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# Global intercomparison of functional pyrodiversity from two satellite sensors

M. Vanesa Moreno, Pierre Laurent, Florent Mouillot

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M. Vanesa Moreno, Pierre Laurent, Florent Mouillot. Global intercomparison of functional pyrodiversity from two satellite sensors. International Journal of Remote Sensing, 2021, 42 (24), pp.9523-9541. 10.1080/01431161.2021.1999529. hal-04224966

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Global intercomparison of functional pyrodiversity from two satellite
sensors

M. Vanesa Moreno<sup>a,*</sup>, Pierre Laurent<sup>b</sup> and Florent Mouillot<sup>a</sup>

<sup>a</sup> UMR CEFE 5175, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France; <sup>b</sup> Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France

* Corresponding author.

UMR CEFE 5175, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3,
Montpellier, France. E-mail: [morenodominguez.mariavanesa@ird.fr](mailto:morenodominguez.mariavanesa@ird.fr) (M.V. Moreno).

* Present address.

Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Cadenazzo,
Switzerland. E-mail: [maria.moreno@wsl.ch](mailto:maria.moreno@wsl.ch) (M.V. Moreno).

This document is the accepted manuscript version of the following article:
Moreno, M. V., Laurent, P., & Mouillot, F. (2021). Global intercomparison of
functional pyrodiversity from two satellite sensors. *International Journal for Remote
Sensing*, 42(24), 9523-9541. https://doi.org/10.1080/01431161.2021.1999529
# Global intercomparison of functional pyrodiversity from two satellite sensors

Fires create substantial ecosystem disturbances, which affect biodiversity and climate processes. Previously, satellite-derived burned areas were assessed, and fire patches and morphological traits related to fire spread functions identified. The cut-off value in days is a critical parameter for the algorithms used to identify fire patches, as it identifies when two contiguous burned pixels should be grouped together as one patch. Our understanding of how fire patches and their morphologies change with the cut-off values between satellite sensors is currently insufficient for model development and pyrogeographic studies. We have addressed this using the FRY v1.0 global database of fire patch functional traits, with cut-off values of 3, 5, 9, and 14 days, derived from two burned area products, the MERIS FireCCI version 4.1 and MODIS MCD64A1 collection 6, from two satellite sensors with different spatial and temporal resolutions. We intercompared the FRY products with functional diversity trait methods that assess their morphological pyrodiversity structures and dissimilarities. We have assessed the functional pyrodiversity of the FRY products with four cut-off values, for fire patches $≥ 300$ ha, on 1° resolution tiles for the world and Global Fire Emissions Database (GFED) region scales, from 2005–2011. The functional structures and dissimilarities between the FRY products varied with the regions, while cut-off value sensitivity varied with the regions and between the satellite sensor products. These variations in functional structures and dissimilarities mirrored the regional fire spread processes and satellite sensor resolutions. The functional pyrodiversity that varied with the regions and similar cut-off values did not lead to the best agreement between the satellite sensor products. These variations reinforce the need to combine high temporal resolution optical sensors with radar in tropical regions, to consistently meet the monitoring requirements and for international organisations, to enable improved assessments of climate and biodiversity.

## 1. Introduction

Fire is a major ecosystem disturbance, affecting the abundance and diversity of vegetation (Kelly et al. 2020) and influencing climatic processes (Bowman et al. 2009), and as a result, fire, vegetation, and climate have coevolved (Marlon et al. 2008). Fire disturbances are thus an essential variable in climate (GCOS 2016) and biodiversity assessments (GEO BON 2017).
From the early 2000s, satellite-derived burned area analyses from the visible and near-infrared spectrums (Chuvieco et al. 2019) have allowed global fire emissions to be accounted for (e.g., Andreae 2019), and they were omitted from atmospheric carbon budgets (Field and Fung 1999). Additionally, burned area products have enabled the calibration and validation of fire modules embedded into the Dynamic Global Vegetation Models (DGVMs; Bistinas et al. 2014; Mouillot et al. 2014; Hantson et al. 2016). However, limited fire spread information and a general inability to provide insights into whether differences are because of fire ignition or size, has led to questions about the usability of burned area analyses (Yue et al. 2014).

Recent analyses of satellite-derived global burned area products at the pixel level have led to individual fire patch identification and improved the study of fire size distributions (Archibald and Roy 2009; Hantson, Pueyo, and Chuvieco 2015) and fire-patch morphologies (Nogueira et al. 2017; Laurent et al. 2018). Furthermore, they have led to the identification of individual fire events using active fire data, enabling the study of fire behaviour (Loboda and Csiszar 2007) and regimes (Artés et al. 2019), and providing a fire atlas (Andela et al. 2019). Fire patch information has also become a promising tool that may enable disaster risk reductions in developing countries, as there is currently a lack of reliable data to facilitate this (UNISDR 2015).

To identify fire patches using pixel-level burn day information, the flood-fill algorithm aggregates neighbouring burned pixels into patches, if their burn day falls below a time parameter cut-off, measured in days. However, there are satellite sensor uncertainties (Brennan et al. 2019), which include assessments of the burned day in relation to cloud presence (Humber, Boschetti, and Giglio 2020; Humber et al. 2019). Although algorithms are emerging to handle such uncertainties (Artés et al. 2019; Andela et al. 2019; Campagnolo et al. 2019; Oom et al. 2016), other remains from omitted and committed pixel burned area products (Padilla et al. 2015; Boschetti et al. 2019). Furthermore, regional fire spread can generate a fast-
or slow-growing fire front, making the cut-off parameter (Andela et al. 2019; Oom et al. 2016; Moreno et al. 2020; Laurent et al. 2018) and suitability of the satellite sensors resolutions a source of discussion in the delivered products (Moreno et al. 2020). The current empirical assumption is that the cut-off values are generic for all satellite sensors, but optimal cut-off values to match the actual fire shape represented in reference data may change with regions (Hantson, Pueyo, and Chuvieco 2015; Oom et al. 2016; Andela et al. 2019; Fusco et al. 2019).

Thus, as burned area products, fire-patch products should be validated and intercompared to use them in models and pyrogeographic analysis, and to help provide the best estimates for pyrosphere, biosphere, and atmosphere interactions (Mouillot et al. 2014; Rabin et al. 2017; GCOS 2016). However, fire patch validation, using independent reference data (Boschetti, Roy, and Justice 2006) on a global scale, is not currently feasible due to a lack of widespread validation sites.

To understand how fire patches and their morphologies might change with the cut-off values between satellite sensors and to ensure proper use of these freely available datasets to the scientific community, we have used the FRY v1.0 global database of fire patches and their morphological features as functional traits, as created by Laurent et al. (2018). Furthermore, we have used the flood-fill algorithm with cut-offs at 3, 6, 9, and 14 days, from two available global burned area products; the MERIS FireCCI version 4.1 (FireCCI41; Chuvieco et al. 2016) and MODIS MCD64A1 collection 6 (MCD64A1; Giglio et al. 2018), that were from two satellite sensors with contrasting temporal (from ~1 day with MODIS to 3 days with MERIS) and spatial resolutions (from ~300 m with MERIS to 500 m with MODIS). We intercompared the FRY products with a previously described trait-based approach applied to fire patches in North America (Moreno et al. 2020), initially developed for community ecology assessments (Villéger, Mason, and Mouillot 2008; Villéger, Grenouillet, and Brosse 2013; Mouillot et al. 2013). These approaches assessed the functional pyrodiversity of the FRY products in terms of
functional fire patches and their size, elongation, and direction traits. We compared their
functional structures when represented by 3D volumes of the traits, and their dissimilarity was
assessed by comparing their overlap.

**2. Materials and Methods**

**2.1. FRY v1.0 database**

We used the FRY v1.0 global database of fire patch functional traits (Pierre Laurent et al. 2018),
which consists of fire patches whose morphological features are related to the fire spread
function (Laurent et al. 2018; Moreno et al. 2020). It includes the fire patch size and geometric
features, and the parameters for a fitted ellipse for each fire patch (Laurent et al. 2018). We
selected the three essential fire spread functional traits of size, elongation, and direction. Size
is the area of the fire patch (ha). Elongation is the ellipse eccentricity. It describes how round
or flat is the ellipse shape. Direction (°) is the rotation angle of the ellipse. It corresponds to the
directional azimuthal angle between the north and the big half axis of the ellipse (Laurent et al.
2018).

