School of Business, New York University, New York, United States of America
14 Laboratorio de Planificaci´
on Territorial, Departamento de Ciencias Ambientales, Facultad de Recursos Naturales—Universidad
Cat´
olica de Temuco, Coyhaique, Chile
15 Universidad de Aysen, Coyhaique, Chile
16 Departamento de M´
etodos Cuantitativos, Universidad de Buenos Aires, Facultad de Agronomía, Buenos Aires, Argentina
17 Consejo Nacional de Investigaciones Científicas y T´
ecnicas, Argentina
E-mail: adison.altamirano@ufrontera.cl
Keywords: remote sensing, augmented visual interpretation, land use and land cover change, tree cover, forest plantation
Supplementary material for this article is available online
Abstract
Distinguishing between natural forests from exotic tree plantations is essential to get an accurate
picture of the world’s state of forests. Most exotic tree plantations support lower levels of
biodiversity and have less potential for ecosystem services supply than natural forests, and
differencing them is still a challenge using standard tools. We use a novel approach in south-central
of Chile to differentiate tree cover dynamics among natural forests and exotic tree plantations.
Chile has one of the world’s most competitive forestry industry and the region is a global
biodiversity hotspot. Our collaborative visual interpretation method combined a global database of
tree cover change, remote sensing from high-resolution satellite images and expert knowledge. By
distinguishing exotic tree plantation and natural forest loss, we fit spatially explicit models to
estimate tree-cover loss across 40 millions of ha between 2000 and 2016. We were able to
distinguish natural forests from exotic tree plantations with an overall accuracy of 99% and
predicted forest loss. Total tree cover loss was continuous over time, and the disaggregation
revealed that 1 549 909 ha of tree plantations were lost (mean =96869 ha year1), while
206 142 ha corresponded to natural forest loss (mean =12 884 ha year1). Mostly of tree
plantations lost returned to be plantation (51%). Natural forests were converted mainly (75%) to
transitional land covers (e.g. shrubland, bare land, grassland), and an important proportion of
these may finish as tree plantation. This replacement may undermine objectives of increased
© 2020 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
carbon storage and biodiversity. Tree planting as a solution has gained increased attention in recen
years with ambitious commitments to mitigate the effects of climate change. However, negative
outcomes for the environment could result if strategies incentivize the replacement of natural
forests into other land covers. Initiatives to reduce carbon emissions should encourage
differentiating natural forests from exotic tree plantations and pay more attention on protecting
and managing sustainably the former.
1. Introduction
Differentiating natural forests from exotic tree plant-
ations at large scales represents a global relevant issue
because different tree covers may produce disparate
estimations of changes in local biodiversity and in rel-
evant ecosystem services such as climate regulation,
carbon storage, and water supply (Hall et al 2012,
Van Holt et al 2012,2016, Viña et al 2016, Lewis et al
2019). However, this still constitutes a technical chal-
lenge for the remote-sensing communities (Zhao et al
2016, Curtis et al 2018).
Evidence has demonstrated that exotic tree plant-
ations are rather distinct from natural forests in their
role in terms of biodiversity conservation, ecosys-
tem services, and the social impacts that they provide
(Reyes et al 2015, Naudts et al 2016, Jones et al
2017, Martínez-Jauregui et al 2018, Lewis et al 2019).
Therefore, these tree cover types need to be carefully
differentiated, especially in areas with high conserva-
tion values, where it has many implications for biod-
iversity and human wellbeing (Newbold et al 2016).
Current policy discourses related to forest con-
servation, and particularly to restoration, use or
imply the forest definition by the United National
Food and Agricultural Organization (FAO), which
aggregates natural forests and exotic tree plantations
(FAO 2010). Under this definition, exotic tree planta-
tions are under intensified forestry management and
some authors point out that could be classified as
‘tree farms’ (Van Holt and Putz 2017, Curtis et al
2018). However, misclassification of natural forests
and exotic tree plantations could bring misinterpret-
ation in environmental policy and social impact eval-
uation (Van Holt and Putz 2017, Hua et al 2018), as
tree plantations are typically subjected to intensified
forestry management practices that cause environ-
mental impacts similar to those produced by intensive
agriculture (Karp et al 2012, Naudts et al 2016, Lewis
et al 2019, Heilmayr et al 2020, Osuri et al 2020).
