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Article

Exploring the Climate-Suitable Forestation Area Under Species Distribution and Growth Modeling for Larix kaempferi and Chamaecyparis obtusa in the Republic of Korea

by
Du-Hee Lee
1,
Hyeon Kwon Ahn
2,
Han Doo Shin
3,
Jeong-Gwan Lee
1,
Chul-Hee Lim
4,* and
Hyun-Jun Kim
1,*
1
Department of Forest Resources, Chonnam National University, Gwangju 61187, Republic of Korea
2
Department of Forest Resources, Kookmin University, Seoul 02707, Republic of Korea
3
Department of Agriculture, Forestry and Bioresources, Seoul National University, Seoul 08826, Republic of Korea
4
Department of Forestry, Environment, and Systems, Kookmin University, Seoul 02707, Republic of Korea
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(3), 530; https://doi.org/10.3390/f16030530
Submission received: 17 February 2025 / Revised: 5 March 2025 / Accepted: 12 March 2025 / Published: 17 March 2025
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Climate change has been transforming forest ecosystems globally, affecting the sustainability of conventional forest management practices. This study investigates the suitable forestation area (SFA) for Larix kaempferi and Chamaecyparis obtusa and their growth potential in South Korea under various climate change scenarios. Using species distribution models (SDMs) based on machine learning ensembles, we analyzed potential spatial shifts in the climatic suitability for these species. Growth models based on field data were also developed to evaluate growth variation between Köppen–Geiger climatic zones. The results indicate a substantial reduction in the SFA for L. kaempferi, with its habitat range confined to high-altitude regions due to rising temperatures. In contrast, the forestation potential for C. obtusa is predicted to expand nationwide, particularly in inland areas, under climate change scenarios. However, extreme increases in temperature and atmospheric CO2 concentrations exceeding 600 ppm may inhibit growth, highlighting the need for the development of adaptive management strategies. This study provides useful information for climate-resilient forestation planning by combining growth-weighted suitability indices with projected habitat shifts. These findings emphasize the importance of prioritizing high-altitude conservation zones for L. kaempferi and employing C. obtusa for inland afforestation as a means to ensure sustainable forest management and carbon neutrality objectives.

1. Introduction

Climate change has significantly disrupted the balance of many ecosystems worldwide. Forest ecosystems are particularly sensitive to long-term changes in climate, with rising temperatures, precipitation patterns, and more frequent extreme weather events leading to ecosystem disturbances and shifts in the range of forest-dwelling species [1,2,3]. Consequently, systematic evaluation of suitable forestation zones and the growth characteristics of individual tree species is required in order to establish adaptive forest management strategies.
In Korea, Larix kaempferi and Chamaecyparis obtusa are forestation species that are widely used for both timber production and ecological restoration, accounting for approximately 37% and 36% of the annual coniferous forestation area, respectively [4,5,6]. However, given the anticipated changes in temperature and precipitation patterns due to climate change, current forestation zones may become less suitable for growth and habitats in the future. For example, studies have shown that rising temperatures are likely to hinder the early growth and root development of L. kaempferi, while the suitable range of C. obtusa may shift from southern to central coastal regions [7,8]. Because the distribution of tree species is largely determined by topographical and climatic factors, understanding how climate change will affect the habitats of these two species is required for future forestation planning in Korea.
Species distribution models (SDMs) predict species distributions by correlating presence data with environmental factors, but these predictions can differ depending on the model used, reducing the reliability of single models [9,10]. Machine learning ensembles address this issue by aggregating multiple model outputs for more accurate predictions [11]. SDMs using ensemble methods can predict species responses, assess future habitat suitability, and support adaptive management decisions [12,13].
Because tree growth is strongly associated with environmental conditions, it is highly sensitive to climate-driven changes [14]. Thus, given the complex environmental impacts associated with climate change, effective management plans, including forestation strategies, should also consider tree growth, rather than focusing on habitat suitability alone. However, although SDMs can project future distribution based on occurrence data, they are limited in their ability to predict growth dynamics [15,16]. Consequently, an integrated approach that considers the interaction between tree growth and environmental conditions is required. While many studies have predicted species distribution using SDMs under various climate scenarios, few have also looked to include tree growth processes [17,18,19]. By integrating SDMs with the growth models developed in this study, it becomes possible to more accurately predict the impacts of climate change on tree growth and forestation suitability.
This study aims to assess the impact of climate change on afforestation suitability and tree growth by integrating species distribution modeling with growth models. To achieve this, the present study utilizes machine learning ensembles to identify regions with future climates that are likely to be similar to current afforested areas. Additionally, growth models for L. kaempferi and C. obtusa are developed based on field data to evaluate species-specific growth variations across different climatic zones. These quantified growth differences are then indexed and integrated with future afforestation suitability, to develop optimal site selection strategies that account for growth changes in the two species under future climate scenarios. This approach has the potential to be used as the foundation for sustainable forest management and climate-resilient forestation strategies.

