# Seasonal hazard-vulnerability patterns between drought and wildfire in New Caledonia derived from remote sensing products

Mathis Neuhauser, Alexandre Peltier, Thomas Ibanez, Marc Despinoy, Michel Le Page

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## ► To cite this version:

Mathis Neuhauser, Alexandre Peltier, Thomas Ibanez, Marc Despinoy, Michel Le Page. Seasonal hazard-vulnerability patterns between drought and wildfire in New Caledonia derived from remote sensing products. International Journal of Applied Earth Observation and Geoinformation, 2026, 146, pp.105159. 10.1016/j.jag.2026.105159. hal-05520745

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Submitted on 20 Feb 2026

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# Seasonal hazard-vulnerability patterns between drought and wildfire in New Caledonia derived from remote sensing products

Mathis Neuhauser<sup>a,∗</sup> <sup>ID</sup>, Alexandre Peltier<sup>b</sup>, Thomas Ibanez<sup>c</sup>, Marc Despinoy<sup>a ID</sup>, Michel Le Page<sup>d</sup>

## ARTICLE INFO

### Keywords:

- Remote sensing

- Vegetation drought

- Burned areas

- Directional interaction analysis

- Fire risk

- Feedback effects

## ABSTRACT

Climate change is escalating the frequency and intensity of droughts and wildfires globally. In New Caledonia, future intensification is projected, yet the seasonal and directional temporal relationships between vegetation drought and wildfire activity remain insufficiently characterized at the regional scale. This study presents a regional case study combining remote sensing and in-situ datasets spanning 2000–2024, aggregated at monthly and municipal scales, to analyse drought-fire temporal interactions in New Caledonia. The approach quantifies the temporal sequencing of interactions between vegetation condition and burned area occurrence, distinguishing periods when vegetation stress precedes fire activity (fire hazard) from periods when fire occurrence is followed by altered vegetation conditions (post-fire vulnerability and potential feedbacks). Lagged and seasonal correlation analyses were conducted using the Vegetation Health Index (VHI) as a proxy of vegetation drought and the Burned areas Anomaly Index (BAI) to characterize wildfire activity. Results reveal robust and spatially coherent correlations between vegetation drought and burned area extent, highlighting the strong association between VHI and subsequent wildfire activity. Distinct seasonal interaction patterns emerge, with vegetation condition preceding fire activity during the early dry season (August–September), while fire occurrence is followed by modified surface conditions over subsequent months (November–May), consistent with post-fire ecosystem vulnerability and feedback mechanism. Marked geographic contrasts are observed, particularly between the west and east coasts. These findings improve understanding of drought-fire temporal linkages in New Caledonia and provide actionable, region-specific insights for seasonally targeted and spatially explicit wildfire risk management.

## 1. Introduction

Extreme events such as droughts and wildfires are becoming more frequent due to climate change, including in Pacific island territories (Barnett, 2020; Dutheil et al., 2021; Kumar et al., 2020). In this context, understanding the interactions between vegetation drought and wildfires is crucial to anticipate fire hazard and assess ecosystem vulnerability (Calvin et al., 2023). While drought is a well-known driver of wildfire activity, the mutual feedback between drought and fire—especially regarding post-fire surface conditions—remain insufficiently explored, including in tropical island ecosystems.

Droughts are triggered by rainfall deficits linked to large-scale

climate anomalies, notably ENSO, PDO, and MJO in the Pacific (Madden and Julian, 1994; Power et al., 1999; Timmermann et al., 2018) and propagated through land surface as vegetation or hydrological drought (Mishra and Singh, 2010; Taufik et al., 2017). Vegetation drought directly impacts the availability and flammability of fuels. Woody vegetation often withstands drought better due to deep rooting systems, whereas grasses and shrubs are more sensitive but recover rapidly (Amri et al., 2011; Cardil et al., 2019), which contributes to maintaining high fire susceptibility (Baudena et al., 2020; Pau et al., 2010). Such traits can generate positive fire-vegetation feedback loops, as highlighted in Mediterranean (Baudena et al., 2020) and savanna ecosystems (D'Antonio and Vitousek, 1992).

<footnote><sup>a</sup> ESPACE-DEV, Univ New Caledonia, Univ Montpellier, Univ Antilles, Univ Guyane, Univ Réunion, IRD, 98800 Nouméa, New Caledonia, France</footnote>

<footnote><sup>b</sup> METEO-FRANCE, 98800 Nouméa, New Caledonia, France</footnote>

<footnote><sup>c</sup> AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, 98800 Nouméa, New Caledonia, France</footnote>

<footnote><sup>d</sup> CESBIO, University of Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: mathis.neuhauser@ird.fr (M. Neuhauser).</footnote>
Wildfire regimes are shaped by both ignition sources (often anthropogenic) and fire weather conditions—precipitation, wind, temperature, vegetation type—that determine fire spread and intensity (Curt et al., 2015; Roos et al., 2023). Drought enhances fire hazard not only by reducing fuel moisture, but also by increasing fuel continuity through the curing and spatial connection of fine fuels, thereby facilitating fire propagation at landscape scales (Adeyeri et al., 2024; Nogueira et al., 2017; Richards et al., 2023; Richardson et al., 2022). Numerous indicators exist to quantify drought severity and fire potential, including Standardized Precipitation Index (SPI; McKee et al., 1993), Standardized Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2010), and Vegetation Health Index (VHI; Kogan, 1997, 1995) for drought, and the Fire Weather Index (Van Wagner, 1987) or land surface temperature anomalies for fires (Giglio et al., 2016; Justice et al., 2002).

Remote sensing offers a robust framework to monitor these processes, capturing surface conditions and fire events over large areas and extended periods. Vegetation indices (NDVI, EVI), temperature (LST), and soil moisture (SWI) from satellite data have been used to map drought propagation in Mediterraneans environment (Le Page and Zribi, 2019). In tropical regions, previous studies have shown contrasting anomaly responses depending on whether indicators are sensitive to vegetation moisture or canopy structure (Anderson et al., 2010), and marked differences between rainforest and dry forest systems, with composite indices such as VHI proving particularly effective in capturing large-scale drought impacts during major events (Chua et al., 2020; Pau et al., 2010). Earth observation approaches for wildfire monitoring broadly encompass fire weather indices, which indirectly quantify fire hazard from meteorological or drought conditions, and active fire or burned area products that document fire occurrence and extent in near-real time and retrospectively (Barbero et al., 2011; Van Wagner, 1987; Westerling and Swetnam, 2003). Together with human-related drivers such as land use or ignition pressure (Curt et al., 2015; Gomez et al., 2015), long-term satellite records from sensors such as MODIS, VIIRS or Sentinel-2 have substantially advanced the understanding of fire-weather interactions and enabled operational fire monitoring and risk assessment (Filipponi, 2018; Giglio et al., 2016; Justice et al., 2002; Roy et al., 2014).

