Research

Open AccessArticle

Trends of Peatland Research Based on Topic Modeling: Toward Sustainable Management under Climate Change

by

,

,

,

and

Forests 2023, 14(9), 1818; https://doi.org/10.3390/f14091818 - 06 Sep 2023

Viewed by 469

Abstract

Peatlands are wetlands with an accumulation of peats, partially decomposed organisms, under waterlogged and anoxic conditions. Despite peatlands being extensively studied due to their wide distribution and various functions, the trends in peatland research have hardly been analyzed. We performed dynamic topic modeling [...] Read more.

Peatlands are wetlands with an accumulation of peats, partially decomposed organisms, under waterlogged and anoxic conditions. Despite peatlands being extensively studied due to their wide distribution and various functions, the trends in peatland research have hardly been analyzed. We performed dynamic topic modeling (DTM) and network analysis to investigate the changes in the global trends in peatland research. Among the searched studies using the keyword ‘peatland’ from ScienceDirect, titles and abstracts from 9541 studies (1995–2022) were used for the analysis. They were classified into 16 topics via DTM (geomorphology, land use and land cover, production, greenhouse gas, habitat, permafrost, management, deposit, fire, soil organic matter, peatland formation, forest, past environmental change, microbe, metal, and hydrology). Among these, the proportion of ‘management’ was the largest and increased the fastest, showing the transition of research trends toward the sustainable management of peatlands under climate change. The keywords used within topics tended to change dynamically when related to a large number of studies and increasing trends. Network analysis among topics suggested that studying peatlands as a response measure to climate change will promote overall peatland research because the greenhouse gases topic had the greatest impact on other topics. Despite increasing research on peatland management under climate change, a gap between academia and policies was found in the field of using peatlands as a response measure to climate change, indicating the necessity for effective policies, research, and technology. This study demonstrates that DTM and network analysis are useful tools for understanding the temporal shift of views on peatlands and finding a gap we need to focus on in the near future. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Deepening the Accuracy of Tree Species Classification: A Deep Learning-Based Methodology

by

,

,

,

and

Forests 2023, 14(8), 1602; https://doi.org/10.3390/f14081602 - 08 Aug 2023

Viewed by 505

Abstract

The utilization of multi-temporally integrated imageries, combined with advanced techniques such as convolutional neural networks (CNNs), has shown significant potential in enhancing the accuracy and efficiency of tree species classification models. In this study, we explore the application of CNNs for tree species [...] Read more.

The utilization of multi-temporally integrated imageries, combined with advanced techniques such as convolutional neural networks (CNNs), has shown significant potential in enhancing the accuracy and efficiency of tree species classification models. In this study, we explore the application of CNNs for tree species classification using multi-temporally integrated imageries. By leveraging the temporal variations captured in the imageries, our goal is to improve the classification models’ discriminative power and overall performance. The results of our study reveal a notable improvement in classification accuracy compared to previous approaches. Specifically, when compared to the random forest model’s classification accuracy of 84.5% in the Gwangneung region, our CNN-based model achieved a higher accuracy of 90.5%, demonstrating a 6% improvement. Furthermore, by extending the same model to the Chuncheon region, we observed a further enhancement in accuracy, reaching 92.1%. While additional validation is necessary, these findings suggest that the proposed model can be applied beyond a single region, demonstrating its potential for a broader applicability. Our experimental results confirm the effectiveness of the deep learning approach in achieving a high accuracy in tree species classification. The integration of multi-temporally integrated imageries with a deep learning algorithm presents a promising avenue for advancing tree species classification, contributing to improved forest management, conservation, and monitoring in the context of a climate change. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Altitudinal Differentiation of Forest Resilience to Drought in a Dryland Mountain

by

,

,

,

and

Forests 2023, 14(7), 1284; https://doi.org/10.3390/f14071284 - 21 Jun 2023

Viewed by 578

Abstract

Drought is one of the major climate disasters leading to forest degradation in dryland mountains. Hence, revealing the response of forest resilience to drought is crucial to predict forest succession in dryland mountains under future global warming. Here, we chose the Qilian Mountains [...] Read more.