2. Materials and Methods

2.1. Study Area

The study area covered national, public, and commercial forest areas from across South Korea (east longitude: 124°54′ to 131°6′; north latitude: 33°9′ to 38°14′), selected for their potential as practical forestation areas (Figure 1). Tree growth was also analyzed in climatic zones defined using the Köppen–Geiger climate classification system (Table 1) [20]. This classification determines climate zones based on monthly temperature and precipitation extremes and seasonal patterns [20]. In South Korea, seven distinct climate types were identified, consisting of three temperate climates (Cwa, Cfa and Cfb) and four continental climates (Dfa, Dfb, Dwa, and Dwb) (Figure 1; Table 1).

2.2. Research Process for Present Study

This study investigated the spatial variability in climate zones and species growth patterns under changing climatic conditions in three primary stages (Figure 2). In the first stage, spatial changes in climate zones caused by climate change were analyzed to determine suitable forestation sites for L. kaempferi and C. obtusa. In the second stage, current forestation sites were classified according to Köppen–Geiger climate zones, and field surveys were conducted in each zone to collect growth data. These data were used to develop growth models for each species. The third stage integrated the spatial changes in the climate zones with species-specific growth data to assess the potential impact of climate change on growth rates.

2.3. Suitable Forestation Area (SFA)

2.3.1. Machine Learning Ensembles

Ensemble modeling was conducted by combining models developed using 10 machine learning algorithms provided by the BIOMOD2 package in R Studio (ver. 4.2): Random Forest (RF), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), Maximum Entropy (Maxent), Generalized Linear Models (GLM), Multivariate Adaptive Regression Spline (MARS), Classification Tree Analysis (CTA), Flexible Discriminant Analysis (FDA), Generalized Additive Models (GAM), and Generalized Boosted Models (GBM). Presence–absence data were utilized for species distribution modeling, with the exception of Maxent, which only requires presence data [9]. The occurrence coordinates for L. kaempferi and C. obtusa were obtained from the 7th National Forest Inventory (NFI7; 2016–2020), and absence data were generated randomly at a 1:1 ratio with the presence data. The performance of the individual models was determined by calculating the area under the curve (AUC) from the receiver operating characteristic (ROC). Those models with an AUC of 0.7 or higher were selected for the final ensemble [21].
The accuracy of the ensemble model was also assessed using the AUC. For model validation, the dataset was divided into 80% for training and 20% for testing [22,23]. The resulting habitat suitability for the species was expressed on a scale from 0 to 1, with areas scoring 0.7 or higher considered suitable for forestation [24].

2.3.2. Input Data

Data for afforestation site analysis were obtained from NFI7 by extracting geographic coordinates for forest stands classified as L. kaempferi and C. obtusa. The dataset contained 512 coordinates for L. kaempferi forests and 166 coordinates for C. obtusa forests (Figure 1).
Forestation suitability predictions were made for three time periods: the present, the near future (2041–2060), and the distant future (2081–2100). Climate data for these periods were obtained from the Korea Meteorological Administration using MK PRISM v2.1 (2000–2019). Climate projections under Shared Socioeconomic Pathway (SSP)1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were used to generate minimum temperature, maximum temperature, and precipitation data for 2041–2060 and 2081–2100. A total of 19 bioclimatic variables were generated using the ‘dismo’ package in R Studio (ver. 1.3), and Pearson correlation analysis was performed to remove variables exhibiting high inter-variable correlations [25]. However, despite a correlation greater than 0.7 between the mean annual temperature (MAT) and the minimum temperature of the coldest month (MTCM), both were retained, as they independently influence tree growth patterns and distribution limits, respectively [26,27,28]. Eight variables in total were selected for the analysis: MAT, mean diurnal range (MDR), MTCM, temperature annual range (TAR), annual precipitation (AP), precipitation of the wettest month (PWM), precipitation of the driest month (PDM), and precipitation of seasonality (PS) (Table 2). In addition, data for topographic variables (altitude, slope, and aspect) were derived from a digital elevation model (DEM) provided by WorldClim [29]. Soil texture data were obtained from a Forest Soil Map (scale 1:25,000) to account for site-specific factors influencing growth.
Forests were categorized into national, public, and commercial forest zones based on land ownership data provided by the Ministry of Land, Infrastructure, and Transport. Further classification was conducted using economic forest development site data from the Korea Forest Service to identify areas with the potential for forestation.