The FRY global database includes two products that have four cut-off values of 3, 5, 9, and 14 days. Fire patches are aggregations of neighbouring burned area pixels according to the burned day. They were identified by the flood-fill algorithm. Thus, the four cut-off values determined the maximum burned day difference between the neighbouring pixels belonging to the same patch (Laurent et al. 2018). We used both FRY products with their four cut-off values and only fire patches ≥ 300 ha, as smaller fire patches showed inaccurate morphologies (Nogueira et al. 2017) and were often omitted in burned area products (Padilla et al. 2015).

The two satellite-derived burned area products at the pixel level were used to derivate
the fire patches, FireCCI41 and MCD64A1, created by hybrid algorithms combining thermal
(Giglio, Schroeder, and Justice 2016) and post-fire reflectance information, from the visible
and near-infrared spectrum. FireCCI41 used the data from the Envisat-MERIS satellite sensor operated by the European Space Agency (ESA; Chuvieco et al. 2016) and MCD64A1 from the Terra and Aqua-MODIS satellite sensor operated by the National Aeronautics and Space Administration (NASA; Giglio et al. 2018). Besides their post-fire reflectance differences, MERIS had a higher spatial resolution than MODIS (300 m vs. 500 m), whereas the temporal resolution of MODIS was higher (1 vs. 3 days). While the products from MCD64A1 were available for a more extended period than those of FireCCI41, we considered only the overlapping years, 2005–2011.

## 2.2. Functional diversity trait-based approach

To intercompare the satellite-derived FRY products, we applied a functional diversity trait-based approach, previously used by Moreno et al. (2020) and adapted from Mouillot et al. (2013) and Villéger et al. (2008; 2013). We considered that each fire-patch product was a community, and each fire patch was an individual. The approach consisted of four steps, from sampling to complete assessment of the fire-patch products (Figure 1).

### 2.2.1. Sample of fire-patch products

We sampled the fire-patch products into 1° tiles across the globe, using the global pool of fire patches ≥ 300 ha from the FRY database, which included two products, each with four cut-off values. Thus, eight fire-patch products were sampled (Figure 1(a)). The 1° tiles balanced the analysis requirements and computing costs. Although a larger tile at the regional scale has been used (Moreno et al. 2020), it was unsuitable for African regions, as the larger tiles account for more fire patches, which significantly extends the computing time. We assessed only tiles with more than three fire patches in each product (4064 tiles). Each tile was identified using the geographic coordinates for its bottom left corner and each fire-patch product using its cut-off value, as an ID name for further steps.
### 2.2.2. Building the functional space

We built the functional space within each tile for each fire-patch product, using the three essential non-correlated fire spread functional traits from the FRY database: size, elongation, and direction (Figure 1(b)). The three continuous functional traits were normalised using the Box-Cox method, which enables the best normalising transformation for the data to be found, using the 'boxcoxfit' function from the 'geoR' R-package (Ribeiro and Diggle 2016). This was likely to make the 3D functional spaces comparable within and across the fire-patch products, the functional traits were standardised overall to a 0 mean and variance of 1, using the 'decostand' function from the 'vegan' R-package (Oksanen et al. 2016). Unlike functional spaces built with > 3 traits, here, the three essential fire spread functional traits were suitable without using ordination methods to reduce them (Villéger, Mason, and Mouillot 2008). We then intercompared the fire-patch products based on the built functional spread spaces.

### 2.2.3. Assessment of functional structures

We assessed the functional structures of the fire-patch products, defined as the variation in fire patch traits in each functional space built in each tile, based on the following functional diversity indices. Functional fire patches (Figure 1(c-i)) were the number of fire patches present in each tile and each product. The functional richness (Figure 1(c-ii)) was the volume of the 3D functional space, gathering all the fire patches into a tile and for each product (Mouillot et al. 2013; Villéger, Mason, and Mouillot 2008). The functional dispersion (Figure 1(c-iii)) was the mean distance of the individual fire patches to the mean trait values, for all fire patches present in the 3D functional space (Mouillot et al. 2013; Villéger, Mason, and Mouillot 2008). Both functional richness and dispersion indices ranged from 0 to 1. The functional diversity indices were computed using the 'dbFD' function from the 'FD' R-package (Laliberté, Legendre, and Shipley 2015).
We then regressed the functional diversity indices tile to the tile computed from each FRY product against each product, using a Standardised Major Axis (SMA) method (Warton et al. 2012), given the uncertainties in both products derived from the burned area products (Padilla et al. 2015; Boschetti et al. 2019). When the squared correlation coefficient (r²) was significant at the 95% significance level, inference testing was run if the slope equalled 1, and the intercept equalled 0, at the 95% significance level for each SMA line. If the functional structure estimates from the two-satellite sensor FRY products were equal, the slope would be 1, and the intercept would be 0. As per this rationale, we quantified the biases using the following descriptions (Mesplé et al. 1996). An unbiased relationship (with slope ≈ 1 and intercept ≈ 0) was when both products had good agreement (Figure 1(c-iv)). A constantly biased relationship (with slope ≈ 1 and intercept ≠ 0), was when a product was overestimating or underestimating the other (Figure 1(c-v)). A proportionally biased relationship (with slope ≠ 1 and intercept ≈ 0) was when one product gave values that were higher or lower than those from the other, proportional to the observation value (Figure 1(c-vi)). A constantly and proportionally biased relationship (with slope ≠ 1 and intercept ≠ 0) was the combination of both (Figure 1(c-vii)). When more than one SMA line falls in the same outcome, the grade of the r² will determine the SMA line of best fit. A non-significant relationship was when the r² was not significant at the 95% significance level (Figure 1(c-viii)). A negative relationship outcome was also added for when the slope was negative (Figure 1(c-ix)).

SMA lines were estimated and inference using the 'sma' function from the 'SMATR' R-package (Warton et al. 2012) and considering the number of functional fire patches and functional dispersion log10-transformed to achieve approximately bivariate normality. The tile to tile spatial relationships were examined for the world and Global Fire Emissions Database (GFED) regions (Figure 2; Giglio, Randerson, and van der Werf 2013). The 1º tiles were assigned to the regions in which their centroid was located.
### 2.2.4. Assessment of functional dissimilarities

Functional dissimilarities were assessed using functional beta diversity and its two components, turnover and nestedness (Villéger, Grenouillet, and Brosse 2013). Functional beta diversity was the dissimilarity in terms of the overlap of convex hull volumes, between the two fire patch products in the 3D functional space. It was the sum of functional turnover and functional nestedness. Functional nestedness was the difference between functional beta diversity and functional turnover. Their values ranged between 0 and 1. When functional beta diversity was 0, it indicated non-dissimilarity due to a complete overlap between the volumes of the two products in the 3D functional space (Figure 1(d-i)). When functional beta diversity was 1, it indicated complete dissimilarity due to a complete non-overlap between the volumes of the two products in the 3D functional space. It can be driven by functional turnover (Figure 1(d-ii and d-iii)), due to the differences in the functional trait values or driven by the functional nestedness (Figure 1(d-iv)) due to the differences in the functional richness. Between these extremes, the functional beta diversity indicates different dissimilarities driven differently by functional turnover and functional nestedness (Figure 1(d-v and d-vi)). We computed the functional beta diversity and its two components using the 'all.intersect' function from the 'rcdd' R package (Geyer, Meeden, and Fukuda 2017). The assessment of the functional dissimilarities complements the assessment of the functional structures, as different communities can lead to similar functional diversity indices (Moreno et al. 2020).

