Landscape assessment and monitoring

Gómez-Sanz, Valentín; Bunce, R.G.H.; Elena-Rosselló, Ramón

doi:10.1007/978-1-4939-0953-7_8

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Again, we reasoned that if there was no bias introduced through binning, the variation in percentage tree cover over the resulting training pixels would be similar without a skew towards specific values of percentage tree cover. We focused on the effects of spatial autocorrelation, which is inherent in most landscapes (Gomez-Sanz, Bunce, & Elena-Rossello, 2014) and in real tree cover gradients, by modelling and comparing (i) fully random uniform and Beta distributions of tree cover, (ii) a sharp boundary landscape, in which half of the landscape was assigned a 100% tree cover and the remaining half a 0% tree cover, (iii) a patchy landscape of autocorrelated Beta distributions across the 0-100% tree cover range, (iv) a linear gradient of tree cover decreasing from 100% to 0% tree cover along the landscape x axis, and (v) a complex gradient representing Beta distributions across the 0-100% tree cover range.…”

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confidence: 99%

MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015)

Hooftman

Langevelde

Veenendaal

et al. 2017

Global Ecol Biogeogr

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Lloyd, Jon. 2017. MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015). Global Ecology and Biogeography, 26 (7). 854-859. 10.1111/geb.12592 Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. ABSTRACTIn their recent paper, Staver and Hansen (Global Ecology and Biogeography, 2015, 24, 985-987) refute the case made by Hanan et al. (Global Ecology and Biogeography, 2014, 23, 259-263) that the use of classification and regression trees (CARTs) to predict tree cover from remotely sensed imagery (MODIS VCF) inherently introduces biases, thus making the resulting tree cover unsuitable for showing alternative stable states through tree cover frequency distribution analyses. We here provide a new and equally fundamental argument why the published frequency distributions should not be used for such purposes.We show that the practice of pre-average binning of tree cover values used to derive cover values to train the CART model will also introduce errors in the frequency distributions of the final product. We demonstrate that the frequency minima found at tree covers 8 % to 18 %; 33 % to 45 %; and 55 % to 75 % can be attributed to numerical biases introduced when training samples are derived from landscapes containing asymmetric tree cover distributions and/or a tree cover gradient. So it is highly likely that the CART, used to produce MODIS VCF, delivers tree cover frequency distributions that do not reflect the real world situation.2 Main body of text:The MODIS VCF tree cover product of Hansen et al. (2002b) provides worldwide estimates of percentage tree cover derived from MODIS data. Discontinuities in tree cover frequency distributions derived from this data have been used to support the hypothesis that the observed distribution of forest, savanna and grassland vegetation in the tropical and boreal regions of the world represent alternative stable states for equivalent environmental conditions (Hirota et al., 2011;Staver et al., 2011;Favier et al., 2012;Murphy & Bowman, 2012;Scheffer et al., 2012;Xu et al., 2016). But recently Hanan et al. (2014Hanan et al. ( , 2015 suggested that the adopted classification and regression trees (CART) approach, used to produce the MODIS VCF tree cover estimates, introduced a systematic bias which makes the MODIS VCF product inappropriate for the analysis of % tree cover frequency distributions. This point was counteredby Staver and Hansen (2015) arguing (i) that the approach taken by Hanan et al. (2014), using simulated EO data and pseudo satellite metrics to demonstrate that an artificial bias is generated by the CART approach, does not reflect the complexity and variability of landscapes and vegetation across the globe and (ii) that the CART model used by Hanan et al. (2014) was highly prun...

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confidence: 99%

MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015)

Hooftman

Langevelde

Veenendaal

et al. 2017

Global Ecol Biogeogr

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“…The development of a relevant theoretical framework and set of methods for studying change is an important legacy of landscape ecology. Gómez-Sanz et al (2014) and forest management and planning should become a priority, and knowledge transfer is an essential but under-used element in developing strategies to help organizations learn to deal with change.…”

Section: Discussionmentioning

confidence: 99%

Forest landscape ecology and global change: what are the next steps?

Azevedo

Pinto

Perera

2014

Forest Landscapes and Global Change

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“…The analyzed extents correspond to 163.84 ha, 655.36 ha and 2621.44 ha, respectively ( Figure 1). The selected widow dimensions are comparable with those commonly used for local and regional forest analysis at landscape scale [40]. As suggested by O'Neil et al [19], the chosen extents are at least two times larger than the largest patch area in the year 1954 (see Figure 1).…”

Section: Multi-scale Analysismentioning

confidence: 95%

Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

et al. 2014

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Abstract:We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011) with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy) reserve at multiple spatial extents (128, 256 and 512 pixels). We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to OPEN ACCESS Remote Sens. 2014, 6 9299 detect forest variations over time. Similar procedures can (i) help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii) minimize the bias when comparing pattern metrics at a single extent and (iii) be extended to other landscapes and scales.

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Landscape assessment and monitoring

Cited by 7 publications

References 44 publications

MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015)

MODIS VCF should not be used to detect discontinuities in tree cover due to binning bias. A comment on Hanan et al. (2014) and Staver and Hansen (2015)

Forest landscape ecology and global change: what are the next steps?

Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

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