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Article

Applying Electrical Resistance Tomography to Diagnose Trees Damaged by Surface Fire

by
Kyeong Cheol Lee
1,†,
Yeonggeun Song
2,†,
Wooyoung Choi
2,
Hyoseong Ju
1,
Won-Seok Kang
3,
Sujung Ahn
4 and
Yu-Gyeong Jung
4,*
1
Department of Crops and Forestry, Korea National University of Agriculture and Fisheries, Jeonju 54874, Republic of Korea
2
Department of Forestry, Jeonbuk National University, Jeonju 54896, Republic of Korea
3
Department of Horticulture and Forestry, Mokpo National University, Muan 58554, Republic of Korea
4
Division of Forest Fire, National Institute of Forest Science, Seoul 02455, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2025, 16(10), 1504; https://doi.org/10.3390/f16101504
Submission received: 20 August 2025 / Revised: 16 September 2025 / Accepted: 22 September 2025 / Published: 23 September 2025
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

The Republic of Korea, with 64% forest coverage, is increasingly vulnerable to large-scale wildfires. This study employed electrical resistance tomography (ERT) to diagnose internal damage in Pinus densiflora trees following a surface fire in spring 2023. Of the 30 monitored trees, 5 died in 2023 and 6 more had died by 2024. Dead trees showed a 41% higher Bark Scorch Index (BSI) and a 10%–15% lower DBH and circumference than survivors. From July, ERT detected significant increases in high- (ERTR) and medium-resistance (ERTY) areas, while low-resistance (ERTB) regions declined. By September, ERTR and ERTY were 2.2 and 1.9 times higher in dead trees. Maximum resistivity (Rsmax) rose 6.1-fold to 3724 Ωm. One year post-fire, healthy areas in dead trees dropped below 18%. These findings indicate that internal defects develop gradually and accelerate in summer and winter, correlating with thermal and freeze–thaw stress. Early diagnosis within two months post-fire was unreliable, while post-summer assessments better distinguished trees at mortality risk. This study demonstrates ERT’s utility as a non-destructive tool for tracking post-fire damage and guiding forest restoration under increasing wildfire threats.

1. Introduction

With about 64% of its land covered by forests, the Republic of Korea is highly susceptible to recurring wildfires that disrupt ecosystems and alter successional dynamics. Between 2011 and 2020, the country experienced an average of 474 wildfires annually, which affected over 1100 hectares. Notably, the scale of these events has increased dramatically in recent years, with the burned area rising from 766 ha in 2021 to 24,782 ha in 2022 and 4992 ha in 2023 [1]. These large-scale wildfires frequently occur along the east coast, where low precipitation, high winds, and dense stands—particularly during winter and spring—create conditions conducive to intense surface fires [2].
Climate change has intensified wildfire risk by lengthening fire seasons and increasing extreme events such as prolonged droughts and heatwaves [3,4,5]. These conditions not only increase the likelihood of ignition but also reduce tree resilience and post-fire recovery [6].
Wildfire impacts on trees include primary thermal injury, causing cell death in leaves, buds, and cambia, and secondary physiological damage, such as reduced hydraulic conductivity, disrupted carbon allocation, and greater susceptibility to pests and pathogens. The severity of damage depends on fire intensity, bark thickness, and species-specific traits [7,8]. Pinus densiflora, the dominant conifer in Korean forests, is particularly vulnerable due to its thin bark and high resin content, which amplify heat damage and combustion [6,9]. Because of its ecological dominance in Korea and high fire susceptibility, we selected P. densiflora as the focal species for evaluating post-fire survival with ERT.
Post-fire restoration in the Republic of Korea typically involves classifying damage severity through field surveys and satellite image analysis, followed by targeted felling and reforestation [2,10]. However, in areas affected by low-intensity surface fires, many trees retain structural and physiological viability and may not require immediate removal. Maintaining such stands offers ecological and economic advantages, including faster natural recovery. Nonetheless, current methods for predicting post-fire tree survival rely heavily on external indicators—such as crown scorch or spectral indices—which fail to capture internal damage progression [7,11].
Because the long-term survival of fire-damaged trees is closely linked to the condition of the sapwood, non-invasive methods for internal diagnosis are essential. Electrical resistance tomography (ERT) is a non-destructive imaging technique that maps the resistivity distribution across tree cross sections using inversion algorithms [12]. This approach allows for early detection of functional tissue degradation within the stem, offering insights beyond visual assessments [13]. Internationally, ERT has been successfully applied to coniferous species such as Picea abies, Abies alba, Pinus sylvestris, and Larix spp. to detect decay, monitor moisture dynamics, and assess internal defects [12,13,14]. However, to our knowledge, there are almost no studies that have systematically applied ERT to diagnose internal damage in post-fire coniferous trees. This gap is particularly evident in East Asian species, including Pinus densiflora, which is highly vulnerable due to its thin bark and high resin content. Highlighting this research gap underscores the novelty and scientific contribution of our study.
This study applies ERT to monitor temporal changes in internal defects of P. densiflora trees affected by surface fire in spring 2023. By assessing resistivity dynamics over a one-year period, we aim to determine the optimal timing for mortality diagnosis and provide evidence-based guidelines for managing fire-damaged trees. Our findings contribute to improving post-fire forest restoration strategies amid growing wildfire threats under climate change.

