Understanding the Long-Term Vegetation Dynamics of North Korea and Their Impact on the Thermal Environment

Abstract

In response to widespread deforestation, North Korea has restored forests through national policy over the past 10 years. Here, the entire process of forest degradation and restoration was evaluated through satellite-based vegetation monitoring, and its effects were also investigated. The vegetation dynamics of North Korea were characterized from 1986 to 2021 using the Landsat satellite 5–7, after which we evaluated the effect of vegetation shifts through changes in surface temperature since the 2000s. Vegetation greenness decreased significantly from the 1980s to the 2000s but increased in recent decades due to forest restoration. During the deforestation period, vegetation in all areas of North Korea tended to decrease, which was particularly noticeable in the provinces of Pyongannam-do and Hamgyongnam-do. During the forest restoration period, increases in vegetation greenness were evident in most regions except for some high-mountainous and developing regions, and the most prominent increase was seen in Pyongyang and Pyongannam-do. According to satellite-based analyses, the land surface temperature exhibited a clear upward trend (average slope = 0.13). However, large regional differences were identified when the analysis was shortened to encompass only the last 10 years. Particularly, the correlation between the area where vegetation improved and the area where the surface temperature decreased was high (−0.32). Moreover, the observed atmospheric temperature increased due to global warming, but only the surface temperature exhibited a decreasing trend, which could be understood by the effect of vegetation restoration. Our results suggest that forest restoration can affect various sectors beyond the thermal environment due to its role as an enhancer of ecosystem services.

1. Introduction

The role of forests is more crucial than ever. Now that climate crisis and carbon neutrality have become important topics, forests have garnered increasing attention due to their role as carbon sinks and a means for disaster reduction [1,2,3]. Additionally, forests are important contributors to human well-being, particularly in urban areas [4]. Considering the concept of ecosystem services and sustainable development goals (SDGs), the value of forests is expected to increase further [5,6,7].

Nevertheless, forests are still being damaged all over the world, and North Korea is a representative example of current deforestation trends [8,9,10]. Huge areas of forest are constantly being damaged at any given time, with the Amazon in South America and the tropical forests of Indonesia and Congo being prime examples. Similar deforestation trends have been recorded in China and India due to the rapid development of these nations. Large tropical forests are often damaged due to commercial logging, corporate agriculture, livestock farming, and mine development, and forests around cities are often damaged due to population growth and urban expansion [11,12]. However, North Korea is a special case. In the case of North Korea, this phenomenon appears to be more closely linked to food and energy shortages due to famine [13,14]. In other words, conversion to agricultural land may be common, but in the case of North Korea, the residents directly engage in logging and clearing activities for their survival.

After the Kim Jong-un regime took office in 2012, North Korea established an active forest restoration plan called the ‘Battle for Forest Restoration (Total Forest Construction Plan)’ [15]. Although there have been financial and technical limitations, afforestation projects have continued despite recent sanctions against North Korea and economic turmoil. Although there is no accurate afforestation performance information, it is estimated that approximately 200,000 ha were planted from 2013 to 2018 [16,17]. Compared to the originally planned 2.2 million hectares by 2042, the current afforestation progress is still small. However, this ambitious initiative by the North Korean government has continued to be implemented to address the environmental issues of this country. The restored ecosystem is expected to be much larger than current estimates, considering the amount of afforestation during the unmeasured period and the occurrence of naturally recovering areas.

Many recent studies have discussed the levels of deforestation in North Korea and their impacts. Jin et al. [18] suggested that 34.2% of forests were degraded, and Kim et al. [19] found that 2.6 million ha of mountain areas were degraded. Cui et al. [20] estimated changes in the forest carbon balance by land cover change, Lim et al. [13] investigated the impact of agriculture on deforestation, and Lim et al. [14] assessed the impact of deforestation on water resource balance. Many studies have been conducted to explore the changes caused by deforestation in North Korea. However, studies on forest restoration are quite limited. North Korea has been promoting forest restoration nationally for the past 10 years, but studies on this have not been conducted yet. Particularly, more attention must be paid to the ramifications of forest restoration in the future.