Drought-fire relationships have been widely investigated using statistical time-series analyses, addressing several complementary research themes. Lagged correlations and related frameworks have primarily focused on quantifying how meteorological and vegetation droughts modulate fire occurrence, extent, and intensity, supporting fire hazard assessment (Enenkel et al., 2016; Fuentes et al., 2022; Littell et al., 2009; McEvoy et al., 2019; Westerling and Swetnam, 2003). In contrast, fewer studies have examined the reverse direction, documenting post-fire feedbacks on surface conditions using satellite-derived indicators, such as delayed vegetation recovery, soil moisture depletion, or surface warming using remote sensing (Bousquet et al., 2022; Goetz et al., 2006; Viana-Soto et al., 2020). These studies highlight the capacity of remote sensing to capture long-term regrowth dynamics, ecosystem resilience, and post-fire vulnerability, yet bidirectional drought-fire interactions are rarely explored within a unified analytical framework.

In New Caledonia, climate projections predict an 18% decrease of annual rainfall and 20% increase of surface wind speed in 2080–2100 compared to 1989–2009 (Dutheil et al., 2021). Under such projected climate, drought frequency and intensity and wildfires hazard would increase in the future. For this reason, New Caledonia is an interesting case study to investigate the spatiotemporal coupling between vegetation drought and wildfires. Moreover, remote sensing has effectively assessed fire patterns and drivers in this region (Curt et al., 2015; Gomez et al., 2015) but the potential existence of fire-drought feedback effects detectable through satellite indicators has not been formally studied.

In this study, we aim to explore these interactions by analysing the temporal relationships between vegetation drought and burned areas from 2000 to 2024 in New Caledonia. Drought data were acquired using in-situ weather stations to monitor meteorological conditions (SPI) and

MODIS/ASCAT space-based sensors for soil and vegetation conditions (MAI, VHI and some other indicators). Wildfires dynamics were assessed using burned areas anomalies (BAI) derived from MODIS/VIIRS active fires. Using lagged cross-correlation and directional interaction analysis, first we analyzed the propagation of drought from atmosphere to surface and more specifically to the vegetation that constitutes the fuel of wildfires. Then, we investigated both the influence of vegetation condition on fire hazard, and the post-fire feedback effects on vegetation drought, across different seasons and regions.

## 2. Material and methods

### 2.1. Study area

#### 2.1.1. Climate, weather and vegetation in New Caledonia

New Caledonia is an archipelago in the southwest Pacific, composed of the main island “Grande-Terre” (≈ 400 × 50 km, 16 600 km²) and smaller islands such as the Belep Islands, Loyalty Islands, and Ile des Pins (Fig. 1b). Located just above the Tropic of Capricorn, it has a subtropical climate (Aw, Köppen–Geiger) with a marked seasonal cycle (Fig. 1c). The hot, humid season extends from November to April, while the cool season (June–September) is generally dry with lower temperatures. Two transition periods occur: a dry inter-season from September to November, corresponding to the fire season with minimal rainfall and rising temperatures, and an intermediate season (April–June) with decreasing rainfall but still warm conditions (Moron et al., 2016). This cycle is modulated by regional climate variability at multiple timescales (Dutheil, 2018), including ENSO, MJO, IPO and PDO, leading to inter-annual changes in the intensity (rainfall anomalies, temperatures) and timing (start and end dates) of the seasons.

Rainfall patterns are strongly shaped by the Central Chain mountain range (Romieux, 2011), whose peaks exceed 1600 m (Mount Panié in the north and Mount Humboldt in the south). Prevailing easterly winds generate high precipitation on the east coast (up to 3500 mm/year, Fig. 1b) and lower values on the west coast plains (≈ 800 mm/year). Three main environmental zones can be distinguished (Fig. 1a):

* West coast and north Grande-Terre – dry plains with thicket, savanna, mining scrub and remnant dry forest; main agricultural areas.

* East coast and south Grande-Terre – humid mountain slopes with tropical rainforest in the east and mining scrub in the south.

* Loyalty Islands and Ile des Pins – low-relief landscapes dominated by dense, humid forests (≈ 1600 mm/year rainfall).

The archipelago has faced recurrent severe droughts over the past decades, notably 1993–1996 and recent events in 2010, 2014, 2015, 2017 and 2019, often linked to El Niño phases. The 2017 and 2019 events set records for winter dryness and high temperatures, strongly impacting vegetation and agriculture.

#### 2.1.2. Wildfires in New Caledonia

Recent analyses (N’Guyen Van Soc et al., 2021) confirmed earlier findings on wildfire patterns (Barbero et al., 2011; Curt et al., 2015; Gomez et al., 2015; Ibanez et al., 2013; Moron et al., 2013). VIIRS data (2012–2018) revealed the highest fire densities along the north-east coast of Grande-Terre, followed by the main east-west transport corridors (e.g., Koné-Poindimié, Fig. 2a). Southern areas and the outer islands are less affected overall.

Extreme events have been recorded, such as 2019 in Ouégoa (north-east coast) with 11 560 ha burned — a historical maximum — and 4 000 ha burned in Thio (south-east coast). The years 2017 and 2019, both marked by severe drought and El Niño conditions, registered the largest burned areas in the satellite record (VIIRS, MODIS, Fig. 2c).

Most fires occur during the dry season between September and December (also known as the fire season), with a peak reached in
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** (a) Land cover/land use 2020 (ESRI Land Cover, 2020) with demarcation of administrative areas over New Caledonia, (b) Average annual precipitation for the period 1981–2010 (Meteo-France, 2018) and (c) Monthly climatology over New Caledonia for the period 1991–2020 (data from Climatic Research Unit, chart adapted from Climate Change Knowledge Portal).</figcaption></figure>

October, but also during July and August when a large number of fires are still observed (bar charts in Fig. 2b). Total burned areas (amount of all burned areas observed in each month on the historical period, red curve in Fig. 2b), have a comparable seasonal trend but with a delayed peak in November, as noticed by (N'Guyen Van Soc et al., 2021).

This study focuses on New Caledonia because its marked east-west climatic gradient, contrasted vegetation types, and recurrent wildfire activity provide a clear and tractable setting in which to examine drought-fire linkages. While the island's limited extent simplifies the interpretation of the processes involved, the diagnostic framework developed here is general and can be adapted to larger and more heterogeneous regions.

## 2.2. Data

The main datasets used in this study are summarized in Table 1 (satellite-derived surface indices) and Table 2 (drought and fire indicators), with additional technical details provided in Supplementary Material S1. Hereafter, “indicators” refer to variables derived from remote sensing or in-situ observations and expressed as anomalies relative to historical references, in order to characterize meteorological drought, vegetation surface conditions, and fire activity.

Meteorological drought was characterized using the 3-month Standardized Precipitation Index (SPI-3), which represents standardized anomalies of cumulative precipitation over a three-month period. SPI-3 was computed and operationally produced by Meteo-France New

Caledonia from daily precipitation records from 1950 to present day at 52 weather stations across the archipelago. Monthly SPI-3 values were obtained directly from Meteo-France products and aggregated at the municipality level (area-averaged). These datasets are publicly available upon request through the Meteo-France New Caledonia climatological data portal.