2. Materials and Methods

2.1. Study Area

The study area is a privately owned pine forest located in Nangok, Gangneung, Gangwon (37°47′37.6″ N, 128°52′53.3″ E), which was the origin of a large wildfire that damaged approximately 120 hectares on 11 April 2023. The site is located approximately 1.4 km inland on a southeast facing slope, with an elevation ranging from 53 to 67 m and an average slope of ~12° (Figure 1A,B). According to the Korea Meteorological Administration, at the time of the initial wildfire, the maximum wind speed reached 26.2 m/s. Over the following year, the annual precipitation was 1773 mm, the relative humidity was 62%, and the average temperature was 14.9 °C, indicating generally high precipitation and mild weather influenced by the nearby sea (Figure 2).
On 18 April 2023, shortly after fire suppression, we selected a study site within a surface-fire-affected stand where the crown layer was not directly removed by the fire. This site had fairly uniform topographical features, such as slope and direction, and a total of 30 Pinus densiflora trees were selected as study samples. The selected 30 trees were initially surveyed for external characteristics and the internal damage rate (ERT). From April to September 2023, changes in the ERT were monitored monthly. However, by April 2024, 10 samples had been felled, leaving only 20 trees for further ERT examination (Figure 1B and Table 1).

2.2. External Morphological Characteristics

In April 2023, the height (Ht), diameter at breast height (DBH), circumference (Circ), and crown base height (CBH) of all the damaged trees were measured, and the status of the crown layers was assessed. Trees whose foliage turned completely brown and failed to recover or be shed by September were classified as dead. To calculate the Bark Scorch Index (BSI), the damaged trees were divided into four directions, and the bark scorch height (BSH) and bark scorch proportion (BSP) were measured for each direction. BSI was then calculated by multiplying the BSH and BSP for each direction and summing the values [6].
The Bark Scorch Index (BSI) was calculated as follows:
B S I = ∑ i = 1 4 ( B S H i × B S P i )
Additionally, based on the status of the crown layer, trees were categorized into groups: dead (D) in 2023, healthy to dead (H-D) in 2024, healthy (H) in 2023 but excluding those that died in 2024, and healthy to healthy (H-H) until 2024 for further analysis (Table 1, Figure 1C).

2.3. Electrical Resistance Tomography

ERT measurements were conducted at breast height (1.2 m above ground), the standard forestry reference point for assessing tree growth and vitality. This level was also chosen because soot deposition and heat damage were clearly observed at this height, ensuring that thermal effects were adequately captured. While resistivity may vary along the vertical stem profile, breast height was selected to maintain consistency across individuals. Monthly measurements were conducted from April to September 2023, with a final measurement in April 2024.
Before ERT measurements, trunk geometry (Geo) was obtained using 12 measuring points (MPs) to minimize mechanical errors by maintaining the minimum spacing between MPs. The locations of MPs were marked with stainless steel electrodes inserted to reach the xylem tissue, with the northernmost MP always designated as number 1 for consistent comparison. Geo was measured using the PiCUS Calliper Version 3 (Argus Electronic, GmbH, Rostock, Germany), generating geometric data for ERT analysis [15].
ERT was then performed using the PiCUS Treetronic instrument (Argus Electronic, GmbH, Rostock, Germany) [12]. The ERT images were standardized for clarity and comparability, with smoothness set at 100 and mesh fineness at 8 (Figure 3A(a,b)). From the enhanced images, the color area ratio (CAR) and resistivity (Rs) values were extracted using ImageJ software version 1.53k (National Institutes of Health, Bethesda, MD, USA) and PiCUS Q74EXPT software version 74.03 (Argus Electronic, GmbH, Rostock, Germany), respectively (Figure 3A(c),B) [16].
Resistivity values were categorized into three relative classes based on the distribution of values observed in this study (minimum to maximum across 30 trees) and the PiCUS software’s color scaling. In our dataset, areas with relatively low resistivity corresponded to regions with high moisture and intact functional sapwood (blue areas), medium resistivity represented partially degraded tissues (green to yellow areas), and high resistivity indicated severely damaged or necrotic tissues with reduced conductivity (red areas). These categories are relative to the measured distribution rather than universal absolute thresholds.
Sapwood, primarily responsible for water and nutrient transport, was identified using t/R < 0.3 (t = residual wall thickness; R = tree radius), as defined in the software [16,17] (Figure 3A(b)). To compare internal damage according to bark scorching, the trunk circumference was divided into 36 directions, and the presence of bark scorch was examined at each point to observe the occurrence rate of ERTR and ERTRY (ERTR + ERTY). To assess wildfire-induced spread within trunk tissues, areas of maximum and minimum resistivity were selected as AOIs in the September 2023 images, and Rsmax and Rsmin values were extracted monthly. Additionally, for one specimen (TN 10) that died in April 2024, wood cores were collected from the AOIs to validate resistivity differences (Figure 3A(c)).

