Does Fire Influence the Greenness Index of Trees? Twelve Months to Decode the Answer in a Rarámuri Mixed Forest

Abstract

Fire is one of the most significant agents of disturbance in forest ecosystems, with implications for their structure and composition. An understanding of its dynamics is essential for the delineation of forest management policies in the context of predicted climate scenarios. Based on the monthly monitoring of greenness index (NDVI) values recorded over one year at the individual crown level, this study aimed to analyze the dynamics of NDVI values for four different genera, growing in a Mexican mixed forest and subjected to a prescribed burn, relative to those of a control (unburned) treatment. The results demonstrated the general effect of burning over time on NDVI values among the genera, with Pinus showing the most significant effect, while the effect on Quercus was not significant. Tree height was related to NDVI values for Pinus and Juniperus in the burned area, where low-growing individuals responded negatively in terms of greenness index values. Further studies are still required, but we can conclude that fire plays a differential role in the dynamics of canopy activity and that tree size is an important variable. The results also contribute to our understanding of forest responses to fire disturbance, providing indicators with which to assess ecosystem stability under the threat of extreme climatic variations.

1. Introduction

Fire is an agent of disturbance that inevitably has ecological implications in terrestrial ecosystems. It becomes even more important in the context of the climatic variability experienced by forests [1]. Moreover, under climate change scenarios, with increasingly severe and frequent periods of droughts [2], forest ecosystems may exhibit significant vulnerability to fire, with increments in the burned areas and, consequently, a rise in C emissions [3,4].

Fire impacts the composition, structure, and functioning of ecosystems, with implications for their ability to maintain health and productivity. This makes fire a matter of central importance in ecological research [5]. The complexity of monitoring ecological interrelationships in heterogeneous forest areas subjected to fire is a challenge, and the spatial scale of analysis plays an important role in the process. In these ecosystems, the assessment of the dynamics of vegetation is enhanced when stand resolution is achieved at the individual tree level, since this represents the fundamental ecological unit [6].

The use of spectral sensors, mounted onto remote sensors, is becoming increasingly common as a reliable approach to characterizing vegetation dynamics [7]. The acquisition of crown time series data with unmanned aerial vehicles (UAVs) or drones provides information for computing the normalized difference vegetation index (NDVI), which is one of the most popular indices for elucidating vegetation responses to the environment [8]. However, despite the advantage of its centimetric resolution and comparative flexibility with respect to satellites, multi-temporal metrics of NDVI are still lacking at the individual tree level, especially in mixed forests, which can be prone to climate-altered fire regimes. These local parameters could prove crucial as strategic inputs for forest management portfolios, as well as for feeding models at larger spatial scales [9] in connection to ecosystem functionality and associated services.

The determination of temporal variation in NDVI values over continuous periods is of crucial importance and can generate robust data with which to better explain the physical variations to which individual trees are subjected in the face of well-differentiated seasonal hydroclimatic variations [10]. In this manner, landscape heterogeneity in mixed forests serves as an ideal natural laboratory for the refinement of the ecological understanding of the effects of fire on vegetation dynamics.

The Sierra Tarahumara or Rarámuri is a major component of the northern Sierra Madre Occidental in Mexico. In this region, the interaction between fire regimes and the indigenous knowledge of the local ethnic groups is of strategic importance in terms of biodiversity conservation and sustainable management [11]. The World Wildlife Fund has designated this area as a priority region for biodiversity conservation, given its significant ecological contribution. These ecosystems are thus ideal for generating further knowledge regarding the plant–fire relationship, given that they also provide a variety of goods and services for local and neighboring communities. These ecosystems are of particular ecological importance as refuges for wildlife. The detailed multi-temporal response of the tree greenness index to fire is therefore valuable for designing management strategies where the traditional influence of human activities associated with global warming threatens these ecosystems [12].

The objective of this study was to investigate the dynamics of monthly NDVI data in individual tree crowns. This was conducted by comparing those areas subjected to fire (e.g., the burned areas) with those that were not burned (control areas) to answer the following specific questions: (a) What is the monthly variation in NDVI values over a year? Do NDVI values exhibit variations that can be attributed to the effects of fire? Does the response of the NDVI values in burned and unburned areas vary according to the dasometric attributes of the trees? We hypothesized that there would be differences in the NDVI values among genera during the study in the burned and control areas.

