\chapter{Decoupling natural and human-caused fires in the Brazilian Cerrado (Research Paper 3)}\label{ch:paper03}


\begin{refsection}

\raggedbottom

\textit{By: Carlota Segura-Garcia, Thomas A. J. Janssen, Renata Libonati, Lucas S. Menezes, Guilherme Mataveli, Sander Veraverbeke, Matthew W. Jones, Imma Oliveras Menor}

\textbf{Linking statement:} In \textbf{Chapter \ref{ch:paper01} (Research Paper 1)} and \textbf{Chapter \ref{ch:paper02} (Research Paper 2)}, this thesis explored the effects of humans on the fire regimes of the Brazilian Cerrado through mechanisms primarily related to the fragmentation of the landscape, which alters the continuity of the fuel bed. However, humans also shape fire occurrence and behaviour by altering the number of fire ignition opportunities and shifting the timing of these in comparison to naturally-ignited fires. Thus, this chapter, the last research paper of this thesis, quantified the proportion of fires ignited by humans and by lightning - the main cause of natural fire ignitions -, and analysed the patterns of fire created by each type of ignition. Even though humans are usually assumed to be the main cause of fire ignitions in the Cerrado, the quantification of human to lightning-ignited fires was a major outstanding question that had not yet been addressed for the whole Cerrado.

This paper was submitted to the journal \textit{Global Change Biology} on 10$\text{th}$ October 2024.

\textbf{Contribution Statement:} I, Carlota Segura-Garcia, conducted the conceptualization, methodology development, data processing and data analysis, investigation, visualization of the results and writing of the manuscript. Dr Matthew W. Jones, Dr Sander Veraverbeke, Dr Thomas Janssen, Dr Guilherme Mataveli, Dr Renata Libonati and Lucas Menezes helped in the conceptualization, methodology, analysis, investigation, and reviewing of the manuscript. Dr Matthew W. Jones and Dr Sander Veraverbeke provided the Vaisala Global Lightning Detection Network GLD360 dataset on lightning strikes. Dr Renata Libonati provided the ground truth data on lightning-ignited fires in Cerrado's protected areas from Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio). Finally, Dr Imma Oliveras Menor supervised and was involved in all steps of this work.

\newpage

\section{Abstract}

The Brazilian Cerrado is a mosaic of savanna and grassland ecosystems that have co-evolved with fire as a disturbance agent. Before humans dispersed across the Cerrado, lightning-ignited fires created landscapes with varied fire histories contributing to a patchwork of vegetation successional stages, hosting high levels of biodiversity. In the past few decades, however, most of the Cerrado has been converted to agricultural land uses, altering its fire regimes. Quantifying lightning and human-caused fires is fundamental to understanding to what extent current patterns of fire may be caused by humans and diverge from these natural regimes. Here, we used geospatial data on individual fire events and lightning occurrence in an ignition-cause attribution method to identify lightning and human-caused fires. We show that humans are the overwhelming ignition cause in the Brazilian Cerrado, responsible for 89.1\% (95\% CI, [86.9, 91.3]\%) of fires between 2019 and 2023, while 7.5\% (95\% confidence interval, CI, [6.7, 8.3]\%) of fires were ignited by lightning, and 3.4\% (95\% CI, [2.7, 4.1]\%) did not have a clear ignition cause. While most lightning-caused fires occur during the transition from the dry to the wet season (September and October), humans advance the fire season up to the start of the dry season in May, extending its total length threefold. Regardless of timing, human-caused fires take place in drier and hotter conditions than lightning fires, thus encouraging more extreme fire behaviour. Indeed, we find that the most extreme human-ignited fires are about ten times larger or two times more intense than the most extreme lightning-ignited fires. We conclude that humans have altered the natural fire regimes of the Cerrado, igniting larger and more intense fires that burn over a longer fire season, with important implications for the biodiversity and carbon cycles in these ecosystems.

% \vspace{0cm}

\textbf{\textit{Keywords -}} neotropical savannas, fire regimes, fire ignitions, lightning, anthropic fires

\section{Introduction}


Many ecosystems in the world co-evolved with fire \citep{bond_fires_2015, bond_fire_2005, pausas_burning_2009, beerling_origin_2006}, as their flora developed a range of strategies to co-exist with this disturbance agent. These strategies include resisting fire, quickly resprouting, flowering, or germinating after fire, or even promoting fire occurrence by providing flammable biomass \citep{hoffmann_comparative_2003, simon_evidence_2012, pausas_flammability_2017}. The ancient origins of such adaptations \citep{he_fire-adapted_2012, he_cretaceous_2016, simon_evidence_2012} indicate that fires, ignited primarily by lightning and volcanic activity, were common on Earth long before the appearance of hominids \citep{glasspool_charcoal_2004}. These fires contributed to the spread of ecosystems with sparse tree canopies in seasonally dry climates, giving rise to the savanna biome \citep{beerling_origin_2006}. Consequently, some of the present-day fire-adapted ecosystems rely on a certain fire regime to maintain their open physiognomies \citep{oliveras_many_2016, pausas_alternative_2020}. The appearance of humans, and particularly their acquired ability to use fire, further shaped fire regimes and ecosystems globally \citep{beerling_origin_2006, archibald_evolution_2012}. However, despite these early contributions, the fast rate of human transformations of many processes and components of the Earth system particularly over the past century have led to rapid alterations of these fire regimes \citep{pausas_burning_2009}, with profound consequences on many ecosystems \citep{barlow_clarifying_2020} and biogeochemical cycles \citep{bowman_fire_2009, archibald_biological_2018}.

Fire regimes are directly and indirectly influenced by human activities \citep{archibald_what_2009} through changes in ignition patterns \citep{balch_human-started_2017, hantson_human-ignited_2022}, landscape fragmentation \citep{archibald_evolution_2012}, active fire suppression \citep{doerr_global_2016}, and anthropic climate change \citep{cattau_anthropogenic_2020, jolly_climate-induced_2015}. Humans start fires in relation to agriculture and landscape management - both in modern and traditional practices -, deforestation, accidents or arson \citep{archibald_what_2009}, and are the main cause of fires along with lightning \citep{veraverbeke_lightning_2017}. Even though lightning densities over land are highest near the Equator and greatly decrease towards the poles \citep{christian_global_2003}, lightning is the main cause of fire in the remote boreal regions \citep{janssen_extratropical_2023}, while humans are considered to be the main cause of fires in the tropics, both in fire-adapted ecosystems like grasslands and savannas \citep{andela_global_2019, archibald_evolution_2012}, and fire-sensitive ecosystems like rainforests \citep{barlow_clarifying_2020}. 
 
Quantifying how much the current patterns of human-ignited fires differ from those started by lightning is fundamental to assessing the pressures on ecosystems adapted to natural fire regimes. In particular, isolating lightning fires and focusing on their impacts can help to reveal more about the fire regimes that these ecosystems evolved with \citep{archibald_evolution_2012}. Humans can expand the so-called fire niche to seasons and areas where lightning densities are low \citep{balch_human-started_2017}, as long as flammability conditions allow. That is,  humans can shift the timing of fires towards drier \citep{menezes_lightning_2022, archibald_evolution_2012} or wetter \citep{balch_human-started_2017} weather conditions than lightning fires, and broaden the geographical range of fire occurrence to landscapes with different characteristics (e.g. in terms of fragmentation or topography) \citep{balch_human-started_2017}. Both these processes can give rise to fires with different behaviour (e.g. in terms of size and intensity) than those naturally occurring \citep{hantson_human-ignited_2022}. In fire-adapted ecosystems, humans can increase or decrease fire frequencies, both with potentially degrading effects due to an excess or a lack of fire \citep{pivello_understanding_2021}, contributing to changes in vegetation composition over time \citep{pinheiro_dinamica_2009}.

The balance between lightning and humans as causes of fire ignition is particularly relevant to the Brazilian Cerrado, the world's most biodiverse tropical savanna \citep{ratter_analysis_2003}. This region is formed by fire-adapted grasslands and savannas with different levels of canopy cover intermingled with patches of riverine forests where fire-sensitive species are present \citep{ribeiro_cerrado_2008}. Such diversity is argued to stem from nearby ecosystems evolving adaptation strategies to fire \citep{simon_recent_2009, simon_evidence_2012} which, along with the stochastic nature of fire and other factors such as soil composition \citep{eiten_cerrado_1972}, contributed to creating the mosaic of vegetation that characterises the Cerrado \citep{billings_ecological_1982}. In addition, studies on the use of fire by indigenous peoples indicate that, over millennia, different communities developed a refined knowledge of fire management \citep{mistry_community_2016, mistry_new_2019, mistry_indigenous_2005, welch_indigenous_2013} that further contributed to the Cerrado's heterogeneity \citep{eloy_seasonal_2019, durigan_no_2020, fidelis_year_2018}. In these traditional patch mosaic burning practices \citep{mistry_community_2016}, fire is used with varying frequencies, and at different times throughout the dry season for a range of purposes, including regenerating pastures, clearing vegetation for cultivation, hunting and landscape management \citep{mistry_indigenous_2005, welch_indigenous_2013, melo_regime_2011}.

Nowadays, however, industrial agriculture is the main economic activity in the Cerrado, producing both livestock and commercial crops like soybean and sugarcane \citep{rekow_socio-ecological_2019}. This economic transformation has been particularly acute over the past few decades, when over 50\% of the Cerrado's native vegetation has been lost to land use changes \citep{alencar_mapping_2020}. The current patterns of fire in this region clearly reflect this expansion \citep{silva_putting_2021, segura-garcia_human_2024, da_silva_arruda_assessing_2024}, with reductions in burned area where fragmentation is extensive, and increases associated with human activities and climate change where the Cerrado has remained more intact \citep{conciani_human-climate_2021, segura-garcia_human_2024, silva_putting_2021, da_silva_arruda_assessing_2024}. Humans are considered to be the main cause of fire ignitions in the Cerrado based on the widespread presence of modern agricultural activities and their concomitant use of fire \citep{billings_ecological_1982, pivello_use_2011}, the high fire frequencies observed in some areas \citep{da_silva_arruda_assessing_2024}, the mismatch between the fire season and the lightning season \citep{schumacher_characteristics_2022}, and the disproportionately larger percentage of human fires compared to lightning fires in nearby ecoregions like the Pantanal \citep{menezes_lightning_2022}. However, different studies in some protected areas of the Cerrado suggest that lightning strikes are still a prevalent cause of fires \citep{ramos-neto_lightning_2000, pereira_identificacao_2005, medeiros_incendios_2004}. A formal assessment of fire ignition causes across the whole region, however, is still lacking.