Vegetation surface conditions were derived from long-term satellite time series of biophysical indices obtained from MODIS (since 2000) and ASCAT (since 2007) sensors, including vegetation indices (NDVI, EVI, NDWI), land surface temperature (LST), and soil moisture proxies (SWI). These products are freely provided online by the NASA/USGS program (MODIS) and Copernicus Land Monitoring Service (ASCAT). For each variable, historical monthly-averaged time series were first constructed over the entire period available, then drought-related vegetation indicators (e.g., VCI, TCI, VHI, VAI, TAI, MAI) were computed by normalizing each index relative to its historical range or climatology for a given month and location (pixel). This normalization removes the mean seasonal cycle and allows anomalies to be compared consistently across space and time, highlighting deviations associated with vegetation stress or moisture surplus. These indicators were then spatially-averaged at the municipality level. To study the temporal relationship between drought and fires, VHI from NDWI and LST was selected for its correlation with SPI-3 and capacity to capture drought variability; values were inverted so that higher values indicate more severe drought, aligning with fire dynamics.

Fire activity was quantified using a Burned areas Anomaly Index
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** (a) Total number of fires detected per administrative area from VIIRS between 2012 and 2024, (b) Total number of fires and total burned areas detected per month from VIIRS (2012–2024) and MODIS (2000–2024) over New Caledonia, (c) Time evolution of burned areas, summed per month over New Caledonia.</figcaption></figure>

(BAI) derived from daily burned-area detections based on spatially-aggregated MODIS and VIIRS thermal anomaly products (active fires detections or “hotspots”, supplied by NASA/FIRMS service since 2000 for MODIS and 2012 for VIIRS). The spatial aggregation process is performed by the Environment Observatory in New Caledonia (OEIL), which combines the hotspots of single fire event according to space–time criteria (detailed description in Supplementary Material S1.4). Burned area datasets are freely accessible through OEIL fire monitoring and alert platform (Vulcain), ensuring transparency and reproducibility of the fire indicators. In this study, burned areas were first accumulated at the monthly scale, then spatially unioned within each administrative unit to obtain total burned surfaces per municipality and month. Anomalies were computed relative to historical monthly reference conditions, yielding positive (negative) BAI values for above- (below-) average fire activity.

All drought and fire indicators were harmonized at a monthly temporal resolution—consistent with the SPI-3 products—and spatially aggregated to the 33 municipalities of New Caledonia. This spatial scale reduces the impact of missing data, matches operational fire management needs, and remains relevant for climatic and environmental analyses, as municipal boundaries partly reflect topographic and climatic contrasts (Fig. 1a–b). This harmonization preserves the dominant seasonal signals required to investigate drought–fire interactions. Two

municipalities with contrasting climatic conditions and high fire activity—Koné (west coast) and Poindimié (east coast)—are used as illustrative examples throughout the analysis.

Because vegetation indices differ in their biophysical sensitivity and response time to meteorological drought, multiple indices (VCI, TCI, VAI, MAI, VHI) were analysed in parallel. This multi-index framework allows testing the consistency of drought–fire interaction patterns across indicators with different sensitivities to vegetation structure, moisture, and temperature.

The temporal coverage of the datasets varies depending on the indicator and application. Drought-only analyses were conducted over periods ensuring homogeneous availability of vegetation indices for robust comparison (2000–2021 for MODIS, 2007–2021 for ASCAT). In contrast, drought–fire interaction analyses were extended to the most recent year available (2000–2024) to capture recent fire activity. Fire-related analyses based on VIIRS products were necessarily restricted to the post-2012 period, corresponding to the start of VIIRS observations.

## 2.3. Analytical framework for drought–fire temporal linkages

In this section, we present the analytical framework used to analyse the temporal linkages between drought and fire products. The methodology is based on correlations analysis applied to two monthly time
<caption>**Table 1**</caption>

Remote-sensing surface indices used to monitor surface state (vegetation, soil, burned areas) in New Caledonia. The table summarizes the values range (on land, water bodies excluded), sensors, spatial and temporal resolutions, and source products. Indices marked with an asterisk (*) correspond to source products directly used in the analysis without additional index formulation.

<table><thead><tr><th>SURFACE INDICES</th><th>EQUATION</th><th>INFORMATION</th><th>RANGE</th><th>SENSOR</th><th>SPATIAL RESOLUTION</th><th>TEMPORAL RESOLUTION</th><th>SOURCE PRODUCTS</th><th>REFERENCES</th></tr></thead><tbody><tr><td>* Normalized Difference Vegetation Index (NDVI)</td><td>NDVI = <sup>NIR - R</sup>&frasl;<sub>NIR + R</sub></td><td>Photosynthetic activity and productivity of plants</td><td>0 to 1</td><td>MODIS</td><td>250 m</td><td>16-day composite (from daily data)</td><td>MYD13Q1/ MOD13Q1 NDVI (NASA-USGS program)</td><td>(Didan et al., 2015; Tucker and Sellers, 1986)</td></tr><tr><td>* Enhanced Vegetation Index (EVI)</td><td>EVI = <sup>G*<sup>NIR - R</sup>&frasl;<sub>NIR + C<sub>1</sub>*R - C<sub>2</sub>*B + L</sub></sup><sup>t<sub>i</sub></sup></td><td>Photosynthetic activity and canopy structure</td><td>0 to 1</td><td>MODIS</td><td>250 m</td><td>16-day composite (from daily data)</td><td>MYD13Q1/ MOD13Q1 EVI (NASA-USGS program)</td><td>(Huete et al., 1994, 2002; Qiu et al., 2022; Didan and Munoz, 2019)</td></tr><tr><td>Normalized Difference Water Index (NDWI)</td><td>NDWI = <sup>NIR - MIR</sup>&frasl;<sub>NIR + MIR</sub></td><td>Photosynthetic activity and plants water content</td><td>-0.5 to 0.5</td><td>MODIS</td><td>500 m</td><td>Daily</td><td>Equation applied to MYD09GA/ MOD09GA surface reflectances (NASA-USGS program)</td><td>(Gao, 1996; Serrano et al., 2000; Szabó et al., 2016)</td></tr><tr><td>* Land Surface Temperature (LST)</td><td>—</td><td>Evapotranspiration (negatively correlated)</td><td>min LST to max LST(in °C)</td><td>MODIS</td><td>1 km</td><td>Daily</td><td>MOD11A1/ MYD11A1 LST (NASA-USGS program)</td><td>(Wan, 2014; Wan and Dozier, 1996)</td></tr><tr><td>* Soil Water Index (SWI)</td><td>SWI(t) = <sup>&Sigma;<sub>i</sub>SSM(t<sub>i</sub>)e<sup>t<sub>i</sub>t</sup></sup>&frasl;<sub>&Sigma;<sub>i</sub>e<sup>t<sub>i</sub>t</sup></sub></td><td>Root zone soil moisture (T = 20, with t<sub>i</sub> ≤ t)</td><td>0 to 1</td><td>ASCAT</td><td>12.5 km</td><td>Daily</td><td>SWI – version 3 (Copernicus Land Monitoring Service)</td><td>(Wagner et al., 1999)</td></tr><tr><td>* Burned areas (BA)</td><td>—</td><td>Burned areas detected from thermal anomalies</td><td>0 to max BA(in ha)</td><td>MODIS, VIIRS</td><td>1 km, 375 m</td><td>Daily</td><td>Potentially burned areas from hotspots aggregated products (OEIL geoportal)</td><td>(N'Guyen Van Soc et al., 2021; De Clercq et al., 2020; Luis et al., 2019)</td></tr></tbody></table>

series x(t) and y(t) aggregated at the municipality scale, where x(t) represents a drought or vegetation surface condition indicator (e.g., SPI-3 or VHI) and y(t) represents vegetation indicator (VHI, VCI, etc.) or fire activity through the burned area anomaly index (BAI). Correlations are computed for each municipality independently, and over the period common to both series (in case of products acquired over different time periods).