2. Materials and Methods

2.1. Study Area

A prescribed surface burn was applied by the personnel of the Mexican National Forestry Commission on 23 March 2021 at a site in the Sierra Tarahumara, located north of the Sierra Madre Occidental (SMO) of northern Mexico (27.125° N, 107.116° W; 2400 masl) (Figure 1). This area is characterized by the convergence of at least four genera: Pinus, Quercus, Juniperus, and Arbutus. The dominant species in the area are Pinus engelmannii and Juniperus deppeana (50.8% and 35%, respectively). The shrub stratum is dominated by Ceanothus buxifolius Willd. ex Schult. and Arctostaphylos pungens HBK. The diversity of herbaceous plants is mainly represented by Bouvardia ternifolia (Cav.) Schltdl., Houstonia rubra Cav, Eryngium heterophyllum Engelm., Dysphania graveolens Mosyakin and Clemants, Bouteloua gracilis (Kunth) Lag. ex Griffiths, and Cyperus esculentus L., among others. These mixed-species forests of the SMO contain a combination of fire-tolerant species and others with post-fire sprouting strategies that provide high resilience to wildfires [12,13]. The dominant land use is timber forest management for commercial purposes. This is maintained under common ownership through the land tenure system of “ejidos”, in which the predominantly Indigenous population maintains a traditional relationship of subsistence through the use of forest resources. These groups also play an important role in the fire regime by burning forested areas [12].

The site presents a slight (10%) slope, low stoniness, and Cambisol soils. The climate is temperate, with a mean annual temperature of 13.7 °C, a minimum temperature of −15 °C and highly variable precipitation, which ranges from 470 to 683 mm per year [14]. On the day of the prescribed burning, the temperatures ranged from a minimum value of 2 °C to a maximum value of 20 °C. In the study site, the load of green material used was in the order of 18.5 ton ha⁻¹, and we used 2.5 ton ha⁻¹ of litter and dry soil organic matter… These values were estimated according to the methodology of Caballero-Cruz et al. (2018) [15] (Figure 2). Likewise, the relative humidity of fuels was 26%. This was estimated according to the soil humidity in the region [16]. Overall, the burning treatment applied at the study site involved a low-intensity prescribed burn (Figure 2B).

2.2. Data Collection

On 10 April 2021, burned and unburned trees adjacent to the burned area were also selected. The average distance between the burned and unburned areas was 75 m. We assessed some dasometric variables at the individual tree level (Table 1): we measured basal diameter and diameter at breast height using a BDH tape (Forestry Suppliers Inc., Jackson, MS, USA), and assessed tree commercial and total heights with a tape measure and an electronic clinometer (Haglöf, Sweden).

A total of 12 flights were conducted on the 15th of each month between April 2021 and April 2022. These flights were conducted using a DJI Phantom 4 multispectral quadcopter drone (Innovation Technology Co., Shenzhen, China), equipped with an RGB digital camera and five multispectral cameras. These cameras covered the blue (450 nm ± 16 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), red edge (730 nm ± 16 nm), and near-infrared (840 nm ± 26 nm) wavelengths bands. The quadcopter was also equipped with a 2 MP global shutter [see DJI P4 Multispectral Specifications, available online at the following site: https://www.dji.com/p4-multispectral/specs (accessed on 2 May 2024)]. The flight altitude was set at 60 m, with overlaps between images and flight paths of 80 and 75%, respectively.

The images were processed and analyzed using photogrammetric procedures in the software OpenDroneMap [version ODM: 2.8.4; Cleveland Metroparks, Cleveland, OH, USA (https://github.com/OpenDroneMap/ODM (accessed on 13 February 2024))]. This process yielded multispectral orthophotos, in which each tree under study was geolocated and its crown area was manually digitized. From the center of each canopy, monthly NDVI values were extracted over a year as the variable of interest (Equation (1)) using the raster calculator of the Open Source QGIS software (QGIS Development Team, 2023. QGIS Geographic Information System. Open-Source Geospatial Foundation Project (http://qgis.osgeo.org (accessed on 13 February 2024)).