Here, we use geospatial data on individual fire perimeters and lightning occurrence, along with an ignition-cause attribution method \citep{larjavaara_lightning_2005}, to identify those fire events in the Cerrado most likely caused by a lightning strike or by human activities over the period 2019-2023. First, we aim to quantify the proportion of lightning and human-caused fires and study how these change throughout the year. Then, we quantify the spatial patterns of lightning and human-caused fires to identify areas of the Cerrado potentially dominated by a certain ignition cause. Finally, we compare the fire frequencies of both ignition types, and their fire behaviour in relation to the level of human presence in the landscape and the weather conditions at ignition. We expect current Cerrado's fire regimes to be largely dominated by human-caused fires, and for these fires to present more extreme behaviour than lightning fires, as previously suggested \citep{ramos-neto_lightning_2000, medeiros_incendios_2004, fiedler_ocorrencia_2006}.



\section{Methods}


\subsection{The study area}

\begin{figure*}[th]%
\centering
\includegraphics[width=0.7\textwidth]{_chapter_03/natural_area_studyArea_stateNames.png}
\caption{Location of the study area (left panel), the Brazilian Cerrado (red), within Brazil (yellow) in South America. The Brazilian Cerrado is coloured according to the natural area percentage of each 30 km grid cell in the year 2022 (right panel). We used the land use data from the MapBiomas Collection 8.0 \citep{souza_reconstructing_2020} dataset, considering as natural all MapBiomas subcategories under "Forest" (which includes savannas) along with "Wetlands" and "Grassland". Values range from 0-10\% natural area (orange), indicating cells mostly converted to human land uses, to 90-100\% natural area (green), indicating mostly unconverted landscapes. Grey lines represent the limits of the Brazilian states, and the labels refer to those states with some area within the Cerrado: MT, Mato Grosso; MS, Mato Grosso do Sul; GO, Goiás; DF, Distrito Federal; SP, São Paulo; PR, Paraná; MG, Minas Gerais; MA, Maranhão; TO, Toncantins; PI, Piauí; BA, Bahía.}
\label{fig:cerrado}
\end{figure*}

The Brazilian Cerrado (Figure \ref{fig:cerrado}) extends over more than 2.2 million km$^2$, and is composed of very heterogeneous vegetation including primarily grasslands and savannas, but also forest patches \citep{ribeiro_cerrado_2008}. The region stands out for its high levels of biodiversity and endemism \citep{klink_conservation_2005}, comprising fire-adapted species and, to a lesser extent, fire-sensitive species. This heterogeneity is reflected in the different ecoregions identified within the Cerrado \citep{sano_cerrado_2019}. The climate of the Cerrado presents a strong seasonality, with well-defined wet and dry seasons. Annual precipitation varies between 800 mm to over 1,800 mm roughly increasing from east to west \citep{nascimento_clima_2020}. Average annual temperatures vary between 18 $^\text{o}$C and 28 $^\text{o}$C increasing along a latitudinal gradient towards the Equator \citep{nascimento_clima_2020}.

Currently, most of the Cerrado's original native vegetation has been converted to agricultural land uses, and ongoing conversion is still occurring in its northern half, the Matopiba region - comprising the Cerrado of the Brazilian states of Maranhão, Tocantins, Piauí and Bahía \citep{teixeira_pitta_impacts_2017}. This ongoing large-scale conversion process in such a biodiverse region makes the Cerrado one of the world's biodiversity hotspots for conservation \citep{myers_biodiversity_2000}.


\subsection{Data sources}

\subsubsection{Individual fire data} \label{sec:GFA_data}

We used data on individual fire events from the Global Fire Atlas (GFA) \citep{andela_2024_GFA}, a global dataset derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) MCD64A1 Collection 6.1 \citep{giglio_collection_2016} burned area data with a daily frequency and 500 m spatial resolution. For the period 2002-present, the GFA provides information at the individual fire level in the form of georeferenced polygons representing the fire scar and the estimated coordinates of the ignition point, as well as the start and end dates of the fires, their duration, size, perimeter, average length of the daily fire line, and the rate and direction of spread \citep{andela_global_2019, andela_2024_GFA}. 

To compare the fire behaviour of lightning and human-caused fires, we focused on the size of the fires and on their intensity. The fire size was readily available from the GFA dataset, with a minimum fire size of 0.21 km$^\text{2}$ because of the MODIS spatial resolution. To study the intensity of fires, we combined the GFA data with active fire (AF) observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) \citep{schroeder_new_2014}. This instrument detects sub-pixel fire activity based on thermal anomalies with a daily overpass frequency and 375 m resolution \citep{schroeder_new_2014}, and provides fire radiative power (FRP), a measure of the radiative energy of the fire. As such, we restricted our analysis to those GFA polygons overlapping with at least one VIIRS AF within a 500 m distance from the GFA perimeter and within the duration period of the fire indicated by the GFA. Then, we calculated the median FRP of each fire out of all the VIIRS AF overlapping the polygon in space and time. 

We used the coordinates of the ignition point and the estimated date of ignition to determine the plausibility that each fire perimeter was ignited by lightning (see Section \ref{sec:attribution_method}). To estimate the date and location of the ignition point, the GFA uses an algorithm that searches for local minima of the mapped burned dates of the MCD64A1 pixels within each fire perimeter \citep{andela_global_2019}. As a consequence, the estimated date of ignition has an associated uncertainty between $\pm1$ day and $\pm2$ days \citep{andela_global_2019, pinto_deep_2020}. In addition, we estimated an upper bound for the uncertainty of the location of the ignition point to be around 2.2 km (Supplementary Figure \ref{fig:lightning_ellipse} a). We estimated this uncertainty by calculating, for each GFA polygon, the maximum distance between the coordinates of the ignition point and the edge of the fire's sub-polygon formed by all pixels with the same burned date contiguous to that of the ignition point. The burned date raster data was also provided by the GFA dataset \citep{andela_2024_GFA}.


\subsubsection{Lightning data} \label{sec:GLD_data}

We used lightning data from the Vaisala Global Lightning Detection Network GLD360, a global dataset on lightning strikes based on a network of lightning sensors. This network records the occurrence of lightning strikes, both cloud-to-ground and cloud-to-cloud, and provides information for each lightning strike: location, timing of occurrence and intensity. For the spatial uncertainty on the lightning ignition location, the dataset provides the angle and lengths of the semi-major and semi-minor axes of the confidence ellipse.

In this study, we used the GLD360 cloud-to-ground data on lightning strikes over the Cerrado for the period January 2019 to December 2023. Based on the information provided by the GLD360 dataset, we estimated that, over the Cerrado, the uncertainty on the location of a lightning strike - which we considered as the median length of the semi-major axis of the 95\% confidence ellipse - was around 3.4 km (95\% confidence interval, CI, [2.3, 7.8] km) (Supplementary Figure \ref{fig:lightning_ellipse} b). The uncertainty associated with the time of the lightning strike is the order of seconds - and hence negligible when compared to the temporal uncertainty of GFA ignition points.


\subsubsection{Ground truth data on lightning-caused fires}

To assess the effectiveness of the fire cause attribution method, we used reference data on observations of fires caused by lightning recorded by the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), which is responsible for managing, protecting, monitoring and inspecting the 340 Federal Conservation Units across Brazil. The data provided consisted of two types of datasets recording lightning fires observed in several protected areas across the Cerrado for the period 2020-2023: (a) a georeferenced dataset providing manually drawn polygons of lightning fires with the date of ignition (Supplementary Table \ref{tab:icmbio_shp_01}), and (b) a non-georeferenced tabular dataset containing records on the detection of lightning fires (Supplementary Table \ref{tab:icmbio_csv_02}). This non-georeferenced dataset (b) contained information on the protected area where the fire occurred, the estimated date of ignition, and the type of management intervention (e.g. if the fire was suppressed or no action was taken). The tabular dataset (b) included all the fire events recorded in the georeferenced data (a), in addition to other lightning fire occurrences.


\subsubsection{Land use data}

To quantify the level of landscape fragmentation across the Cerrado, and hence the level of agricultural presence, we used land use and land cover data from the MapBiomas project (Collection 8.0 \citep{souza_reconstructing_2020}), which uses the random forest algorithm to classify annual mosaics of Landsat images (30 m spatial resolution). MapBiomas is an open source and open data project that uses Google Earth Engine \citep{gorelick_google_2017} to develop their products.

We divided the Cerrado into a 30 km grid, and calculated for each grid cell and year the percentage area occupied by non-converted vegetation - as opposed to human land use area. We simplified MapBiomas Collection 8.0 land use and land cover classes into "anthropic" and "natural" groups, the first comprising "Urban Area", "Mining" and all subcategories under "Farming"; the second comprising all subcategories under "Forest", as well as "Wetland" and "Grassland". Hence, the percentage of natural area was calculated as the percentage of pixels classified as "natural" out of the union of "natural" and "anthropic". Because the MapBiomas Collection 8.0 \citep{souza_reconstructing_2020} was the last available version at the time of the study and it went only up to the year 2022, we used the land use and land cover data from this year for all those fires in the year 2023.


\subsubsection{Climate data}

We used climate data to characterise the weather conditions in which the fires took place. We used daily precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS, version 2.0), a quasi-global rainfall dataset with 0.05$^\text{o}$ spatial resolution for the period 1981-present \citep{funk_climate_2015}. We used the CHIRPS data product for precipitation as it has been identified as the most reliable dataset for precipitation in tropical climates \citep{burton_inter-comparison_2018}. For each ignition point, we calculated the accumulated precipitation over the 10 days prior to the ignition day.