### 2.3.1. Lagged correlations

To explore temporal propagation from meteorological to vegetation drought, and from surface conditions to fire activity, we computed lagged Pearson correlation score $r_{lag}$ (also defined later as R-score) between reference time series x(t) and lagged observed time series y(t):

$$r_{\text{lag}}(\tau) = \frac{\text{Cov}(x(t), y(t + \tau))}{\sigma_x \sigma_y} \qquad (1)$$

with Cov standing for covariance between x(t) and y(t), $\sigma_x$ and $\sigma_y$ the standard deviation of x(t) and y(t) respectively, and $\tau$ time lag from -4 to +4 months. Positive lags indicate that the observed series lags behind the reference, while negative lags suggest it precedes it.

### 2.3.2. Seasonal correlations

We then examined whether relationships varied seasonally by correlating specific months from the two series. Seasonal correlations are computed separately for each calendar month in order to isolate seasonal timing effects and avoid spurious correlations driven by the annual cycle. For each month pair ($m_x$, $m_y$), the seasonal correlation $r_{season}$ was computed:

$$r_{\text{season}}(m_x, m_y) = \frac{\text{Cov}(x_{m_x}(t), y_{m_y}(t))}{\sigma_{x_{m_x}} \sigma_{y_{m_y}}} \qquad (2)$$

With $x_{m_x}(t)$ and $y_{m_y}(t)$ corresponding to x(t) and y(t) obtained only for specific months $m_x$ and $m_y$ respectively, $\sigma_{x_{m_x}}$ and $\sigma_{y_{m_y}}$ the standard deviation of $x_{m_x}(t)$ and $y_{m_y}(t)$ respectively.

When $m_x = m_y$, this highlights same-season links (seasonal correlations); when $m_x \neq m_y$, it reveals cross-season connections (seasonal lagged correlations). An example of the analysis resulting from this correlation is shown in Fig. 3 (left diagram).

### 2.3.3. Directional interaction analysis

Although statistical methods like cross-correlation do not provide a formal proof of causation, they allow the identification of temporal ordering and relative directionality between processes, which are essential for interpreting hazard-vulnerability relationships. Here, we analyze the seasonal timing and relative direction of interactions between vegetation surface conditions and wildfire activity using seasonal lagged correlations between VHI (NDWI + LST) and BAI (VIIRS). The underlying assumption is that anomalies in surface conditions—expressed as vegetation stress or moisture—may contribute to fire hazard when they systematically precede fire activity, whereas fire occurrence may affect surface conditions afterward, reflecting ecosystem vulnerability and potential feedbacks. Throughout this study, the term “directional” will refer to temporal ordering inferred from lagged correlations, and will not imply formal causality in the strict statistical or mechanistic sense (this interpretation is further discussed in the 4.3 Limitations and future directions section).

To explicitly account for the annual cycle, we defined a signed cyclic lag $\Delta_m$ as:
<caption>**Table 2**</caption>

Meteorological drought, vegetation drought, and wildfire indicators used in this study. The indicators are derived through different processing chains depending on data type: (i) SPI-3 values are provided directly at the monthly scale by Meteo-France New Caledonia and subsequently aggregated at the municipality level; (ii) vegetation drought indicators are computed from monthly aggregated remote-sensing indices (Table 1), normalized over the reference period, and then spatially aggregated by municipality; (iii) burned area anomalies are derived from monthly burned areas aggregated by municipality and compared to historical seasonal reference values.

<table><thead><tr><th>DROUGHT/FIRE INDICATORS</th><th>EQUATION</th><th>INFORMATION</th><th>RANGE</th><th>SENSOR</th><th>SPATIAL RESOLUTION</th><th>TEMPORAL RESOLUTION</th><th>SOURCE PRODUCTS</th><th>REFERENCES</th></tr></thead><tbody><tr><td>* Standardized Precipitation Index (SPI-3)</td><td>—</td><td>Meteorological drought from rainfall anomalies</td><td>centered in 0, SPI &lt; -1 indicates meteorological drought</td><td>In-situ rain gauge network</td><td>Municipality level (area-averaged)</td><td>Monthly (3-month precipitation accumulated)</td><td>Station-based Meteo-France New Caledonia SPI-3</td><td>(Ji and Peters, 2003; McKee et al., 1993)</td></tr><tr><td>Vegetation Condition Index (VCI)</td><td>VCI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">VI - VI<sub>min</sub></div><div style="padding: 0 5px;">VI<sub>max</sub> - VI<sub>min</sub></div></div></td><td>Vegetation drought from plant moisture and photosynthetic activity</td><td>0 to 1, VCI &lt; 0.4 indicates vegetation drought</td><td>MODIS</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived Vegetation Index (NDVI, EVI or NDWI)</td><td>(Kogan, 1997, 1995)</td></tr><tr><td>Temperature Condition Index (TCI)</td><td>TCI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">LST<sub>max</sub> - LST</div><div style="padding: 0 5px;">LST<sub>max</sub> - LST<sub>min</sub></div></div></td><td>Vegetation drought from evapotranspiration</td><td>0 to 1, TCI &lt; 0.4 indicates vegetation drought</td><td>MODIS</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived temperature (LST)</td><td>(Kogan, 1997, 1995)</td></tr><tr><td>Vegetation Health Index (VHI)</td><td>VHI = α*VCI + (1 - α)*TCI</td><td>Vegetation drought from plant moisture, photosynthetic activity and evapotranspiration (α = 0.5)</td><td>0 to 1, VHI &lt; 0.4 indicates vegetation drought</td><td>MODIS</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived Vegetation Index and temperature</td><td>(Kogan, 1990, 1995, 1997)</td></tr><tr><td>Vegetation Anomaly Index (VAI)</td><td>VAI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">VI - √VI</div><div style="padding: 0 5px;">σ<sub>VI</sub></div></div></td><td>Vegetation drought from vegetation anomalies</td><td>centered in 0, VAI &lt; -1 indicates vegetation drought</td><td>MODIS</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived Vegetation Index (NDVI, EVI or NDWI)</td><td>(Anderson et al., 2010; Le Page and Zribi, 2019)</td></tr><tr><td>Temperature Anomaly Index (TAI)</td><td>TAI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">LST - LST</div><div style="padding: 0 5px;">σ<sub>LST</sub></div></div></td><td>Vegetation drought from temperature anomalies</td><td>centered in 0, TAI &gt; 1 indicates vegetation drought</td><td>MODIS</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived temperature (LST)</td><td>(Le Page and Zribi, 2019)</td></tr><tr><td>Moisture Anomaly Index (MAI)</td><td>MAI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">SWI - SWI</div><div style="padding: 0 5px;">σ<sub>SWI</sub></div></div></td><td>Vegetation drought from root zone soil moisture anomalies</td><td>centered in 0, MAI &lt; -1 indicates vegetation drought</td><td>ASCAT</td><td>Municipality level (area-averaged)</td><td>Monthly (sub-monthly satellite data averaged)</td><td>Satellite-derived soil water index (SWI)</td><td>(Le Page and Zribi, 2019)</td></tr><tr><td>Burned areas Anomaly Index (BAI)</td><td>BAI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">BA - √BA</div><div style="padding: 0 5px;">σ<sub>BA</sub></div></div></td><td>Burned areas anomalies</td><td>centered in 0, BAI &gt; 0 indicates abnormally high burned areas</td><td>MODIS, VIIRS</td><td>Municipality level (area-unioned)</td><td>Monthly (sub-monthly satellite data accumulated)</td><td>Satellite-derived burned areas (BA)</td><td>—</td></tr></tbody></table>

$$ \Delta_m(m_x, m_y) = ((m_x - m_y + 6) \mod 12) - 6 \quad (3) $$

This formulation constrains $\Delta_m$ between -6 and +5 months and enables a consistent interpretation of temporal alignment:

* $\Delta_m < 0$: Surface condition anomalies precede fire activity, indicative of a hazard configuration (vegetation stress potentially favoring fire occurrence).

* $\Delta_m > 0$: Fire activity precedes surface condition anomalies, reflecting ecosystem vulnerability, including post-fire vegetation degradation, regrowth dynamics, and potential feedbacks increasing future fire susceptibility.

* $\Delta_m = 0$ or $-6$: Synchronous or ambiguous timing (in-phase or anti-phase), for which no dominant direction can be inferred.

For each fire month $m_y$, the reference month $m_x$ with the maximum absolute correlation ($Rmax$) was retained, and the corresponding lag $\Delta_{Rmax}$ was used to classify the interaction patterns according to their direction (see Fig. 3 for an example with $m_y = 1$).

The strength (intensity) of the interaction patterns was categorized according to their correlation level, as **High** ($|Rmax| > 0.7$ and $p < 0.05$) or **Medium** ($0.25 < |Rmax| < 0.7$, or $|Rmax| > 0.7$ with $p > 0.05$), and labelled as positive or negative depending on the sign of $Rmax$.

Additional sensitivity analyses assessing the robustness of the inferred drought-fire relationships to both index selection and correlation thresholds are presented in Supplementary Material (Section S5).

In the following sections, we refer to early dry season, peak dry season, and late dry season when describing the temporal alignment of drought and fire. These terms correspond to the climatic subdivisions defined above (section 2.1.1): the early dry season (June-September, cool and progressive surface drying), the peak dry/fire season (September-November, hot and very dry), and the late dry/post-fire season (November-January, transitional toward the hot and humid season, fires still possible, gradual moisture recovery). We use this terminology consistently throughout the results and discussion to describe the phase relationships between vegetation stress and fire activity.

## 3. Results

### 3.1. Drought analysis

This section presents the results of the drought-only analysis, focusing on the temporal relationships between meteorological drought, characterized by the 3-month SPI (hereafter abbreviated as SPI-3 or only SPI for simplicity) and soil and vegetation conditions (VHI and other indicators). This analysis was performed over the 2000-2021 period, corresponding to the longest common period of availability for the satellite-based drought indicators.

Preliminary analyses of satellite surface indices (NDVI, NDWI, EVI, LST, SWI) showed marked seasonal and spatial contrasts, with a strong east-west coast duality and index-specific propagation patterns (see Supplementary Material S2). Supplementary Material S3 further details
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Methodology to define directional interaction patterns between surface condition and fires. From seasonal (lagged) correlations represented on the left diagram (specific case $m_x = m_y$ is evidenced by the diagonal dotted line), the reference month with the strongest absolute correlation $R_{max}$ was retained (cool colors indicate positive correlations, while warm colors indicate negative correlations). Then the corresponding cyclic lag $\Delta R_{max}$ was used to classify interaction patterns represented on the right diagram. Numbers inside the diagram indicate all possible $\Delta_m$ values. Negative lags indicate surface-condition-driven fire hazard (vegetation stress preceding fire activity), while positive lags indicate fire-driven surface vulnerability (post-fire impacts on surface conditions). Synchronous and opposite-phase configurations correspond to simultaneous or ambiguous timing.</figcaption></figure>

time series of SPI, VHI, and MAI, confirming these contrasted dynamics in two representative municipalities (Koné, west coast, and Poindimié,

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** (a) Maximum correlations (cooler colors indicate higher correlations) and (b) corresponding time lags, obtained between SPI-3 and VHI indicators for the period 2000–2021. Although not clearly visible on the maps, the municipality of Belep (islands at the northern tip of Grande-Terre) shows a lag of –3 months for the three maps. Note that the significance of all the correlation tests is sufficient (p-values < 0.05).</figcaption></figure>
east coast).

### 3.1.1. Lagged correlations

We have compared VHI obtained from different satellite indices (NDVI, EVI, NDWI) to lagged SPI-3 (Fig. 4). Maximum correlations (R-scores max) were found generally higher on the west coast than on the east coast, and scores were higher in the south of the west coast (R-score > 0.7) than in the north (R-score > 0.5). The lowest correlations for VHI were found on the east and south coasts, notably near Yaté where VHI-NDVI and VHI-EVI correlations were near zero. Similar spatial disparities appeared for other drought indicators (see Supplementary Fig. S3.2a), with more areas showing weak correlations (<0.25), particularly for VAI and VCI-EVI. In contrast, soil moisture anomalies (MAI) showed consistently good correlations across both coasts (Fig. S3.2a).

For most areas and products, the strongest correlations were obtained with no temporal shift (LAG = 0), meaning that the satellite drought indicators were synchronized with the meteorological indicator (Fig. 4b). Nevertheless, non-negligible delays were observed in certain cases. On the north and east coast areas, as well as on one of the Loyalty Islands (Lifou), correlation peaks were found when VHI-NDWI preceded SPI-3 by one month (LAG = -1). On Belep islands (northern tip), a delay of 3 months (LAG = -3) was obtained for the three VHI indicators. Negative lags (-1 and -2) were also found on other indicators like VAI, VCI and MAI (Supplementary Fig. S3.2b), for a majority of areas. This means that vegetation stress and root-zone soil moisture deficit seemed to be detected between one and two months after a meteorological drought had occurred.

### 3.1.2. Seasonality

To reveal interannual seasonal relationships, correlation analysis

was also carried out by month, with identical months between series (r<sub>season</sub> with m<sub>x</sub> = m<sub>y</sub>). For each month, we then have a maximum of 22 observations between 2000 and 2021. Once again, a very different pattern is revealed between the east and west coast.

In Koné (Fig. 5a), VHI indicators showed consistently strong seasonal patterns, with highest correlations during the dry (Sept–Dec) and intermediate (Jan-May) seasons (R = 0.6–0.8), and lowest in June and August (R = 0.2–0.4). The consistency of VHI’s performance across months, and among its components (VCI and TCI, not shown), highlights its ability to integrate vegetation and temperature signals even when vegetation indices alone fall short (Supplementary Fig. S3.3), and makes it effective in ecologically diverse, seasonally variable regions like Koné (general significant correlation tests were obtained, except for June and August – available upon request).