(1)$NDVI={\displaystyle \frac{NIR-RED}{NIR+RED}}$

where NDVI = normalized difference vegetation index, NIR = near-infrared (840 ± 26 nm), and RED = red (=650 ± 16 nm) wavelengths bands.

2.3. Statistical Analysis

A mixed-effects model was fitted for each genus. In these models, the months of the NDVI measurement and treatment (burned vs. unburned) were included as fixed effects, and the individual tree was included as a random effect. Furthermore, a multiple linear regression was conducted to evaluate the influence of dasometric variables on the NDVI. Since total height was identified as the variable that explained the greatest proportion of variance, it was included in the mixed model as a fixed effect. The models were fitted using the lme4 package of the software R version 4.0.0 [17]. For each model, the assumptions regarding the normality and homoscedasticity of the residuals were verified graphically. When any of the fixed factors proved significant, means comparisons were performed using the emmeans package [18]. A graphical representation of the results was produced using the ggplot2 package of R [19].

3. Results

Over the course of the twelve-month analysis period, the greenness index demonstrated uniform seasonal fluctuations among genera. As expected, both for burned and unburned trees, the NDVI values declined considerably during the dry season in the months of May and June, when some trees lost their foliage, and their leaf activity was consequently diminished (Figure 3). During the rainy season in the months of July to August, and in the subsequent period of monsoon rains in October, the NDVI values reached their maxima. Finally, during the winter months, the index decreased slightly, but it increased once again during the coming spring.

Nevertheless, in all genera, the statistical analyses indicate a significant increase in NDVI values over time during the measurement year (p < 0.005 in all cases; Table A1). Similarly, a general negative effect of burning on NDVI was observed (Figure 4), except in Quercus, for which the NDVI values did not change with burning (p = 0.645; Table A1).

With regard to the dasometric variables, a positive relationship was observed between tree height and NDVI values (p < 0.01), except for Arbutus (p = 0.412; Table A1). However, in Juniperus and Pinus, this relationship was only significant for the trees undergoing the burning treatment (total height × burning interaction, p < 0.05 in both genera). Specifically, it was observed that low-growing trees reduced their NDVI values in sites that underwent prescribed burning (Figure 5).

4. Discussion

4.1. Intra-Annual Variation in NDVI Values at Individual Tree Crown Level

Previous studies have investigated forest stability following fire events using imagery sensed using UAVs [20,21], but efforts to understand the multi-temporal response of multispecies forests remain incomplete. The structural complexity of these forests confers ecological importance, with direct implications for productivity [22] and stand stability. Moreover, the interconnection of the Rarámuri community with the environment contributes to the cultural richness of the ancestral knowledge of fire management [11]. The anthropogenic effect serves as a modulator of forest resilience. As suggested by Forzieri et al. (2022) [1], the degree of external disturbances is contingent upon the structural complexity of the forest.

Viewing the aerial perspective of the forest canopy offers an advantage that is limited for the manager working from the ground alone. The remote sensing data obtained from the UAV represent an important data source in the context of the growing body of literature that documents the close relationship between NDVI values and vegetation dynamics, including in terms of climatic variability [23,24]. Above all, procuring data at the individual tree level elucidates mechanisms that have hitherto been unclear and that complementarily enhance the findings by relating them to the biometric parameters of individual trees. Subject to extending the temporal window of analysis with subsequent UAV flights and testing other flight configurations [25], the results obtained to date have been consistent with those reporting intra-annual variation in NDVI values [10,26].

Our scientific findings contribute to the understanding of the intra-annual variation in the greenness index. The NDVI variations throughout the year followed similar graphical trends for all the studied genera, with marked oscillations occurring in the seasonal periods of the prevailing climate in this region, which is subject to frequent episodes of drought associated with fire occurrence [27]. The lowest reflectance values occurred during the dry season (NDVI~3, April–June, Figure 3) because of the reduction in photosynthetic activity due to water limitations. This phenomenon is particularly evident in leafy species that, during this season, lose their leaves as a strategy with which to reduce drought stress [10]. This, in turn, affects their foliar area index [28]. Concomitantly, there were also increases in NDVI values in late spring and throughout the summer. These were attributed to the high rates of photosynthesis seen due to the availability of moisture combined with warm temperatures.