Both deforestation and restoration are commonly characterized by monitoring vegetation with long-term observation data. Satellite images are particularly suitable for monitoring vegetation in North Korea due to the low accessibility of this country [14,19]. The normalized difference vegetation index (NDVI) is the most representative index for vegetation monitoring, as it allows for the evaluation of the vitality, health, and presence of vegetation, including forests [21,22,23]. Hossain et al. [24] used the NDVI to describe the global vegetation dynamics, and Kim et al. [19] used the NDVI to identify deforestation areas. Particularly, long-term observation satellites with historical data from both deforestation and forest restoration patterns (e.g., the Landsat satellite) would enable the long-term monitoring and land-use assessment of inaccessible regions.

This study conducted satellite-based monitoring of the vegetation dynamics in North Korea, where deforestation and forest restoration have been prominent for the past 30–40 years. Particularly, our study sought to confirm the numerical changes in vegetation greenness at each stage of deforestation and forest restoration. Moreover, this study sought to explore the effects of vegetation greenness, which has recently exhibited considerable changes due to forest restoration, on the thermal environment of North Korea. Collectively, our findings provide important insights into the long-term vegetation dynamics of North Korea to identify the effects of vegetation recovery and areas in need of additional forest restoration.

2. Data and Methods

2.1. Study Area

The Korean Peninsula is located in the mid-latitude region in Eastern Asia, and North Korea occupies the northern half of the peninsula (Figure 1). North Korea is specifically located at 37.41° N–43.01° N and 128.17° E–130.41° E and covers an area of approximately 122,000 km², with mountainous areas in the northeast and plains in the western part. North Korea is a temperate monsoon, according to the Köppen climate classification, and has a cold, dry winter and hot summer. [14]. The average annual precipitation and annual mean temperature are 1000 mm and 10 °C, respectively [25]. Mountains and uplands cover approximately 80% of North Korea’s land. The Baekdudaegan mountainous region is located along the northern and eastern coast of the Korean Peninsula [26,27]. The Kaema Plateau, the highest region in the Korean Peninsula, is located in the northern part of the country, including Baekdu Mountain.

Approximately 60% of North Korea’s land is covered by forests, with croplands constituting the second-highest percentage of land use [19]. Although North Korea features many mountainous areas, forest resources have not been historically well managed. Nevertheless, prior to a great famine called the ‘Arduous March’ in the 1990s, forest resources were abundant in this country [28]. Many residents used forests as fuel and cleared mountains to create agricultural land. However, despite the extensive damage to forest ecosystems in response to this famine, food shortages have not been resolved [13]. Forest restoration in North Korea has been strongly promoted since the 2010s. Cooperative projects of various international organizations and non-governmental organizations emerged, and a new forest policy was promoted under the Kim Jong-un regime. The ‘Total Forest Construction Plan’ established by the North Korean government aims to restore 2.2 million hectares of forests by 2042 [16], which would return the landscape of North Korea to its pre-1990s state.

2.2. Data

2.2.1. Satellite-Based Vegetation Data

For the long-term and national-scale vegetation dataset, we used the Landsat satellite series database. From 1972 to the present year, Landsat, the world’s most representative land observation satellite, has been widely used among medium-resolution multi-spectral satellites. Jointly operated by NASA and USGS, the Landsat series was first launched in 1972, and Landsat 9 was most recently launched in 2021. In this study, Landsat 5 satellites were used from 1986 to 2011, Landsat 7 satellites from 2012 and 2013, and Landsat 8 satellites from 2014 to 2021.

In the Google Earth Engine environment, our study used the surface reflectance collections available for the Landsat 5, 7, and 8 satellites. We followed the data processing procedure of Fassnacht et al. [29]. Our collections of all satellite scenes were corrected for atmospheric effects via the LASRC method for Landsat 8 (USGS Landsat Surface Reflectance Tier 1) and the LEDAPS method for Landsat 5 and 7 [30,31]. For all images collected in the highest greening season for North Korea, which is between the 1st of June and the 30th of September of each year, clouds, cloud shadows, and snow were masked via the CFMASK method, and the NDVI was calculated based on near-infrared (NIR) and red (R) band data [32]. A more detailed data processing procedure was described by Fassnacht et al. [29]. The NDVI value was derived from the reflectance ratio of NIR and R bands using Equation (1):

$$\mathrm{NDVI}=\left(NIR-R\right)/\left(NIR+R\right)$$

(1)

In the case of Landsat 8 data, we applied an offset and intercept to compensate for band designations with Landsat 5/7 following a previous study [29,33]. NDVI values higher and lower than 0.9 and −0.9 were masked out to remove remaining artificial features that occurred locally such as the scanning error of Landsat 7. Afterward, we produced annual NDVI mosaics by calculating the median NDVI value using all available images for the selected period (June–September) within a year. We then used the median value instead of the mean to minimize the effect of outlier values [29].