2.4. Growth Model Development

Growth models were developed to compare and analyze the height growth in different climate zones for L. kaempferi and C. obtusa. Using NFI5 wood samples, age and height information were extracted for 2004 L. kaempferi and 349 C. obtusa trees. The extracted samples were classified into Köppen–Geiger climate zones (Cwa, Cfa, Cfb, Dfa, Dfb, Dwa, and Dwb), and height growth models were developed for each zone.
Because the NFI5 data contained limited height information for younger trees, additional data were collected for early growth estimates from 18 forestation sites with stand ages of 12 years for both species. Stem analysis was conducted on three trees per site, and the resulting height data were incorporated into the growth models to improve their accuracy.
The Chapman–Richards equation was employed to develop the height growth model, with tree age as the primary parameter (Equation (1)) [30,31]:
H = a × (1 − exp(−b × age))c,
where H is the dominant tree height, and a, b, and c are coefficients to be estimated. This equation has been widely used, because it can define sigmoid curves using three parameters influenced by biological processes and behaviors [32,33]. Shapiro–Wilk tests were used to verify data normality.

2.5. Climate-Weighted SFA

Using the developed growth models, weights were calculated to account for variations in height growth between different climatic zones. These weights were then employed to evaluate the suitability of these zones for forestation. In this analysis, the height of L. kaempferi and C. obtusa at the age of 50 was considered for each climatic zone, because, at this age, tree growth tends to decelerate, making it an appropriate indicator of long-term growth potential. These weights were calculated using Equation (2):
W = h/H,
where h is the difference between the minimum height and the 50-year height in each climatic zone, while H is the range between the maximum and minimum heights. The data were normalized to produce values constrained within the interval of 0 to 1. The weights derived from the growth models for each climate zone were then incorporated into the ensemble model to more precisely assess the suitability of climate zone-specific forestation sites.

3. Results

3.1. Changes in Forestation Suitability Under Climate Change

Using eight climatic and five environmental variables as input (Table 2), the ensemble model produced an AUC of 0.89 for L. kaempferi and 0.97 for C. obtusa. This higher prediction accuracy for C. obtusa was likely due to its smaller afforested area compared to L. kaempferi, because biased occurrence data can inflate prediction accuracy [34]. Of the variables influencing L. kaempferi distribution, the MTCT and PS were the most significant factors (Table 3). For C. obtusa, key variables included the PDM, PS, and MAT.
The suitable forestation area (SFA) in the present for L. kaempferi was concentrated in Gangwon-do, excluding coastal regions (Figure 3a). Projections for the 2050s and 2090s under climate change scenarios revealed a significant reduction in the suitable area. By 2050, the area was expected to decrease by 55% under SSP1-2.6, 73% under SSP2-4.5, and 81% under SSP5-8.5. By 2090, these reductions were projected to reach 62%, 86%, and 89%, respectively (Figure 3b–g).
The present C. obtusa forestation areas were concentrated in the coastal areas of Jeolla-do and Gyeongsangnam-do (Figure 3a). By the 2050s, the SFA was projected to expand significantly, particularly under SSP5-8.5, with most areas becoming suitable, except for parts of Gyeonggi-do, Gangwon-do, and Gyeongsangbuk-do (Figure 3b,d,f). However, under the SSP5-8.5 scenario, a sharp decrease in the SFA by the 2090s was predicted.