We used the 2-metre temperature and 2-metre dewpoint temperature from the climate re-analysis dataset ERA5-Land Daily Aggregated product \citep{munoz-sabater_era5-land_2021}, with a 9 km resolution, to calculate the vapour pressure deficit (VPD) - the difference between saturation pressure (e$_\text{s}$) and actual vapour pressure (e$_\text{a}$) \citep{allen_crop_1998}. We calculated the VPD on the day of ignition. We used temperature data from ERA5-Land dataset because it is one of the most reliable data products for temperature for tropical climates \citep{burton_inter-comparison_2018}.


\subsubsection{Infrastructure data}

We used data on two types of infrastructure, roads and urban areas (Supplementary Figure \ref{fig:distance_fragmentation_striplot}). We obtained the road data as georeferenced files from the MapBiomas website (\url{https://brasil.mapbiomas.org/dados-de-infraestrutura/}, last accessed 27/07/2024), which in turn compiled this data from various sources, including the Geographic and Statistics Institute of Brazil (Instituto Brasileiro de Geografia e Estatística, IBGE). We used the MapBiomas Collection 8.0 \citep{souza_reconstructing_2020} classifications to create georeferenced polygons of urban areas by drawing polygons of all "Urban Area" pixels for each year.


\subsection{Data analysis}

All data processing and analysis in this study were carried out using Python (Python Software Foundation, \url{https://www.python.org/}) and Jupyter Notebook \citep{kluyver_jupyter_2016}.


\subsubsection{Ignition cause attribution method}
\label{sec:attribution_method}

To identify those fires most likely ignited by lightning, we searched for those GFA polygons that co-occur in space and time with a lightning strike. However, because of the uncertainties in the spatial and temporal location of the fire events and lightning strikes of the datasets used, any attribution method had to allow a certain time lag and spatial gap between the time and location of the observed lightning strikes and those of the ignition points.

Here, we followed the method of \citet{larjavaara_lightning_2005} that assigns an attribution score, $B$, to every fire quantifying how likely each one is to have been ignited by lightning \citep{larjavaara_lightning_2005}, and which has been previously applied to the Pantanal savanna-like wetland \citep{menezes_lightning_2022} in Brazil. Following this method, all lightning strikes within certain spatial and temporal windows from the ignition point are considered to be ignition candidates, but their contribution to the attribution score depends on how far in space and time they are from the ignition point. 

This method is implemented in four steps. First, the temporal, $\Delta t_\text{max}$, and spatial, $\Delta x_\text{max}$, windows are selected. Then, every lightning strike, $i$, and fire, $j$, pair is assigned a proximity index, $A_{i,j}$, that weights their spatial and temporal separation,
\begin{equation}
A_{i, j} =  \begin{cases}
    \left( 1 - \dfrac{\Delta t_{i, j}}{\Delta t_{\text{max}}}\right) \left( 1 - \dfrac{\Delta x_{i, j}}{\Delta x_{\text{max}}}\right) & \text{if } \Delta t_{i,j} < \Delta t_{\text{max}}, \Delta x_{i,j} < \Delta x_{\text{max}} \\
    0 & \text{otherwise,}
    \end{cases}
\end{equation}
where $\Delta t_{i, j}$ expresses the time lag and $\Delta x_{i, j}$ the spatial distance between the lightning strike and the ignition point. This proximity index ranges between 1 (perfect co-occurrence) and 0 (the lightning strike falls outside the temporal or the spatial windows).

Once the lightning strike candidates are identified and their proximity index quantified, the attribution scores, $B_{j}$, for a fire $j$ are calculated as
\begin{equation}
B_{j} = 1 - \prod_{i=1}^{n_{j}} (1 - A_{i, j}),
\end{equation}
where $n_j$ is the number of lightning candidates for fire $j$. Finally, all those fires with an attribution score larger or equal than some threshold, $B_\text{thresh}$, are considered to be ignited by lightning.

Hence, this attribution method requires making a number of decisions concerning the values of $\Delta t_\text{max}$, $\Delta x_\text{max}$, and $B_\text{thresh}$. The choice of these values should aim to balance omission and commission errors. 

\subsubsection{Attribution score thresholds, $B$}

Following \citet{menezes_lightning_2022}, we used a rather high attribution score threshold, $B_\text{thresh} = 0.8$, so that, once the temporal and spatial windows were chosen, only those fires with higher attribution scores would be identified as likely caused by lightning. Then, we carried out a preliminary sensitivity analysis (Supplementary Figure \ref{fig:sensitivity}) to understand how the proportion of lightning fires varied with different temporal and spatial windows considering a wide range of values, $\Delta t_\text{max} \in [1, 10]$ days and $\Delta x_\text{max} \in [2, 25]$ km. 

To diminish confusion when comparing lightning and human-caused fires, we imposed a second attribution score threshold to identify those fires most likely caused by humans. Using again a conservative score, only those fires with $B \leq 0.2$ were identified as caused by humans. Consequently, fires with attribution scores between 0.2 and 0.8 were considered as not having a clear ignition cause. 


\subsubsection{Attribution method windows and uncertainty}

Instead of selecting a unique value for the spatial ($\Delta x_\text{max}$) and temporal ($\Delta t_\text{max}$) windows, as has been done before \citep{dorph_modelling_2022, moris_lightning-caused_2020, perez-invernon_lightning-ignited_2021, perez-invernon_meteorological_2022}, we selected a set of plausible combinations, $\{$($\Delta t_\text{max}$, $\Delta x_\text{max}$), $\Delta t_\text{max} \in [3,7]$ days and  $\Delta x_\text{max} \in [6,12]$ km$\}$. For each $(\Delta t_\text{max}, \Delta x_\text{max})$ pair, we identified those fires with $B\geq 0.8$ as caused by lightning, those with $B\leq 0.2$ as caused by humans, and the remaining fires ($B$ between 0.2 and 0.8)  as having no clear ignition cause. Then, we quantified the number of fires per ignition category (lightning, humans or not clear) and calculated the percentage of fires and the percentage of total burned area corresponding to each ignition category. Then, we estimated the final percentages for each ignition category as the average values obtained from all temporal and spatial window pairs, $\{(\Delta t_\text{max}, \Delta x_\text{max})\}$. Finally, we assessed how much these percentages varied when using different $(\Delta t_\text{max}, \Delta x_\text{max})$ values, which we expressed as a 95\% CI around the mean using the bootstrapping algorithm \citep{mokhtar_confidence_2023}.

The time elapsed between the lightning strike and the moment in which the fire is first detected by an observer or detection device is commonly referred to as holdover time \citep{moris_global_2023}. In this case, the detection time corresponds to the day of ignition provided by the GFA dataset, so the holdover time depends on when the Terra and Aqua satellites overpass the fire, the presence of clouds \citep{moris_global_2023} - particularly in the Cerrado \citep{pereira_burned_2017, libonati_algorithm_2015} -, when the spectral signal from the burn scar becomes detectable to the MODIS instrument, and the uncertainties on the day of burn from the MCD64A1 burned area algorithm \citep{giglio_collection_2016} and from the GFA dataset (around 2 to 4 days \citep{andela_global_2019}).

Considering these sources of uncertainties, most studies identifying lightning-caused fires use a $\Delta t_\text{max}$ around 5 days \citep{menezes_lightning_2022}. In this study, we considered a range of values, $\Delta t_\text{max} \in [3,7]$ day, because the MODIS uncertainty on the day of burn can reach up to 6 days in the Cerrado \citep{pinto_deep_2020}. In addition, because MODIS' uncertainty on the day of burn is  $\pm 1$ day in 45\% of the cases \citep{pinto_deep_2020}, the GFA dataset could be reporting ignition dates around one day or more earlier than the actual start of the fire. Therefore, when running the attribution method algorithm, we included as candidates all lightning strikes falling from $\Delta t_\text{max}$ days before the GFA day of ignition to one day after.


Regarding $\Delta x_\text{max}$, the mismatch between the lightning strike location and that of the ignition point depends on the uncertainty of these two locations, as well as spatial mismatches derived from delays in the detection of the fire. As indicated earlier, the uncertainty on the location of the lightning strike could go up to 8 km, and that of the GFA ignition point up to 2.2 km (Supplementary Figure \ref{fig:lightning_ellipse}). Therefore, we considered $\Delta x_\text{max} \in [6,12]$ km to identify fire-igniting lightning candidates. Again, this range is also in accordance with other studies, which usually consider spatial windows around 8-10 km \citep{moris_lightning-caused_2020, dorph_modelling_2022, perez-invernon_lightning-ignited_2021}.



\subsubsection{Validation of the attribution method with ground truth data}

Next, we used both the georeferenced dataset (with fire polygons) and the non-georeferenced dataset (in tabular format) provided by ICMBio containing observed lightning-caused fires as ground truth data for our attribution method. Even though the number of records in the ICMBio dataset on lightning fires was too small to allow a full statistical validation, we used the two datasets to verify whether the attribution method was able to identify any of the ground truth records as caused by lightning. Therefore, although we could not use the ground truth data to select the $\Delta t_\text{max}$ and $\Delta x_\text{max}$ values that balanced commission and omission errors of identification, at least we could have an indication of whether it was able to identify any lightning fires at all.

Regarding the georeferenced vector dataset, a total of 11 fire polygons were selected after inspection of the data (Supplementary Table \ref{fig:lightning_ellipse}). Out of these, we found that 6 events had been mapped by the GFA dataset (i.e. GFA polygons overlapping in time and space with the corresponding ICMBio ground truth records). Next, we evaluated whether any of these 6 fires were identified as caused by lightning by the attribution method and found that 5 of these had an attribution score $B>0.8$ for at least one of the $(\Delta t_\text{max}, \Delta x_\text{max})$ combinations considered. The remaining fire had a score $B$ between 0.7 and 0.8 for $\Delta x_{\text{max}} = $ 12 km and all $\Delta t_{\text{max}}$ considered (Supplementary Figure \ref{fig:attribution_scores}). 