2.4. Statistical Analysis

Statistical analyses were performed using SPSS Statistics 19.0 (SPSS Inc., Chicago, IL, USA). One-way Analysis of Variance (ANOVA) was performed for the color area ratio of all samples, with Levene’s test used to confirm homogeneity of variance assumptions. Post hoc tests were conducted using the Tukey Honestly Significant Difference (HSD) test (p < 0.05). When analyzing groups based on mortality, non-parametric tests were used due to the varying sample sizes and the presence of small groups, which did not assume normal distributions. The Mann–Whitney U test or the Kruskal–Wallis test was used depending on the number of comparison groups, with significance tests conducted at the p < 0.05 level.

3. Results

3.1. Tree Mortality and External Characteristics

Among the total 30 trees, there were 5 dead trees (dead; D) in 2023 and 6 newly dead trees (healthy to dead; H-D) one year after the wildfire. The logging of 10 trees in the winter of 2023 included not only the 5 dead trees from 2023 but also 5 trees that exhibited healthy crowns until September 2023. Therefore, excluding the trees that died by 2024 (H) from those that were healthy until September 2023, there were 19 trees, and the total number of trees confirmed to be healthy until 2024 (healthy to healthy; H-H) was 14 (Table 1 and Figure 1C).
Until September 2023, the height (Ht), diameter at breast height (DBH), and circumference (Circ) of the D group were approximately 9%–10% lower than those of the H group. Conversely, the Bark Scorch Index (BSI) was 12.8, which was about 41% higher for the D group. For the H-D in 2024, the DBH and Circ were 15% and 16% lower, respectively, compared to the H-H. Additionally, the BSI was 10.8, which was 34% higher (Table 1), whereas there were no significant differences in height, slope, or crown base height.

3.2. Seasonal Progression of Internal Damage Assessed by ERT

Electrical resistance tomography (ERT) determines the resistivity based on the electrical conductivity of the trunk and can image functional defects within the wood [17,18]. In ERT images, red areas indicate high-resistivity regions with major damage, while blue areas indicate regions with high moisture and cation contents, representing low resistance [7,8,18].
Examining the monthly changes in color area ratio (CAR) of the 30 P. densiflora affected by surface fires in 2023, there was a significant increase in high-resistance (ERTR) and medium-resistance (ERTY) areas, while low-resistance (ERTB) areas decreased (p < 0.05). This trend continued to intensify through September, with increased variance indicating substantial individual differences (Figure 3B and Figure 4A).
Furthermore, the bark scorched areas generally had a higher proportion of ERTR compared to non-scorched areas, with an even greater difference observed in ERTY. While the proportion of ERTR and ERTY in the scorched areas gradually increased over the growth period, there was no significant change in ERTR and ERTY in non-scorched areas compared to immediately after the wildfire (Figure 3B and Figure 4B).
Comparing the dead (D) and healthy (H) trees in 2023, both ERTR and ERTY were very low and almost indistinguishable between dead and healthy trees immediately after the wildfire in April. However, the ERTR of H trees began to increase somewhat from August, and the ERTY began to increase from July. For the Ds, both ERTR and ERTY started to increase significantly from July (Figure 4 and Figure 5). However, the increase was more pronounced in the D group than in the H group, resulting in ERTR and ERTY levels being 2.2 times and 1.9 times higher, respectively, in September compared to healthy trees. Additionally, the ERTR and ERTY of healthy trees in September were 14% and 36%, showing approximately 3.0-times and 1.9-times increases compared to April, while dead trees showed ERTR and ERTY values of 32% and 69% in September, representing approximately 4.5-times and 2.9-times increases compared to April (Figure 4 and Figure 5).
Examining the differences in ERTR and ERTRY reveals that the variance in ERTRY for dead trees was very high in June but significantly decreased in July. In contrast, the variance in ERTR was notably high in August and September. Comparing healthy to dead trees (H-D) and healthy to healthy trees (H-H) as of 2024, the ERTR and ERTRY of H-H trees showed little change from 5% to 7% and from 21% to 25%, respectively, in September 2023. However, by April 2024, there was a markable increase, with ERTR and ERTRY reaching 17% and 50%, respectively. For the H-D group ERTR and ERTRY gradually increased from July 2023 and reached 72% and 82%, respectively, by April 2024, indicating that internal defects occupied most of the trunk (Figure 3B and Figure 5).
Analyzing the changes in resistivity due to wildfire, both the D and H groups in 2023 showed increases in maximum resistivity (Rsmax) and minimum resistivity (Rsmin). The H group exhibited a slight increase starting in August, while the D group began showing significantly higher resistivity in June, two months after the wildfire. By September, the Rsmax for the D group reached 3724 Ωm, and the Rsmin reached 1964 Ωm, which values were 2.1- and 2.0-times higher, respectively, compared to the H group. This represented 6.1-times and 4.7-times increases, respectively, compared to April.
Additionally, in 2024, a comparison of the H-H and H-D groups showed different trends in resistivity. The H-H group exhibited a slight increase in April 2024, with an Rsmax of 2021 Ωm and an Rsmin of 1117 Ωm compared to the previous year. In contrast, the H-D group displayed significantly higher values, with an Rsmax of 15,264 Ωm and am Rsmin of 7834 Ωm. Notably, the H-D group had already shown twice the resistivity of H-H in terms of both Rsmax and Rsmin by September 2023 (Figure 6).
Analyzing the correlations between internal defect ratios (ERTR and ERTRY) and various indices, BSI demonstrated a positive relationship with both ERTR and ERTRY, while DBH and circumference exhibited a negative relationship. No clear relationship was observed with crown base height, overall height, and slope. Specifically, BSI had a steeper slope for ERTR compared to ERTRY, but ERTRY showed a stronger correlation with BSI (R2 = 0.49, p < 0.01). Similarly, DBH and circumference had more pronounced decreasing slopes for ERTR, yet the coefficient of determination (R2) was higher for ERTRY at R2 = 0.3787 and R2 = 0.3883, respectively (Figure 7).