3.2. Development of Growth Models for Climatic Zones

Height growth models were developed to predict the growth of L. kaempferi and C. obtusa in different climatic zones (Table 4). For L. kaempferi, the height growth was lower in the Cwa and Cfa zones, with recorded values of 13 m and 12 m, respectively, at a rotation age of 30 years. In contrast, the Dwa and Dfa zones had recorded heights of 16 m, while those for Dwb and Dfb were 15 m. Similarly, for C. obtusa, the height growth at a rotation age of 40 years was highest in the Dwa zone (15 m) and lowest in the Cwa (13 m) and Cfa (12 m) zones (Figure 4b).
These findings suggest that L. kaempferi and C. obtusa exhibit suboptimal growth in the Cwa and Cfa zones, which are found in coastal regions, while interior regions such as the Dwa zone provide more suitable growth conditions.

3.3. Predicting Future Forestation Areas Using Weighted Growth Models

When growth weights were incorporated into the ensemble model for L. kaempferi, the weighted suitable forestation area (WSFA) was found to be 23.7% lower than the SFA in the present-day projections (Figure 5; Table 5). However, in the future under the climate change scenarios, the differences between the SFA and WSFA were less dramatic. This indicates that, despite the changes in the climatic zones caused by climate change, regions with higher growth potential for L. kaempferi were identified as part of the SFA. Additionally, with the northward shift of the SFA driven by climate change, regions with poor growth potential, such as the Cwa and Cfa zones, were not considered as future forestation areas.
More significant differences between the SFA and WSFA were observed for C. obtusa (Figure 5; Table 5). The WSFA for the present was lower than the SFA by approximately 35%. This reduction was attributed to the fact that C. obtusa is predominantly distributed in regions with a large proportion of coastal areas, particularly within the Cwa zone, where its growth is relatively poor. Furthermore, major afforestation regions along the western coast of Jeollanam-do and the eastern coast of Gangwon-do, where C. obtusa is widespread, fall within the Cwa zone, which has suboptimal growth conditions.
Conversely, climate change is anticipated to result in inland regions of South Korea being reclassified as Cwa and Dwa climatic zones, suggesting that these areas may become newly viable for C. obtusa forestation in the near future. The Dfa zone, where C. obtusa exhibits relatively poor growth, is projected to reduce significantly due to rising temperatures. Consequently, with increasing temperatures, the SFA for C. obtusa is expected to expand nationwide, creating opportunities for various forest management strategies.

4. Discussion

4.1. Vulnerability of L. kaempferi and Adaptability of C. obtusa to Climate Change

The results highlight the vulnerability of L. kaempferi to climate change, with substantial reductions in the SFA projected under all SSP scenarios. The correlation with the MTCM highlights the sensitivity of this species to extreme cold, which restricts its presence in northern and high-altitude regions [35]. The strong impact of PS also suggests that L. kaempferi is sensitive to seasonal changes in rainfall and soil moisture. In particular, its well-developed root system allows it to efficiently manage excess soil moisture in wet conditions [36]. However, this adaptation also means that it can experience adverse growth when the soil moisture levels fluctuate dramatically between seasons.
Because L. kaempferi prefers regions with an MAT of 6.9 to 10.2 °C, rising temperatures will likely restrict its habitat. Previous research predicting habitat changes for various tree species due to climate change has also found that with climate change, the habitat of L. kaempferi will decrease [37]. To mitigate the impact of climate change on L. kaempferi, conservation strategies should prioritize areas with stable moisture conditions and cooler climates, particularly in Gangwon-do’s high-altitude zones.
In contrast, C. obtusa demonstrated greater adaptability to climate change, with its SFA projected to expand nationwide under the SSP scenarios. The water-conserving traits of this species allow it to maintain soil moisture during dry periods, supporting its survival in relatively dry regions [38,39]. Furthermore, just as previous research has reported a strong correlation between C. obtusa growth and the MAT, the present study also identified temperature as an important factor influencing its distribution [40]. The association of C. obtusa with the MAT reflects its warm-temperate species characteristics. Consequently, compared to L. kaempferi, C. obtusa shows greater responsiveness to climate change, leading to an expansion in its distribution range. These results are consistent with previous research on habitat suitability for domestic timber species under SSP scenarios, in which the habitat suitability area of C. obtusa was found to expand with climate change [18].
An increase in atmospheric CO2 concentration driven by climate change is known to have a positive impact on tree growth, due to the carbon fertilization effect [41,42,43]. Atmospheric CO2 concentrations are projected to rise to 603 ppm by 2090 under the SSP2-4.5 scenario, and to 1135 ppm under SSP5-8.5. While C. obtusa benefits from the carbon fertilization effect at concentrations below 600 ppm, levels exceeding this threshold act as a growth-inhibiting factor. Excessive photosynthetic activity may lead to high transpiration rates, resulting in drought stress due to the relatively low drought tolerance of C. obtusa.
Periods with the highest forestation potential for C. obtusa are projected to occur in the 2090s under SSP1-2.6, the 2090s under SSP2-4.5, and the 2040s under SSP5-8.5. During these periods, the projected increases in the temperature are 2.3 °C, 3.5 °C, and 2.9 °C, respectively. Under the SSP5-8.5 scenario in 2090, a 6.3 °C increase is anticipated. Moderate temperature increases appear to enhance the forestation potential of C. obtusa, whereas extreme warming (6.3 °C) could suppress growth [44]. These findings emphasize the need for adaptive forestation strategies that consider CO2 thresholds and regional climate conditions.