Under the FAO definition (2015), not all forests
contribute to climate change mitigation (Naudts et al
2016). Planting trees, and particularly some con-
ifer species for fast growth tree plantations, is not
enough to stave off global warming (Lewis et al 2019).
Given the current global challenge of forest restora-
tion (Chazdon and Brancalion 2019) it is paramount
to accurately discriminate between natural forests
and tree plantations. For instance, China reported a
significant increase in forest cover in around 1.6% of
its territory (Viña et al 2016). However, native forests
are not returning and this forest recovery was mostly
due to exotic tree plantations (Van Holt and Putz
2017). Moreover, while net tree cover in southwest-
ern China grew by 32% (2000–2015), this increase
was mainly due to the conversion of croplands to tree
plantations, but tree plantations also displaced native
forests with a gross loss of 6.6% (Hua et al 2018).
The biggest effort to assess tree cover change is
the global database developed by Hansen et al (2013),
who mapped annual global tree cover loss and gains
from 2001 to 2018 using Landsat satellite program.
Based on this dataset, several studies have assessed
the global and local forest changes in a number of
places/countries (Viña et al 2016, Heilmayr et al 2016,
Potapov et al 2017, Curtis et al 2018, Hua et al
2018). At a global scale, the main driver of tree cover
loss is associated to permanent land use change for
commodity production like forestry and agriculture
(Curtis et al 2018). But not considering the natural
or productive character of these forests can lead to
a substantial misestimation of the natural forest loss
(Tropek et al 2014), compromising its value for local
and global policy decisions.
Identification of different types of forests using
Hansen et al (2013) dataset is mainly based on
tree cover percentage (Tyukavina et al 2018) or in
combination with forest height maps derived from
LIDAR images (Yu et al 2020). However, those cri-
teria do not differentiate natural forests from tree
plantations. In this work, we use a novel approach
to illustrate the importance of accurately discriminat-
ing natural forests from tree plantations when quan-
tifying tree cover losses. We use a method which can
be applied to large-scale land cover monitoring. We
use a collaborative augmented visual interpretation
method that uses the Hansen et al (2013) database,
the Google Earth-Engine platform, high-resolution
satellite images, and expert knowledge through net-
working collaboration (figure 1).
The method is tested in south-central Chile,
which is one of the global leaders in pulp produc-
tion from exotic tree plantations (Cubbage et al 2007),
and also classified as a global biodiversity hotspot
(Mittermeier et al 2004). This area harbors the last
confined and endangered sclerophyllous and tem-
perate forests of South America. Our method was
applied for the period 2000–2016. We discuss the
2
Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
Figure 1. Workflow of the methodological framework.
implications for global and large-scale tree cover
monitoring, by providing an accurate, cost-efficient,
and replicable tool, which can be useful for future
biodiversity conservation and climate mitigation
actions.
2. Methods
2.1. Study area
The Chilean biodiversity hotspot, also called ‘Chilean
winter rainfall–Valdivian forests’ (25–47S) (Arroyo
et al 2004) covers about 400 000 km2(figure 2). It
comprises half of the temperate forests in the south-
ern hemisphere, but also suffers the greatest land-use-
change pressure in the country due to the high con-
centration of economic activities in Chile (Miranda
et al 2017). This area comprises 79% of the coun-
try’s urban and industrial zones, 94% of its agri-
culture, and 98.7% of the total exotic tree planta-
tion extent (mostly Pinus radiata and Eucalyptus spp.)