2.2.2. Satellite-Based Land Surface Temperature Data and Meteorological Data

For land surface temperature (LST) data, LST products (MOD11A1) of the MODIS satellite were utilized. The target period was from 2000 to 2021, when MODIS satellite data were available, and a total of 22 years of data were used. Specifically, the LST data that were applied in this study were acquired from the MODIS Terra land surface temperature and emissivity 8-day global dataset in the Google Earth Engine (GEE) platform. These data contain an average of 8-day land surface temperatures at 1 km spatial resolution in a 1200 × 1200 km width [34]. The ‘LST_Day_1 km’ band with a 1 km spatial resolution was used to focus on the daytime LST. Summer LST mosaics were produced by calculating the median value of all available images for the summer period (1 July to 31 August) in North Korea. Given that the temperature for the MODIS LST data is provided in Kelvin, we applied a function using the mathematical calculation provided in GEE to retrieve LST data in degrees Celsius. Finally, we used the LST data by dividing the entire period from 2000 to 2021 and the forest restoration period from 2012 to 2021. Atmospheric temperature data were also used to compare with LST changes. Observed meteorological information for the entire region of North Korea was provided by the Korea Meteorological Administration. North Korea’s atmospheric temperature observed at a total of 27 Automated Synoptic Observing System points was averaged and applied to the analysis.

2.3. Methods

Mann–Kendall trend analyses were conducted to numerically calculate time-series changes in vegetation greenness and the land surface temperature of North Korea. This method is a widely used nonparametric approach for assessing the presence of monotonic trends in time-series data [35,36]. The approach has been continually applied for the analysis of vegetation data [37], including a number of recent studies that analyzed changes in various environmental variables [36,38]. This approach has benefits for spatiotemporal environmental data analysis because it does not assume a specific data distribution and is not affected by outliers [39]. The Theil–Sen method was applied to calculate the slope of the Mann–Kendall trend [40]. The estimator of the Theil–Sen slope is the median of the slopes calculated for the observed values at all pairwise period steps for a total of n (n − 1)/2 slopes.

The Mann–Kendall test statistic S [41,42] is computed as follows:

$$\mathrm{S}={\mathsf{\Sigma }}_{i=1}^{n-1}{\mathsf{\Sigma }}_{j=i+1}^n\mathrm{sgn}\left(x_j-x_i\right)$$

(2)

where n represents the number of data points, $x_i$ and $x_j$ are the $i$ and $j$ ($j$ > $i$) values of time-series data, respectively, and sgn($x_j$ − $x_i$) is the sign function, computed as follows:

$$\mathrm{sgn}\left(x_j-x_i\right)=\{\begin{array}{c}+1, if x_j-x_i>0\\ 0, if x_j-x_i=0\\ -1, if x_j-x_i<0\end{array}$$

(3)

The value ranges from −1 to +1. A value of +1 indicates a trend that continually increases and never decreases, whereas −1 indicates the opposite.

Gridded Pearson correlation analysis was also conducted to explore the numerical relationship between vegetation greenness and land surface temperature. Prior to analysis, vegetation data with a spatial resolution of 30 m² were converted to 1 km² using the nearest resampling method for spatial matching with LST data. The correlation coefficient r is an estimate of the strength of the linear relationship between vegetation greenness and the land surface temperature by calculating the per-pixel relationship coefficient from 2012 to 2021. The correlation coefficients were computed as follows:

$$r_{xy}=\frac{{{\displaystyle \sum }}_{i=1}^n\left(x_i-\overline{x}\right)\left(y_i-\overline{y}\right)}{\sqrt{{{\displaystyle \sum }}_{i=1}^n{\left(x_i-\overline{x}\right)}^2\sqrt{{{\displaystyle \sum }}_{i=1}^n{\left(y_i-\overline{y}\right)}^2}}}$$

(4)

where $x_i$ and $y_i$ describe vegetation greenness and variables such as land surface temperature in year $i$, respectively, and $\overline{x}$ and $\overline{y}$ describe the corresponding averages over the analyzed period [36]. The ranges of correlation coefficient also ranged from −1 to +1. The explanation of the association varies depending on the two variables, and a pixel with a strong positive or negative correlation can be interpreted as a region that is influenced by changes in land vegetation.