4.2. Growth Patterns Between Climatic Zones

The lower growth height of L. kaempferi in the Cwa and Cfa zones is indicative of its sensitivity to soil fertility and moisture. Coastal areas, particularly those with high salinity, appear to negatively impact its growth [36]. This is in accordance with the distribution pattern observed in the NFI7 dataset, which showed that L. kaempferi forests are primarily located outside of these zones (Figure 1). Because this species demonstrates better growth in inland regions with stable soil moisture levels, forestation efforts should focus on Dwa, Dwb, Dfa, and Dfb zones, where its growth potential is highest.
For C. obtusa, the superior growth observed in the Dwa zone suggests that inland regions offer optimal conditions for its cultivation. Conversely, the poor growth recorded in the Cwa and Cfa zones reflects the detrimental effects of strong sea winds and high salinity for this species [45]. Because C. obtusa is highly sensitive to site-specific factors, forestation in coastal areas should be avoided to ensure long-term success. These results reinforce the importance of careful site selection based on climatic and environmental conditions. Inland areas classified as Dwa zones should be prioritized for C. obtusa forestation, while regions within the Cwa and Cfa zones should be considered less suitable for its cultivation.

4.3. Implications of SFA and WSFA Analysis for Sustainable Forest Management

The difference between the current SFA and WSFA for L. kaempferi arises from the fact that some L. kaempferi forestation areas are in the Dfb zone, where its growth is relatively poor. In contrast, the minimal differences between the SFA and WSFA under climate change scenarios suggest that regions with higher growth potential are naturally included in the SFA under the projected climatic shifts. In addition, with the predicted northward shift in suitable regions, high-altitude areas are likely to become the primary forestation sites for L. kaempferi. This highlights the need for targeted forestation and conservation strategies in high-elevation regions, where the species can maintain optimal growth, despite the rising temperatures.
For C. obtusa, the significant reduction in WSFA compared to SFA emphasizes the limitations of current forestation sites, particularly in coastal regions exposed to adverse conditions such as high salinity and strong sea winds. The poor growth observed in coastal areas means that forestation efforts should be refocused towards inland regions, where better growth conditions are expected as the climate zones shift. The projected expansion of suitable inland areas, particularly in the Dwa and Cwa zones, thus presents new opportunities for sustainable forestation.
With rising temperatures and the corresponding changes in climatic zones, the potential for C. obtusa forestation is expected to increase significantly, offering opportunities for the implementation of various forest management strategies. These strategies could include not only forestation, but also the sustainable management of forest resources, recreation, and therapeutic uses centered around C. obtusa [39]. The projected nationwide expansion of the SFA for C. obtusa suggests that this species has the potential to play a key role in sustainable circular forest management in South Korea.
Conversely, the reduction in the SFA for L. kaempferi highlights the challenges associated with climate change. Proactive measures, including the development of adaptive forestation strategies that prioritize high-altitude regions and consider the long-term viability of the species under future climatic conditions, are thus required.

4.4. Limitations and Future Research Directions

Despite the valuable insights provided by this study, ecological processes, such as seed dispersal ability and forest successional trends, were not considered. Seed dispersal ability and forest successional trends play a crucial role in determining future habitat suitability and species distribution. These processes affect the species distribution over time, and should be incorporated into future modeling. Field-based long-term monitoring of seed dispersal ability ranges and forest successional trends should also be conducted to enhance the accuracy of forestation models and provide more realistic projections for future forest management strategies.