3.3. Multivariate Patterns Distinguishing Tree Mortality

The PCA results showed that the PC1 axis had an explanatory power of 39.6%, whereas the PC2 axis had an explanatory value of 16.8%. The factors influencing PC1 included Rsmin, Rsmax, slope, height, BSI, ERTR, and ERTRY, which could be grouped together, while DBH, ERTY, and ERTB formed another group. For PC2, Rsmin, Rsmax, slope, BSI, ERTR, and ERTRY formed one group, while ERTY, ERTB, DBH, Circ, and CBH formed another. Trees affected by surface fires could be distinguished into the H-D group in the positive region of PC1 and the H-H group in the negative region, with the D group in between. The indicators with the highest variance were Rsmin, Rsmax, ERTR, and ERTRY, while height showed relatively low variance (Figure 8).

4. Discussion

4.1. Influence of Tree Morphometrics and Bark Scorch Index (BSI) on Mortality

Even surface fires can sharply reduce tree survival if damage extends beyond trunk injury, causing crown loss or impairing key physiological functions such as photosynthesis [19,20]. The maintenance and gradual recovery of the crown layer’s integrity are critical indicators for assessing tree mortality. At this study site, immediately after the wildfire, approximately 50% of the trees (15 trees, data not presented) showed browning of the crown layer, which was seemingly dead due to charring or high ambient heat. However, by June, more than two months after the fire was extinguished, new leaves emerged from surviving buds, and most of the crown layer (approximately 87%, data not presented) appeared healthy as the soot was washed away by rain. By September 2023 (six months after the fire), 16.7% of the trees had died. Another six trees died a year later, but the exact mortality rate could not be determined because five apparently healthy trees were logged during the winter of 2023. Nonetheless, it was evident that ongoing mortality was occurring after the surface fire (Table 1).
Hood et al. (2010) [21] reported that most tree mortality, such as that of ponderosa pine, due to the California wildfires from 2000 to 2004 occurred within two years after the fires, stabilizing in the third year. Similarly, the surface-fire-affected area in Samcheok, Gangwon Province, in 2017 showed a maximum mortality rate of 14% for P. densiflora in the first year after the wildfire [6], mirroring the trend found in this study.
However, tree mortality rates can vary significantly within the same region affected by wildfires, depending on site conditions such as soil covering surface thickness, slope, elevation, and tree characteristics such as height, DBH, wood density, bark thickness, scorch height, and volatile substance content [6,9,22,23,24]. Height, circumference, and diameter at breast height (DBH) are indicators of overall tree growth conditions, with superior conditions known to reduce wildfire-induced mortality rates [6,7,9,23]. Trunk volume, indicated by DBH and circumference, is closely related to bark thickness, where thinner bark provides minimal insulation, leading to xylem and phloem deformation and increased mortality rates [9,25].
In this study, both the trees that died in 2023 (D) and those that newly died in 2024 (H-D) had diameters and circumferences approximately 10%–15% lower than those of healthy trees (H-H), indicating poorer growth conditions (p < 0.05). No significant differences were found in crown base height or slope, likely because the sampled trees were in stands with similar site conditions and crown base heights (Table 1). Despite the relatively poorer growth indicators of the trees that died in 2024 (H-D) compared to those that died in 2023 (D), the time of death tended to be earlier. This may be related to the H-D group’s greater height and lower Bark Scorch Index (BSI). Taller trees can reduce the exposure time of the crown layer to heat, thereby reducing crown-layer damage [26,27]. A lower BSI indicates a shorter fire residence time, resulting in relatively less bark damage [6,9]. However, it should be noted that the study involved only 30 trees from a single site in Gangneung, the Republic of Korea. This limited sample size and geographic scope constrain the generalizability of the results. Future research across multiple sites and larger sample sizes is necessary to strengthen the conclusions.
BSI is a representative indicator for assessing wildfire damage levels [6,9]. In the 2017 wildfire in Samcheok, P. densiflora with a DBH of 30 cm and a BSI of 10 had a mortality rate of 56%, while a tree with the same DBH but a BSI of 15 had a higher mortality rate of 89% [6]. In our study, the BSI of the H-H group in 2024 was significantly lower at 8.1 ± 3.2 compared to dead trees (p < 0.05). This indicated that even trees with larger trunk characteristics could die earlier if they had a higher BSI (Table 1).
Therefore, tree height, diameter, and circumference, along with BSI, are direct factors influencing tree mortality rates. Understanding damage to internal trunk tissues, such as the inner bark, cambium, and xylem, is essential for accurately diagnosing the potential mortality risk of trees due to wildfires.