Even though the non-georeferenced tabular dataset did not provide the exact location of fires, it indicated that in specific protected areas of the Cerrado and in specific dates some lightning-caused fires had occurred. Hence, as a further check to the attribution method, we explored whether there were any fire polygons in the GFA dataset overlapping these protected areas within a period up to five days before and one day after the ignition date recorded in the tabular ICMBio data, and whether any of these records had $B \geq 0.8$. In the non-georeferenced tabular data, there were 20 records reporting lightning-caused fires in different protected areas and dates (Supplementary Table \ref{fig:sensitivity}). The GFA dataset detected 14 fire polygons corresponding to 7 out of these 20 tabular records. Out of these 14 fire polygons, we identified 13 polygons as likely caused by lightning ($B\geq0.8$) (Supplementary Figure \ref{fig:attribution_scores}). The remaining polygon had, again, an attribution score $B$ between 0.7 and 0.8. We also noted that these 13 fire polygons included the 6 fire polygons contained in the ICMBio georeferenced data.


\subsubsection{Characterising the spatial patterns of fires and lightning strikes}

Next, we explored how the lightning and human-caused fires were distributed across the landscape. For each 30 km grid cell, year, and combination of spatial and temporal windows, we obtained the number of fires in each ignition category (lightning, human or not clear). Then, for each cell and year, we calculated the average number and percentage of fires in each ignition category out of all the $\{(\Delta t_\text{max}, \Delta x_\text{max})\}$ combinations, and the corresponding 95\% CI using bootstrapping. Finally, for each cell, we calculated the average annual number of fires of each ignition category, along with the 95\% CI of the mean. Even though we took into account that a number of fires had no clear ignition cause when calculating the percentages, we focused the spatial analysis only on the lightning and human-caused fires.

Regarding lightning densities, we counted the number of strikes per grid cell and year and calculated the annual average number of lightning densities. Again, we also calculated the 95\% CI around the mean.


\subsubsection{Characterising the fire frequencies of lightning and human fires}

We then quantified the fire frequencies of humans ($B\leq 0.2$) and lightning ($B\geq 0.8$) ignited fires. In this case, we used $\Delta t_\text{max} = $ 5 days and $\Delta x_\text{max} =$ 9 km, the middle values out of all temporal and spatial window combinations considered earlier, to identify the ignition causes of each GFA polygon. Then, to calculate the fire frequencies, we counted how many times each pixel had burned in the period for each ignition cause. Therefore, fire frequencies would range from 1 year to 5 consecutive years in the period 2019 to 2023. Finally, to compare the fire frequencies between lightning and human-caused fires, we normalised the pixel count (equivalent to burned area) of each frequency and ignition cause by the corresponding total number of affected pixels (the total area that burned at least once for that type of ignition).

\subsubsection{Characterising the climatic and the landscape fragmentation conditions of fires}

Next, we compared the conditions in which the human-caused ($B\leq$ 0.2) and the lightning-caused ($B\geq 0.8$) fires took place, both in terms of the weather and fuel conditions at ignition, and in terms of the level of fragmentation of the landscapes where the fires happened. Both weather and fragmentation are known to affect fire behaviour \citep{archibald_what_2009}. Again, we used $\Delta t_\text{max} = $ 5 days and $\Delta x_\text{max} =$ 9 km to calculate the attribution scores $B$. 

Regarding the weather conditions, we compared the distribution of the precipitation accumulated over the ten days prior to the ignition of the fire and the VPD on the ignition day, which we used as proxies of fuel flammability. We did so for all fires in the study period and per month.

Then, each GFA fire polygon was assigned to the grid cell containing the fire ignition point. In this manner, the landscape characteristics where the fire took place were assessed in terms of the natural area percentage of the cell and year where the fire started. We did the same for each lightning strike in the study period. Then, we classified the occurrences of each type of event (lightning-caused fires, human-caused fires and lightning strikes) according to the decile of natural area percentage - a proxy for the level of fragmentation. To understand how each type of event was distributed across landscapes with different levels of natural area percentage, we divided the number of occurrences in a decile by the total area occupied by that decile.

Finally, for each GFA polygon, we calculated the distance from the ignition point to the closest road and to the closest urban area. Because the distance distributions we obtained for lightning-caused fires were very skewed towards small values for both types of infrastructure, we also calculated the shortest distances for all lightning strikes in the period. In this manner, we could interpret whether the skewness of the distance distribution could be related to lightning strikes also being more likely to fall close to these two types of infrastructure.


\subsubsection{Comparing fire behaviour between lightning and human fires}

We compared the fire behaviour of lightning and human-caused fires focusing on two measures of fire behaviour: fire size and median FRP. We compared the distributions of each of these two variables for lightning ($B\geq 0.8$) and human ($B\leq 0.2$) ignitions for the whole study period and by month. We also explored the distribution of these two variables per decile of natural area percentage because the level of landscape fragmentation can also affect fire behaviour. Again, we used $\Delta t_\text{max} = $ 5 days and $\Delta x_\text{max} =$ 9 km.

Finally, we compared the largest (fire size $\geq$ 150 km$^\text{2}$) and the most intense (median FRP $\geq$ 100 MW) fires separately by ignition cause. For each of these two variables, we explored the weather conditions at ignition against the level of natural area percentage of the landscape where they took place.


\section{Results}


\subsection{Quantifying lightning fires}

\sloppy Using the attribution method for a set of temporal and spatial windows, $\{(\Delta t_\text{max}, \Delta x_\text{max}),\text{ } \Delta t_\text{max} \in [3,7] \text{ days and } \Delta x_\text{max} \in [6,12] \text{ km}\}$, we estimated that, in the period 2019-2023, 7.5\% (95\% CI, [6.0, 9.0]\%) of all fires in the Brazilian Cerrado were ignited by lightning, 89.1 \% (95\% CI, [86.9, 91.3]\%) were ignited by humans, and a remaining 3.4\% (95\% CI, [2.7, 4.1]) had no clear ignition cause. Similarly, around 7.9\% (95\% CI, [5.6, 10.4]\%) of the annual burned area was caused by lightning fires, 88.7\% (95\% CI, [85.3, 92.0]\%) was caused by humans, and 3.4\% (95\% CI, [2.4, 4.4]\%) had no clear ignition cause.

Exploring these percentages by year, we observed some inter-annual variability both in the number of lightning fires and in the burned area associated with this type of fires (Figure \ref{fig:perc_numBA_year}). In both cases, 2021 was the year with the lowest percentage of lightning fires, 6.5 [6.0, 7.0]\%, and burned area caused by lightning, 5.8\% (95\% CI, [5.2, 6.4]\%); while 2023 was the year with the highest values, 10.1\% (95\% CI, [9.3, 11.1]\%) and 11.7\% (95\% CI, [10.7, 12.8]\%) respectively.

\begin{figure*}[th]%
\centering
\includegraphics[width=\textwidth]{_chapter_03/barerrorplot_numberBA_lighAnt_fires_per_year_3cat.pdf}
\caption{Inter-annual variability throughout the study period (2019-2023) of the number of fires, \textbf{a}, caused by humans (brown) and lightning (green), or with no clear cause of ignition (grey), and the corresponding annual burned areas, \textbf{b}. Numbers indicate the percentage of fires ignited by lightning in the year (\textbf{a}), and the percentage of burned area from lightning fires (\textbf{b}). Fires were considered to be ignited by lightning if their attribution score was $B \geq 0.8$, ignited by humans if $B\leq 0.2$, and the remaining fires were not considered to have a clear ignition cause. Given these attribution score thresholds, the different values shown by the coloured bars were obtained as the average out of those numbers of fires, or average burned areas, obtained when using different combinations of spatial and temporal windows in the attribution method, $\{(\Delta t_\text{max}, \Delta x_\text{max}),\text{ } \Delta t_\text{max} \in [3,7] \text{ days and } \Delta x_\text{max} \in [6,12] \text{ km}\}$ (see Methods). Error bars indicate the 95\% confidence interval (CI) around these averages when using the different window combinations and were obtained using bootstrapping.}
\label{fig:perc_numBA_year}
\end{figure*}

Most lightning fires occurred in the transition from the dry to the wet season, in September and October (Figure \ref{fig:monthly_timeSeries} a). While the lightning-fire season was concentrated in these two months - and to a lesser extent in November -, the human-caused fire season commenced notably earlier, spanning from May to October, and peaking in August and September (Figure \ref{fig:monthly_timeSeries} a).

Quantifying the balance between lightning and human-caused fires in each month, we observed that the dry season was clearly dominated by human fires, as they caused more than 80\% of fires from May to October (Figure \ref{fig:monthly_timeSeries} b). In contrast, during the wettest months of the period, December to February, the percentage of lightning fires was similar or even larger than those of human fires, albeit in remarkably smaller numbers than during the dry season (Figures \ref{fig:monthly_timeSeries} a and b). Interestingly, in October, the month with the highest number of lightning fires, these accounted for 20.9\% (95\% CI, [13.9, 27.7]\%) of fires, while in September this percentage was only 8.1\% (95\% CI, [2.6, 13.6]\%). Finally, July was the month with the lowest percentage of lightning fires, causing less than 1\% of fires.


\begin{figure*}[t!]%
\centering
\includegraphics[width=\textwidth]{_chapter_03/timeSeries_nfires_percfires_nstrikes_successStrikes_strikesPrec_stderr_3cat_fixed.png}
\caption{\textbf{a} Average number of human-caused (brown) and lightning-caused (green) fires per month in the study period, 2019-2023. The numbers indicate the average number of fires per month and type of ignition and are included for reference. In each year and month, a number of fires may have no clear ignition cause (not represented in this figure). \textbf{b} Average monthly percentage of  fires for each type of ignition. Lightning and human-caused fire percentages in each month do not necessarily add up to 100\% as there can be fires with no clear ignition cause (see Methods). \textbf{c} Average number of lightning strikes per month (pink), average monthly precipitation (blue) and vapour pressure deficit (VPD, red). \textbf{d} Lightning ignition efficiency, the number of lightning strikes igniting fires divided by the total number of lightning strikes. \textbf{e} Ratio of the number of total lightning strikes to precipitation per month and grid cell. Error bars were obtained from the standard deviation and represent the 95\% confidence interval (CI).}
\label{fig:monthly_timeSeries}
\end{figure*}


\subsection{Spatial patterns of lightning-caused and human-caused fires across the Cerrado}

Next, we looked at the spatial distribution of lightning and human-caused fires across the Cerrado in the period 2019-2023 (Figures \ref{fig:maps_subplots} a and b respectively). Here, we excluded those cells that experienced less than 10 fires in total over the period to prevent results that may not be representative of ignition dynamics but rather capturing noise. The excluded cells were concentrated in the south of the Cerrado (Figure \ref{fig:maps_subplots} d) where landscapes are highly fragmented (Figure \ref{fig:cerrado}) and usually present fewer and smaller fires associated with longer human occupation \citep{silva_putting_2021}, and are also prone to larger omission errors \citep{rodrigues_how_2019}.