4.2. Variations in Tree NDVI values in Response to Fire Effects Are Contingent on the Tree Genus

This study demonstrates the impact of a prescribed burn treatment on the variation in tree crown NDVI values, measured using monthly scales. Our findings also revealed a significant relationship between tree size and this aforementioned variation. This is important, considering the importance of anthropogenic factors as drivers of climate change. These factors include the actions of mankind and its habit of arson, which can cause catastrophic fires that are further exacerbated by adverse environmental conditions [4].

Nevertheless, the trees subjected to fire differed temporally in their NDVI values according to genus. The statistical modeling demonstrated that the application of fire elicited a response in terms of NDVI values in the crowns burned over the subsequent twelve months. The crowns of the burned trees showed slightly lower greenness index values than those of the control trees, except for Quercus (Table A1). One possible explanation for this phenomenon is that fire stress initially impairs soil moisture, which in turn limits the hydraulic conductivity of the tree [29,30]. This then affects the distribution of resources to the canopy, which is required in order to maintain the functioning of the tree. Moreover, the concurrence of burning with the dry season may exacerbate the decline in photosynthetic capacity, thereby diminishing resistance to disturbances [5]. Nevertheless, a study of Pinus pinea revealed that a crown partially recovered in the spring following a fire [31].

In contrast to the other genera, Quercus did not exhibit significant differences between the two treatments, at least in the intra-annual assessment. This could be explained by the thickness of the phellem in the bark of trees of this genus, which serves to mitigate the impact of fire damage, as evidenced by the research of Burrows et al. (2016) [32]. However, further anatomical evidence and hydraulic conductivity tests are still pending. According to Graves et al. (2014) [33], the Quercus species, which is typical of fire-prone habitats, invests more in the outer bark close to the ground, where heat is more likely to damage the outer tissue, making it thicker and moister. Another fire tolerance strategy employed by oak species involves rapid recovery through resprouting after the aerial portions of the plant have been damaged [34]. Additionally, these species exhibit marginal foliage loss in cases of low fire severity, as observed in our study, which can also help to explain the similarity of the NDVI values between burned and control trees. It has been reported that oak physiology is complex, and, in some cases, the legacy of environmental effects persists over temporal scales greater than one year [10,35]. Consequently, the impacts of fire on this genus may not have been apparent within the time frame of the study. The elimination of the understory, the release of nutrients, and their link to the aerial part of the tree may present temporary delays following a prescribed burning, which in turn is a function of the intensity of the fire. This is consistent with the research of Camarero et al. (2023) [36], which shows that an increase in their resilience to environmental dryness disturbances should be anticipated. Consequently, it is recommended that the adaptive monitoring of these trees be conducted.

4.3. The NDVI Responses to Fire Are Influenced by the Size of the Trees

Forest landscapes comprise both horizontal and vertical structures. For this reason, assessing canopy responses, as reflected solely by NDVI values, is not necessarily sufficient. In this sense, the relationship with tree size is complementary and helps to establish the role of tree attributes in tree responses. In this study, tree size was found to be associated with post-burn NDVI values since the scorched crown of low trees was heat-stressed. This was consistent with Fernandes et al. (2008) [31]. Thus, the needles are susceptible to damage from fire, which results in a reduction in leaf area and, consequently, a decline in canopy greenness index values. Another potential explanation is that juveniles or low-height trees possess thinner bark and, when subjected to fire, experience damage to the cambium, potentially impairing photosynthesis. For example, Talucci et al. (2020) [8] documented that the photosynthetic rate (~NDVI) has an allometric dependence on basal dimensions. However, improved sapwood and heartwood area metrics are necessary. Moreover, the analysis of the fuel load is critical due to its influence on fire intensity and the resulting impact on tree growth [37].