4.2. Seasonal Expansion of Internal Defects Detected by ERT

High thermal energy from wildfires first evaporates moisture in the intercellular spaces near the bark. This causes necrosis of living cells and reduces the functionality of sapwood for water transport [8,28]. In particular, cellular heat damage is initiated by gross structural changes in cell membranes. Heat-induced protein denaturation and phase changes in membrane lipids lead to permeability alterations or lesions and, consequently, to a release of cellular contents [5,29]. When observing the wood core with the naked eye, areas with high resistance appeared a darker brown than those with low resistivity (Figure 3A(c)).
For P. densiflora affected by surface fires, most initially displayed a blue region (ERTB), indicating low resistance. Starting in July, three months after the wildfire, ERTR and ERTRY areas began to increase gradually (Figure 4). This suggests that damage continued to spread throughout the stand even after the fire was extinguished, with significant disruptions in water movement becoming apparent 2–3 months post-fire.
The surface temperature of wildfire-exposed areas can approach 100 °C [30], exceeding the insulation capacity of the outer bark. This damages the cork layer and cork cambium cells, preventing regeneration and prolonging bark recovery [5]. Additionally, high temperatures kill vascular cambium cells, preventing the regeneration of new xylem and phloem tissues [7,8,31].
Scorched bark areas showed significantly higher ERTR and ERTRY values compared to non-scorched areas early in the wildfire (Figure 4), indicating that larger scorched areas correlate with increased internal damage.
Moreover, the notable increase in ERTR and ERTRY in July and August, corresponding to the Korean summer season with average daily temperatures of 27.5 °C and 26.2 °C, respectively (Figure 2), likely accelerated tree mortality due to restricted water movement and high water demand. P. densiflora, being isohydric, rapidly close their stomata to retain internal water in response to high temperatures or drought [32]. Frequent stomatal closure during hot summer months [33] prevents the maintenance of pressure in conductive tissues through active transpiration, leading to reduced hydraulic conductivity [34]. Thus, significant tracheid closure due to wildfires observed from summer onward can be attributed to these ecological characteristics of pine trees.
P. densiflora transport water through tracheids, which have walls and pits composed of viscoelastic polymers. High temperatures cause thermal softening of these polymers, deforming tracheid walls in response to sap tension, reducing tracheid diameter, or causing rupture or collapse [30,35,36]. This leads to cavitation and xylem discoloration. Discolored tracheids lose conductivity, reducing the sapwood cross-sectional area and hydraulic capacity [8,30]. Temperatures above 60 °C can cause necrosis of parenchyma cells, which aid in refilling tracheids, potentially permanently disrupting the continuous water column in the xylem [31,37].
These internal defects were distinctly observed to expand through changes in the color area ratio. In the first year of fire damage (2023), both healthy (H) and dead (D) groups showed an increase in ERTR and ERTRY. However, the D group started to show an increase earlier, from June, and the increase was more significant. Particularly, the ERTRY values of the D group showed large individual differences in June, but by July, as ERTY transitioned to ERTR, the variance significantly decreased. This indicates that internal defects progressed from ERTB through ERTY to the high-resistance ERTR stage (Figure 5).
One year after the fire (2024), newly dead trees (H-D) also showed significantly higher ERTR values compared to healthy trees (H-H). This trend began in August 2023, with more than a twofold difference, and by one year post-fire, the ERTR had expanded to cover 72% of the sapwood area, compared to just 17% in the H-H group, indicating a 4.1-times increase in damage spread. This highlights the need for continuous monitoring if a significant increase in ERTR is observed after the summer season, even if the canopy layer appears healthy (Figure 5).
The differences in ERTRY between the H-H and H-D groups were not as large as those in ERTR, but the overall trend was similar. One year after the surface fire, the ERTRY of the H-D group occupied about 82% of the sapwood area. The ERTRY of the H-D group began to increase almost linearly from July, slowing down in the winter, with only a 10% difference between the ERTR and ERTRY by April 2024. This demonstrates the progression from ERTB to ERTY to ERTR, indicating that an increase in ERTRY is also a significant indicator of tree mortality progression due to wildfire (Figure 5).
The expansion of defect areas due to wildfires can be observed through changes in the color area ratio (CAR) using ERT images. Unlike the expansion observed through color area ratio changes, the loss of hydraulic conductivity and severity of damage can be diagnosed by tracking the resistance of specific internal defect areas [14].
The changes in the maximum (Rsmax) and minimum (Rsmin) resistivity of P. densiflora experiencing surface fire damage showed significant increases in all groups in 2023 and 2024, with more pronounced increases in dead trees (D and H-D), showing trends similar to CAR (Figure 6). This demonstrates that as the damage area in the sapwood expands due to fire, the resistance itself also significantly increases. The steep increase in Rsmax indicates substantial moisture and cation loss in the affected tissue, clearly predicting loss of functionality. The increase in Rsmin should also be noted, as it indicates rising resistance in healthy areas, suggesting overall reduced functionality of xylem tissue and potential cumulative heat damage. The significant increase in ERTR, ERTRY, and both Rsmax and Rsmin in the H-D group during the winter season indicates that even without active water movement in winter, internal defects expand and become more severe. This suggests that embolism in xylem tissues, exacerbated by winter drought and cold, prevents effective recovery in the spring (Figure 6).
Winter drought and cold typically induce embolism in trees; as xylem sap freezes, air bubbles are released due to lower gas solubility in ice compared to water. These bubbles rapidly refill the tracheids upon thawing, causing embolism [38]. This phenomenon occurs even in conifers with smaller, more freeze–thaw-resistant tracheids. Winter hydraulic conductivity losses of 35% in Pinus albicaulis and 25% in Larix lyallii have been reported [39], with survival rates critically impacted when hydraulic conductivity losses exceed 50% [40].
However, most conifers overcome embolism in summer as root pressure recharges cavitated xylem tissues during the spring thaw, keeping hydraulic conductivity losses below 16% [38,40,41]. Trees with low fire-resistance traits, such as lower height, DBH, and circumference and higher BSI, fail to recover from cumulative damage experienced in winter, ultimately leading to mortality.
As shown in Figure 7, BSI, DBH, and Circ are correlated with CAR, indicating internal defect rates. The higher coefficient of determination for ERTRY compared to ERTR suggests that the consistent decline in healthy portions and loss of functionality serve as persuasive indicators for early diagnosis of tree mortality.
Finally, wildfire-damaged trees decline gradually as defects expand from scorched to healthy areas. The process begins with reduced water transport, followed by necrosis and irreversible loss of function. These changes are not immediately visible and typically appear only after two months. Thus, early post-fire assessments are unreliable. In contrast, monitoring after the summer season clearly distinguishes survivors from non-survivors. Seasonal heat stress and freeze–thaw events accentuate internal defects, making post-summer monitoring the most reliable period for mortality diagnosis.