Drought is one of the major climate disasters leading to forest degradation in dryland mountains. Hence, revealing the response of forest resilience to drought is crucial to predict forest succession in dryland mountains under future global warming. Here, we chose the Qilian Mountains as the study area and calculated the recovery time and drought intensity along elevation from 1982 to 2020 using the Leaf Area Index (LAI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the forest resilience to drought was calculated using the area of an exponentially fitted curve between drought intensity and corresponding recovery time. Finally, the dominant climate factors underlying altitude differentiation of forest resilience were analyzed using a random forest (RF) regression model, and correlations were determined based on a generalized additive model (GAM). The results indicate that forests in the elevation range of 2600–3900 m exhibited faster recovery rates and greater resilience compared to those in 1700–2600 m. The attributional analysis shows that altitudinal differentiation of forest resilience to drought was mainly constrained by precipitation with a non-monotonic correlation, and resilience was strongest when monthly precipitation reaches 30 mm. In terms of the occurrence of historical drought events, increased potential evapotranspiration improved resilience in the elevation range of 2600–3900 m and enhanced cloud cover initially enlarged the resilience and then decreased it in the elevation range of 3000–3400 m and 3400–3900 m, with resilience being strongest when cloud cover reached 24% and 33%, respectively. Under future climate change, global warming will further exacerbate the drought impact in arid regions, increasing the risk of primary forest collapse. The results of this study provide a scientific basis for predicting the potential changes in vegetation resilience and developing policies for ecological protection in dryland mountains, and we will take addressing the difficult study of the quantitative effects of tree species on resilience altitude differentiation based on ecosystem scales as our future direction. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Uncovering the Potential of Multi-Temporally Integrated Satellite Imagery for Accurate Tree Species Classification

by

,

,

,

and

Forests 2023, 14(4), 746; https://doi.org/10.3390/f14040746 - 05 Apr 2023

Cited by 1 | Viewed by 674

Abstract

In this study, prior to the launch of compact advanced satellite 500 (CAS500-4), which is an agriculture and forestry satellite, nine major tree species were classified using multi-temporally integrated imageries based on a random forest model using RapidEye and Sentinel-2. Six scenarios were [...] Read more.

In this study, prior to the launch of compact advanced satellite 500 (CAS500-4), which is an agriculture and forestry satellite, nine major tree species were classified using multi-temporally integrated imageries based on a random forest model using RapidEye and Sentinel-2. Six scenarios were devised considering the composition of the input dataset, and a random forest model was used to evaluate the accuracy of the different input datasets for each scenario. The highest accuracy, with accuracy values of 84.5% (kappa value: 0.825), was achieved by using RapidEye and Sentinel-2 spectral wavelengths along with gray-level co-occurrence matrix (GLCM) statistics (Scenario IV). In the variable importance analysis, the short-wave infrared (SWIR) band of Sentinel-2 and the GLCM statistics of RapidEye were found to be sequentially higher. This study proposes an optimal input dataset for tree species classification using the variance error range of GLCM statistics to establish an optimal range for window size calculation methodology. We also demonstrate the effectiveness of multi-temporally integrated satellite imageries in improving the accuracy of the random forest model, achieving an approximate improvement of 20.5%. The findings of this study suggest that combining the advantages of different satellite platforms and statistical methods can lead to significant improvements in tree species classification accuracy, which can contribute to better forest resource assessments and management strategies in the face of climate change. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Modeling-Based Risks Assessment and Management of Climate Change in South Korean Forests

by

,

,

,

,

,

,

,

,

,

and

Forests 2023, 14(4), 745; https://doi.org/10.3390/f14040745 - 05 Apr 2023

Viewed by 864

Abstract

The IPCC emphasizes the role of forests in the sequestration of greenhouse gases, a significant cause of climate change. Accordingly, it shows the importance of predicting changes in forests due to climate change, evaluating them to reduce vulnerability under adaptive capacity, and finding [...] Read more.