We estimated the differences in the functional beta diversity and its two components between the cut-off values using ANOVA analysis and compared the means using Fisher's least significant difference test, at a 95% significance level, with the 'aov' function from the 'stats' R-package (R Core Team 2017) and the 'LSD.test' function from the 'agricolae' R-package (De Mendiburu 2014). For the world and GFED regions (Figure 2; Giglio, Randerson, and van
der Werf 2013), we identified the cut-off values with the minimum average functional beta diversity.

# 3. Results

## 3.1. Functional structures

The SMA regressions for the functional diversity indices for the tiles between the FRY products and the cut-offs at 3, 5, 9, and 14 days, revealed some biases in their functional structures, which varied with the regions (Figure 3). Here, we focused on the SMA line of best fit between the satellite sensor products, whose cut-off values varied with the regions and products (Table 1 and Figure S1, S2 and S3 in Supplementary material).

The best fit for the functional fire patch relationships (Figure 3(a)) between the FRY products were unbiased in BONA, CEAM, EURO, MIDE, SEAS, and AUST. This suggested that there was good agreement for the number of fire patches identified by the satellite sensor products in the 3D functional space. However, they were constantly biased for WORLD, TENA, NHSA, SHSA, SHAF, and CEAS, suggesting that one satellite sensor product was systematically overestimating the number of fire patches of the other by a constant value. Such relationships became proportionally biased in BOAS and constantly and proportionally biased in NHAF. In EQAS, such correlations were not significant (p > 0.05). The SMA lines with the best fit (Table 1) were those with small cut-off values for WORLD (r² = 0.69), TENA (r² = 0.42), MIDE (r² = 0.70), and BOAS (r² = 0.62), whereas they had high cut-off values in NHSA (r² = 0.47) and EURO (r² = 0.74) and contrasted cut-off values in BONA (r² = 0.55), CEAM (r² = 0.16), SHSA (r² = 0.33), SHAF (r² = 0.64), AUST (r² = 0.71), NHAF (r² = 0.64), CEAS (r² = 0.54), and SEAS (r² = 0.50). However, the relationships were similar for the cut-off values from CEAM, NHSA, SHSA, NHAF, and EQAS, suggesting that the functional fire patches had
a low sensitivity to the cut-off values of the regions, between the satellite sensor products (Figure S1).

The best fit for the functional richness relationships (Figure 3(b)) between the FRY products was unbiased for BONA, EURO, MIDE, SEAS, and AUST. This suggested that there was consistency between the volumes of the satellite sensor products in the 3D functional space. The relationships became constantly and proportionally biased for the WORLD, NHAF, SHAF, and CEAS regions. In the NHSA, such correlations were not significant (p > 0.05), and they were negative in TENA, CEAM, SHSA, and EQAS. The SMA lines of best fit (Table 1) for the small cut-off values were identified for NHAF ($r^2$ = 0.16) and SHAF ($r^2$ = 0.09), while for the high cut-off values they were for EURO ($r^2$ = 0.06), CEAS ($r^2$ = 0.05), and AUST ($r^2$ = 0.16), and the contrasted cut-off values were for the WORLD ($r^2$ = 0.10), BOAS ($r^2$ = 0.13), BONA ($r^2$ = 0.08), MIDE ($r^2$ = 0.10), and SEAS ($r^2$ = 0.02). However, the relationships were similar for all the cut-off values for WORLD, NHSA, NHAF, SHAF, and EQAS, indicating that there was a low sensitivity of functional richness to the cut-off values in these regions, between the satellite sensor products (Figure S2).

The best fit for the functional dispersion relationships (Figure 3(c)) between the FRY products were unbiased in TENA, CEAM, NHSA, EURO, MIDE, SHAF, BOAS, CEAS, SEAS, and AUST. This suggested that there was consistency in the mean distance of individual fire patches to the mean trait values for the two satellite sensor products in the 3D functional space across the tiles. Such relationships became constantly biased in the WORLD and NHAF, proportionally biased in BONA, and constantly and proportionally biased in SHSA. For the functional fire patches in EQAS, the functional dispersion correlations were not significant (p > 0.05). The SMA lines of best fit (Table 1) were with the small cut-off values for WORLD ($r^2$ = 0.28), SHAF ($r^2$ = 0.53) and AUST ($r^2$ = 0.20), whereas there were high cut-off values for NHSA ($r^2$ = 0.30), and contrasted cut-off values for BONA ($r^2$ = 0.11), NHAF ($r^2$ = 0.28),
BOAS (r² = 0.15), TENA (r² = 0.07), SHSA (r² = 0.15), CEAM (r² = 0.16), EURO (r² = 0.19), SEAS (r² = 0.15), MIDE (r² = 0.08), and CEAS (r² = 0.19). However, the relationships were similar for all the cut-off values for NHSA, SHSA, and EQAS, suggesting a low sensitivity of functional dispersion to the cut-off values in these regions, between the satellite sensor products (Figure S3).

## 3.2. Functional dissimilarities

The overlap between the convex hull volumes of the FRY products in the 3D functional space within the tiles revealed their functional dissimilarities, in the functional beta diversity and its two components of functional turnover and functional nestedness, which also varied with the GFED regions (Figure 4). We focused on the minimum average functional beta diversity, whose cut-off values varied regionally and with the satellite sensor products (Table 2 and Figure S4).

The minimum average functional beta diversity (Figure 4) was lower in NHAF (0.24 ± 0.21) and SHAF (0.26 ± 0.21), suggesting a large overlap between the volumes of the satellite sensor products in the 3D functional space. Such minimum average functional beta diversity became moderate in most of the regions, such as for the WORLD (0.41 ± 0.23), SHSA (0.45 ± 0.23), EURO (0.49 ± 0.19), MIDE (0.48 ± 0.19), CEAS (0.43 ± 0.20), SEAS (0.42 ± 0.20), and AUST (0.42 ± 0.22), suggesting a partial overlap between the volumes of the satellite sensor products in the 3D functional space. The larger non-overlap, with values of functional beta diversity higher than 0.50, was between the volumes of the two satellite sensor products in the 3D functional space for BONA (0.59 ± 0.18), TENA (0.58 ± 0.17), CEAM (0.54 ± 0.20), NHSA (0.50 ± 0.22), BOAS (0.52 ± 0.19), and EQAS (0.58 ± 0.18).

The minimum average functional beta diversity (Figure 4) was driven on average by a functional turnover of more than 50% in BONA (0.43 ± 0.20), TENA (0.35 ± 0.21), EURO (0.27 ± 0.20), MIDE (0.26 ± 0.18), BOAS (0.32 ± 0.20), CEAS (0.26 ± 0.18), and AUST (0.23 ± 0.17), due to the differences in the functional trait values between the two satellite sensor
products, whereas it was driven on average by functional nestedness by more than 50% in
NHAF (0.15 ± 0.20) and SHAF (0.16 ± 0.19), CEAM (0.34 ± 0.23), NHSA (0.36 ± 0.25),
SHSA (0.31 ± 0.25), and EQAS (0.34 ± 0.25), due to the differences in functional richness
between the satellite sensor products. For the SEAS and WORLD, the functional beta diversity
was driven on average equally by both functional turnover and nestedness, due to the
differences in both functional trait values and richness between the satellite sensor products.

The minimum average functional beta diversity was with the small cut-off values in
NHSA, whereas it was with high cut-off values in CEAM, SHSA, SHAF, CEAS, SEAS, and
EQAS. In the other regions, it was with contrasted cut-off values (Table 2). However, the
functional beta diversities on average, were not significantly different (p > 0.05) with cut-off
values in TENA, CEAM, NHSA, SHSA, MIDE, NHAF, SHAF, BOAS, and AUST. This
suggested that there was a low sensitivity for the functional beta diversity of the cut-off values
from these regions between the satellite products, yet with some significant differences in terms
of functional turnover in NHSA, SHSA, and NHAF, and functional turnover and nestedness in
BOAS (Figure S4).