4.3. Key Indicators Differentiating Post-Fire Survivors from Non-Survivors

Figure 8 presents the Principal Component Analysis (PCA) used to identify factors influencing the distribution of three groups classified by mortality and timing of surface fire damage. Notably, the newly deceased group in 2024 (H-D) is clearly separated from the 2023 deceased group (D) and the surviving group (H-H) along the PC1 axis. This distinction is closely related to the significant increase in ERT and resistance changes observed after the winter season. Overall, the H-D group is characterized by higher values of Rsmin, Rsmax, slope, tree height, BSI, ERTR, and ERTRY, whereas the H-H group is distinguished by higher DBH and ERTB values.
In the case of ERTY, the PCA results show a relatively higher tendency in the H-H group. This is likely because ERTY represents an intermediate level of wildfire damage, with a relatively high proportion even in healthy trees.
In summary, PCA identified indicators that distinguish dead from healthy trees. Larger sample sizes in future studies will improve the reliability of these relationships and help identify the most effective predictors of mortality. In particular, PCA confirmed that resistivity-based indicators measured after the summer season provided the clearest separation between surviving and non-surviving trees. This reinforces our conclusion that post-summer monitoring is the optimal window for the practical application of ERT in post-fire assessments.
Although our study provides longitudinal evidence for ERT-based diagnosis after surface fire, several limitations should be noted. The dataset comprised 30 trees from a single site in Gangneung, the Republic of Korea, which constrains the statistical power and geographic generalizability. Uncertainty is inherent in ERT measurements because resistivity can vary with electrode contact, sapwood moisture, and seasonal temperature; we minimized these effects by fixing MP positions, standardizing the color scale, and repeating measurements, yet some variability remained. In addition, the current feasibility of ERT in operational forestry is limited by equipment cost and the need for specialized expertise, which may restrict broad deployment. To partly overcome the limitation of site-specificity, this study also incorporated external morphological indicators that are widely used to classify wildfire damage severity and combined them with monthly repeated ERT monitoring to strengthen the reliability of our findings. Future work across multiple sites and species, with larger samples and simplified field protocols, will be essential to validate, generalize, and operationalize these results.