The composition of mixed stands, i.e., the complexity of their diversity, is evidently responsible for their differentiated response to fire. For example, a study by Isbell et al. (2015) [38] demonstrated that species richness within a stand confers greater stability and resilience. For these reasons, it is useful to evaluate the resilience of the growth of these species following exposure to fire [35].

4.4. Study Limitations

From a scientific perspective, the stand must be a functionally interconnected forest management system. Understanding the complex relationships that underpin its dynamics is therefore of great importance. Although this study established a correlation between the crown greenness index and burned and unburned trees, including the elucidation of its relationship with certain dasometric attributes, several scientific questions remain unanswered.

In the first instance, the date of the burn application and the prevailing seasonal climatic conditions (e.g., drought) were found to influence the results [5,39]. In other words, the phenological state of the individual plant responds differently to fire, depending on the temporal timing of the disturbance. Although the timing of the applied prescribed burn coincided with the typical season for fires in the area [27], further investigation is warranted to examine other temporal windows and their intrinsic relationships with climate. For example, the application of a burn in the fall following a dry year produced enhanced resilience in Pinus nigra ssp. salzmannii Dunal (Franco) relative to pines treated in the spring [40].

Extending the temporal window of horizons, as well as the availability of climatic data, is critical in these investigations. It has been demonstrated that NDVI values are closely related to the post-fire climatic seasonal environment [9,24], especially in areas prone to recurrent and extreme droughts, as evidenced by our study site [5]. Similarly, documentation of the timing of phenological changes (e.g., the initiation, duration, and termination of leaves, buds, fruits, etc.) could serve to complement the findings, enabling the anticipation of changes in stand dynamics. New flight configurations are also recommended [25]. By leveraging a high spatial resolution, more detailed insights into stand ecological heterogeneity can be obtained, including interactions with the forest understory [5,8]. This makes the use of UAVs an ideal alternative, providing data that traditional satellite image-based studies cannot. It does not assume that the forest structure presents no temporal alterations, which introduces biases into the NDVI values as a consequence [1,5]. Nevertheless, it is recommended to reinforce these findings by evaluating the performance of other spectral indices, given that NDVI values are limited to representing the structural features of a stand [41]. The incorporation of other modern sensors should not be precluded if economic conditions permit [42].

The integration of these findings with those pertaining to radial growth, including anatomical data, presents a potential opportunity to enhance our understanding of the impact of fire. As demonstrated by Acosta-Hernández et al. (2024) [10], stem growth is closely linked with the crown and can help to discern its implications for vegetation’s productivity rates.

5. Conclusions

NDVI values were found to be a reliable indicator of the impact of fire on canopies, as well as a potential indicator of stand dynamics. The modeling results demonstrated a strong correlation between fire and NDVI metrics. Intra-annual post-fire NDVI anomalies differed between burned and control trees in the genera Juniperus, Pinus, and Arbutus, but not in Quercus. We strongly recommended extending the temporal scope of the analysis given that some long-term tree responses remain to be explored. The interactions of burned and control areas with dasometric attributes demonstrated that tree size was a variable that was statistically associated with fire responses. These findings provide a perspective on the resilience rates of these species, which impacted forest productivity in the context of fire and the climatic extremes experienced in the study area. The results demonstrate the potential applications of this in the fire management, restoration, and monitoring of post-burn landscapes. It is recommended that continuous monitoring be increased, and that climatic variability data be associated with changes in phenology as a means by which to anticipate changes in stand behavior.

Author Contributions

Conceptualization, writing—original draft preparation, M.P.-G.; data analysis and visualization, J.A.S.; writing—review and editing, F.d.J.R.-F. and D.A.R.-T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Acknowledgments

To the DendroRed working group that helped conduct flight missions and cartographic manipulations (Eduardo Vivar, Javier Castellanos, Nancy Silva Avila, Andrea Acosta, and Alexis Martínez). To CONAFOR for their technical support in conducting the prescribed burn. To the staff of the forestry development department of the municipality of Guachochi who helped conduct the fieldwork. To the state government for coordinating efforts to achieve the field experiment.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Table

Figure 1. The location of the study area in the Rarámuri region, Mexico (top), and a 3D-modeled view of the experimental site (bottom).