(CONAF 2011). In this hotspot, natural forests cover
approximately 9.5 millions of ha (Zhao et al 2016),
which are distributed mainly from 33S southwards
(figure 2(A)). In the country the last official figure of
exotic forest plantations is equivalent to 3.1 millions
of ha (CONAF 2019).
2.2. Data source of tree cover loss
We use the updated tree cover loss database
developed by Hansen et al (2013) (available at
https://glad.umd.edu/dataset). This global database
has a high spatial resolution (30 m) of annual tree
cover change. We used the tree cover loss product
(patches lost) for each year between 2000 and 2016.
We applied an image filtering technique to elimin-
ate tree loss patches smaller than 0.27 ha, and the
eight-cells neighborhood rule (Mcgarigal et al 2012).
We use the defined forest loss as a ‘stand replace-
ment disturbance’ (Hansen et al 2013), meaning the
removal or mortality of all tree cover in a Landsat
pixel with more than 50% of tree crown cover.
2.3. The collaborative survey
We performed a random sampling through a sur-
vey to differentiate tree cover loss between natural
forest and exotic tree plantation, and identify the
contributors of change (i.e. the land cover type after
forest loss), for the whole period. The sampling con-
sidered a total of 2623 points in patches of tree cover
loss (figure 1(C)). To get a representative sample
of each loss patch, five points were randomly dis-
tributed in each accumulated forest loss patch for
the whole period which was constrained to the
patch size (minimum distance among sample points
was 30 m). Therefore a total of 1219 patches were
sampled.
The total sample points were distributed between
ten local experts who were selected given their expert-
ise on remote sensing and on land use/land cover
monitoring. Each expert received a minimum of 250
sample points and all instructions and files to under-
take the survey.
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Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
Figure 2. Map of (A) the current distribution of natural forest and exotic tree plantations (Zhao et al 2016), (B) whole tree cover
loss by Hansen et al (2013) updated to 2016, and (C) the distribution of sampling points on tree cover loss patches in the study
area. Plots represent latitudinal and longitudinal density distribution of land cover-types in each map.
Based on their experience each local expert iden-
tified at each sample point the land-cover type prior
to tree cover loss (i.e. natural forest and tree planta-
tion), but was not possible to distinguish the species
of origin. After that, at the same sample point, the
expert identified the land-cover type in the last year
of the period (2016), among the following options:
natural forest, exotic tree plantation, cropland, grass-
land, settlement, wetland, shrubland, bare land, and
other land (appendix 1, which is available online
at https://stacks.iop.org/ERL/15/124055/mmedia).
Local experts also identified errors associated to no
presence of tree cover loss in Hansen et al (2013)
database, and all these points were discarded from
the final analysis and the spatial and temporal vari-
ations of cover loss.
We tested the sensitivity of our results to assess
the variation of land cover identification according
to the sample size (appendix 3). Furthermore, we
assessed the accuracy of the local experts to differen-
tiate between ‘natural forest’ and ‘tree plantation. We
collected a total of 450 independent points from field-
work on natural forest and tree plantation. We ran-
domly selected 100 of these field points (50% of each
land cover). All local experts received the same sample
points and identified the land-cover type on each
one. We compared the relationship between known
reference data (field points) and the results of the
local expert identification for accuracy estimation.
Additionally, we measured agreement among experts
using Fleiss’ kappa (K), which measures reliability
among a group of experts. We calculated Kusing the
R software package irr (Gamer et al 2014).
The survey was designed through Augmen-
ted Visual Interpretation and implemented in
‘Open Foris Collect’ software (www.openforis.
org/tools/collect.html) (Bey et al 2016). This platform
is open source and provides a flexible solution for data
management, allowing full customization of survey
structure, variables, and data checks. To apply the sur-
vey, we used ‘Collect Earth’, which is a free and open
source tool for land monitoring that uses Google
Earth and Google Earth Engine, and was developed
by FAO (Bey et al 2016). For land cover identification
of each local expert we mostly used historical Google
Earth images but in years with no availability of these
images we used Landsat images.