We found considerable overlap between the areas where most lightning and most human fires occur (Figure \ref{fig:maps_subplots} a and b). However, there was a notable exception to these overlapping patterns in the northern ecoregion of Alto Parnaíba \citep{sano_cerrado_2019}, where fires were predominantly caused by human ignitions, which caused more than 90\% of fires (Figure \ref{fig:maps_subplots} d). Nonetheless, in general, the Cerrado's fire regimes were dominated by anthropic fires, as humans were the main ignition cause of fires ($\geq$ 50\%) in 94.5\% of the grid cells with more than 10 fires on average in the period (Figure \ref{fig:maps_subplots} d).

The percentage of lightning fires across grid cells ranged from 0 to around 60\%, and only less than 2\% of the Cerrado's area presented percentages above 40\%. In general, the average number of lightning fires per year was small, around 4 fires in the grid cells with the highest occurrences. Lightning fires were concentrated in the ecoregions of the Bananal, the west of the Araguaia Tocantins, and the northwest of Planalto Central \citep{sano_cerrado_2019} (Figure \ref{fig:maps_subplots} a). The southern parts of these areas presented the largest percentages of lightning fires (Figure \ref{fig:maps_subplots} d).


\begin{figure*}[t!]%
    \centering
    \includegraphics[width=0.8\textwidth]{_chapter_03/numFires_lightning_percFires_annualAverage_labels.png}
    \caption{Average annual number of \textbf{a} lightning and \textbf{b} human-caused fires, and \textbf{c} lightning density in each 30 km cell over the Cerrado in the period 2019-2023. Note that maps \textbf{a} and \textbf{b} have different scales so that the spatial variation in the number of lightning-caused fires is visible despite its numbers being overall much smaller than those of human-caused fires. \textbf{d} map showing the grid cells dominated by lightning (green) or human ignitions (yellow and oranges), that is, where one of the two causes of ignitions accounts for 50\% or more of the ignitions in a grid cell. Grid cells coloured in pink do not have an ignition cause accounting for more than 50\% of fires (as there are fires with no clear ignition cause). Cells with less than 10 fires on average in the period appear in grey. Areas and numeric labels highlighted in red correspond to the Cerrado ecoregions referred to in the text: 1, Alto Parnaíba; 2, Araguaia Tocantins; 3, Bananal; 4, Planalto Central. Grey labels indicated the Brazilian states intersecting the Cerrado: MT, Mato Grosso; MS, Mato Grosso do Sul; GO, Goiás; DF, Distrito Federal; SP, São Paulo; PR, Paraná; MG, Minas Gerais; MA, Maranhão; TO, Tocantins; PI, Piauí; BA, Bahía.}
    \label{fig:maps_subplots}
\end{figure*}


\subsection{The spatiotemporal patterns of lightning}

We also characterised the seasonality of lightning and its spatial patterns across the Cerrado. First, as expected, we observed a strong temporal co-occurrence of precipitation and lightning (Figure \ref{fig:monthly_timeSeries} c). Accordingly, in the middle of the dry season, from June to August, the number of lightning strikes was lowest, while they were highest during the wet season, December to February. In fact, there were 100 times more lightning strikes during the later period than the former. However, the lightning ignition efficiency was highest from May to October (between 0.5 and 1.8\%, Figure \ref{fig:monthly_timeSeries} d), while from December to March only about 0.04\% of lightning strikes ignited fires.

Interestingly, September and October were the months with the largest numbers of lightning strikes per unit volume of precipitation (Figure \ref{fig:monthly_timeSeries} e), exactly those months in which the number of lightning-caused fires peaked. Hence, during the transition from the dry to the wet season, when the first rains begin but the monthly precipitation has not yet reached its peak, there is a higher number of lightning strikes per precipitation volume compared to the rest of the year.

Lightning strikes were not uniformly distributed across the Cerrado (Figure \ref{fig:maps_subplots} c). In particular, the state of Tocantins presented the highest levels of lightning density, along with the northern parts of Mato Grosso and the north of Goiás, in the centre-west of the Cerrado. The state of Mato Grosso in the south-west, and the north-eastern tip of the Cerrado experienced intermediate levels of lightning activity. In contrast, the south-eastern regions of the Cerrado along with an area roughly corresponding to the Alto Parnaíba ecoregion \citep{sano_cerrado_2019} in the north presented the lowest levels of lightning densities, about ten times fewer strikes than the grid cells with highest levels. Finally, during the dry season and particularly in June, many areas of the Cerrado experienced no lightning strikes - especially in the centre, while the southern- and northern-most parts of the Cerrado did experience some level of lightning activity throughout the year (Supplementary Figure \ref{fig:number_lightStrikes_month}). 

\subsection{The fire frequencies of lightning and human-caused fires}

\begin{figure*}[t!]%
    \centering
    \includegraphics[width=0.5\textwidth]{_chapter_03/fire_frequency.pdf}
    \caption{Fire frequency distributions for lightning-caused (green) and human-caused (brown) fires in the period 2019-2023. Bars represent the areas burned by each type of ignition a certain number of times in the study period divided by the total area affected at least once by that ignition cause. Here, we identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:fire_frequency}
\end{figure*}

We also quantified the fire frequencies of lightning and human-caused fires (Figure \ref{fig:fire_frequency}). Comparing the fire frequency distributions, we found that most of the area affected by fire over the 5 years of the study period burned only once for both types of ignition. However, around 28\% of the areas affected by human-caused fires burned two times or more, while only about 6\% of the areas affected by lightning burned more than once in 5 years. Furthermore, the lightning-affected area that burned four times was very small (3 km$^\text{2}$, $\simeq 0.01\%$) and none burned five times. In contrast, around 3,550 km$^\text{2}$ were affected by human-caused fires four times ($\simeq 1.4$\% of the human-affected area), and 1,190 km$^\text{2}$ ($\simeq 0.5$\%) burned every year between 2019 and 2023.


\subsection{Comparing weather conditions and landscape characteristics}

Next, we explored the weather conditions at the time of each fire occurrence and the landscape characteristics where fires took place, as both these factors can affect fire behaviour. We measured the landscape characteristics in terms of the natural area percentage of the grid cell where each ignition happened, and the distance to the nearest road and urban area.

Regarding weather conditions, we explored the amount of precipitation accumulated over the ten days prior to the ignition on the location where the fire started, as well as the VPD on the day of ignition. We found that lightning-ignited fires took place in more humid conditions than human-started fires, with overall larger accumulated precipitation and lower VPD values (Figure \ref{fig:weather_conditions_boxplot} c and d). Importantly, we observed these same patterns when comparing by month (Figure \ref{fig:weather_conditions_boxplot} a and b), with particularly acute differences from August to October.

Then, we explored how the fire density (number of fires per unit area) for both ignition causes and the lightning strikes density changed with the level of natural area percentage (Supplementary Figure \ref{fig:fragmentation_conditions}). We found that, even though the lightning density was similar across levels of natural area - meaning there was a similar number of lightning ignition opportunities across fragmentation levels -, the number of lightning-caused fires detected increased with the percentage of natural area (Supplementary Figure \ref{fig:fragmentation_conditions} b). Similarly, the number of human-caused fires detected increased towards less converted landscapes (Supplementary Figure \ref{fig:fragmentation_conditions} a). Hence, most fires occurred in landscapes with $\ge$ 60\% of natural area for both ignition causes.

Finally, we also explored the distances from each ignition point to the nearest road and urban area, as well as for a subset of 500,000 lightning strikes selected at random (in the interest of computational time). We found that the distance distributions for these three types of events were skewed towards small values (Supplementary Figures \ref{fig:distance_histrograms} a and c). That is, the proportion of fires (both lightning and human) and lightning strikes happening at shorter distances was larger than the number of fires and strikes occurring further away. Still, we did observe that the distribution of distances for human-caused fires seemed slightly more skewed towards small values than that of lightning-caused fires (Supplementary Figure \ref{fig:distance_histrograms}). In addition, however, the most remote human-caused fires started at similar distances from roads to those of the most remote lightning-caused fires (Supplementary Figure \ref{fig:distance_histrograms}).


\begin{figure*}[t!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/weather_prec_vpd_total&monthly_boxplot.pdf}
    \caption{\textbf{a}, distribution of the precipitation accumulated over the 10 days prior to the fire start per month for lightning (green) and human-caused (brown) fires, and, \textbf{b}, over the study period 2019-2023. \textbf{c}, distribution of vapour pressure deficit (VPD) values on the day of ignition per month, and, \textbf{d}, over the whole period, per ignition cause. Here, we identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:weather_conditions_boxplot}
\end{figure*}



\subsection{Comparing fire behaviour in relation to ignition cause}

Finally, we compared the behaviour of lightning and human-ignited fires examining the distributions of fire size and median FRP (Figure \ref{fig:size_FRP_monthly} and Supplementary Figure \ref{fig:size_FRP_landscape}). While we did not observe apparent differences in the medians (Figure \ref{fig:size_FRP_monthly}), there were remarkable differences in the largest values of the fire size and median FRP distributions (Figure \ref{fig:size_FRP_monthly}). For example, the largest human-caused fires were about one order of magnitude larger than the largest lightning-caused fires (Figures \ref{fig:size_FRP_monthly} b), and the largest median FRP values were about two times larger (Figure \ref{fig:size_FRP_monthly} d). 

Differences in fire properties between lightning and human-caused fires were conditional on the timing of fire ignition. The largest human-ignited fires occurred in August (Figure \ref{fig:size_FRP_monthly} a), when the difference between largest sizes was also greatest; followed by September, when the largest lightning fires were closer in size to the human-caused ones. In contrast, we did not observe remarkable differences between the largest sizes of the two types of fires in October nor during the wet season (Figure \ref{fig:size_FRP_monthly} a).