3. Results and Discussion

3.1. Long-Term Spatial Trend of North Korean Vegetation

Upon analyzing vegetation greenness in all regions of North Korea for 36 years, our findings demonstrate that vegetation dynamics exhibited clear temporal patterns (Figure 2 and Figure 3). Particularly, our findings confirm that social phenomena and natural changes have a complex influence on vegetation dynamics.

Interestingly, there was a low abundance of forest resources in the 1980s even prior to the forest degradation that occurred in the 1990s. Compared with a recent period, the level of vegetation greenness was not high. The average NDVI of the whole land cover of the late 1980s was 0.692 and late 2010s was 0.731. This presumably reflects the fact that national forest management or forest resource development strategies had not been implemented at the time. Additionally, considering the low food production and crop yields at the time, it is thought that the lack of vegetation health in croplands affected the vegetation greenness value [11,25]. Spatially, most of the mountainous areas exhibited high levels of vegetation greenness. However, the vegetation greenness was relatively low in high-mountainous areas such as the Gaema Plateau (Figure 2a).

In the 1990s, the decline in vegetation greenness was remarkable. Particularly, the lowest level of vegetation greenness was observed in 1992–1993 (Figure 3b). The average NDVI of the early 1990s was 0.666. Even considering the past statistics, the 1990s was considered the most serious period of the ‘Arduous March’ [28]. Particularly, food shortages were so severe that food distribution completely stopped in 1992 [43]. Spatially, vegetation greenness levels and cropland areas decreased due to climatic factors such as cold weather damage or drought, and it is thought that the nationwide conversion of forests to croplands as a result of forest degradation also affected the vegetation dynamics of the region (Figure 2b).

In the 2000s, the vegetation greenness was higher than in the 1990s (Figure 3b), the average NDVI of the early 2000s was 0.681 and the late 2000s was 0.708. This can be interpreted as an increase in the overall productivity of terrestrial ecosystems due to a lower incidence of climatic disasters such as droughts. Significant changes appeared after 2012. The Kim Jong-un regime, which promoted the forest restoration policy, started in 2012, and a remarkable change was also confirmed in the satellite-based vegetation greenness. Particularly, the highest level of vegetation greenness in the last 36 years was observed in the recent satellite-based vegetation data. Importantly, these changes are particularly appreciable in the past 10 years, which is when forest restoration began. Spatially, most of the mountainous areas showed high vegetation greenness, and it can be seen that the vegetation greenness was higher than in the 1980s even in the high mountain areas. This may indicate that global warming promoted the growth of the vegetation in the alpine area, and the greenness of other forest areas may be high due to the increase in the age of existing trees. In addition, the CO₂ fertilization effect and N deposition might have affected this greening trend [44] (Piao et al., 2016). However, near the coast or traditional croplands, the distribution of vegetation associated with agricultural land appeared wider than in the past. This may be because traditional forest areas that were cleared during the ‘Arduous March’ have not yet been restored. The spatial trend over the past 30 years confirmed that the vegetation greenness decreased due to deforestation in the North Korea–China border area and inland mountainous areas (Figure 3a). Furthermore, the vegetation greenness increased in the vicinity of Pyongyang where forest restoration was performed, as well as on the edge of the existing mountainous area.

3.2. Vegetation Dynamics on Deforestation and Afforestation Period

The most striking characteristic of the vegetation dynamics in North Korea is the decrease in the 1990s and the increase after the 2010s (Figure 3b). Spatial trends were identified by classifying 1986 to 2000 as the deforestation period and 2012 to 2021 as the afforestation period.

Similar to previously reported cases, there was a clear decrease in vegetation during the deforestation period (average slope = −0.101). Despite regional differences, most regions showed a decreasing trend (Figure 4a). Particularly, a marked decrease in vegetation was confirmed in the provinces of Pyongannam-do and Hamgyongnam-do. According to our statistical analyses, all regions except Kaesong City exhibited negative mean values (

Table 1. Statistics of Theil–Sen slope on deforestation and afforestation period in each local province.

