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Comment on “High-resolution global maps of 21st-century forest cover change”

Authors:

Abstract

Hansen et al. (Reports, 15 November 2013, p. 850) published a high-resolution global forest map with detailed information on local forest loss and gain. We show that their product does not distinguish tropical forests from plantations and even herbaceous crops, which leads to a substantial underestimate of forest loss and compromises its value for local policy decisions.
TECHNICAL COMMENT
FOREST SURVEYS
Comment on High-resolution global
maps of 21st-century forest
cover change
Robert Tropek,
1,2
*Ondřej Sedláček,
3
Jan Beck,
1
Petr Keil,
4,5
Zuzana Musilová,
6
Irena Šímová,
3,5
David Storch
3,5
Hansen et al. (Reports, 15 November 2013, p. 850) published a high-resolution global
forest map with detailed information on local forest loss and gain. We show that their
product does not distinguish tropical forests from plantations and even herbaceous
crops, which leads to a substantial underestimate of forest loss and compromises its
value for local policy decisions.
The high-resolution global map of forest cov-
er loss and gain in Hansen et al.(1)isa
fascinating and much-needed tool for both
research and conservation planning. The
authors claim that [t]he information con-
tent of the presented data sets...provides a trans-
parent, sound, and consistent basis on which to
quantify critical environmental issues, includ-
ing...(iv) the status of remaining natural forests
of the world and threats to biodiversity(v) the
effectiveness of existing protected-area networks
(vi) the economic drivers of natural forest con-
version to more intensive land uses.After study-
ing the supplementary data application (http://
earthenginepartners.appspot.com/science-2013-
global-forest) in detail, we express serious con-
cernsabouttheappropriatenessoftheproduct
for these purposes.
The main problem lies in Hansen et al.sdef-
inition of forest as all vegetation taller than 5m
in height[supplementary materials for (1)]. Such
a structural definition includes types of planta-
tions that have already replaced substantial parts
of tropical (and also extratropical) forests. Mono-
cultures of oil palm, rubber, or Eucalyptus are
recognized as some of the biggest threats to
tropical biodiversity (24), and their expansion
into forest systems continues at an alarming rate
[see (5) for details]. Although these plantations
are technically forestsin the definition above,
they do not provide the benefits of forest vege-
tation as enumerated by the authorsi.e., eco-
system services, including biodiversity richness,
climate regulation, carbon storage, and water
supplies(69). Classifying plantations as forests
confuses an endangered habitat with its greatest
threats and thus underestimates real forest loss.
To evaluate Hansen et al.sforestmap,we
compiled sites for which we had detailed infor-
mation (e.g., from our previous fieldwork). We
compared these validation sites to the forest map
and identified three ways in which the product
failed to accurately assess forest cover gain and
loss (see Table 1 and Fig. 1 for specific cases):
1) Areas deforested and converted into planta-
tions before 2000 are classified as forests (cases
1 to 19, Table 1, and Fig. 1, A to E), which leads to
an overestimation of total forested area. Further-
more, plantation management such as cutting
old growth plantation and replanting with new
crops is interpreted as forest gain/loss.
2) Areas deforested around 2000 and re-
plantedbytreeplantationsbefore2012are
identified as forest gain,although their conser-
vation value has been largely lost in such cases
(case 20, Table 1).
3) Contrary to the given definition of forest,
vegetation lower than 5 m (e.g., pineapple, soy-
beans, or tea plantations) is often classified as
forest. Including these types of vegetation as
forestfurther biases estimates of forest cover
gain and loss (cases 21 to 24, Table 1, and Fig. 1, F
to H).
Theeasewithwhichwefoundclassification
errors in every examined tropical region suggests
that these represent systematic misinterpretations
with substantial consequences for inferences
based on the product. Following our personal
knowledge of several tropical regions and a sur-
vey of Hansen et al.s map application, we ten-
tatively estimate that the forest loss may be
underestimated by tens of percents in the trop-
ics. Similar issues may also occur outside the
tropics, where species-poor wood plantations
are widespread (5,10).