**4. Discussion**

**4.1. Functional pyrodiversity**

We have presented here, the first global functional pyrodiversity assessment and identified the
regional variations between the satellite sensor products. These findings mirrored the regional
fire spread processes and satellite sensor characteristics. The temporal resolution of the satellite
sensors determined their ability to not only detect fires, but also follow their spread. Hence, the
MODIS satellite sensor captured more fires than the MERIS satellite sensor. This caused bias
when determining the functional fire patches between the FRY products in regions such as
TENA (Figure 3(a)), where cropland fires are underestimated (Moreno et al. 2020). The
overlooked fires were also found to be related to fires close to human settlements, where fire
suppression policies are more robust (Andela et al. 2019; Fusco et al. 2019).

Similarly, because of considerably higher temporal resolution, the MODIS satellite
sensor more accurately captured the quick-spreading fires, when compared with the MERIS
satellite sensor, which fragmented the extensive fires into smaller ones. These differences
created functional structure biases and dissimilarities between the FRY products because of the
lack of extreme trait values. For example, in BONA, where the fires are widespread (Rogers et
al. 2015), both FRY products agreed structurally (Figure 3) but were functionally dissimilar,
due to the differences in their trait values (Figure 4). Similar results were also found for savanna
fires (Oom et al. 2016), which are quick-spreading as they are highly flammable and lead to
high levels of functional pyrodiversity. Consequently, functional dissimilarities were small in
the African regions NHAF and SHAF (Figure 4), despite both FRY products being structurally
biased (Figure 3).

Furthermore, depending on the temporal coverage of the satellite sensors, smoke or
cloud cover could temporally mask some burned pixels, increasing the burned day uncertainties
and creating fire patch diversities, particularly in tropical regions where fires occur most
frequently during the dry periods (Page et al. 2002; Turetsky et al. 2015) and they have high
cloud coverage. EQAS was the most critical region with more biases in functional structure
(Figure 3) and functional dissimilarities (Figure 4). Similarly in NHSA and SHSA, the FRY-
MCD64A1 product doubled the fire patches from the FRY-FireCCI41 product (Figure S6 and
S9, and Table S2 and S3), leading to negative relationships for functional richness from tile to
tile (Figure 3(b)), and causing a high nested structure within the tiles between the FRY products
(Figure 4). This has been related to a lack of MERIS satellite images (Tum et al. 2016).

Our results are consistent with previous intercomparisons of burned area products (e.g.,
Hantson et al. 2016; Humber et al. 2019). The variability reported for most fire-prone regions
in the savanna showed the lowest variation, less than 40%, whereas most other regions were
between 40% and 100%, and EQAS showed variations that were larger than 100% (Hantson et
al. 2016). The validation of the MCD64A1 product for peatland and non-peatland fires in
Indonesia, showed that the product underestimated approximately half of the burned area with
slightly better accuracies for larger fires (Vetrita et al. 2020). Differences in the boreal fires
were also highlighted when validating the burned area products (Chuvieco et al. 2016; Boschetti
et al. 2019). For example, the MCD64A1 product underestimated by approximately 16% the
burned area for the croplands, grasslands, and forests fires in Eurasia (Zhu et al. 2017).

4.2. Cut-off values

The first global assessment was made for the cut-off value correspondence between the satellite
sensor products, which showed the regional and satellite sensor dependent cut-offs. If the cut-
off values were only regionally dependent, then the functional structures and dissimilarities
between the two products for all regions should be optimal with similar cut-off values.
However, the SMA line of best fit (Figure 3 and Table 1) between the satellite sensor products
and the minimum average functional beta diversity (Figure 4 and Table 2) for some regions
were in contrast with the cut-off values, demonstrating that the cut-off values are satellite sensor
and regionally dependent.

Like the regional variations in functional pyrodiverstiy, the regional fire spread processes and
satellite sensor characteristics determined the correspondence between the cut-off values of the
satellite sensor products. For example, boreal fires in BONA were more intense than in BOAS
(Rogers et al. 2015; Wooster 2004), and fire patches in these regions were more complex overall
(Laurent et al. 2018), and consequently, more challenging to match across the satellite sensors.
The functional structures and dissimilarities (Figure S1, S2, S3 and S4) between the FRY
products varied with the cut-off values more strongly in BONA than in BOAS. These results
agreed well with the validation of the FRY products, using the reference data based on high-resolution fire perimeters in North America, which indicated that the FRY-MCD64A1 product better conserved fire patches than the FRY-FireCCI41 in BONA (Moreno et al. 2020), suggesting that there were differences between the two satellite sensor products. However, for some regions the functional structural relationships between the satellite sensor products were similar for all cut-off values and some functional dissimilarities on average were not significantly different (p > 0.05) with the cut-off values (Figure S4). It is not clear whether the sensitivity to the cut-off values varies only among regions and satellite sensors, or whether the functional trait-based method based on 1° tiles did not capture the extreme trait value differences in some regions.

### 4.3. Challenges and opportunities

The statistical framework used, which followed Moreno et al. (2020), was a functional pyrogeographic approach for comparative analysis of fire patch products relying on their morphological features. It provided an efficient and synthetic assessment and helped to advance global pyrodiversity quantification as an ecological process, which was previously only studied at a continental level (Hempson et al. 2018; Beale et al. 2018). Although a 1° tile size was used across the globe, the tile size should be adapted to the fire patch number, to prevent potential methodological bias in functional richness and dissimilarities, where a high fire patch number would limit the capture of differences in extreme trait values, such as in African regions. Also, more effort should be made to understand its distribution across scales. These issues were observed previously in investigations of taxonomical and functional diversity (e.g., Chase et al. 2019; Jarzyna and Jetz 2018) and burned areas (Turco et al. 2019).

Our results extend and agree with the findings of previous studies, which argue that the suitability of cut-off values are regionally dependent (Hantson, Pueyo, and Chuvieco 2015; Oom et al. 2016; Andela et al. 2019; Fusco et al. 2019), by demonstrating that the cut-off values
are also satellite sensor dependent. Thus, we suggest considering not only the regional specific
fire spread processes but also the characteristics of the satellite sensor, when choosing cut-off
values; particularity in tropical regions, where biases may influence interpretations or
estimations in climate and biodiversity assessments. We acknowledge that cut-off values can
change over time, as fires are dynamic and have the potential to change landscapes and fire
spread processes. Fire patch traits derived from fire patches are key indicators of the underlying
fire spread processes (Laurent et al. 2019), fire regimes and their impact (Artés et al. 2019).
More effort should be made to accurately obtain this information from satellite-derived
products and better understand the temporal consistency of cut-off values.

We discussed the importance of the temporal resolution in the correspondence of the
cut-off value and thus fire patches and their morphological traits between the satellite sensor
products. The temporal resolution was the most important for the satellite-derived fire patch
identification compared to reference data in North America (Moreno et al. 2020). Shorter revisit
time reduced the uncertainties in the burned day that fed the flood-fill algorithm, particularly
for small and fast-spreading fires (Laurent et al. 2018; Moreno et al. 2020). Despite remaining
a critical issue in burned area products (Brennan et al. 2019), the inconsistencies are decreasing
with improved technologies (Humber et al. 2019; Giglio et al. 2018). For tropical regions,
where burned pixel detection is highly affected by cloud coverage, the optical sensors can now
be combined with radar and function in all weather conditions with minimal atmospheric effect
(e.g., Belenguer-Plomer et al. 2019; Lohberger et al. 2018).

Furthermore, we require a better understanding of the other characteristics of satellite
sensors, such as their spatial resolutions, which may cause differences, when determining fire
patch complexity (Laurent et al. 2018; Humber, Boschetti, and Giglio 2020). The continual
comparison of the new and upcoming products, such as the latest MODIS-based FireCCI51
(Chuvieco et al. 2018), with a 250-m spatial resolution, and alternative fire patch identification algorithms (Artés et al. 2019; Andela et al. 2019) could address these questions.

**5. Conclusions**

The functional pyrodiversity varied with the regions and similar cut-off values did not result in the highest level of agreement between the satellite sensor products, because of regional fire spread processes and different temporal resolutions of the satellite sensors. These variations suggest that fire-patch identification from satellite-derived burned areas can be improved using regional and satellite sensor cut-off values. Our results reinforce the need to consider high temporal resolution optical and radar satellite sensors to achieve consistent and accurate fire disturbance products, and thus meet the requirements for improved climate and biodiversity assessments.