2.4. Natural forest and tree plantation losses
estimation
We estimated the tree cover loss area in the whole hot-
spot distinguishing between tree plantation and nat-
ural forest loss for the entire period using boosted
regression trees. For the training data set we use one
sample per patch (discarding all duplicate samples
in a patch) which is equivalent to a total of 1219
samples. Then, on that way the samples are relatively
spatially independent. The decision trees were gen-
erated first by linking the potential explanatory vari-
ables to the response variables. As response variable
we used the binary variable of forest loss (natural
forest =1, and tree plantation =0). We specified
three parameters to fit the model: tree complexity
(5), learning rate (0.01), and bag fraction (0.8). We
constructed a set of environmental variables maps as
explanatory variables from spatially-explicit data on
geography, landscape characteristics, and tree cover
loss patch metrics (appendix 2). The environmental
explanatory variables were: latitude, longitude, elev-
ation, slope, distance to cities, distance to villages,
distance to roads, and different patch-lost met-
rics (year loss, area, perimeter, area/perimeter, and
perimeter/area). We thus examined the correlation
matrix of all these explanatory variables and excluded
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Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
Figure 3. Annual tree cover loss in the Chilean hotspot according to Hansen et al (2013) updated database.
those that were highly correlated (|r| > 0.6) to avoid
multicollinearity.
This technique generates many regression trees
that are combined into one ultimate regression tree
model, boosting the ultimate model’s accuracy and
predictive performance (Elith et al 2008). After train-
ing the model, a validation accuracy score estimates
the performance of the model on an independent
dataset (20%). When the dataset of observations are
divided into kdisjoint subsamples (or folds), then is
taken a group as a hold out or test dataset and the
remaining groups as a training dataset, this proced-
ure is known as K-fold cross-validation. In our study,
we adopted the latter procedure (with K=5) to val-
idate, to avoid overfitting and to estimate the aver-
age classification. Then, the majority of model pre-
dictions were applied across all study area.
We calculated usual measures of model perform-
ance as the Area under the ROC curve (AUC), the cor-
relation between the observed and predicted values,
sensibility and sensitivity (Shabani et al 2016). Sensit-
ivity is the percentage of positive observations that are
correctly classified whereas sensibility is the percent-
age of negative observations that are correctly identi-
fied. AUC assess the overall accuracy of the classifier’s
performance. AUC value near 0.5 means that the pre-
dictive ability of the model is completely random and
a value of 1.0 represents a perfect prediction without
misclassification.
We assessed the uncertainty of the model estima-
tions across the latitudinal gradient of the study area.
The model estimates the relative influence of each
explanatory variable. We thus chose those explanat-
ory variables with 4% of influence in BTR mod-
els (a strong relationship with the response variable).
The influence was based on the number of times a
variable was selected for splitting, weighted by the
squared improvement to the model as a result of each
split, and averaged over all trees (Elith et al 2008). We
fitted the model using the dismo package implemen-
ted in R(R Development Core Team 2016).
3. Results
Total tree cover loss in the entire Chilean biodiversity
hotspot for the period 2000–2016 was 1 756 052 ha
(109 753 ha year1in average). Overall, there was
a continuous increment in tree cover loss dur-
ing the whole period, ranging from approximately
70 000 ha year1in 2001–160 000 ha year1in
2016 (figures 2(B) and 3). The main contributors
of change (i.e. the land-cover types after tree cover
loss) were tree plantation (44%), bare land (36%),
and shrubland (11%).
3.1. Land cover classification accuracy assessment
The global assessment of local experts showed high
accuracy values for differentiation between natural
forest and exotic tree plantation, as well as for the
other land covers. Mean global accuracy from the
independent field samples applied to the ten local
experts was 99% (natural forest and exotic tree plant-
ation rised 99% and 98% respectively). This res-
ult was consistent with Fleiss’ Kappa analysis where
K=0.95. Sensitivity analysis showed that the influ-
ence of sample size variation on land-cover type iden-
tification prior and after the tree cover loss decreases
as sample size increases (appendix 3).