We warn that classification of high-resolution
satellite data based on a single and simplistic
algorithm can provide only limited insight into
real forest dynamics at local scales. This forest
map may provide preliminary identification of
ongoing changes [e.g., (11)], but without locally
specific calibration and evaluation and/or accom-
panying maps of pixel-specific classification un-
certainty, it will mislead conservation policy-makers
and managers, with potentially serious conse-
quences for biodiversity and socioeconomic is-
sues. The fact that the product comes with an
easy-to-use online application further enhances
the potential for uncritical use by nonspecialists
and various interest groups.
Although the global loss of tree cover reported
by Hansen et al.(1) represents a serious en-
vironmental issue, the replacement of natural
forests by plantations (often with comparable
tree cover) is a more important environmental
and biodiversity problem at the local scale. Plan-
tations are often characterized by considerably
lower diversity than extensively used open coun-
tryside, including nonintensive pastures and small
fields (2,12). In this respect, the results of Hansen
et al. are misleading and can potentially lead to
abuse by local policy-makers who could consider
an increase of tree cover a conservation success,
even if this change is accompanied by decreases
in biological diversity. The stated conservation
relevance and utility of the approach of Hansen
et al. is thus seriously compromised and calls for
a critical reevaluation.
REFERENCES AND NOTES
1. M. C. Hansen et al., Science 342, 850853 (2013).
2. J. Barlow et al., Proc. Natl. Acad. Sci. U.S.A. 104, 1855518560
(2007).
3. L. P. Koh, D. S. Wilcove, Conserv. Lett. 1,6064 (2008).
4. D. S. Wilcove, X. Giam, D. P. Edwards, B. Fisher, L. P. Koh,
Trends Ecol. Evol. 28, 531540 (2013).
5. E. G. Brockerhoff, H. Jactel, J. A. Parrotta, C. P. Quine, J. Sayer,
Biodivers. Conserv. 17, 925951 (2008).
6. R. Guo, R. M. Gifford, Glob. Change Biol. 8, 345360 (2002).
7. E. B. Fitzherbert et al., Trends Ecol. Evol. 23, 538545
(2008).
8. L. Gibson et al., Nature 478, 378381 (2011).
9. A. D. Ziegler et al., Glob. Change Biol. 18, 30873099
(2012).
10. F. T. Maestre, J. Cortina, For. Ecol. Manage. 198, 303317
(2004).
11. R. Koenig, Science 320, 14391441 (2008).
12. H. M. Pereira, G. C. Daily, Ecology 87, 18771885 (2006).
ACKNO WLED GME NTS
We thank K. Mertes for English proofreading and valuable
comments on the manuscript. This work was partly supported
by the Czech Science Foundation (14-36098G).
20 November 2013; accepted 4 April 2014
10.1126/science.1248753
RESEARCH
1
Department of Environmental Science (Biogeography),
University of Basel, St. Johanns-Vorstadt 10, CH-4056 Basel,
Switzerland.
2
Institute of Entomology, Biology Centre,
Academy of Sciences of the Czech Republic, Branisovska 31,
CZ-370 05 Ceske Budejovice, Czech Republic.
3
Department
of Ecology, Faculty of Science, Charles University in Prague,
Vinicna 7, CZ-128 44 Praha 2, Czech Republic.
4
Department
of Ecology and Evolutionary Biology, Yale University, 165
Prospect Street, New Haven, CT 06520, USA.
5
Center for
Theoretical Study, Charles University in Prague and Academy
of Sciences of the Czech Republic, Jilska 1, CZ-110 00 Praha
1, Czech Republic.
6
Zoological Institute, University of Basel,
Vesalgasse 1, CH-4051 Basel, Switzerland.
*Corresponding author. E-mail: robert.tropek@gmail.com
SCIENCE sciencemag.org 30 MAY 2014 VOL 344 ISSUE 6187 981-d
Table 1. Examples of serious misclassifications by Hansen et al.(1). Geographic coordinates allow easy checking of current vegetation in the
supplementary online map application (http://earthenginepartners.appspot.com/science-2013-global-forest). Hundreds of additional examples of similar
errors can easily be found in the application map by simply following plantation maintenance roads. Often, even details such as individual oil palms or
soybean rows are clearly visible.