**Acknowledgements**

The European Space Agency, contract N° 4000115006/15/I-NB, supported this study and P.L.'s work.
M.V.M.'s work was supported by UM Post-Doc program co-financed by the ESA Fire_cci project
[contract N° 4000115006/15/I-NB], and an IRD post-doc fellowship. We thank P. Ciais for his feedback
on a draft of this manuscript.

**Declaration of interest**

No potential competing interest was reported by the authors.

**Funding**

This work was supported by the European Space Agency under Grant [N° 4000115006/15/I-NB].

**Data availability statement**

The data supporting the findings of this study are available in the Supplemental online material of this
article. Further information can be obtained from the corresponding author upon reasonable request.
The FRY v1.0 global database is available online: Laurent, Pierre, Mouillot Florent, Yue Chao, Moreno M Vanesa, Ciais Philippe, Nogueira Joana M P. 2018. FRY: a global database of fire patch functional traits. OSU OREME. doi: /10.15148/0e999ffc-e220-41ac-ac85-76e92ecd0320.

References

Andela, Niels, Douglas C. Morton, Louis Giglio, Ronan Paugam, Yang Chen, Stijn Hantson, Guido R. Van Der Werf, and James T. Anderson. 2019. “The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction.” *Earth System Science Data* 11 (2): 529–552. doi:10.5194/essd-11-529-2019.

Andreae, Meinrat O. 2019. "Emission of Trace Gases and Aerosols from Biomass Burning - An Updated Assessment." *Atmospheric Chemistry and Physics* 19 (13). Copernicus Publications: 8523–8546. doi:10.5194/acp-19-8523-2019.

Archibald, S., and D. P. Roy. 2009. "Identifying Individual Fires from Satellite-Derived Burned Area Data." In *International Geoscience and Remote Sensing Symposium (IGARSS)*, 3:III-160-III-163. doi:10.1109/IGARSS.2009.5417974.

Artés, Tomàs, Duarte Oom, Daniele de Rigo, Tracy Houston Durrant, Pieralberto Maianti, Giorgio Libertà, and Jesús San-Miguel-Ayanz. 2019. “A Global Wildfire Dataset for the Analysis of Fire Regimes and Fire Behaviour.” *Scientific Data* 6 (1): 296. doi:10.1038/s41597-019-0312-2.

Beale, Colin M, Colin J Courtney Mustaphi, Thomas A Morrison, Sally Archibald, T Michael Anderson, Andrew P Dobson, Jason E Donaldson, Gareth P Hempson, James Probert, and Catherine L Parr. 2018. “Pyrodiversity Interacts with Rainfall to Increase Bird and
Mammal Richness in African Savannas.” Edited by Margaret Mayfield. *Ecology Letters*
21 (4). John Wiley & Sons, Ltd: 557–567. doi:10.1111/ele.12921.

Belenguer-Plomer, Miguel A, Mihai A Tanase, Angel Fernandez-Carrillo, and Emilio
Chuvieco. 2019. "Burned Area Detection and Mapping Using Sentinel-1 Backscatter
Coefficient and Thermal Anomalies." *Remote Sensing of Environment* 233 (November):
111345. doi:10.1016/j.rse.2019.111345.

Bistinas, I, S P Harrison, I C Prentice, and J M C Pereira. 2014. "Causal Relationships versus Emergent Patterns in the Global Controls of Fire Frequency." *Biogeosciences* 11 (18).
Copernicus Publications: 5087–5101. doi:10.5194/bg-11-5087-2014.

Boschetti, Luigi, David Roy, and Christopher O. Justice. 2006. *International Global Burned Area Satellite Product Validation Protocol Part I – Production and Standardization of Validation Reference Data (to Be Followed by Part II – Accuracy Reporting)*.
*Assessment*. In CEOS-Ca. Vol. 1998. USA: Committee on Earth Observation Satellites.

Boschetti, Luigi, David P. Roy, Louis Giglio, Haiyan Huang, Maria Zubkova, and Michael L. Humber. 2019. "Global Validation of the Collection 6 MODIS Burned Area Product."
*Remote Sensing of Environment* 235: 111490. doi:10.1016/j.rse.2019.111490.

Bowman, David M.J.S., Jennifer K. Balch, Paulo Artaxo, William J. Bond, Jean M. Carlson, Mark A. Cochrane, Carla M. D'Antonio, et al. 2009. "Fire in the Earth System." *Science* 324 (5926). American Association for the Advancement of Science: 481–484.
doi:10.1126/science.1163886.

Brennan, J, J L Gómez-Dans, M Disney, and P Lewis. 2019. "Theoretical Uncertainties for Global Satellite-Derived Burned Area Estimates." *Biogeosciences* 16 (16). Copernicus
Publications: 3147–3164. doi:10.5194/bg-16-3147-2019.

Campagnolo, M. L., D. Oom, M. Padilla, and J. M.C. Pereira. 2019. "A Patch-Based Algorithm for Global and Daily Burned Area Mapping." *Remote Sensing of Environment* 232: 111288. doi:10.1016/j.rse.2019.111288.

Chase, Jonathan M, Brian J McGill, Patrick L Thompson, Laura H Antão, Amanda E Bates, Shane A Blowes, Maria Dornelas, et al. 2019. "Species Richness Change across Spatial Scales." *Oikos* 128 (8): 1079–1091. doi:10.1111/oik.05968.

Chuvieco, Emilio, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, et al. 2018. "Generation and Analysis of a New Global Burned Area Product Based on MODIS 250 m Reflectance Bands and Thermal Anomalies." *Earth System Science Data* 10 (4): 2015–2031. doi:10.5194/essd-10-2015-2018.

Chuvieco, Emilio, Florent Mouillot, Guido R van der Werf, Jesús San Miguel, Mihai Tanase, Nikos Koutsias, Mariano García, et al. 2019. "Historical Background and Current Developments for Mapping Burned Area from Satellite Earth Observation." *Remote Sensing of Environment* 225 (May): 45–64. doi:10.1016/j.rse.2019.02.013.

Chuvieco, Emilio, Chao Yue, Angelika Heil, Florent Mouillot, Itziar Alonso-Canas, Marc Padilla, Jose Miguel Pereira, Duarte Oom, and Kevin Tansey. 2016. "A New Global Burned Area Product for Climate Assessment of Fire Impacts." *Global Ecology and Biogeography* 25 (5). John Wiley & Sons, Ltd (10.1111): 619–629. doi:10.1111/geb.12440.

De Mendiburu, Felipe. 2014. "Agricolae: Statistical Procedures for Agricultural Research. R
Package Version 1.2-0.”

Field, Christopher B., and Inez Y. Fung. 1999. “The Not-so-Big U.S. Carbon Sink.” *Science* 285 (5427): 544–545. doi:10.1126/science.285.5427.544.

Fusco, Emily J., John T. Finn, John T. Abatzoglou, Jennifer K. Balch, Sepideh Dadashi, and Bethany A. Bradley. 2019. “Detection Rates and Biases of Fire Observations from MODIS and Agency Reports in the Conterminous United States.” *Remote Sensing of Environment* 220: 30–40. doi:10.1016/j.rse.2018.10.028.

GCOS. 2016. *The Global Observing System for Climate: Implementation Needs*. Geneva.

GEO BON. 2017. *GEO BON Implementation Plan 2017-2020. Version 1.3*. Leipzig.

Geyer, Charles J., Glen D. Meeden, and Komei Fukuda. 2017. “Rcdd: Computational Geometry. R Package Version 1.2.”

Giglio, Louis, Luigi Boschetti, David P. Roy, Michael L. Humber, and Christopher O. Justice. 2018. “The Collection 6 MODIS Burned Area Mapping Algorithm and Product.” *Remote Sensing of Environment* 217: 72–85. doi:10.1016/j.rse.2018.08.005.