3.2. Tree cover losses
Based on the sampling, most of tree cover loss
turned to exotic tree plantations in the whole time
period (2000–2016), explaining 85% of the tree
patches lost, while only 15% of the samples were nat-
ural forests loss. These tree plantations lost mostly
returned to be plantation (51%), even in some cases
they were changed to bare land (38%), grassland
(5%), shrubland (5%), and other land-cover types
(1%). On the contrary, natural forests were conver-
ted mainly to shrubland (40%), bare land (27%),
grassland (11%), cropland (10%), tree plantation
(7%), and other land covers (5%) (see examples in
appendix 4).
5
Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
Figure 4. Map of sample points by type of tree cover loss and period (red points =tree plantation, green points =natural forest).
Table 1. Model performance statistics for boosted regression tree
model of disaggregated tree forest loss prediction. s.e. =standard
error for cross-validation.
Indicator Training Cross-validation s.e.
AUC 0.98 0.96 0.02
Correlation 0.94 0.87 0.02
Sensibility (%) 98 92 1.84
Sensitivity (%) 97 85 1.71
Total tree cover loss was mostly concentrated in
the north-central area of the hotspot (the peak at
37S) (figure 4). However, there was a skewed lon-
gitudinal pattern towards the coastal range, which is
consistent with the peak of spatial distribution of tree
plantation (figure 2). Natural forest loss was more
scattered throughout the region in most time periods,
though it was more evident further south.
Overall, tree plantation and natural forest showed
an increasing pattern of tree cover loss during the
whole period (figure 4, appendix 5). Tree plantation
clear cutting was relatively constant until the period
2012–2013. However, from the period 2014–2015
onwards it increased substantially. Natural forest loss
remained relatively constant with minor fluctuations
until 2010, then increased, first steadily, and after
2013–2014 more sharply (appendix 5).
3.3. Disaggregating tree cover losses
Our model to predict disaggregated tree cover loss in
the whole study area obtained high accuracy under
different indicators (table 1). It is especially relevant
the high AUC using train and cross-validation data.
Model performance results were consistent due to the
high values of correlation, sensibility and sensitivity,
the last ones over 85%.
Our initial model was reduced to five main
explanatory variables (appendix 6), with far latitude
the most influencing variable (73%). Other relev-
ant variables were longitude (10%), elevation (9%),
perimeter/area relationship (4%), and distance to
cities (4%).
The disaggregation of total tree cover loss
in the study area revealed that from the total
1 756 052 ha, 1 549 909 ha corresponded to tree
plantation loss (mean =96 869 ha year1), while
206 142 ha corresponded to natural forest loss
(mean =12 884 ha year1). The spatial pattern of
disaggregated tree cover loss predictions (figure 5)
showed the same pattern of the sample points, which
also indicated the consistence of our results. Uncer-
tainty of estimations across the whole latitudinal
range of the study area showed low error. Higher
error are located in the southern area, however
the maximum error remains being lower than 10%
(appendix 7).
4. Discussion
4.1. Natural forest and tree plantations losses
We present an application to test disaggregation of
tree cover loss into natural forests and tree plantations
in a representative place, where forestry is a relevant
economic activity. Chile is one of the top ten coun-
tries in the world in terms of land dedicated to forestry
based on exotic tree plantations and the fifth in the
Americas (Cubbage et al 2007), with 3.2 million ha
(CONAF 2019). Moreover, forestry has been estim-
ated as the main driver of tree cover loss. In particular,
in Europe, North America, Russia/China/South Asia,
and Australia/Oceania it represents 99%, 56%, 41%,
and 29% of tree cover loss respectively (Van Holt et al
2016, Curtis et al 2018).
We differentiated tree-cover loss among natural
forest and exotic tree plantation with high accuracy.