5. Conclusions

This study employed a machine learning ensemble approach to predict the future SFA for L. kaempferi and C. obtusa under various climate change scenarios. Species-specific growth models were also developed by incorporating growth characteristics for different climatic zones. The results indicate that, with increasing temperatures, the SFA for L. kaempferi is expected to decrease significantly, emphasizing the need for new forestation strategies that ensure long-term viability. Specifically, as temperatures rise, suitable forestation conditions for L. kaempferi are projected to remain only in high-altitude regions, necessitating conservation and forestation efforts in these areas. In contrast, the potential forestation area for C. obtusa is expected to expand nationwide under future climate scenarios. This expansion highlights the opportunities available to identify new forestation sites and develop sustainable management strategies tailored to the long-term cultivation of C. obtusa. As the range of suitable climate zones for C. obtusa increases, the utility of its forest resources is likely to improve, rendering it a potentially important species for future forest management. The findings of this study provide valuable baseline data for the development of forest management strategies that align with climate change adaptation and carbon neutrality. By predicting species growth and analyzing forestation site suitability under shifting climatic conditions, this study contributes to ensuring the long-term health and stability of forest ecosystems. Furthermore, it offers a foundation for developing adaptive forest management strategies that can effectively respond to the impacts of climate change.

Author Contributions

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

Funding

This study was supported by the Korea Forest Service Research Project (2022464B10-2224-0201) and a Kookmin University grant.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Köppen–Geiger climate and occurrence points of L. kaempferi and C. obtusa.
Figure 1. Köppen–Geiger climate and occurrence points of L. kaempferi and C. obtusa.
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Figure 2. Overall research process for present study.
Figure 2. Overall research process for present study.
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Figure 3. SSP climate scenario-based predictions of suitable forestation area (SFA) for L. kaempferi and C. obtusa: (a) current distribution and predicted SFA under (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 in 2050s, and (e) SSP1-2.6, (f) SSP2-4.5, and (g) SSP5-8.5 in 2090s.
Figure 3. SSP climate scenario-based predictions of suitable forestation area (SFA) for L. kaempferi and C. obtusa: (a) current distribution and predicted SFA under (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 in 2050s, and (e) SSP1-2.6, (f) SSP2-4.5, and (g) SSP5-8.5 in 2090s.
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Figure 4. Height growth model based on stand age for (a) L. kaempferi and (b) C. obtusa.
Figure 4. Height growth model based on stand age for (a) L. kaempferi and (b) C. obtusa.
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Figure 5. SSP climate scenario-based predictions of weighted suitable forestation area (WSFA) for L. kaempferi and C. obtusa: (a) current distribution and predicted WSFA under (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 in 2050s, and (e) SSP1-2.6, (f) SSP2-4.5, and (g) SSP5-8.5 in 2090s.
Figure 5. SSP climate scenario-based predictions of weighted suitable forestation area (WSFA) for L. kaempferi and C. obtusa: (a) current distribution and predicted WSFA under (b) SSP1-2.6, (c) SSP2-4.5, and (d) SSP5-8.5 in 2050s, and (e) SSP1-2.6, (f) SSP2-4.5, and (g) SSP5-8.5 in 2090s.
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Table 1. Overview of Köppen–Geiger climate classification system.
Table 1. Overview of Köppen–Geiger climate classification system.
Letter Symbol
1st2nd3rdDescriptionCriteria a
C Temperate climateThot > 10 and 0 < Tcold < 18