Figure 2. The study area in a Rarámuri mixed forest, Mexico, before (A), during (B), and after (C) the application of a prescribed burning.

Figure 3. Intra-annual variation in NDVI values in the genera Arbutus, Juniperus, Pinus, and Quercus in the study area: unburned trees (green boxes and lines) and burned trees (red boxes and lines). Dotted vertical lines denote the climatic seasons of the study area.

Figure 4. Violin and box plots of mean NDVI values in Arbutus, Juniperus, Pinus, and Quercus trees in areas with no burning (green) and with prescribed burning (red). The violin plot represents the distributions of the NDVI data for each group, presented as density curves. The upper horizontal brackets indicate a statistical comparison between treatments within each genus (ns = no significant, * p [less than] 0.05, *** p [less than] 0.001).

Figure 5. Relationship between total height and NDVI values in trees of genera Arbutus, Juniperus, Pinus, and Quercus in areas without burning (green) and with prescribed burning (red). lines and shaded areas denote predicted values and their 95% confidence intervals, respectively.

Table 1

Biometric description of the trees considered for estimation of NDVI values.

Genus	Variable	n		Min		Max		Mean		Sd		Se
		C	B	C	B	C	B	C	B	C	B	C	B
Arbutus	BD (cm)	6	6	13	11	32	25	21	21	7	5	3	2
	DBH (cm)	6	6	8	6	22	19	14	14	5	4	2	2
	CH (m)	6	6	1.60	0.70	3.00	2.10	2.03	1.41	0.52	0.45	0.21	0.18
	TH (m)	6	6	4.8	3.7	8.2	8.1	6.22	5.22	1.35	1.6	0.55	0.67
Juniperus	BD (cm)	26	25	4	4	29	32	13	12	6	6	1	1
	DBH (cm)	26	25	0	0	23	25	9	8	5	5	1	1
	CH (m)	26	25	0.90	0.40	2.40	3.40	1.80	1.97	0.40	0.59	0.10	0.20
	TH (m)	26	25	1.60	1.30	6.30	7.90	3.82	4.23	1.25	1.52	0.25	0.30
Pinus	BD (cm)	195	194	7	7	64	66	16	15	8	9	1	1
	DBH (cm)	195	194	5	5	52	56	12	11	7	7	1	1
	CH (m)	195	194	1.70	1.50	10.3	12.6	3.06	3.43	1.5	2.11	0.10	0.15
	TH (m)	195	194	2.90	0	21.4	20.8	6.94	6.94	3.17	3.38	0.23	0.24
Quercus	DBH (cm)	35	35	7	7	58	60	32	35	16	16	3	3
	ND (cm)	35	35	4	4	49	49	25	27	13	14	2	2
	CH (m)	35	35	0.4	0.6	5	9.20	2.35	3.05	1.12	1.80	0.19	0.30
	TH (m)	35	35	2.70	3.0	14.8	16.9	8.97	10.8	3.16	3.85	0.53	0.65

C: control trees; B: burned trees; n = number of individuals; var = variable; n = number of trees; BD = basal diameter; DBH = diameter at breast height; CH = commercial height; TH = total height; min = minimum; max = maximum; mean = average; sd = standard deviation; se = standard error.

Appendix A

Table A1

ANOVA results of fixed effects on NDVI values from mixed-effects, models fitted by tree genus.

Genus	Source of Variation	F	Pr (>F)
Arbutus	Date	71.87	<0.0001
	Burning treatment (B)	6.443	0.0348
	Total height (TH)	0.738	0.4153
	B × TH	0.645	0.4450
Juniperus	Date	8.454	0.0038
	Burning treatment (B)	5.808	0.0199
	Total height (TH)	7.920	0.0071
	B × TH	5.220	0.0269
Quercus	Date	388.20	<0.0001
	Burning treatment (B)	0.2097	0.6485
	Total height (TH)	14.958	0.0002
	B × TH	1.9575	0.1665
Pinus	Date	187.93	<0.0001
	Burning treatment (B)	21.840	<0.0001
	Total height (TH)	13.726	0.0002
	B × TH	6.1573	0.01351

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