This is not an easy task, especially at large scales given
6
Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
Figure 5. Map of spatial predictions of tree cover loss
disaggregated by forest plantation and natural forest.
the difficulties of remote sensing techniques and data.
The main reason of that is because tree plantations are
easily visible on the satellite images given its homo-
geneous structure (Wang and Huang 2012, Van Holt
et al 2016) which local experts are able to identify.
Our method takes advantage of freely available data
and remote sensing techniques combined with expert
local knowledge that have the potential to reproduce
the analysis for any region in the world. Moreover,
our results illustrate well the advantages of combining
remote-sense measurements and expert knowledge
than to use remote-sense technology alone (Cayuela
et al 2006, El Hajj et al 2009, Huang and Jia 2012,
Mialhe et al 2015). In this sense, we provide trans-
parent, comprehensive, confident and a cost-efficient
data-set given its several advantages of this approach
as stability, replicability, easy to share, testable and
low cost.
Based on the sampling, we also found that the
most important contributor of whole tree cover loss
is tree plantation, which account for 44% of total
tree cover loss. When we separate the tree cover loss,
more than 50% of tree plantation loss finished as tree
plantation at the end of our assessment period, and
this pattern is consistent with last global assessment
(Curtis et al 2018). After tree cover loss some land
cover types are transitional (i.e. bare land and grass-
land), but usually and in particular in Chile these land
cover types represent a stage in the intensive harvest-
ing activities of the tree plantation dynamic (Aguayo
et al 2009, Patterson and Hoalst-Pullen 2011). There-
fore, it would be likely that most tree plantation
remain over time with the same land use type. Also is
important to highlight that the main drivers of forest
plantations loss can be associated to both harvest-
ing and fires which we have not differentiated. Fur-
ther research is needed about this topic and especially
about the underlying causes of tree plantation and
natural forest loss given that both are related to dif-
ferent dynamics of change.
Natural forest loss continues to be an important
concern in one of the most endangered areas world-
wide, where our results show that approximately
13 000 ha year1are replaced by shrublands, bare
lands, grasslands, croplands, and exotic tree plant-
ations. This result contradicts some research point-
ing out that the expansion of planted forests has
the potential to reduce pressure on natural forests
(Köhlin and Parks 2001, Kauppi et al 2006). Other
studies support our findings and shows that exotic
tree plantation expansion has resulted in a contrac-
tion of natural forests (Heilmayr 2014, Sloan and
Sayer 2015, Van Holt et al 2016, Miranda et al
2017, Hua et al 2018) and can potentially increase
deforestation in certain regions (Pirard et al 2016).
After natural forest loss, transitional land cover types
(e.g. shrubland, bare land, grassland) can be found
which will finish as permanent ones (e.g. agricultural
crops, tree plantation). These transitional land cover
types account for more than 75% of natural forest
loss. An important proportion of these may finish
as tree plantation as illustrated by several examples
(Aguayo et al 2009, Patterson and Hoalst-Pullen 2011,
Altamirano et al 2016, Austin et al 2019).
4.2. Implications for public policies on forest
management and restoration
Inconsistences in terms of the information and
applied monitoring methods are recognized by FAO
for national forest monitoring systems (Macdicken
2015). This is particularly important in Chile, where
the Global Forest Resources Assessment report
indicated a net increase of natural primary forests
from 4 631 000 (1990) to 5 355 000 ha (2015), and
other natural regenerated forest from 8925 000
to 9 336 000 ha (FAO 2014). Our results high-
light the implications of information misinter-
pretation, and represent an opportunity for local,
but also global policies related to forest man-
agement and conservation and large-scale forest
monitoring.
Increasing the world’s forest cover have been
settled as the most important goal for fighting against
and adapting to climate change (Chazdon 2014,
Bastin et al 2019, Lewis et al 2019, Carey 2020).