Case no. Figure Country Region Latitude Longitude Hansen et al.Actual
vegetation
1 1A Philippines Davao, Mindanao 7°26'1.29''N 125°38'6.26''E Stable forest Banana
2 1B Ecuador Quevedo, Los Rios 1°0'46.76''S 79°29'59.33''W Forest with
large regrowth
Oil palm
3 1C Costa Rica Damas, Puntarenas 9°31'59.50''N 84°14'16.20''W Forest with
large regrowth
Oil palm
4 1D Malaysia Sepang, Kuala Lumpur
International Airport
2°43'55.97''N 101°40'49.64''E Forest with
large regrowth
Oil palm
5 1E Cameroon Mundemba, Southwest 4°57'1.16''N 8°52'18.66''E Forest with large
clearings and regrowth
Oil palm
6 Cameroon Bafut-Ngemba, Forest Reserve
Northwest Province
5°54'11.90''N 10°11'43.61''E Stable forest Eucalyptus
7 Cameroon Penda Mboko,
Southwest Province
4°16'14.68''N 9°26'12.66''E Stable forest Rubber
8 Malaysia (Borneo) Left bank of
Kinabatangan River, Sabah
5°32'40''N 118°166''E Forest with clearings
and regrowth
Oil palm
9 Philippines South of Tagum,
Davao del Norte
7°21'28.04''N 125°47'52.59''E Stable forest Coconut
10 Papua New Guinea Gusap, Morobe 6°4'53.08''S 146°0'12.95''E Large forest regrowth Oil palm
11 Indonesia Bogor, West Java 6°30'47.64''S 106°43'35.64''E Large forest regrowth Oil palm
12 Indonesia North Konawe,
Southeast Sulawesi
3°12'40.73''S 122°7'30.66''E Large forest regrowth Oil palm
13 Venezuela Ciudad Guayana,
Bolívar State
8°35'33.84''N 62°35'54.19''W Forest with large
clearings and regrowth
Pine tree
14 Peru Santa Lucía,
San Martín
8°19'40.71''S 76°29'50.67''W Forest with
large regrowth
Oil palm
15 Benin Saketé, Plateau
Department
6°48'36.02''N 2°30'10.52''E Forest with
clearings and regrowth
Oil palm
16 Côte dIvoire Ebobo,
Sud-Comoé
5°15'43.42''N 3°1'46.12''W Forest with
clearings and regrowth
Oil palm
17 Nigeria Benin City,
Edo State
6°9'39.23''N 5°41'0.33''E Forest with
large regrowth
Oil palm
18 Liberia Kakata, Margibi 6°32'6.47''N 10°22'57.34''W Forest with
clearings and regrowth
Rubber tree
19 Guinea - Conacry Samaya/Kemaya, Dubréka 10°2'28.65''N 13°48'44.74''W Large forest regrowth Oil palm
20 Cameroon Northern border of Campo
Maan National Park,
South Province
2°40'47.17''N 10°13'8.11''E Large forest regrowth Newly established
rubber trees instead
of freshly cut forest
21 1F Madagascar Ambatoharanana, Sava 14°32'30.80''S 49°35'44.26''E Large forest regrowths Various field crops
22 1G Brazil Tailândia, Pará 2°39'50.20''S 48°53'17.43''W Forest with
large regrowth
Soybeans
23 1H Philippines Tupi, South Cotabato 6°18'31.14''N 124°58'17.75''E Stable forest Pineapple
24 Cameroon Ndawara Belo Ranch,
Northwest Province
6°4'41.37''N 10°22'46.00''E Forest with
large regrowth
Te a
981-d 30 MAY 2014 VOL 344 ISSUE 6187 sciencemag.org SCIENCE
RESEARCH |TECHNICAL COMMENT
Fig. 1. Selected examples of Hansen et al.s(1) failures in classifying of tree plantations (A to E) and herbal crops (F to H) as forest. All the maps are
screenshots from Hansen et al.s supplementary online map application (http://earthenginepartners.appspot.com/science-2013-global-forest) taken in November
2013 and modified to highlight details by adding the yellow squares.The colors in the right halves of each panel indicate stable forest (green), forest loss (red), forest
gain (blue), and forest loss and gain (magenta). See Table 1 for more details, including coordinates, and for several additional examples.