The IPCC emphasizes the role of forests in the sequestration of greenhouse gases, a significant cause of climate change. Accordingly, it shows the importance of predicting changes in forests due to climate change, evaluating them to reduce vulnerability under adaptive capacity, and finding ways to find climate resilient development pathways. In this study, the KO-G-Dynamic model, a Korean growth model, was linked with the frameworks of AR5 and 6 to assess risk dynamics in the forest growth sector. At this time, the sensitivity is a variability due to the reduction in forest growth, the exposure is the forest as an object, the hazard is climate change, the adaptive capacity is forest management, and the vulnerability is a mechanism that sensitivity could not be adjusted according to adaptive capacity. The risk was assessed by ranking overall risks derived from the process of vulnerability generated by the interaction of the above factors. As a result, the current forests in Korea are age class imbalanced, and the effects of distribution are centered on fast-growing tree species. If climate change and overprotection continue, the vulnerable area expands as sensitivity increases, since the total growth reduces due to increasing over-matured forests. From the regional-based analysis, Gangwon-do and Gyeongsangnam-do mostly consist of the higher V age class, the ratio of ‘very high’ risk grade was high and the area of ‘high’ risk grade changed rapidly. However, after applying forest management scenarios of adaptive capacity such as harvesting, reforestation, and thinning based on Republic of Korea’s forest management policy, the ratio of ‘Low’ risk grades increased according to the reduction of vulnerability areas. Therefore, forest management can act as an important factor to reduce the risk of forest growth in response to climate change. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Long-Term Effects of Altered Precipitation Patterns on Alpine Vegetation Species Composition on the Qinghai-Tibet Plateau

by

,

,

,

and

Forests 2023, 14(1), 47; https://doi.org/10.3390/f14010047 - 26 Dec 2022

Cited by 1 | Viewed by 941

Abstract

Changes in global precipitation patterns have had important impacts on terrestrial ecosystems. However, the relationship between alpine vegetation species composition and precipitation patterns remained uncertain. Based on in situ observations, long-term datasets of monthly aboveground biomass (AGB) and daily precipitation were applied in [...] Read more.

Changes in global precipitation patterns have had important impacts on terrestrial ecosystems. However, the relationship between alpine vegetation species composition and precipitation patterns remained uncertain. Based on in situ observations, long-term datasets of monthly aboveground biomass (AGB) and daily precipitation were applied in an alpine grassland on the Qinghai–Tibet Plateau (QTP), in order to characterize the responses of multi-species biomass to changing rainfall patterns. In this study, vegetation species composition exhibited obvious variations during 1997–2011 in alpine grasslands on the Qinghai–Tibet Plateau. Rapid increases in weed, Kobresia humilis, and Poa crymophila Keng squeezed the living space of the dominant species, Stipa sareptana var. krylovii. Meanwhile, effective precipitation had stronger effects on vegetation biomass, which were heterogeneous in different precipitation periods. Therefore, the crucial effective precipitation, accounting the effective precipitation in crucial periods, could better explain vegetation biomass variations, which could be a new representative climatic indicator to accurately describe vegetation change in alpine grasslands. In addition, crucial periods of effective precipitation appeared to influence heterogeneity for different vegetation species, which showed the heterogeneous adaptability of species to the changes in precipitation patterns. Precipitation patterns during 1997–2011 were more conducive to the growth of Poa crymophila Keng and Kobresia humilis, thereby changing the species composition in alpine grasslands. The coupling of biological environmental adaptability and abiotic crucial effective precipitation determined the variations of vegetation species composition. The new indicator of crucial effective precipitation could provide a new perspective for studying and predicting the species dynamics of alpine grassland. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessFeature PaperEditor’s ChoiceArticle

Contribution of Dry Forests and Forest Products to Climate Change Adaptation in Tigray Region, Ethiopia

by

,

,

,

,

Shibire Bekele Eshetu

,

Katharina Löhr

and

Stefan Sieber

Forests 2022, 13(12), 2026; https://doi.org/10.3390/f13122026 - 29 Nov 2022

Viewed by 2495

Abstract

Despite their ecological importance, dry forests’ contribution to climate change adaptation is often neglected. Hence, this study was initiated to assess the socioeconomic contribution of dry forests to climate change adaptation in Tigray Region, Ethiopia. A mixed quantitative and qualitative research design was [...] Read more.