5. Conclusions

Wildfires are a critical threat to forest ecosystems, often prompting extensive salvage logging and reforestation. In cases of relatively low-intensity surface fires, however, conserving affected stands based on accurate survival assessments can accelerate natural recovery. This study examined post-fire physiological responses in Pinus densiflora following a major wildfire in Gangneung, the Republic of Korea (2023), combining field observations with electrical resistance tomography (ERT) to monitor internal trunk damage over one year.
Our results showed that scorched bark regions had significantly higher ERTR and ERTRY values than unaffected areas, reflecting strong associations between thermal injury and internal defect formation. Defects expanded outward from low-resistance zones (ERTB) and intensified over time. Six months after the fire, trees that later died already displayed distinct resistivity patterns (32% ERTR and 69% ERTRY). After one year, healthy tissue in dead trees declined to below 18% of the cross-sectional area, demonstrating the utility of ERT for visualizing progressive internal degradation.
Early assessments within two months were unreliable because soot obscured crown conditions, highlighting the limitations of image-based diagnostics during the immediate post-fire period. In contrast, post-summer assessments clearly distinguished survivors from non-survivors. Seasonal stressors include summer heat increasing transpiration demand and winter freezing further exacerbating internal damage. Trees with a larger DBH, height, and circumference and lower Bark Scorch Index (BSI) showed reduced internal degradation and higher survival potential.
Overall, mortality-prone trees exhibited progressive internal defects originating at heat-injured sites, with acceleration during summer and winter stress periods. For reliable diagnosis and effective management, post-summer assessment in the first year is recommended as the optimal window for mortality prediction in surface-fire-affected stands. Although this study was limited to 30 trees from a single site, its longitudinal design provides valuable insights. Future research with larger and multisite datasets will be essential to validate and generalize these findings, and further work is needed to simplify and adapt ERT for broader practical use.