Giglio, Louis, James T Randerson, and Guido R van der Werf. 2013. “Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire Emissions Database (GFED4).” *Journal of Geophysical Research: Biogeosciences* 118 (1): 317–328. doi:10.1002/jgrg.20042.

Giglio, Louis, Wilfrid Schroeder, and Christopher O. Justice. 2016. “The Collection 6 MODIS Active Fire Detection Algorithm and Fire Products.” *Remote Sensing of Environment* 178: 31–41. doi:10.1016/j.rse.2016.02.054.
Hantson, Stijn, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, et al. 2016. "The Status and Challenge of Global Fire Modelling." *Biogeosciences* 13 (11): 3359–3375. doi:10.5194/bg-13-3359-2016.

Hantson, Stijn, Salvador Pueyo, and Emilio Chuvieco. 2015. "Global Fire Size Distribution Is Driven by Human Impact and Climate." *Global Ecology and Biogeography* 24 (1). Wiley Online Library: 77–86. doi:10.1111/geb.12246.

Hempson, Gareth P., Catherine L. Parr, Sally Archibald, T. Michael Anderson, Colin J. Courtney Mustaphi, Andrew P. Dobson, Jason E. Donaldson, Thomas A. Morrison, James Probert, and Colin M. Beale. 2018. "Continent-Level Drivers of African Pyrodiversity." *Ecography* 41 (6): 889–899. doi:10.1111/ecog.03109.

Humber, Michael L., Luigi Boschetti, and Louis Giglio. 2020. "Assessing the Shape Accuracy of Coarse Resolution Burned Area Identifications." *IEEE Transactions on Geoscience and Remote Sensing* 58 (3): 1516–1526. doi:10.1109/TGRS.2019.2943901.

Humber, Michael L., Luigi Boschetti, Louis Giglio, and Christopher O. Justice. 2019. "Spatial and Temporal Intercomparison of Four Global Burned Area Products." *International Journal of Digital Earth* 12 (4): 460–484. doi:10.1080/17538947.2018.1433727.

Jarzyna, Marta A., and Walter Jetz. 2018. "Taxonomic and Functional Diversity Change Is Scale Dependent." *Nature Communications* 9 (1): 2565. doi:10.1038/s41467-018-04889-z.

Kelly, Luke T, Katherine M Giljohann, Andrea Duane, Núria Aquilué, Sally Archibald, Enric Batllori, Andrew F Bennett, et al. 2020. "Fire and Biodiversity in the Anthropocene."
*Science* 370 (6519): eabb0355. doi:10.1126/science.abb0355.

Laliberté, Etienne, Pierre Legendre, and Bill Shipley. 2015. "FD: Measuring Functional Diversity from Multiple Traits, and Other Tools for Functional Ecology." *R Package, Version 1.0-12*.

Laurent, P., F Mouillot, M V Moreno, C Yue, and P Ciais. 2019. "Varying Relationships between Fire Radiative Power and Fire Size at a Global Scale." *Biogeosciences* 16 (2): 275-288. doi:10.5194/bg-16-275-2019.

Laurent, Pierre, Florent Mouillot, Chao Yue, Philippe Ciais, M. Vanesa Moreno, and Joana M.P. Nogueira. 2018. "FRY, a Global Database of Fire Patch Functional Traits Derived from Space-Borne Burned Area Products." *Scientific Data* 5: 180132. doi:10.1038/sdata.2018.132.

Loboda, T. V., and I. A. Csiszar. 2007. "Reconstruction of Fire Spread within Wildland Fire Events in Northern Eurasia from the MODIS Active Fire Product." *Global and Planetary Change* 56 (3-4): 258-273. doi:10.1016/j.gloplacha.2006.07.015.

Lohberger, Sandra, Matthias Stängel, Elizabeth C Atwood, and Florian Siegert. 2018. "Spatial Evaluation of Indonesia's 2015 Fire-Affected Area and Estimated Carbon Emissions Using Sentinel-1." *Global Change Biology* 24 (2). John Wiley & Sons, Ltd: 644-654. doi:https://doi.org/10.1111/gcb.13841.

Marlon, J R, P J Bartlein, C Carcaillet, D G Gavin, S P Harrison, P E Higuera, F Joos, M J Power, and I C Prentice. 2008. "Climate and Human Influences on Global Biomass Burning over the Past Two Millennia." *Nature Geoscience* 1 (10): 697-702. doi:10.1038/ngeo313.
Mesplé, Fabrice, Marc Troussellier, Claude Casellas, and Pierre Legendre. 1996. "Evaluation of Simple Statistical Criteria to Qualify a Simulation." *Ecological Modelling* 88 (1–3): 9–18. doi:10.1016/0304-3800(95)00033-X.

Moreno, M. Vanesa, Pierre Laurent, Philippe Ciais, and Florent Mouillot. 2020. "Assessing Satellite-Derived Fire Patches with Functional Diversity Trait Methods." *Remote Sensing of Environment* 247 (September). Elsevier Inc.: 111897. doi:https://doi.org/10.1016/j.rse.2020.111897.

Mouillot, David, Nicholas A.J. Graham, Sébastien Villéger, Norman W.H. Mason, and David R. Bellwood. 2013. "A Functional Approach Reveals Community Responses to Disturbances." *Trends in Ecology and Evolution* 28 (3). Elsevier: 167–177. doi:10.1016/j.tree.2012.10.004.

Mouillot, Florent, Martin G. Schultz, Chao Yue, Patricia Cadule, Kevin Tansey, Philippe Ciais, and Emilio Chuvieco. 2014. "Ten Years of Global Burned Area Products from Spaceborne Remote Sensing-A Review: Analysis of User Needs and Recommendations for Future Developments." *International Journal of Applied Earth Observation and Geoinformation* 26 (1): 64–79. doi:10.1016/j.jag.2013.05.014.

Nogueira, Joana M.P., Julien Ruffault, Emilio Chuvieco, and Florent Mouillot. 2017. "Can We Go beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?" *Remote Sensing* 9 (1): 7. doi:10.3390/rs9010007.

Oksanen, Jari, Roeland Kindt, Legendre Pierre, Bob O'Hara, Gavin L. Simpson, Peter Solymos, M.H.H Henry H Stevens, et al. 2016. "Vegan: Community Ecology Package, R Package Version 2.4-0." *R Package Version* 2.2-1.
Oom, Duarte, Pedro C. Silva, Ioannis Bistinas, and José M.C. Pereira. 2016. "Highlighting Biome-Specific Sensitivity of Fire Size Distributions to Time-Gap Parameter Using a New Algorithm for Fire Event Individuation." *Remote Sensing* 8 (8): 663.
doi:10.3390/rs8080663.

Padilla, Marc, Stephen V. Stehman, Ruben Ramo, Dante Corti, Stijn Hantson, Patricia Oliva, Itziar Alonso-Canas, et al. 2015. "Comparing the Accuracies of Remote Sensing Global Burned Area Products Using Stratified Random Sampling and Estimation." *Remote Sensing of Environment* 160 (April): 114–121. doi:10.1016/j.rse.2015.01.005.

Page, Susan E, Florian Siegert, John O Rieley, Hans-Dieter V Boehm, Adi Jaya, and Suwido Limin. 2002. "The Amount of Carbon Released from Peat and Forest Fires in Indonesia during 1997." *Nature* 420 (6911): 61–65. doi:10.1038/nature01131.

R Core Team. 2017. "A Language and Environment for Statistical Computing." *R Foundation for Statistical Computing*. Vienna, Austria.

Rabin, Sam S., Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, et al. 2017. "The Fire Modeling Intercomparison Project (FireMIP), Phase 1: Experimental and Analytical Protocols with Detailed Model Descriptions." *Geoscientific Model Development* 10 (3): 1175–1197. doi:10.5194/gmd-10-1175-2017.

Ribeiro Jr, Paulo J, and Peter J Diggle. 2016. "GeoR: Analysis of Geostatistical Data. R Package Version 1.7-5.2."