SCIENCE sciencemag.org 30 MAY 2014 VOL 344 ISSUE 6187 981-d
RESEARCH |TECHNICAL COMMENT
... An effective and precise forest monitoring system is essential to address forest loss and deforestation rates in the world. Early detection of forest loss is a key tool to reduce emissions from deforestation, preserve habitats, and reduce the alarming acceleration of climate change (Hansen et al. 2013(Hansen et al. , 2014Tropek et al. 2014). However, creating a reliable tool for forest monitoring is not an easy task, as complex remote sensing algorithms are still making progress in screening forests regularly (some examples of the different algorithms and techniques, are reported: Bajocco et al. (2012); Dutrieux et al. (2015); Guo et al. (2022); Hansen and DeFries (2024); Ørka et al. (2022); Panta et al. (2008); Potapov et al. (2015); Song et al. (2014Song et al. ( , 2015. ...
... Besides forest overestimation in densely forested areas, there is a relevant problem concerning GFCD, which we mentioned before and discuss here in more detail, namely, the fact that palm oil plantations and other crops are detected as forest (see also Tropek et al. 2014). In the present study, we do not consider palm oil plantations as forests, as many papers point to the disruption that this monoculture is causing in tropical forests, endangering species and biodiversity (Meijaard et al. 2020). ...
... The other relevant drawback of GFCD is that of miss-classifying lakes, rivers, palm oil and other plantations as forest. Despite the definition of forest given in GFCD, even vegetation lower than 5 m (e.g., pineapple, soybeans, or tea plantations) is often classified as forest (Tropek et al. 2014). Erroneous consideration of plantations represents a serious problem as they do not have the carbon value of forests and often contribute to endanger species and biodiversity (Brockerhoff et al. 2017). ...
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... Therefore, an effective way of understanding fire effects on forest landscapes is to assess its dynamics through changes in structure and ecosystem functions (Forman 1995;Turner and Gardner 2015). Particularly, land cover change (LCC) analysis can reveal the transformation of spatial patterns and trajectories of land cover over time (i.e., structure change), and is the most efficient way to quantitatively assess, manage and understand spatiotemporal dynamics of landscape (Ellis and Ramankutty 2008;Tropek et al. 2013;Turner and Gardner 2015;Song et al. 2018;Radwan et al. 2021). Ecosystem functions usually refer to the combination of processes and structures of an ecosystem, which can be represented as the potential capacity to deliver ecosystem services (Costanza and Daly 1992;Müller et al. 2010). ...
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... Despite the disturbances caused by historical fires, our study shows that this forest landscape could be considered mostly intact or with low anthropization, which is in agreement with global studies (Potapov et al. 2017;Jacobson et al. 2019). This is an important observation in the context of current trends in global change and the accelerated rates of land use and land cover change both locally in Chile Locher-Krause et al. 2017;Miranda et al. 2017;Otavo and Echeverría 2017;Altamirano et al. 2020) and global (Tropek et al. 2013;Venter et al. 2016;Allan et al. 2017;Song et al. 2018;Potapov et al. 2020). This suggests that for isolated landscapes such as Patagonia, it is still possible to maintain IFL proportions even in times of global change. ...
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Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA’s shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.
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Oil palm is one of the world's most rapidly increasing crops. We assess its contribution to tropical deforestation and review its biodiversity value. Oil palm has replaced large areas of forest in Southeast Asia, but land-cover change statistics alone do not allow an assessment of where it has driven forest clearance and where it has simply followed it. Oil palm plantations support much fewer species than do forests and often also fewer than other tree crops. Further negative impacts include habitat fragmentation and pollution, including greenhouse gas emissions. With rising demand for vegetable oils and biofuels, and strong overlap between areas suitable for oil palm and those of most importance for biodiversity, substantial biodiversity losses will only be averted if future oil palm expansion is managed to avoid deforestation.
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In 2004, Navjot Sodhi and colleagues warned that logging and agricultural conversion of Southeast Asia's forests were leading to a biodiversity disaster. We evaluate this prediction against subsequent research and conclude that most of the fauna of the region can persist in logged forests. Conversely, conversion of primary or logged forests to plantation crops, such as oil palm, causes tremendous biodiversity loss. This loss is exacerbated by increased fire frequency. Therefore, we conclude that preventing agricultural conversion of logged forests is essential to conserving the biodiversity of this region. Our analysis also suggests that, because Southeast Asian forests are tightly tied to global commodity markets, conservation payments commensurate with combined returns from logging and subsequent agricultural production may be required to secure long-term forest protection.
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