Author Contributions

Conceptualization, K.C.L., W.-S.K. and Y.-G.J.; methodology, K.C.L., W.-S.K. and Y.-G.J.; validation, K.C.L., W.-S.K., S.A. and Y.-G.J.; formal analysis, Y.S., W.C., H.J. and W.-S.K.; investigation, Y.S., W.C., H.J. and Y.-G.J.; data curation, Y.S., W.C. and H.J.; writing—original draft preparation, K.C.L. and Y.S.; writing—review and editing, S.A. and Y.-G.J.; visualization, Y.S., W.C. and H.J.; supervision, W.-S.K. and S.A.; project administration, K.C.L.; funding acquisition, S.A. and Y.-G.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Institute of Forest Science (Project No. FE0100-2022-02-2024).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 01504 g001
Figure 1. Location of the experimental site (A) and images of the site before and after the felling of dead trees (B). Observed changes in tree crown conditions from April 2023 to April 2024 (C).
Figure 1. Location of the experimental site (A) and images of the site before and after the felling of dead trees (B). Observed changes in tree crown conditions from April 2023 to April 2024 (C).
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Figure 2. Daily mean temperature and precipitation recorded from April 2023 to April 2024.
Figure 2. Daily mean temperature and precipitation recorded from April 2023 to April 2024.
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Figure 3. ERT images of tree number 10 with (A(a)) and without (A(b)) heartwood area. Resistivity values obtained from the ERT measurements, including the areas of interest (A(c)); blue line indicates the boundary between sapwood and heartwood, identified using the criterion t/R < 0.3 (t = residual wall thickness; R = tree radius). Monthly ERT images of dead (D), healthy-to-dead (H-D), and continuously healthy (H-H) trees (B).
Figure 3. ERT images of tree number 10 with (A(a)) and without (A(b)) heartwood area. Resistivity values obtained from the ERT measurements, including the areas of interest (A(c)); blue line indicates the boundary between sapwood and heartwood, identified using the criterion t/R < 0.3 (t = residual wall thickness; R = tree radius). Monthly ERT images of dead (D), healthy-to-dead (H-D), and continuously healthy (H-H) trees (B).
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Figure 4. Monthly changes in color area ratios (A) and comparison of ERTR and ERTRY in areas with and without soot (B) from April to September 2023. Different letters above boxes indicate significant differences among months or groups (p < 0.05, Tukey’s HSD). Asterisks (* p < 0.05; ** p < 0.01) denote significant differences between soot and non-soot areas at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
Figure 4. Monthly changes in color area ratios (A) and comparison of ERTR and ERTRY in areas with and without soot (B) from April to September 2023. Different letters above boxes indicate significant differences among months or groups (p < 0.05, Tukey’s HSD). Asterisks (* p < 0.05; ** p < 0.01) denote significant differences between soot and non-soot areas at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
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Figure 5. Comparison of ERT ratios between healthy and dead trees from April 2023 to April 2024. Asterisks (* p < 0.05; ** p < 0.01) indicate significant differences between healthy and dead trees at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
Figure 5. Comparison of ERT ratios between healthy and dead trees from April 2023 to April 2024. Asterisks (* p < 0.05; ** p < 0.01) indicate significant differences between healthy and dead trees at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
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Figure 6. Changes in maximum (Rsmax) and minimum (Rsmin) resistivity values of healthy and dead trees from April 2023 to April 2024. Asterisks (* p < 0.05; ** p < 0.01) denote significant differences between groups at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
Figure 6. Changes in maximum (Rsmax) and minimum (Rsmin) resistivity values of healthy and dead trees from April 2023 to April 2024. Asterisks (* p < 0.05; ** p < 0.01) denote significant differences between groups at the same time point. “ns” indicates no significant difference. In each boxplot, the central line represents the median, the box indicates the interquartile range (IQR), and whiskers show the minimum and maximum values excluding outliers.
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Figure 7. Correlations between defect ratios (ERTR and ERTRY) and external tree characteristics (Bark Scorch Index, DBH, and Circumference). Solid lines represent regression fits for ERTR, and dashed lines represent regression fits for ERTRY. The coefficient of determination (R2) is shown for each relationship.
Figure 7. Correlations between defect ratios (ERTR and ERTRY) and external tree characteristics (Bark Scorch Index, DBH, and Circumference). Solid lines represent regression fits for ERTR, and dashed lines represent regression fits for ERTRY. The coefficient of determination (R2) is shown for each relationship.
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Figure 8. Principal Component Analysis (PCA) of topographical characteristics, external tree characteristics, and internal defect factors in surface-fire-damaged trees. Different colored circles (red: D, blue: H-H, black: H-D) indicate the approximate distribution ranges of each group for visualization purposes. The partial overlap among ellipses does not affect the interpretation of group separation or the scientific conclusions.
Figure 8. Principal Component Analysis (PCA) of topographical characteristics, external tree characteristics, and internal defect factors in surface-fire-damaged trees. Different colored circles (red: D, blue: H-H, black: H-D) indicate the approximate distribution ranges of each group for visualization purposes. The partial overlap among ellipses does not affect the interpretation of group separation or the scientific conclusions.
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Table 1. Topographical and morphological characteristics, including the number of samples, at the study site.
Table 1. Topographical and morphological characteristics, including the number of samples, at the study site.
Elevation
(m)		TypeSlope
(°)		Height
(m)		DBH
(cm)		Circ.
(mm)		CBH
(m)		BSINumber of TreesNumber of Logged Trees
53–67Total11.8 ± 3.115.5 ± 1.036.9 ± 5.61178 ± 17712.4 ± 1.910.0 ± 3.630
H11.8 ± 3.315.9 ± 0.838.5 ± 5.81229 ± 18412.3 ± 2.09.1 ± 3.5195
H-H11.7 ± 3.715.8 ± 0.839.3 ± 5.01254 ± 16112.3 ± 2.08.1 ± 3.214
D12.0 ± 3.614.4 ± 1.134.6 ± 5.81124 ± 18312.2 ± 2.412.8 ± 4.755
H-D12.0 ± 2.215.3 ± 0.633.8 ± 2.51065 ± 7312.6 ± 1.210.8 ± 1.16
p value0.988<0.035<0.032<0.0280.953<0.042
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Lee, K.C.; Song, Y.; Choi, W.; Ju, H.; Kang, W.-S.; Ahn, S.; Jung, Y.-G. Applying Electrical Resistance Tomography to Diagnose Trees Damaged by Surface Fire. Forests 2025, 16, 1504. https://doi.org/10.3390/f16101504

AMA Style

Lee KC, Song Y, Choi W, Ju H, Kang W-S, Ahn S, Jung Y-G. Applying Electrical Resistance Tomography to Diagnose Trees Damaged by Surface Fire. Forests. 2025; 16(10):1504. https://doi.org/10.3390/f16101504

Chicago/Turabian Style

Lee, Kyeong Cheol, Yeonggeun Song, Wooyoung Choi, Hyoseong Ju, Won-Seok Kang, Sujung Ahn, and Yu-Gyeong Jung. 2025. "Applying Electrical Resistance Tomography to Diagnose Trees Damaged by Surface Fire" Forests 16, no. 10: 1504. https://doi.org/10.3390/f16101504

APA Style

Lee, K. C., Song, Y., Choi, W., Ju, H., Kang, W.-S., Ahn, S., & Jung, Y.-G. (2025). Applying Electrical Resistance Tomography to Diagnose Trees Damaged by Surface Fire. Forests, 16(10), 1504. https://doi.org/10.3390/f16101504

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