Regarding FRP, we observed similar seasonality patterns (Figure \ref{fig:size_FRP_monthly} b), with generally small values during the wet season for both types of fire. The most intense human-caused fires took place in the dry season, while the most intense lightning fires occurred in the dry-wet transition period. Again, human-caused fires had higher FRP values than lightning ones during these periods, while they were overall similar during the wet season.

Lastly, we explored the weather conditions and landscape characteristics of the largest ($\geq 150$ km$^2$) and most intense ($\geq 100$ MW) fires (Figure \ref{fig:size_FRP_largest}). We found that the largest human-caused fires took place in the less fragmented landscapes ($\geq 80$ \%) and in the driest conditions (Figures \ref{fig:size_FRP_largest} a and b, and Supplementary Figure \ref{fig:size_FRP_landscape} a). In contrast, the largest lightning-fires were more spread out across landscapes ($\geq$ 50 \% natural area) and practically all of them occurred in areas that had experienced some rainfall in the days before the fire (Figure \ref{fig:size_FRP_largest} a). In addition, the largest lightning fires occurred overall in lower VPD conditions than human ones (Figure \ref{fig:size_FRP_largest} b). Regarding FRP values, the most intense fires were spread out across all levels of fragmentation (Figures \ref{fig:size_FRP_largest} c and d, and Supplementary Figure \ref{fig:size_FRP_landscape} b), and the most intense human-caused fires took place in drier and hotter conditions than the most intense lightning fires.  

\begin{figure*}[t!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/size_FRP_monthly_stripplot.png}
    \caption{Distribution of the fire sizes and median fire radiative power (FRP) of lightning (green) and human-caused (brown) fires. The distributions are shown as a stripplot of the observations - that is, each brown or green dot in the graph represents a fire event. The white dots represent the median value of each distribution, and the black box-and-whisker show the quartiles of the distributions. \textbf{a} and \textbf{c} show the distributions per month for fire size and median FRP respectively. \textbf{b} and \textbf{d} show the fire size and median FRP distributions over the period 2019-2023 respectively.  We identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:size_FRP_monthly}
\end{figure*}


\begin{figure*}[t!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/size_FPR_largest_fixed.pdf}
    \caption{Weather conditions (y-axis) and natural area percentage (x-axis) of the ignition points of the largest ($\geq$ 150 km$^2$) fires (top row) and for those with the highest median fire radiative power (FRP) values ($\geq$ 100 MW) (bottom row). Each dot represents a fire observation coloured by the type of ignition, either human-caused (brown) or lightning-caused (green). The size of the dot represents the size (top row) or the median FRP value (bottom row) of the fire. \textbf{a} and \textbf{c}, show the relation between the precipitation accumulated in the 10 days prior to ignition and the percentage of natural area of the cell where the fire took place for the largest and most intense fires respectively. \textbf{b} and \textbf{d}, show the relation between the VPD conditions on the day of ignition and the percentage of natural area for the largest fires and most intense fires respectively. We identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:size_FRP_largest}
\end{figure*}


\section{Discussion}


Across the Brazilian Cerrado, we estimated that 7.5\% (95\% CI, [6.0, 9.0]\%) of all fire ignitions are caused by lightning, while human activities were attributed 89.1\% (95\% CI, [86.9, 91.3]\%) of fire ignitions - be it for agricultural purposes, for native vegetation conversion, as part of fire management practices or accidentally -, and 3.4\% (95\% CI, [2.7, 4.1]\%) did not have a clear ignition cause. Our findings highlight the extent of the human footprint on the Cerrado's current fire regimes, and how these substantially differ compared to lightning-caused fire regimes. Indeed, we found that human ignitions dominate across the Cerrado, as they account for more than 50\% of fires \citep{balch_human-started_2017} in over 90\% of its area (Figure \ref{fig:maps_subplots} d). Apart from increasing the number of ignitions, humans lengthen three-fold the fire season and shift it towards months with drier weather conditions (Figure \ref{fig:monthly_timeSeries} a and c). Moreover, we found that humans are the cause of the largest and most intense fires (Figure \ref{fig:size_FRP_largest}), with extreme fire behaviour that is not observed amongst the fires ignited by lightning. We found that these differences may be attributed to lightning often being accompanied by rainfall and moist weather conditions that prevent extreme fire behaviour, which is not the case for human ignitions (Figures \ref{fig:weather_conditions_boxplot} and \ref{fig:size_FRP_largest}).

We estimated that most lightning-caused fires, around 75\%, took place in the transition period from the dry to the wet seasons (September and October, Figure \ref{fig:monthly_timeSeries} a), in agreement with previous studies in different areas of the Cerrado \citep{schumacher_characteristics_2022, ramos-neto_lightning_2000, medeiros_incendios_2004, fiedler_ocorrencia_2006, de_souza_neves_dinamica_2018}, while they were much lower during the core months of the dry and wet seasons (June to August and December to March). The trade-off between vegetation dryness and the occurrence of sufficient ignition opportunities enabling the fire seasons \citep{bond_fire_2005} may explain these temporal patterns. The moisture content of the vegetation diminishes throughout the dry season and larger ratios of biomass become flammable \citep{zanzarini_flammability_2022}, explaining the higher lightning-ignition efficiency observed during the dry season (Figure \ref{fig:monthly_timeSeries} d), as noted in other ecosystems \citep{hessilt_future_2022}. However, the density of lightning strikes from June to August is very low (Figure \ref{fig:monthly_timeSeries} c) - precisely due to the lack of atmospheric events leading to cloud formation -, possibly acting as a limiting factor to the occurrence of lightning-ignited fires. Conversely, during the wet season, lightning density is not limited (Supplementary Figure \ref{fig:number_lightStrikes_month}), but high precipitation and low VPD impair fuel flammability, thus limiting fire occurrence regardless of ignition cause (Figure \ref{fig:monthly_timeSeries} a). However, the quantification of fires during this period must be considered with care because wet-season fires tend to be small, patchy and of low intensity \citep{ramos-neto_lightning_2000, medeiros_incendios_2004, fiedler_ocorrencia_2006}, and the cloud cover much denser, making them less likely to be identified in satellite-based burned area products \citep{rodrigues_how_2019, prudente_limitations_2020}. Therefore, our results seemingly underestimate the amount of fires occurring during the wet season, a period during which studies in different Cerrado protected areas and other savanna-like ecosystems have observed numerous lightning fires \citep{ramos-neto_lightning_2000, medeiros_incendios_2004, fiedler_ocorrencia_2006, menezes_lightning_2022}. In the dry-to-wet transition periods, however, we observed that lightning density increases as rainfall restarts, while precipitation has not yet reached maximum levels and VPD is still high (Figure \ref{fig:monthly_timeSeries} c), so large amounts of fuel may still have low moisture content. In addition, we find a larger ratio of lightning strikes per precipitation volume in September and October (Figure \ref{fig:monthly_timeSeries} e). This confluence of factors can explain the higher incidence of lightning fires in this period. 

The majority of fires, however, are caused by humans and take place over a longer period (May to October), precisely during drier months (Figure \ref{fig:monthly_timeSeries} a and c), as suggested in previous studies \citep{pivello_use_2011, mataveli_satellite_2018, mataveli_analysis_2017, silva_putting_2021}. This shift in the fire seasonality implies that Cerrado vegetation could be experiencing fires in a phase of the phenological cycle that may not correspond to the timing of fire it co-evolved with \citep{fontenele_what_2023}. Such mismatch, combined with the drier and hotter conditions in which most fires now happen, could have negative impacts on native vegetation, even if adapted to fire \citep{pivello_understanding_2021}. Further, human-caused fires present higher fire frequencies when compared to lightning-ignited fires (Figure \ref{fig:fire_frequency}), possibly as a result of the use of fire in modern agricultural practices to burn residues or renew pastures carried out regularly in the same areas \citep{pivello_use_2011}. The stochastic nature of lightning strikes and the necessary co-occurrence with appropriate flammability conditions, however, may limit the probability of high frequencies associated with lightning-caused fires.

Moreover, using accumulated precipitation and the VPD on the day of ignition as proxies for fuel moisture content \citep{spessa_relationships_2005, russell-smith_bushfires_2007}, we found that even when happening in the same month, human-caused fires tend to take place in drier and hotter conditions than lightning-caused fires (Figure \ref{fig:weather_conditions_boxplot}). These differences emphasise the fact that, as long as fuels are sufficiently flammable and continuous, human ignitions are less constrained than lightning ignitions, which tend to co-occur with rainfall events even during the dry season \citep{schumacher_characteristics_2022}. In addition, the remarkable differences in weather conditions during the transition period also suggest that human ignitions may have a tendency to target drier weather, especially when set to fulfill a purpose \citep{pivello_use_2011}. By elongating and shifting the fire season, humans expand the climatic niche of fires \citep{balch_human-started_2017} in Cerrado to weather conditions that would otherwise be rare if solely ignited by lightning, a process that may have been occurring since the first humans arrived in the Cerrado \citep{miranda_fire_2002}.

We found that both lightning and human-caused fires are concentrated in regions of the Cerrado with larger native vegetation remnants, while the incidence of both types of fires is low in highly fragmented areas (Supplementary Figure \ref{fig:fragmentation_conditions}). Such patterns indicate that the distribution of fires in the Cerrado may be more dependent on bioclimatic and landscape factors, as previously suggested \citep{mataveli_satellite_2018, silva_putting_2021, da_silva_arruda_assessing_2024, segura-garcia_human_2024}, than on the type of ignition. Therefore, while humans modify the rates of fire ignition above natural levels across the Cerrado, they also exert strong controls on fire spread through fragmentation and active fire suppression \citep{silva_putting_2021, archibald_managing_2016, archibald_what_2009} on both lightning and human-caused fires.

Still, satellite burned area products with coarse resolution, such as MCD64A1 from which the GFA is derived, can only capture fires larger than a certain size (around 0.21 km$^\text{2}$ in the GFA) and have high omission errors in agricultural landscapes of Cerrado where fires tend to be small and their spectral signal can be confounded \citep{rodrigues_how_2019, campagnolo_comprehensive_2021}. Using a higher spatial resolution (20 m) burned area product, \cite{ramo_african_2021} found that in sub-Saharan Africa, small fires ($<$ 100 ha) make up 41\% of total burned area, but only 5\% of these were mapped in the MCD64A1 product \citep{ramo_african_2021}. Hence, we are probably underestimating the number of small fires in the Cerrado as well \citep{rodrigues_how_2019, campagnolo_comprehensive_2021}. Additionally, the lightning strikes dataset may also contain omission errors, thus contributing to the underestimation of lightning-ignited fires.