D Continental climateThot > 10 and Tcold ≤ 0
w Dry winterPwdry < Pswet/10
f Without dry seasonNot (Dw)
aHot summerThot ≥ 22
bWarm summerNot (a) and Tmon10 ≥ 4
a Variable definitions: Tcold = air temperature of coldest month (°C); Thot = air temperature of warmest month (°C); Tmon10 = number of months with air temperature > 10 °C (unitless); Pwdry = precipitation in driest month in winter (mm month−1); Pswet = precipitation in wettest month in summer (mm month−1); summer and winter are defined as six-month periods April–September and October–March, respectively.
Table 2. Input variables used in ensemble model.
Table 2. Input variables used in ensemble model.
Variable TypeVariableAbbreviationSource
Climate dataAnnual mean temperatureAMTKorea Meteorological Administration
Mean diurnal rangeMDR
Minimum temperature of coldest monthMTCM
Temperature annual rangeTAR
Annual precipitationAP
Precipitation of wettest monthPWM
Precipitation of driest monthPDM
Precipitation of seasonalityPS
Environmental dataAspect DEM
Slope
Altitude
Soil texture Forest Soil Map (1:25,000)
Topography
Table 3. Mean importance of variables for L. kaempferi and C. obtusa.
Table 3. Mean importance of variables for L. kaempferi and C. obtusa.
Variable TypeVariableL. kaempferiC. obtusa
Climate dataAnnual mean temperature0.02830.1805
Mean diurnal range0.03450.0053
Minimum temperature of coldest month0.36770.0158
Temperature annual range0.07790.0060
Annual precipitation0.01050.0373
Precipitation of wettest month0.02680.1012
Precipitation of driest month0.02390.3000
Precipitation of seasonality0.23040.2525
Environmental dataAspect0.01130.0015
Slope0.00240.0009
Altitude0.06550.1700
Soil texture0.02620.0004
Topography0.01460.0019
Table 4. Parameters and statistics for L. kaempferi and C. obtusa growth models by climate zone.
Table 4. Parameters and statistics for L. kaempferi and C. obtusa growth models by climate zone.
SpeciesClimate
Zone		ParametersR2
abc
L. kaempferiCwa13.41420.18233.01370.9023
Cfa12.67070.19533.16930.9083
Dwa20.99150.05471.29920.9733
Dwb15.72260.13402.33670.9320
Dfa17.54370.11102.06530.9418
Dfb15.93470.12892.26080.9104
C. obtusaCwa16.61530.04021.08560.9601
Cfa12.39640.10331.64430.8544
Dwa18.60980.05091.36860.9568
Dfa17.32230.04941.35990.9769
Table 5. Suitable forestation area (SFA; km2) and weighted suitable forestation area (WSFA; km2) for L. kaempferi and C. obtusa.
Table 5. Suitable forestation area (SFA; km2) and weighted suitable forestation area (WSFA; km2) for L. kaempferi and C. obtusa.
Species SSP1-2.6SSP2-4.5SSP5-8.5
2020s2050s2090s2050s2090s2050s2090s
L. kaempferiSFA10,765486040892876146719511142
WSFA8210471239162815144119121109
C. obtusaSFA667716,50129,90523,10128,14934,34819,033
WSFA432411,94324,21417,41722,68125,69214,217
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Lee, D.-H.; Ahn, H.K.; Shin, H.D.; Lee, J.-G.; Lim, C.-H.; Kim, H.-J. Exploring the Climate-Suitable Forestation Area Under Species Distribution and Growth Modeling for Larix kaempferi and Chamaecyparis obtusa in the Republic of Korea. Forests 2025, 16, 530. https://doi.org/10.3390/f16030530

AMA Style

Lee D-H, Ahn HK, Shin HD, Lee J-G, Lim C-H, Kim H-J. Exploring the Climate-Suitable Forestation Area Under Species Distribution and Growth Modeling for Larix kaempferi and Chamaecyparis obtusa in the Republic of Korea. Forests. 2025; 16(3):530. https://doi.org/10.3390/f16030530

Chicago/Turabian Style

Lee, Du-Hee, Hyeon Kwon Ahn, Han Doo Shin, Jeong-Gwan Lee, Chul-Hee Lim, and Hyun-Jun Kim. 2025. "Exploring the Climate-Suitable Forestation Area Under Species Distribution and Growth Modeling for Larix kaempferi and Chamaecyparis obtusa in the Republic of Korea" Forests 16, no. 3: 530. https://doi.org/10.3390/f16030530

APA Style

Lee, D.-H., Ahn, H. K., Shin, H. D., Lee, J.-G., Lim, C.-H., & Kim, H.-J. (2025). Exploring the Climate-Suitable Forestation Area Under Species Distribution and Growth Modeling for Larix kaempferi and Chamaecyparis obtusa in the Republic of Korea. Forests, 16(3), 530. https://doi.org/10.3390/f16030530

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