Province	Deforestation Period		Afforestation Period
	Mean	Std	Mean	Std
Hamgyungbuk-do	−0.122	0.227	−0.051	0.287
Naseon	−0.168	0.217	−0.137	0.313
Yanggang-do	−0.057	0.219	−0.005	0.316
Jagang-do	−0.113	0.221	0.047	0.312
Hamgyungnam-do	−0.140	0.228	0.112	0.298
Pyeonganbuk-do	−0.144	0.234	0.155	0.325
Pyeongannam-do	−0.164	0.250	0.195	0.311
Kangwon-do	−0.112	0.222	0.195	0.295
Pyongyang City	−0.080	0.264	0.339	0.269
Hwanghaebuk-do	−0.112	0.245	0.175	0.297
Hwanghaenam-do	−0.002	0.244	0.159	0.293
Kaesung City	0.028	0.218	0.128	0.309
Kumgangsan	−0.127	0.187	0.256	0.285
Average	−0.101	0.229	0.121	0.301

). This may have intentionally blocked deforestation in Kaesong City, which is adjacent to the Republic of Korea and constitutes a communication route for Koreans to come and go through the Kaesong Industrial Complex. Naseon City (Najin-Seonbong) bordering Russia and Pyongannam-do exhibited the largest decrease in vegetation. However, decreases were also observed in the provinces of Hamgyongnam-do and Hamgyeongbuk-do (Table 1), which was consistent with the results of our spatial trend analyses.

Conversely, there was a marked increase in vegetation greenness during the afforestation period (average slope = 0.121). Vegetation greenness increased in most of North Korea except in the northeast and northern mountainous regions. This confirms that there was a significant improvement in vegetation throughout North Korea. Particularly, the improvement was most conspicuous in Pyongannam-do and neighboring areas where deforestation was prominent (Figure 4b). Statistically, negative numbers were found only in the provinces of Hamgyeongbuk-do, Naseon, and Yanggang-do, all of which are located in the north (Table 1). Positive numbers were found in all other regions, and significant increases in average vegetation greenness were confirmed (Table 1). Particularly, the improvement in vegetation in the central region of North Korea was remarkable including Pyongyang, the capital of North Korea, and the province of Pyongannam-do.

3.3. Relationship to Land Surface Temperature

After analyzing the LST spatial trend for 22 years from 2000 to 2021, our findings indicate that the temperature increased significantly in most regions (average slope = 0.130) (Figure 5a). The most affected areas were the alpine and inland regions, as well as the province of Hwanghae-do, where deforestation was significant. In the case of the alpine regions, the increase in atmospheric temperature was presumably caused by climate change. However, the results of the spatial trend from 2012 to 2021 after the start of the forest restoration efforts were completely different. Our results confirm that the temperature decreased in most areas except for the alpine region (Figure 5b). The LST still increased in the northern mountains including the Gaema Plateau, but this is thought to be because the effect of global warming is greater in the boreal region [45]. Upon comparing both the surface temperature and the observed atmospheric temperature, the observed atmospheric temperature exhibited an increasing trend, whereas the surface temperature tended to decrease (Figure 6b). Although our findings reflect the increase in LST due to the record heatwave in 2018, there was an overall decreasing tendency. Despite the average temperature continuing to rise due to global warming and the long-term LST also rising, it was unusual for the LST to decrease in the past 10 years. This phenomenon could be identified through the spatial correlation between vegetation change and LST change. Most of the regions that exhibited significant decreases in LST showed a high level of negative correlation (R = −0.1~−0.4) with vegetation greenness (i.e., greenness levels were significantly increased) (Figure 6a). That is, in many regions where vegetation greenness increased, LST decreased despite the increase in atmospheric temperature. This means that the improvement in the vitality of vegetation through forest restoration is contributing to the reduction in surface temperature in North Korea.

This phenomenon becomes more evident when the results of our correlation analyses were assessed as a function of altitude. A negative correlation was observed between LST and the vegetation below 1000 m, where forest restoration was performed, and the highest correlation was observed at 600–800 m (R = −0.322) (

Table 2. Correlation coefficient of vegetation greenness and land surface temperature by altitude.