Rogers, Brendan M, Amber J Soja, Michael L Goulden, and James T Randerson. 2015. "Influence of Tree Species on Continental Differences in Boreal Fires and Climate
Feedbacks.” *Nature Geoscience* 8 (3). Nature Publishing Group: 228.
doi:10.1038/ngeo2352.

Tum, Markus, Kurt P. Günther, Martin Böttcher, Frédéric Baret, Michael Bittner, Carsten Brockmann, and Marie Weiss. 2016. "Global Gap-Free MERIS LAI Time Series (2002-2012)." *Remote Sensing*. doi:10.3390/rs8010069.

Turco, Marco, Sixto Herrera, Etienne Tourigny, Emilio Chuvieco, and Antonello Provenzale. 2019. "A Comparison of Remotely-Sensed and Inventory Datasets for Burned Area in Mediterranean Europe." *International Journal of Applied Earth Observation and Geoinformation* 82 (October): 101887. doi:10.1016/j.jag.2019.05.020.

Turetsky, Merritt R., Brian Benscoter, Susan Page, Guillermo Rein, Guido R. Van Der Werf, and Adam Watts. 2015. "Global Vulnerability of Peatlands to Fire and Carbon Loss." *Nature Geoscience* 8 (1). Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.: 11–14. doi:10.1038/ngeo2325.

UNISDR, UNO. 2015. *Sendai Framework for Disaster Risk Reduction 2015--2030*. Proceedings of the 3rd United Nations World Conference on DRR. Sendai, Japan.

Vetrita, Yenni, Mark A. Cochrane, Suwarsono, Muhammad Priyatna, Kusumaning Ayu Dyah Sukowati, and Muhammad Rokhis Khomarudin. 2020. "Evaluating Accuracy of Four MODIS-Derived Burned Area Products for Tropical Peatland and Non-Peatland Fires." *Environmental Research Letters*, December. doi:10.1088/1748-9326/abd3d1.

Villéger, Sébastien, Gaël Grenouillet, and Sébastien Brosse. 2013. "Decomposing Functional β-Diversity Reveals That Low Functional β-Diversity Is Driven by Low Functional Turnover in European Fish Assemblages." *Global Ecology and Biogeography* 22 (6):
671-681. doi:10.1111/geb.12021.

Villéger, Sébastien, Norman W.H. Mason, and David Mouillot. 2008. "New Multidimensional Functional Diversity Indices for a Multifaceted Framework in Functional Ecology." *Ecology* 89 (8). Ecological Society of America: 2290-2301.
doi:10.1890/07-1206.1.

Warton, David I, Remko A Duursma, Daniel S Falster, and Sara Taskinen. 2012. "Smatr 3-an R Package for Estimation and Inference about Allometric Lines." *Methods in Ecology and Evolution* 3 (2): 257-259. doi:10.1111/j.2041-210X.2011.00153.x.

Wooster, M J. 2004. "Boreal Forest Fires Burn Less Intensely in Russia than in North America." *Geophysical Research Letters* 31 (20). John Wiley & Sons, Ltd: L20505.
doi:10.1029/2004GL020805.

Yue, C., P. Ciais, P. Cadule, K. Thonicke, S. Archibald, B. Poulter, W. M. Hao, et al. 2014.
"Modelling the Role of Fires in the Terrestrial Carbon Balance by Incorporating
SPITFIRE into the Global Vegetation Model ORCHIDEE - Part 1: Simulating Historical
Global Burned Area and Fire Regimes." *Geoscientific Model Development* 7 (6): 2747-
2767. doi:10.5194/gmd-7-2747-2014.

Zhu, Chunmao, Hideki Kobayashi, Yugo Kanaya, and Masahiko Saito. 2017. "Size-Dependent Validation of MODIS MCD64A1 Burned Area over Six Vegetation Types in Boreal Eurasia: Large Underestimation in Croplands." *Scientific Reports* 7 (1): 4181.
doi:10.1038/s41598-017-03739-0.
<figure><img src="image_3.png" /><figcaption>Figure 1. Illustration of the functional diversity trait-based approach following Moreno et al. (2020) to intercompare fire-patch products using four steps. The functional trait ($F_{trait}$) is a morphological feature of the fire patch. The functional diversity indices ($FDIs$) are functional fire patches ($F_{fp}$), functional richness ($F_{ric}$), and functional dispersion ($F_{dis}$). The assessment includes tile-to-tile standardized major axis (SMA) regression of the</figcaption></figure>
FDIs, and tile-level functional beta diversity ($F_β$), functional turnover ($F_{turn}$), and functional nestedness ($F_{nest}$). For a simple graphical display, the first (a) step shows tiles from only two products. Similarly, the second (b), third (c), and fourth (d) steps show a functional space, with only two functional traits in one tile; in addition, the third (a-ii) step and the fourth (d) step show convex hull volumes as a circle. A Venn diagram is provided as illustration.

BONA (n = 81)

TENA (n = 224)

CEAM (n = 93)

NHSA (n = 51)

SHSA (n = 431)

EURO (n = 85)

MIDE (n = 63)

NHAF (n = 510)

SHAF (n = 679)

BOAS (n = 465)

CEAS (n = 602)

SEAS (n = 289)

EQAS (n = 65)

AUST (n = 426)

Figure 2. Map of the 14 regions defined in the Global Fire Emissions Database (GFED; Giglio, Randerson, and van der Werf 2013) and the 1° tiles. Boreal North America (BONA), Temperate North America (TENA), Central America (CEAM), Northern Hemisphere South America (NHSA), Southern Hemisphere South America (SHSA), Europe (EURO), Middle East (MIDE), Northern Hemisphere Africa (NHAF), Southern Hemisphere Africa (SHAF), Boreal Asia (BOAS), Central Asia (CEAS), Southeast Asia (SEAS), Equatorial Asia (EQAS), and Australia and New Zealand (AUST). Number of 1° tiles (n).
<figure><img src="image_4.png" /><figcaption>Figure 3. Bias of the standardized major axis (SMA) line of the best fit for the functional</figcaption></figure>
diversity indices between the FRY v1.0 products with cut-offs of 3, 5, 9, and 14 days for the
Global Fire Emissions Database (GFED) regions. Functional fire patches (Ffp), functional
richness (Fric), and functional dispersion (Fdis). As explained in the methods section, unbiased
is a relationship with slope ≈ 1 and intercept ≈ 0. Constantly biased is a relationship with slope
≈ 1 and intercept ≠ 0. Proportionally biased is a relationship with slope ≠ 1 and intercept ≈ 0.
Constantly and proportionally biased is a relationship with slope ≠ 1 and intercept ≠ 0. Not
significant is a relationship with squared correlation coefficient (r²) not significant at the 95%
significance level. Negative is a relationship with negative slope. The cut-off values and r² are
provided in Table 1. The r² and SMA line parameters of the full cross-analysis are provided in
the Supplementary material.
<figure><img src="image_5.png" /></figure>