We observed human-ignited fires at large distances from roads and urban areas (Supplementary Figure \ref{fig:distance_histrograms}), indicating some human presence in the less fragmented areas of the Cerrado. In addition, many large patches of Cerrado vegetation limit with agricultural lands \citep{medeiros_incendios_2004}, and the use of fires to renew pastures that escape human control has been documented in some of these areas \citep{marimon_floristica_2012, de_souza_neves_dinamica_2018}. Such ignition dynamics may be coupling with climate change contributing to the increasing fire frequency and burned area observed in the more intact parts of the Cerrado \citep{silva2024climate, segura-garcia_human_2024}, exerting further pressures on native vegetation. 

Importantly, we found a continuous region in the north of the Cerrado strongly dominated by human ignitions (causing $\geq$90\% of fires, Figure \ref{fig:maps_subplots} d). This area corresponds to the ecoregion of Alto Parnaíba \citep{sano_cerrado_2019}, within the so-called Matopiba region. Matopiba is considered to be the Cerrado's last agricultural frontier and has experienced large-scale native vegetation conversion over the past two decades \citep{teixeira_pitta_impacts_2017, rekow_socio-ecological_2019}, where fire may be used in relation to agri-business activities \citep{silva_drivers_2020, ribeiro_time_2024}. Interestingly, this area presents a lower density of lightning strikes than other parts of the Cerrado (Figure \ref{fig:maps_subplots} c), which could explain the lower number of lightning-caused fires. 

Comparing the fire behaviour of lightning and human fires, we found the most remarkable differences in the largest values of fire size and FRP distributions rather than in the medians, as observed in previous studies comparing fire behaviour for different fire categories \citep{libonati_twenty-first_2021}. For both types of ignitions, the largest and most intense fires are concentrated in the dry season (Figure \ref{fig:size_FRP_monthly}) as expected \citep{ramos-neto_lightning_2000}, as this is the period when the vegetation is more flammable, giving rise to higher fire intensities and effectively extending the fuel bed continuity \citep{archibald_what_2009}. However, the largest ($\geq$150 km$^\text{2}$) and most intense ($\geq$ 100 MW) human-ignited fires occurred in overall drier conditions than the lightning fires counterparts (Figure \ref{fig:size_FRP_largest}). In both cases, the largest fires occurred in the least converted landscapes ($>$80\% natural area, Figure \ref{fig:size_FRP_largest}), also as expected because land use fragmentation exerts a strong control on fire size \citep{archibald_what_2009, hantson_global_2015, haas_global_2022}. Therefore, given similar levels of landscape fragmentation, the different weather conditions at the time of the fire start appear as a determining factor explaining the more extreme behaviour of human fires, even though the weather conditions over the duration of fires will also contribute to their size and intensity.

Throughout this study, we have focused on lightning and human ignitions. However, there is a fundamental distinction that must be made between small-scale traditional uses of fires, the use of prescribed fires in integrated fire management (IFM) practices \citep{santos_prescribed_2021, de_andrade_implementation_2021}, and those uses associated with modern agricultural activities \citep{pivello_use_2011, de_medeiros_manejo_2002}. Indigenous peoples and pastoral communities in the first case, and environmental agencies and other organisations in the second, use mosaic burning practices where fire is planned and managed \citep{mistry_indigenous_2005, pivello_use_2011}. These practices intend to create landscapes of patches with different fire histories, which can contribute to shaping and maintaining Cerrado's heterogeneous vegetation \citep{eloy_seasonal_2019, durigan_no_2020, fidelis_year_2018}. In modern agricultural uses, however, fire occurrence in native vegetation is usually a by-product of its use in the cultivation of commercial crops and cattle-ranching fields \citep{pivello_use_2011} or in native vegetation conversion \citep{pivello_use_2011} and arson \citep{medeiros_incendios_2004}. In these cases, fire is used with high frequencies, concentrated in the dry and late-dry season, and with little control on fire escape into adjacent native vegetation \citep{pivello_use_2011}. Future studies could further discriminate ignition cause amongst IFM, traditional practices, modern agricultural activities and lightning, to deepen our understanding about the fire regimes created in each case and how they may be impacted by climate change.


\section{Conclusions}


Our results highlighted the profound differences between the Cerrado's fire regimes caused by lightning and by human ignitions. We provided quantitative evidence that humans are the main cause of fires across the Brazilian Cerrado, creating an excessive number of ignitions and higher fire frequencies in comparison to lightning. Still, we also showed that lightning ignites a small but relevant fraction of fires, particularly in the transition period from the dry to the wet season, when adequate flammability conditions and high lightning densities concur. In contrast, humans lengthen threefold the fire season by starting fires throughout the drier months of the year and in generally more flammable conditions than lightning, leading to more extreme fire behaviour. Further, we found that both types of fires are primarily concentrated in the least converted areas of the Cerrado, seemingly constrained by the level of landscape fragmentation \citep{silva_putting_2021, da_silva_arruda_assessing_2024, segura-garcia_human_2024}. Therefore, our results indicate that, through fire ignitions, humans are altering the fire regimes of the largest remnants of Cerrado native vegetation towards more frequent fires and extreme behaviour than would naturally occur. Even though most Cerrado ecosystems are adapted to fire, these patterns of human ignitions can have negative impacts affecting the structure of the vegetation and lead to biodiversity losses \citep{pivello_understanding_2021}. In this sense, the recently adopted Brazilian National Policy on IFM (Lei n. 14,944) that regulates the use of fire for agricultural practices - apart from recognising the role of fire in Cerrado ecosystems and promoting its use for conservation \citep{schmidt_fire_2020} - is a promising way forward in light of the findings presented in this study. However, the extent to which Cerrado has been converted and its fire regimes altered stress the urgency and scale of conservation needed to protect this region.


\section{Data and code availability statement}

The climate data used in this study are publicly available from ERA5-Land Monthly Averaged at \url{https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land}, and Climate Hazards Group InfraRed Precipitation with Station data version 2.0 at \url{https://data.chc.ucsb.edu/products/CHIRPS-2.0/}. The land use data are publicly available from MapBiomas collection 8.0 at \url{https://brasil.mapbiomas.org/en/colecoes-mapbiomas/}. The Global Fire Atlas data are publicly available from \url{https://zenodo.org/records/11400062}. The lightning strikes data from the Vaisala Global Lightning Detection Network GLD360 will be made publicly available (and a link to the repository provided) upon acceptance or if requested during review process in a coarsened format as it is subject to contractual agreement. The code supporting this study will also be made available through a repository and a link will be provided.


\section{Acknowledgements}

We thank Esther Brambleby for subsetting and formatting the lightning data. We thank the Ecosystems Lab Group at the Environmental Change Institute, School of Geography and the Environment, University of Oxford, for helpful feedback during discussions. We also thank the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) for the ground truth data on lightning-ignited fires in Cerrado's protected areas. C.S.G. was supported by the UK Natural Environment Research Council under grant number NE/S007474/1.


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\begin{refsection}

\supplementarysection

\section{Supplementary Information}

\begin{figure*}[h!]%
\centering
\includegraphics[width=\textwidth]{_chapter_03/lightning_ellipse_ignitionPoint_CI.pdf}
\caption[]{\textbf{a} distribution of uncertainties around the ignition point of each Global Fire Atlas (GFA) fire polygon. These uncertainties were calculated as the maximum distances between the ignition point and the border of the sub-polygon formed by all pixels in the GFA perimeter with the same burn date as the ignition point contiguous to it. \textbf{b}, distribution of the lengths of the semi-major axis of the 95\% confidence ellipse for the location of the lightning strikes in the Vaisala Global Lightning Detection Network GLD360 dataset in the period 2019 to 2023. Dashed red lines show, from left to right, the 2.5$^\text{th}$, the 50$^\text{th}$ and the 97.5$^\text{th}$ quantiles of the distributions.}
\label{fig:lightning_ellipse}
\end{figure*}

\begin{figure*}[h!]%
\centering
\includegraphics[width=\textwidth]{_chapter_03/heatmap_scatter.pdf}
\caption[]{Percentage of lightning-caused fires identified when using different temporal and spatial windows with an attribution score threshold of 0.8 (see Methods). \textbf{a}, shows the percentage of lightning-caused fires only for those temporal and spatial distances within the set considered in the study, $\{$($\Delta t_\text{max}$, $\Delta x_\text{max}$), $\Delta t_\text{max} \in [3,7]$ days and  $\Delta x_\text{max} \in [6,12]$ km$\}$. \textbf{b}, for reference, this plot shows the percentage of lightning fires for a larger set of spatial and temporal window combinations.}
\label{fig:sensitivity}
\end{figure*}

\begin{figure*}[h!]%
\centering
\includegraphics[width=0.9\textwidth]{_chapter_03/attribution_scores_CSVfile_formatted.pdf}
\caption[]{Attribution scores for different combinations of temporal and spatial windows for the different Global Fire Atlas (GFA) polygons identified as corresponding to a protected area, year and period where lightning fires were observed by the Instituto Chico Mendes de Conservação de Biodiversidade (ICMBio) in the non-georeferenced dataset. Each plot corresponds to a single GFA polygon out of all the records in the ICMBio (Supplementary Table 2). Those GFA polygons that were recorded in the georeferenced data from ICMBio are identified as such in the bottom right label of the plots along with month, year and the national park (PN) where the fire took place.}
\label{fig:attribution_scores}
\end{figure*}



\begin{figure*}[h!]%
\centering
\includegraphics[width=\textwidth]{_chapter_03/number_lightning_strikes_monthly.png}
\caption[]{Average annual lightning density (number of lightning strikes per unit area) for each 30 km grid and month over the Cerrado in the period 2019-2023.}
\label{fig:number_lightStrikes_month}
\end{figure*}