Elevation(m)	Correlation Coefficient
	Mean	Std
0–200	−0.129	0.322
200–400	−0.101	0.325
400–600	−0.057	0.326
600–800	−0.322	0.331
800–1000	−0.01	0.324
1000–1200	0.184	0.318
1200–1400	0.009	0.325
>1400	0.005	0.314

). This is consistent with the fact that the mid-mountainous areas that had been devastated were recently restored through reforestation [45,46]. The significance of this relationship even below 400 m is thought to be influenced by the restoration of low mountain areas around farmlands and urban areas, including Pyongyang.

3.4. Implications and Limitations

North Korea is a representative region in which the national vegetation greenness has changed rapidly. Several studies have discussed the progression of deforestation and strategies for forest restoration. However, our study adopted a more holistic approach to characterize the spatiotemporal changes in the vegetation of North Korea in response to forest degradation and restoration.

Unlike previous studies [18,19] that characterized forest degradation in terms of land cover, our analyses were based on vegetation greenness. Degraded land accounted for a large proportion of the total study area. However, vegetation loss was smaller than expected because the majority of the lands were converted to grasslands or croplands rather than urban areas. Therefore, although the forest was degraded, there were still many places with vegetation. On the other hand, during the forest restoration period, the increase in vegetation greenness was significantly higher than that of restored forest areas investigated until recently [16]. This was likely caused by an increase in vegetation greenness due to global warming, which is prominent in the mid-latitude region, as well as an increase in the age of existing trees [21,46].

The most notable feature of this study is that numerical and spatial evaluations were performed to assess the outcomes of forest restoration policies that were actively pursued after the Kim Jong-un regime took office in North Korea. North Korea aims to restore the forest area to the level of the 1970s and 1980s by the 2040s [47]. Restoration projects have been implemented for the past 10 years through international cooperation projects or national reforestation programs, but there has been no concrete evaluation yet [48]. Although many areas have not been restored, the positive impact of forest restoration activities was confirmed through this study.

The positive effect of forest restoration was confirmed at the surface temperature. Several authors, including Kim et al. [49], have largely focused on carbon sequestration in forests, and their findings provide insights into the effects of forest restoration on carbon budgets. Our study confirmed the multifaceted effects of forest restoration by performing a numerical analysis of surface temperature relaxation among ecosystem services. Particularly, our findings demonstrate that the surface temperature of the forest restoration area decreased despite the recent increases in atmospheric temperature, which highlights the important role of reforestation efforts in counteracting the effects of global warming. Therefore, forest restoration provides several benefits in addition to conventional forest functions such as timber, natural ecosystem restoration, and carbon absorption. Future studies should thus evaluate other ecosystem services in future research on North Korean deforestation.

Nevertheless, our study has limitations. Structurally, it is difficult to show a high correlation coefficient when performing grid-level correlation analysis in terms of analytics. In addition, since one grid becomes each sample, there is a limit to presenting the p-value because the number of samples is large. It is reasonable to recognize the correlation coefficient in our study as a trend dimension. Moreover, the fact that the effect of vegetation restoration was confirmed only as a fragmentary part of the thermal environment can also be a limitation. More effects need to be confirmed in future studies.

4. Conclusions

A thorough spatiotemporal approach for analyzing national vegetation dynamics and their impact improved our understanding of North Korean deforestation and recent restoration. Vegetation greenness decreased significantly from the 1980s to the 2000s and increased in the recent decade due to forest restoration. The recent increase in vegetation greenness is expected to be due to various factors including forest restoration policies among those related to climate change. During the deforestation period, vegetation in all areas of North Korea tended to decrease, which was particularly noticeable in the provinces of Pyongannam-do and Hamgyongnam-do. During the forest restoration period, the vegetation greenness increase was evident in most regions except for some high-mountainous and developing regions, and the most prominent increase was observed in the provinces of Pyongyang and Pyongannam-do. In the past 20 years, the satellite-based land surface temperature exhibited a clear upward trend, but significant regional differences were observed when the analysis was shortened to the last 10 years. Particularly, there was a very high correlation between the area with improved vegetation and the area where the surface temperature is decreasing. At the same time, the observed atmospheric temperature increased due to global warming. However, vegetation restoration steadily decreased the surface temperature over the past decade. By adopting a holistic approach, our study characterized the spatiotemporal changes in the vegetation of North Korea in response to forest degradation and restoration. In turn, these findings provide a basis for the development of more effective forest management strategies. However, additional studies are needed to further assess the effects of forest restoration and subsequent phenomena.