In Poindimié (Fig. 5b), the correlation between VHI and SPI was lower overall and less seasonally structured, and inconsistent between indices.

For comparison, results for other vegetation and surface indicators (VAI, MAI, TAI, in Fig.S3.3) revealed similar dry-season peaks in Koné, particularly for VAI and TAI, while MAI maintained a more stable year-round correlation. In Poindimié, however, their performance was more erratic and generally weaker. It suggests that, on the east coast, dominated by trees, the satellite based indicators are less reliable for detecting or monitoring meteorological drought than on the western agricultural coast.

### 3.2. Wildfires and drought relationship

In the previous section, we examined the relations between meteorological drought indicators and vegetation drought indicators, highlighting a general agreement on the west agricultural coast, but a

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Monthly correlations between SPI-3 and VHI indicators for the period 2000–2021 on (a) Koné and (b) Poindimié. Trapezoid indicates dry season interannual varying timing (start and end dates), possibly linked to climate regional oscillations (ENSO, PDO, IPO).</figcaption></figure>
disagreement on the forest east coast. We now analyze the relations of one of those indicators (VHI) with wildfires (BAI) over the 2000–2024 period (2012–2024 for VIIRS products), taking advantage of the extended availability of burned area products to include recent fire events. A preliminary analysis is provided in Supplementary Materials S4.1, S4.2 and S4.3. Comparable dynamics of VHI and BAI were observed, with drought and fire peaks generally coinciding during major El Niño years, though fire events were more intermittent and spatially variable. The link was clearer with VIIRS than with MODIS, both in time series (Fig.S4.1) and in lagged correlations (Fig.S4.2), confirming the better ability of VIIRS to capture vegetation–fire interactions. Seasonal correlations further showed a stronger VHI–BAI relationship during the dry season in high fire density areas, but also a moderate peak during the intermediate season (April–May), suggesting additional seasonal effects (see Supplementary Figs. S4.3 and S4.4).

### 3.2.1. Lag and seasonality

In this section, we combine the lag and seasonality analysis between VHI and BAI. In Ouégoa (Fig. 6a), significant positive correlations revealed that July VHI anticipated BAI by 1–2 months (R > 0.7, see

detailed numerical matrices in Supplementary Fig.S4.5a-d), while late dry season BAI (Oct–Jan) was followed by delayed VHI responses of 1 to 4 months, with January BAI correlating with March–May VHI (up to 0.72). Negative correlations were also observed, notably between March VHI and June–July BAI, suggesting that healthier vegetation early in the year may promote fire occurrence mid-year. Similar patterns of anticipation (Aug–Sept VHI preceding BAI) and delay (Oct–Jan BAI preceding VHI) were observed in the three other areas (Fig. 6b–d), though with local differences: no January delay in Poindimié, and no Oct–Nov delay in Thio and Koné. Negative correlations also emerged in Poindimié and Thio, but were weaker. Overall, Thio showed more near-zero or negative scores, while Koné displayed stronger positive correlations closer to the Obs = Ref diagonal, a pattern also found in other western coastal regions such as Kaala Gomen and Bourail (Supplementary Fig.S4.5e-f).

From these results, two interesting informations were provided: 1) a lag exists between interannual dynamics of BAI and VHI whose direction (positive/negative) and duration is seasonalized, and 2) geographic disparities were observed, with a highly seasonalized and lagged fire/ drought relationship on the east coast (Ouégoa, Poindimié, Thio) and a relatively less season-dependent relationship on the west coast (Koné,

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Heatmaps of seasonal-lagged-correlations obtained between VHI and BAI<sub>VIIRS</sub> (2012 – 2024) on (a) Ouégoa (north-east coast), (b) Poindimié (north-east coast), (c) Koné (north-west coast) and (d) Thio (south-east coast). Cooler colors indicate positive correlations, while warmer colors indicate negative correlations. Bolded cells indicate significant correlation test (p-values lower than 0.05), and diagonal dotted line indicate simultaneous months (Obs = Ref).</figcaption></figure>
Kaala Gomen, Bourail).

To highlight the overall trend of seasonal lagged correlations across the territory, a Kernel Density Estimation (KDE) was performed by extracting, for each area and month (BAI<sub>VIIRS</sub>), the VHI month with the highest significant correlation (Rmax). These Rmax values ("Real values" in Fig. 7) were then weighted by their absolute score in the KDE. Results confirmed earlier observations (Fig. 6): 1) at the start of the dry season, VHI often anticipates BAI, with vegetation stress in July linked to fires in August–September, corresponding on the graph in Fig. 7 to the high densities of positive Rmax located above the Obs = Ref diagonal (left tip of the 80% contour), 2) at the end of the dry season, the pattern reverses, with BAI preceding VHI, indicating post-fire vegetation stress (moderate density for Jan–Jun VHI below the Obs = Ref diagonal). Strong negative correlations mainly involved Feb–Mar VHI and May–Jul BAI, again suggesting that healthier early-season vegetation can increase later fire risk.

### 3.2.2. Drought-fire directional interactions

In this section, we analyze both the prevalence and the intensity of directional temporal relationships between VHI and BAI<sub>VIIRS</sub>. Patterns of directions ("Surface-condition-driven fire hazard", "Fire-driven surface vulnerability", "Synchronous/Opposite-phase variability") reveal strong seasonal and spatial trends. During the peak dry season / fire season, a shift occurs around October (Fig. 8b): before this month, vegetation condition mainly drives fire activity (also during intermediate season), while afterwards fire events dominate, indicating a transition from fire hazard to fire vulnerability. A prevalence of patterns with no dominant direction (synchronous/opposite-phase) was observed along the western coast (Fig. 8a), confirming earlier regional findings. Geographic contrasts also emerged: prior to October, vegetation influenced fires mainly in the southeast, while the opposite held for the northeast, with

trends reversing later in the season.

Intensities (strength) of interactions show that the strongest positive links occurred in September, January, and April (Fig. 9b), while June was characterized by widespread negative relationships, particularly on the east coast (Fig. 9a). These dynamics align with the lagged correlation patterns derived from Kernel Density Estimation. Overall, results highlight a temporal shift from hazard to vulnerability and a strong spatial variability, underlining the need for localized fire risk assessments and adaptive vegetation management.

## 4. Synthesis and discussion

### 4.1. Different surface responses to drought conditions

The results revealed both geographic and seasonal disparities in the dynamics of surface variables and their relationship with meteorological drought. Strong, almost linear correlations between rainfall deficit and surface drought (vegetation, soil) were found on the western coast and northern tip of Grande-Terre, dominated by savannas, thickets, dry forests, and farmland. Shallow-rooted vegetation reduces transpiration and increases surface temperature, explaining the strong link with VHI (Courault et al., 2005; Cuxart and Boone, 2020; Liou and Kar, 2014). In contrast, weaker correlations were seen on the north-eastern coast, dominated by rainforest. For instance, vegetation in Poindimié remained unaffected during droughts (2014, 2017, 2019/20), likely due to deep rooting and reduced water limitation. Pau et al. (2010) observed a similar contrast in Hawaii, with dry forests browning during ENSO droughts while rainforests greened up, highlighting their light limitation. Comparable behavior was also found on the south-eastern tip of Grande-Terre, but with mining scrubs instead of rainforest. Soil moisture anomalies (MAI) correlated well with precipitation deficits except on

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Kernel Density Estimation (KDE) of seasonal-lagged-correlations between VHI and BAI<sub>VIIRS</sub> (2012–2024), considering all areas in New Caledonia. For each area and observed month (BAI), only the significant (p < 0.05) and absolute maximum (positive or negative) correlation score is used as input of KDE. Note that KDE is then weighted by the absolute values of scores. Cool colors indicate density of positive correlations, while warm colors indicate density of negative correlations. Level lines on the graph correspond to iso-proportions of the density: e.g., 20% of the probability mass will lie below the contour drawn for 0.2.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Directions of interaction obtained between fires and vegetation drought on all administrative areas in New Caledonia and all observed fire months. White-filled areas mean that R-score max could not be calculated due to constant input series (VHI or BAI not changing from year to year for the same month).</figcaption></figure>

small islands, where ASCAT signals are affected by ocean proximity (Brocca et al., 2011).