\begin{figure*}[h!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/landscape_fires_lightningStrikes.pdf}
    \caption[]{Density of fires and lightning strikes falling in different types of landscapes classified in deciles of natural area percentage (see Methods). Densities are obtained dividing per unit area of the total extension occupied by each type of landscape (i.e. by the number of cells with the same decile of natural area percentage). \textbf{a}, density of human-caused fires per unit area of each type of landscape. \textbf{b} density of lightning-caused fires per unit area (green, left y-axis), and density of lightning strikes per unit area (pink, right y-axis) of each type of landscape. Here, we identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:fragmentation_conditions}
\end{figure*}

\begin{figure*}[h!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/distance_infrastructure_lightningStrikes_hist_cats.pdf}
    \caption[]{Distances from the ignition points of human-caused (brown) and lightning- caused (green) fires, and from the location of lightning strikes (pink) to the nearest roads (top row) and urban areas (bottom row). \textbf{a}, normalised histogram distribution of distances to nearest roads for each type of event (ignition point or lightning strike). \textbf{b}, percentage of each type of event taking place in each of three different ranges of distances to nearest roads, less than 2 km, between 2 and 10 km, and more than 10 km. \textbf{c}, normalised histogram distribution of distances to nearest urban area for each type of event. \textbf{d}, percentage of each type of event (ignition point or lightning strike) within four different ranges of distances to urban areas, less than 5 km, between 5 and 20 km, between 20 and 50 km, and more than 50 km. Here, we identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:distance_histrograms}
\end{figure*}


\begin{figure*}[h!]%
    \centering
    \includegraphics[width=\textwidth]{_chapter_03/size_FRP_landscape_stripplot.png}
    \caption[]{Distribution of fire sizes, \textbf{a}, and median FRP values, \textbf{b}, for lightning (green) and human (brown) caused fires classified per decile of natural area percentage of the landscape where the fire started. In this figure, we identified lightning ($B\geq 0.8$) and human ($B\leq 0.2$) caused fires using $\Delta t_\text{max} = 5$ days and $\Delta x_\text{max} = 9$ km. The fires with intermediate attribution scores, $B$ between 0.2 and 0.8, were considered as having no clear cause of ignition and were excluded from this analysis.}
    \label{fig:size_FRP_landscape}
\end{figure*}



\begin{figure*}[h!]%
    \centering
    \includegraphics[width=0.5\textwidth]{_chapter_03/roads.png}
    \caption[]{Map of road infrastructure (brown lines) as obtained from the MapBiomas project, including the categories "federal highway", "state highway" and "other roads". The map also shows the urban areas (red polygons), obtained from the MapBiomas Collection 8.0 land use and land cover product.}
    \label{fig:distance_fragmentation_striplot}
\end{figure*}

\begin{landscape}
\begin{table}[h]
\centering
\caption[]{Lightning-caused fires registered in the field record provided by the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) in georeferenced vector format. Record includes the year, protected area, area burned by the fire, the estimated date of ignition of the fire, and the corresponding date expressed as day-of-year (DOY). Out of the 11 records, 6 were also mapped in the GFA dataset. The table also shows the starting and ending DOY date for each fire record that appears in the Global Fire Atlas (GFA), as well as whether the attribution method identified the fire as lightning for some combination of spatial and temporal windows (see Methods). PARNA stands for National Park.}
\label{tab:icmbio_shp_01}
\begin{tabular}{ccccccccc}
\textbf{Year} & \begin{tabular}[c]{@{}c@{}} \textbf{Protected}\\ \textbf{area}\end{tabular} & \begin{tabular}[c]{@{}c@{}} \textbf{Burned}\\ \textbf{area (ha)}\end{tabular} & \begin{tabular}[c]{@{}c@{}} \textbf{Estimated}\\ \textbf{ignition date}\end{tabular} & \textbf{Est. DOY} & \begin{tabular}[c]{@{}c@{}} \textbf{Registered}\\ \textbf{in GFA}\end{tabular} & \begin{tabular}[c]{@{}c@{}} \textbf{Start DOY}\\ \textbf{GFA}\end{tabular} &\begin{tabular}[c]{@{}c@{}} \textbf{End DOY}\\ \textbf{GFA}\end{tabular} & \begin{tabular}[c]{@{}c@{}} \textbf{Identified as}\\ \textbf{lightning fire}\end{tabular} \\ \hline
2021          & PARNA das Emas                 & 7216.9                    & 10/12/2021                  & 344               & Yes                        & 336                    & 346                  & Yes                                   \\
2022          & \begin{tabular}[c]{@{}c@{}} PARNA da\\ Serra da Canastra\end{tabular}     & 947.7                     & 24/01/2022                  & 20                & No                         & -                      & -                    & -                                     \\
2022          & \begin{tabular}[c]{@{}c@{}} PARNA da\\ Serra da Canastra\end{tabular}     & 286.7                     & 16/02/2022                  & 46                & No                         & -                      & -                    & -                                     \\
2022          & \begin{tabular}[c]{@{}c@{}} PARNA da Chapada\\ dos Veadeiros\end{tabular} & 1414.9                    & 17/11/2022                  & 314               & No                         & -                      & -                    & -                                     \\
2022          & \begin{tabular}[c]{@{}c@{}} PARNA Grande\\ Sertão Veredas\end{tabular}   & 117.5                     & 20/01/2021                  & 20                & No                         & -                      & -                    & -                                     \\
2023          & PARNA das Emas                 & 1332.5                    & 05/03/2023                  & 57                & Yes                        & 56                     & 62                   & Yes                                   \\
2023          & Sertão Veredas    & 68.1                      & 18/03/2023                  & 64                & No                         & -                      & -                    & -                                     \\
2023          & \begin{tabular}[c]{@{}c@{}} PARNA da Chapada\\ dos Veadeiros\end{tabular} & 826.0                     & 30/05/2023                  & 148               & Yes                        & 146                    & 148                  & No                                    \\
2023          & \begin{tabular}[c]{@{}c@{}} PARNA da Chapada\\ dos Veadeiros\end{tabular} & 462.6                     & 19/09/2023                  & 259               & Yes                        & 259                    & 263                  & Yes                                   \\
2023          & \begin{tabular}[c]{@{}c@{}} PARNA da Chapada\\ dos Veadeiros\end{tabular} & 594.9                     & 01/10/2023                  & 273               & Yes                        & 272                    & 285                  & Yes                                   \\
2023          & \begin{tabular}[c]{@{}c@{}} PARNA Grande\\ Sertão Veredas\end{tabular}   & 717.7                     & 05/12/2023                  & 336               & Yes                        & 336                    & 336                  & Yes                                   \\ 
\bottomrule
\end{tabular}
\end{table}
\end{landscape}

\begin{landscape}
\begin{table}[h]
\centering
\caption[]{The protected areas in Brazil and the period of the year in which lightning fire observations were recorded by the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) in the non-geoereferenced tabular format. Out of the 20 records of protected areas and months when lightning fires were recorded, we found that the Global Fire Atlas (GFA) had identified at least one fire for 7 of them, totalling 14 polygons. Out of these 14 records, the attribution method identified 13 as caused by lightning for some combination of spatial and temporal windows (see Methods). PARNA stands for National Park.}
\label{tab:icmbio_csv_02}
\begin{tabular}{cclccc}
\textbf{Year}                      & \textbf{Month}       & \multicolumn{1}{c}{\textbf{Protected area}} & \begin{tabular}[c]{@{}c@{}} \textbf{Containing}\\ \textbf{GFA fires}\end{tabular} & \begin{tabular}[c]{@{}c@{}} \textbf{Num. GFA}\\ \textbf{polygons}\end{tabular}                & \begin{tabular}[c]{@{}c@{}} \textbf{Num. lightning}\\ \textbf{fires}\end{tabular}             \\ \hline
2020                               & January   & PARNA das Nascentes do Rio Parnaíba         & No                                     & -                                         & -                                         \\
2020                               & October   & PARNA da Chapada dos Veadeiros              & No                                     & -                                         & -                                         \\
2020                               & November  & PARNA da Chapada dos Veadeiros              & Yes                                    & 2                                         & 2                                         \\
2020                               & December  & PARNA das Emas                              & Yes                                    & 3                                         & 3                                         \\
2021                               & February  & PARNA da Serra da Canastra                  & No                                     & -                                         & -                                         \\
2021                               & September & PARNA das Emas                              & Yes                                    & 1                                         & 1                                         \\
2021                               & November  & PARNA da Chapada dos Guimarães              & No                                     & -                                         & -                                         \\
2021                               & December  & PARNA das Emas                              & Yes                                    & 1                                         & 1                                         \\
2022                               & January   & PARNA Grande Sertão Veredas                 & No                                     & -                                         & -                                         \\
2022                               & January   & PARNA da Serra da Canastra                  & No                                     & -                                         & -                                         \\
2022                               & February  & PARNA da Serra da Canastra                  & No                                     & -                                         & -                                         \\
2022                               & September & Floresta Nacional de Carajás                & No                                     & -                                         & -                                         \\
2022                               & November  & PARNA da Chapada dos Veadeiros              & No                                     & -                                         & -                                         \\
2022                               & December  & PARNA da Chapada dos Veadeiros              & No                                     & -                                         & -                                         \\
2023                               & February  & PARNA das Emas                              & Yes                                    & 3                                         & 3                                         \\
2023                               & April     & PARNA da Chapada dos Veadeiros              & No                                     & -                                         & -                                         \\
2023                               & August    & PARNA da Chapada dos Guimarães              & No                                     & -                                         & -                                         \\
2023                               & September  & PARNA da Chapada dos Veadeiros              & Yes                                    & 3                                         & 2                                         \\
2023                               & October    & PARNA da Chapada dos Veadeiros              & No                                     & -                                         & -                                         \\
2023                               & December   & PARNA Grande Sertão Veredas                 & Yes                                    & 1                                         & 1                                         \\ \hline
\multicolumn{1}{l}{\textbf{Total}} & \multicolumn{1}{l}{} &                                             &     \multicolumn{1}{r}{\textbf{7 out of 20}}                                   &  \multicolumn{1}{r}{\textbf{14 GFA polygons}}    & \multicolumn{1}{r}{\textbf{13 out of 14}} \\
\bottomrule
\end{tabular}
\end{table}
\end{landscape}


\end{refsection}

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