But the current forest restoration strategies at land-
scape scales (and even larger), including different
activities (e.g. from strict restoration to monocul-
ture of tree plantations), may have different impacts
7
Environ. Res. Lett. 15 (2020) 124055 A Altamirano et al
on biodiversity, carbon, water, and eventually on
human wellbeing (Chazdon and Brancalion 2019,
Lewis et al 2019). Therefore, negative outcomes for
the environment could result if strategies incentiv-
ize exotic tree plantations establishment. In this
context, differentiating the cover dynamics of nat-
ural forests and exotic tree plantations is highly
relevant.
Current sectorial policies supporting forest res-
toration ignore the links between biodiversity, water,
soil retention and timber-production (Latawiec et al
2015). With so many multiple benefits, regrowing
forests would be seen as a means for achieving
goals related to sustainability and human livelihoods
(Chazdon et al 2017). But to achieve this, new forest
visions should be encouraged based on a more com-
prehensive understanding of the ecological landscape
impacts of managing natural forest and tree planta-
tions, and eventually built a better base for developing
more efficient economic compensating mechanism to
ensure the multiple functions and benefits these tree
covers may provide (Chazdon and Brancalion 2019).
Our results can be useful to build capacity for
land monitoring and to improve our collective under-
standing of forest loss dynamics at global scale, and
even more it can be expanded to other conflicts of
land use and land cover change. For instance, it could
be used to check FAO statistics in places where we
are unsure or/and accessibility is limited. Currently,
forest certification covers an important area of world
managed forests and tree plantations (FSC 2018) but
it requires accurate monitoring systems. The current
climate change crisis and the related forestry agendas
(e.g. REDD+and Aichi Targets for 2020) require crit-
ical revision in global policy discussions, and at the
same time an accurate and specific land monitor-
ing system which can help to prevent the growing
problem of green grabbing in land use (e.g. Zhao et al
2014, Scheidel and Work 2018).
Chile has currently proposed its goal of National
Determined Contribution to face the climate crisis,
but the current proposal for reducing greenhouse
gas emissions inadequately addresses forest manage-
ment mainly through tree plantations (Chazdon and
Brancalion 2019, Duran and Barbosa 2019, Rudel et al
2019). The proposal considers to plant 200000 ha of
forests mainly oriented to tree plantations (approx-
imately 130 000 ha). Tree plantation is not a perman-
ent land cover as our results demonstrate, and this has
serious implications for climatic goals given harvested
systems, and carbon loss sequestered. Additionally,
this target results clearly insufficient to counterbal-
ance the forest loss area reported in this study. Ini-
tiatives to reduce carbon emissions should encour-
age differentiating natural forests from exotic tree
plantations and pay more attention on protecting and
managing sustainably the former. To advance towards
a global monitoring system, effectively differentiate
global tree cover loss should be an urgent goal as a
climate change mitigation action and to face the cur-
rent environmental global challenges.
Data availability statement
The data that support the findings of this study
are openly available at the following URL/DOI:
http://www.lepfor.ufro.cl/?page_id=530.
Acknowledgments
This research was supported by funding from
Fondecyt Grant No. 1171445 and Direcci´
on de
Investigaci´
on of Universidad de La Frontera. AM and
CZ are thankful to ANID/FONDAP/15110009. AH
thanks the support of ANID NREDI170329 grant.
Figures were refined by V Sontag (@illusscientia).
ORCID iDs
Adison Altamirano https://orcid.org/0000-0002-
9638-7486
Paul Aplin https://orcid.org/0000-0002-9394-
5630
Rosa Reyes-Riveros https://orcid.org/0000-0002-
5251-3954
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10
... Estos planes de manejo autorizados por CONAF fueron calificados como ilegales por el Dictamen de la Contraloría General de la República, la Corte de Apelaciones de Santiago rechazó el recurso de protección presentado por la Sociedad Nacional de Agricultura, y la Corte Suprema confirmó la sentencia de la Corte de Apelaciones. La pérdida sostenida de bosques nativos por cambios de uso de suelo es una realidad (Heilmayr et al., 2016;Miranda et al., 2016;Altamirano et al., 2020). ...
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