Temporal lags also varied: rainfall and VHI were synchronous on the west coast and north, while a one-month lag was observed on the east and some islands, in line with Zhou et al. (2022). LST lagged NDVI/NDWI by 1–2 months, reflecting delayed vegetation stress and rising temperature. NDVI also lagged EVI more on the east than the west, consistent with its lower sensitivity to dense vegetation (Huete et al., 2002). Strong correlations with lag were also observed between SWI and vegetation indices (Le Page and Zribi, 2019; Zribi et al., 2010). NDWI–SWI lags varied widely, reflecting NDWI’s dependence on multiple surface properties including soil moisture and leaf area (Gao, 1996; Wang et al., 2008).

On the west coast (Koné), interannual dynamics of drought indicators correlated strongly with precipitation during the dry season (Aug–Jan), regardless of index type. By contrast, in May–July, temperature-based indicators showed stronger links with SPI than vegetation-based ones, due to agricultural cycles: soil plowing increases evaporation (Bouras et al., 2020; Zribi et al., 2021), while crop senescence enhances heat stress. Similar seasonal peaks in vegetation drought response were noted by Kogan (1997) and Amri et al. (2011). On the wetter east coast, annual rainfall and dense forest explain weaker, less seasonalized correlations (Pau et al., 2010). Microwave soil moisture anomalies (MAI) also showed weaker correlations due to canopy effects (Babaeian et al., 2019; Le Page and Zribi, 2019; Ulaby et al., 1996) and low surface texture variability in rainforest areas.

Although the various vegetation indicators and indices tested in this study exhibited different sensitivities and lagged responses to

meteorological drought, reflecting their sensitivity to distinct biophysical processes (vegetation greenness, moisture, temperature), the main seasonal and spatial interaction patterns were consistent across indices, supporting the robustness of the results.

## 4.2. Fire hazard or fire vulnerability?

A linear relationship emerged between vegetation drought (VHI) and fire activity (BAI), with geographic disparities linked to climate and land cover. Strong links appeared in two periods: the peak dry season (Sept–Jan) and the intermediate/early cool season (Apr–Jul). Given the influence of temperature on fire-drought interactions (Adeyeri et al., 2024; Littell et al., 2016; Richardson et al., 2022), strong relationships during hot, dry months are expected. In the intermediate season, fire peaks may reflect abundant new fuel, as also observed by Nogueira et al. (2017).

Interseasonal connections were evident: VHI anomalies in May–Jul were positively correlated with fire anomalies in early dry season, suggesting vegetation drought as a precursor of fires, consistent with Barbero et al. (2011) and Moron et al. (2013). Later in the dry season, fires correlated with subsequent VHI anomalies (1–6 months later), reflecting fire feedbacks and seasonal fire peaks (Curt et al., 2015; N’Guyen Van Soc et al., 2021). Fires dry vegetation and reduce healthy cover, then promote herbaceous regrowth, increasing future ignition risk (Baudena et al., 2020; D’Antonio and Vitousek, 1992).

A negative correlation was also found between VHI in Feb–Mar and fire activity in May–Jul. This suggests that healthier vegetation in the wet season may increase fire activity later in the year. This pattern can
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Intensities of interaction obtained between fires and vegetation drought on all administrative areas in New Caledonia and all observed fire months. Cool colors indicate positive correlations, while warm colors indicate negative correlations. White-filled areas mean that R-score max could not be calculated due to constant input series (VHI or BAI not changing from year to year for the same month). In this specific case of interaction intensities (compared to interaction directions), white-filled areas can also be obtained when no interaction is found (R-score max between -0.25 and 0.25).</figcaption></figure>

appear counter-intuitive but is well-documented in savanna and Mediterranean ecosystems (Cardil et al., 2019; Russo et al., 2017). During the rainy season, favourable moisture conditions promote vigorous growth of herbaceous and shrubby vegetation, increasing the quantity of fine, easily combustible fuels. As rainfall decreases during the intermediate season, this biomass progressively cures and loses moisture, becoming highly flammable. Thus, periods of high vegetation productivity can lead, after a lag, to greater fire extent once fuels become dry (Keeley et al., 2011; Pausas and Ribeiro, 2017). In New Caledonia, this mechanism is particularly relevant in areas dominated by savannas and mixed shrublands, where fast-growing grasses act as primary fine fuels, and where human ignitions are frequent (N'Guyen Van Soc et al., 2021). By contrast, rainforest areas with deeper rooting systems maintain higher moisture longer and do not contribute similarly to mid-season fuel build-up. Therefore, the observed negative correlations reflect a fuel-accumulation mechanism rather than an inverse drought-fire link: high vegetation health in late wet season increases fuel load, which, after curing, enhances the likelihood and spread of fires the closer it gets to the dry season.

Directional interaction analysis confirmed strong seasonal contrasts: highly lagged fire-drought relationships on the east coast, versus more synchronous ones on the west. Interactions patterns with no dominant temporal direction were observed mainly in the western municipalities and may reflect not only methodological ambiguity but also specific land-use and fire-management regimes. In particular, synchronous dynamics are consistent with irrigated agricultural cycles on the west coast

(ISEE (Institut de la Statistique et des Etudes Economiques, Nouvelle Caledonie) and DAVAR (Direction des Affaires Veterinaires, Alimentaires et Rurales), 2014), where vegetation conditions are partly decoupled from meteorological drought, as well as with eco-burning practices on the east coast (INC, 2012), where anthropogenic ignition can dominate over climate-driven fuel controls. Monthly directional relationships showed a key shift in October: from vegetation drought driving fires to fires impacting vegetation. Thus, in early dry season, surface dryness reflects fire hazard, while in late season, fires create vulnerability through feedbacks. Strongest linkages were found in September, January, and April. In October, this reversal was particularly marked on the south-east coast, possibly due to ultramafic soils and vegetation adaptations. Gomez et al. (2015) mapped similar high-risk areas for biodiversity loss and fire vulnerability in this region, confirming its sensitivity to early dry season drought and fire dynamics.

### 4.3. Limitations and future directions

Although this study provides new insights into drought-fire interactions in New Caledonia, several limitations should be acknowledged.

