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

Tropek, Robert; Sedláček, Ondřej; Beck, Jan; Keil, Petr; Musilová, Zuzana; Šímová, Irena; Storch, David

doi:10.1126/science.1248753

Cited by 244 publications

(211 citation statements)

References 12 publications

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“…Attempting to distinguish natural from planted forest based on tree cover can be problematic when plantations might feature tree cover extents that are comparable with that of natural forests. While this distinction is certainly not one of which the GFCD has claimed to be capable [56], it is still a limitation to the application of the GFCD in conservation studies that has been previously noted [57], and that our study corroborates. Although agreed-upon forest definitions in fact are commonly based on percent forest cover [30], our study indicates that such a discrete classification scheme, based on mutual exclusivity in which attempts are made to define natural forest by a single tree cover threshold, may not be particularly useful when attempting to differentiate natural from planted forests.…”

Section: Discussionsupporting

confidence: 75%

“…However, CLASlite's advantage stemmed from its encapsulation of a single pixel's heterogeneity, allowing for an ontological interpretation of forest pixels that aligns most closely with the biophysical reality of naturally occurring phenomena often comprising spectra from more than one ground material [66]. Meanwhile, the GFCD's more liberal ontology of what constitutes a forest has already been criticised for its conflation of tropical forests with monoculture plantations and even tall herbaceous crops [57]. Inconsistencies in land-cover nomenclature are broadly recognised as main barriers to forest monitoring strategies [67], and our exploration of how the technical differences among the classification techniques used in this study can be reinterpreted as ontological differences largely underscore this claim.…”

Section: Discussionmentioning

confidence: 99%

“…Although the GFCD product as applied within the framework of our study was not the most optimal of the land-cover classification tools employed, the product itself still harbours opportunities for extended analyses as well. While the ramifications of the GFCD's dependence on percent tree cover in defining forest have been explored (notably in [57]), it is perhaps the GFCD's core identity as a percent tree cover product that lends itself to a wealth of extended and more tailored reinterpretations. For example, sensitivity analyses could be conducted to determine specific percent tree cover thresholds that would allow the GFCD product to distinguish certain vegetation types of interest given a particular landscape.…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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“…In past inventories, such errors in assessment can be attributed to limited data and image interpretation as well as the use of inconsistent methods. Similarly, references [64,65] describe the drawbacks of global algorithms and datasets [63] for local level assessments. In the current study, the assessment is based on uniform data, methods and interpretations.…”

Section: Discussionmentioning

confidence: 99%

Mapping Deforestation and Forest Degradation Patterns in Western Himalaya, Pakistan

et al. 2016

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Abstract:The Himalayan mountain forest ecosystem has been degrading since the British ruled the area in the 1850s. Local understanding of the patterns and processes of degradation is desperately required to devise management strategies to halt this degradation and provide long-term sustainability. This work comprises a satellite image based study in combination with national expert validation to generate sub-district level statistics for forest cover over the Western Himalaya, Pakistan, which accounts for approximately 67% of the total forest cover of the country. The time series of forest cover maps (1990, 2000, 2010) reveal extensive deforestation in the area. Indeed, approximately 170,684 ha of forest has been lost, which amounts to 0.38% per year clear cut or severely degraded during the last 20 years. A significant increase in the rate of deforestation is observed in the second half of the study period, where much of the loss occurs at the western borders along with Afghanistan. The current study is the first systematic and comprehensive effort to map changes to forest cover in Northern Pakistan. Deforestation hotspots identified at the sub-district level provide important insight into deforestation patterns, which may facilitate the development of appropriate forest conservation and management strategies in the country.

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“…For example, the attribution of oil palm plantations is an important factor for the differences in area changes between different datasets, especially in Indonesia. Oil palm is taken as cropland rather than forest in the FAO definitions (FAOSTAT, 2015) but detected as tree covers from the remote sensing (Carlson et al, 2012(Carlson et al, , 2013Hansen et al, 2013;Koh et al, 2011;Tropek et al, 2014), including in the CCI LC products. This partly explains that the larger cropland increase in LUH2v2h (Hurtt et al, 2011) and larger forest decrease in Houghton and Nassikas (2017) than those in ESA CCI PFTs and Hansen et al (2013) in Indonesia (Fig.…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

MacBean

Ciais

et al. 2018

Earth Syst. Sci. Data

226

119

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Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km 2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013 or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km 2 respectively, resulting in a net forest loss of 0.6 million km 2 , mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from

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

Cited by 244 publications

References 12 publications

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Mapping Deforestation and Forest Degradation Patterns in Western Himalaya, Pakistan

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

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