Figure 4. Minimum average functional dissimilarities between the FRY v1.0 products with cut-offs of 3, 5, 9, and 14 days for the Global Fire Emissions Database (GFED) regions. Functional beta diversity ($F_{\beta}$) and its two components of functional turnover ($F_{\text{turn}}$) and functional nestedness ($F_{\text{nest}}$). As explained in the methods section, $F_{\beta}$ is the sum of $F_{\text{turn}}$ and $F_{\text{nest}}$. $F_{\text{nest}}$ is the difference between $F_{\beta}$ and $F_{\text{turn}}$. Their values range between 0 and 1. The lower the $F_{\beta}$, the lighter the colour. The higher the $F_{\beta}$, the darker the colour. $F_{\beta}$ driven by $F_{\text{nest}}$ is in blue. $F_{\beta}$ driven by $F_{\text{turn}}$ is in violet. The cut-off values and the minimum average $F_{\beta}$ and its two components of the $F_{\text{turn}}$ and $F_{\text{nest}}$ values are provided in Table 2. The ANOVA and Fisher's least significant difference tests of the full cross-analysis are provided in the Supplementary material.
<table><caption>Table 1. Cut-off values and squared correlation coefficient (r²) of the standardized major axis (SMA) line of best fit for the functional diversity indices between the FRY v1.0 products for the world and Global Fire Emissions Database (GFED) regions.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">F<sub>fp</sub><br>FRY v1.0<br>FireCCI41<br>cut-offs</th><th colspan="2">F<sub>ric</sub><br>FRY v1.0<br>FireCCI41<br>cut-offs</th><th colspan="2">F<sub>dis</sub><br>FRY v1.0<br>FireCCI41<br>cut-offs</th><th rowspan="2">r<sup>2</sup></th></tr><tr><th>FRY v1.0<br>MCD64A1<br>cut-offs</th><th>r<sup>2</sup></th><th>FRY v1.0<br>MCD64A1<br>cut-offs</th><th>r<sup>2</sup></th></tr></thead><tbody><tr><td>WORLD</td><td>3</td><td>3</td><td>0.69</td><td>14</td><td>5</td><td>0.10</td><td>3</td><td>3</td><td>0.28</td></tr><tr><td>BONA</td><td>9</td><td>3</td><td>0.55</td><td>14</td><td>3</td><td>0.08</td><td>9</td><td>3</td><td>0.11</td></tr><tr><td>TENA</td><td>3</td><td>3</td><td>0.42</td><td></td><td></td><td></td><td>3</td><td>14</td><td>0.07</td></tr><tr><td>CEAM</td><td>14</td><td>3</td><td>0.16</td><td></td><td></td><td></td><td>5</td><td>3</td><td>0.16</td></tr><tr><td>NHSA</td><td>14</td><td>14</td><td>0.47</td><td></td><td></td><td></td><td>14</td><td>14</td><td>0.30</td></tr><tr><td>SHSA</td><td>14</td><td>3</td><td>0.33</td><td></td><td></td><td></td><td>3</td><td>14</td><td>0.15</td></tr><tr><td>EURO</td><td>14</td><td>14</td><td>0.74</td><td>14</td><td>14</td><td>0.06</td><td>5</td><td>3</td><td>0.19</td></tr><tr><td>MIDE</td><td>3</td><td>3</td><td>0.70</td><td>5</td><td>14</td><td>0.10</td><td>14</td><td>3</td><td>0.08</td></tr><tr><td>NHAF</td><td>5</td><td>3</td><td>0.64</td><td>3</td><td>3</td><td>0.16</td><td>9</td><td>3</td><td>0.28</td></tr><tr><td>SHAF</td><td>14</td><td>3</td><td>0.64</td><td>3</td><td>3</td><td>0.09</td><td>3</td><td>3</td><td>0.53</td></tr><tr><td>BOAS</td><td>3</td><td>3</td><td>0.62</td><td>14</td><td>5</td><td>0.13</td><td>9</td><td>3</td><td>0.15</td></tr><tr><td>CEAS</td><td>5</td><td>9</td><td>0.54</td><td>14</td><td>14</td><td>0.05</td><td>9</td><td>14</td><td>0.19</td></tr><tr><td>SEAS</td><td>14</td><td>9</td><td>0.50</td><td>3</td><td>5</td><td>0.02</td><td>5</td><td>3</td><td>0.15</td></tr><tr><td>EQAS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AUST</td><td>14</td><td>3</td><td>0.71</td><td>14</td><td>14</td><td>0.16</td><td>3</td><td>3</td><td>0.15</td></tr></tbody></table>

Functional fire patches (F<sub>fp</sub>), functional richness (F<sub>ric</sub>), functional dispersion (F<sub>dis</sub>). Boreal North America (BONA), Temperate North America (TENA), Central America (CEAM), Northern Hemisphere South America (NHSA), Southern Hemisphere South America (SHSA), Europe (EURO), Middle East (MIDE), Northern Hemisphere Africa (NHAF), Southern Hemisphere Africa (SHAF), Boreal Asia (BOAS), Central Asia (CEAS), Southeast Asia (SEAS), Equatorial Asia (EQAS), and Australia and New Zealand (AUST). Cut-off values are only provided for significant r² and positive
slopes. The r² and SMA line parameters of the full cross-analysis are provided in the Supplementary
material (Figure S1, S2 and S3).
<table><caption>Table 2. Cut-off values of the minimum average functional dissimilarities and the minimum average functional dissimilarities between the FRY v1.0 products for the world and Global Fire Emissions Database (GFED) regions.</caption><thead><tr><th></th><th>FRY v1.0<br>FireCCI41<br>cut-offs</th><th>FRY v1.0<br>MCD64A1<br>cut-offs</th><th>F<sub>β</sub></th><th>F<sub>turn</sub></th><th>F<sub>nest</sub></th></tr></thead><tbody><tr><td>WORLD</td><td>9</td><td>9</td><td>0.41 ± 0.23</td><td>0.21 ± 0.19</td><td>0.21 ± 0.21</td></tr><tr><td>BONA</td><td>5</td><td>3</td><td>0.59 ± 0.18</td><td>0.43 ± 0.20</td><td>0.16 ± 0.17</td></tr><tr><td>TENA</td><td>14</td><td>9</td><td>0.58 ± 0.17</td><td>0.35 ± 0.21</td><td>0.23 ± 0.19</td></tr><tr><td>CEAM</td><td>9</td><td>9</td><td>0.54 ± 0.20</td><td>0.19 ± 0.18</td><td>0.34 ± 0.23</td></tr><tr><td>NHSA</td><td>3</td><td>3</td><td>0.50 ± 0.22</td><td>0.15 ± 0.16</td><td>0.36 ± 0.25</td></tr><tr><td>SHSA</td><td>9</td><td>9</td><td>0.45 ± 0.23</td><td>0.14 ± 0.16</td><td>0.31 ± 0.25</td></tr><tr><td>EURO</td><td>14</td><td>9</td><td>0.49 ± 0.19</td><td>0.27 ± 0.20</td><td>0.23 ± 0.20</td></tr><tr><td>MIDE</td><td>5</td><td>3</td><td>0.48 ± 0.19</td><td>0.26 ± 0.18</td><td>0.22 ± 0.19</td></tr><tr><td>NHAF</td><td>9</td><td>5</td><td>0.24 ± 0.21</td><td>0.09 ± 0.09</td><td>0.15 ± 0.20</td></tr><tr><td>SHAF</td><td>9</td><td>9</td><td>0.26 ± 0.21</td><td>0.11 ± 0.12</td><td>0.16 ± 0.19</td></tr><tr><td>BOAS</td><td>5</td><td>3</td><td>0.52 ± 0.19</td><td>0.32 ± 0.20</td><td>0.20 ± 0.18</td></tr><tr><td>CEAS</td><td>14</td><td>14</td><td>0.43 ± 0.20</td><td>0.26 ± 0.18</td><td>0.17 ± 0.16</td></tr><tr><td>SEAS</td><td>14</td><td>14</td><td>0.42 ± 0.20</td><td>0.21 ± 0.17</td><td>0.21 ± 0.17</td></tr><tr><td>EQAS</td><td>14</td><td>14</td><td>0.58 ± 0.18</td><td>0.24 ± 0.20</td><td>0.34 ± 0.25</td></tr><tr><td>AUST</td><td>9</td><td>3</td><td>0.42 ± 0.22</td><td>0.23 ± 0.17</td><td>0.19 ± 0.20</td></tr></tbody></table>

Functional beta diversity (F<sub>β</sub>) and its two components of functional turnover (F<sub>turn</sub>) and functional nestedness (F<sub>nest</sub>). Boreal North America (BONA), Temperate North America (TENA), Central America (CEAM), Northern Hemisphere South America (NHSA), Southern Hemisphere South America (SHSA), Europe (EURO), Middle East (MIDE), Northern Hemisphere Africa (NHAF), Southern Hemisphere Africa (SHAF), Boreal Asia (BOAS), Central Asia (CEAS), Southeast Asia (SEAS), Equatorial Asia (EQAS), and Australia and New Zealand (AUST). The ANOVA and Fisher's least significant difference tests of the full cross-analysis are provided in the Supplementary material (Figure S4).