The analysis was conducted over a relatively small and climatically coherent island, which offers an ideal setting to disentangle environmental drivers but may limit the extrapolation of the findings to larger or more heterogeneous regions. The present study should therefore be viewed as a regional case study, leveraging uniquely detailed and locally
validated burned area products available for New Caledonia. While this specificity constrains direct replication in other regions, it also ensures a high level of internal consistency and observational reliability. Applying the proposed framework to other fire-prone regions would be a valuable next step, but would require comparable long-term burned area datasets and harmonized surface-condition indicators, which are not currently available for many Pacific and tropical island systems.

In addition, data were aggregated at the municipality level to ensure temporal continuity and operational relevance for fire management. However, several municipalities encompass highly heterogeneous monaics of savannas, dry forests, rainforests and agricultural areas. This aggregation may therefore smooth local variability and partially explain the higher proportion of interaction patterns with no dominant direction observed in western municipalities, where landscape heterogeneity is strongest. Such patterns may also reflect heterogeneous land-management practices and anthropogenic fire regimes, including irrigation, prescribed burning, or non-climatic ignition sources, which can locally decouple fire activity from vegetation drought signals at the monthly scale.

More broadly, these results suggest the coexistence of distinct sub-regional drought-fire regimes within New Caledonia, shaped by contrasts in climate exposure (west vs. east coast), dominant vegetation structure (open-canopy vs. dense evergreen systems), and fire-use practices. While some of these contrasts are discussed qualitatively in this study, they were not formalized through an explicit regional classification or typology.

The sensitivity of vegetation indices such as VHI is also likely to vary across vegetation regimes. Open or seasonally dry systems may exhibit stronger and more immediate drought-fire linkages, whereas dense rainforest systems may display buffered or delayed responses due to canopy structure and deeper rooting systems.

In that sense, although lagged relationships were estimated independently for each municipality, future work could explicitly define homogeneous sub-regional regimes (e.g., through clustering based on climate, vegetation type, or fire activity) and model region-specific lag structures. Such approaches could help disentangle wether the observed seasonal patterns are broadly shared across the territory or primarily driven by a limited number of contrasting regimes. However, this would require additional assumptions on spatial homogeneity and regime stability, and was therefore beyond the scope of this exploratory, data-driven study.

Although drought and fire indicators are expressed as monthly standardized anomalies that remove the mean seasonal cycle, substantial interannual variability remains, particularly associated with large-scale climate modes such as ENSO. In this study, ENSO-related variability is therefore implicitly embedded in the anomaly signal rather than explicitly separated or stratified. Similarly, fire-weather conditions such as heatwaves, extreme temperatures, or strong winds were not explicitly accounted for, although they may simultaneously influence vegetation stress and wildfire spread, particularly during peak fire periods. Moreover, monthly aggregation may smooth the influence of short-term fire-weather extremes and limit the detection of rapid feedbacks during extreme events. As a result, some of the observed seasonal correlation patterns may partly reflect shared atmospheric forcing rather than purely surface-mediated drought-fire feedbacks. The objective of this study was not to attribute drought-fire interactions to specific climate or meteorological drivers, but to identify recurrent seasonal patterns in the temporal sequencing between meteorological drought, vegetation stress, and fire activity across multiple years. Disentangling these interactions conditional on ENSO phases or explicit fire-weather regimes, with higher-frequency data and multiscale fire-weather analyses (wavelets approaches for example), would require a dedicated framework and represents a relevant direction for future work.

Potential causality relations were inferred from seasonal lagged correlations rather than formally demonstrated. Such correlation-based

inferences indicate possible causal directions but cannot alone confirm causal mechanisms. Future work could strengthen this aspect by combining complementary statistical approaches, such as Granger causality, with non linear deterministic frameworks like convergent cross mapping (CCM), based on global modelling and chaos theory. These nonlinear approaches, recently benchmarked against statistical causality measures (Mangiarotti et al., 2025), could better capture the feedback loops and state-dependent dynamics linking drought and fire.

Finally, the present framework could be enhanced by incorporating higher-resolution datasets and additional biophysical and anthropogenic drivers (e.g., fuel structure, land cover change, ignition patterns) to provide a more comprehensive understanding of wildfire dynamics under changing climate conditions.

## 5. Conclusion

This study assessed the temporal relationships between drought and wildfires across 33 municipalities in New Caledonia using remote sensing and in-situ data over the 2000–2024 period.

Results show strong spatial and seasonal contrasts in how meteorological drought translates to surface drought, depending on vegetation type and rooting depth. Fast and strong responses were observed in savannas, dry forests, and agricultural areas, while rainforest areas showed slower and weaker correlations due to deeper rooting systems.

Among the tested satellite indicators, the Vegetation Health Index (VHI)—combining NDWI and LST—proved most effective in capturing both the timing and spatial variability of meteorological droughts, outperforming other vegetation- or soil-based indices.

A nearly linear relationship was found between vegetation drought and burned area anomalies, particularly during the dry and intermediate seasons. This confirms the potential of VHI as a reliable wildfire risk indicator within the New Caledonian context, especially in regions characterized by seasonal water limitation and open-canopy vegetation.

Directional interaction analysis revealed seasonal dynamics with two main regimes: 1) early dry season fires were often preceded by vegetation stress, highlighting fire hazard; and 2) post-fire impacts on vegetation were observed at the end of the dry season, reflecting fire vulnerability. These bidirectional links illustrate feedback effects, where drought influences fire occurrence and fires, in turn, alter surface conditions, potentially increasing the likelihood of future events. An inverse pattern was also detected mid-year, where high surface moisture during vegetation growth led to increased fuel loads and later fire activity.

Geographic differences emerged as well, with weaker and more synchronous seasonal links observed on the west coast than on the east coast, likely reflecting the influence of agricultural practices (e.g., irrigation, burning) and land management on vegetation-fire dynamics.

Overall, this work demonstrates the value of remote sensing for quantifying both fire hazard and vulnerability from a single vegetation indicator. The approach offers new insights into wildfire risk patterns under climate pressure and could inform biodiversity conservation, especially in ecologically sensitive areas with high endemism.

Importantly, these results should be interpreted as a regional case study, reflecting the coexistence of contrasting drought-fire regimes within New Caledonia, rather than as a universally transferable framework.

Future improvements could involve using higher-resolution data to define more homogeneous landscape units, exploring internal typologies of climatic or ecological regimes, and adopting advanced analytical tools (e.g., wavelets, deterministic models) to investigate multi-scale coupling and feedback effects between drought and fire.

### CRediT authorship contribution statement

**Mathis Neuhauser:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Alexandre Peltier:**
Writing – review & editing, Resources, Formal analysis, Data curation, Conceptualization. **Thomas Ibanez:** Writing – review & editing, Visualization, Validation. **Marc Despinoy:** Writing – review & editing, Resources, Project administration, Methodology, Funding acquisition. **Michel Le Page:** Writing – review & editing, Visualization, Validation, Supervision, Methodology, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We want to thank N. Sfaksi, G. De Coudenhove and H. Roussaffa for fruitfull discussions and their advice. This work was supported by the Space Climate Observatory (SCO), the French Pacific Fund ("Fonds Pacifique"), the French National Research Institute for Sustainable Development (IRD) and INSIGHT SAS.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jag.2026.105159.

## Data availability

Data will be made available on request.

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