Despite their ecological importance, dry forests’ contribution to climate change adaptation is often neglected. Hence, this study was initiated to assess the socioeconomic contribution of dry forests to climate change adaptation in Tigray Region, Ethiopia. A mixed quantitative and qualitative research design was used to examine the role of dry forests in climate change adaptation. Household questionnaire survey, key informants, and a focus group discussion were used to collect data. The results indicated that 94% of all households visited a dry forest at least once a month to access the forest and forest products. While the dry forest income level varied significantly (p < 0.05), the overall dry forest income level contributed to 16.8% of the total household income. Dry forest income enabled the reduction of the area between the line of equality and the Lorenz curve by 21% in dry evergreen Afromontane Forest users, by 3.02% in Combretum–Terminalia woodland users, and by 3% in Acacia–Commiphora woodland users. Gender, occupation, wealth status, and distance from the forest to their homes are all factors that significantly affected Combretum–Terminalia woodland users’ income level. Among Acacia–Commiphora woodland users, the respondents’ age influenced the dry forest income level, whereas, among dry evergreen Afromontane Forest users, the family size of the household influenced the dry forest income level. The findings of this study could help policy makers understand the crucial role of dry forest income in the livelihood of the community and in climate change adaptation. Policymakers could reduce the pressure on dry forests by introducing policies that recognize the role of dry forest income in reducing poverty and income inequality and by establishing farmer cooperation in commercializing the non-timber forest products which support the long-term coping and adaptation strategy. Further research is needed to understand the increasing role of dry forest products in climate change adaptation over time and its contribution to the national economy at large. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

CO₂ Emissions Accounting and Carbon Peak Prediction of China’s Papermaking Industry

by

,

,

,

and

Forests 2022, 13(11), 1856; https://doi.org/10.3390/f13111856 - 07 Nov 2022

Cited by 2 | Viewed by 1217

Abstract

China has been the world’s largest producer and consumer of paper products. In the context of the “carbon peaking and carbon neutrality goals”, China’s papermaking industry which is traditionally a high energy-consuming and high-emissions industry, desperately needs a nationally appropriate low-carbon development path. [...] Read more.

China has been the world’s largest producer and consumer of paper products. In the context of the “carbon peaking and carbon neutrality goals”, China’s papermaking industry which is traditionally a high energy-consuming and high-emissions industry, desperately needs a nationally appropriate low-carbon development path. From the consumption-side perspective, this paper calculates the CO₂ emissions of China’s papermaking industry from 2000 to 2019 by using carbon emission nuclear algorithm, grain-straw ratio, first-order attenuation method, and STIRFDT decomposition model, etc., to further explore the core stages and basic patterns affecting the industry’s carbon peaking. The results show that the total CO₂ emissions of China’s papermaking industry showed an upward trend from 2000–2013, stable from 2013–2017, and a steady but slight decline from 2017–2019. Meanwhile, the total CO₂ emissions of the full life cycle of paper products in China have decreased to a certain extent in the raw material acquisition, pulp, and paper making and shipping stages, with only the waste paper disposal stage showing a particular upward trend. We find that from 2000 to 2019, China’s CO₂ emissions in the pulping and papermaking stage of paper products accounted for 68% of the total emissions in the whole life cycle, of which 59% was caused by coal consumption. Moreover, the scenario prediction shows that improving the energy structure and increasing the waste paper recovery rate can reduce the CO₂ emissions of the industry, and it is more significant when both work. Based on this and the four core stages of CO₂ emissions of the papermaking industry we proposed ways to promote CO₂ emissions peaking of China’s paper products. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Open AccessArticle

Development of a Methodology for the Conservation of Northern-Region Plant Resources under Climate Change

by

,

,

,

,

,

,

,

,

and

Forests 2022, 13(10), 1559; https://doi.org/10.3390/f13101559 - 23 Sep 2022

Cited by 2 | Viewed by 908

Abstract

According to the guidelines of the Nagoya Protocol, species are now recognized as ‘resources’ and owned by each country, thereby emphasizing the significance of biological resources and the importance of the continuous efforts made to systematically manage them. Despite these efforts, climate change, [...] Read more.

According to the guidelines of the Nagoya Protocol, species are now recognized as ‘resources’ and owned by each country, thereby emphasizing the significance of biological resources and the importance of the continuous efforts made to systematically manage them. Despite these efforts, climate change, which influences climatic factors such as temperature and precipitation, is expected to negatively impact the struggle for conservation of biological resources by affecting species’ habitats. We aimed to devise methodologies that could be utilized for the management of biological resources, especially valuable tree species, that are experiencing difficulties due to climate change. First, changes in habitat of the northern-region plant Needle fir (Abies holophylla) due to of climate change were estimated using the BIOMOD2 package in R under the RCP8.5 scenario. Second, the time period of management was estimated based on the change in habitat area over time. It is expected that 30% of the current habitat of A. holophylla will be lost by 2030 and 50% will be lost by 2042. Third, four management zones (maintenance, reduction, dispersal, and non-habitat areas) were derived by comparing habitats according to the period of management required. In this case, we compared the present and the time point at which 30% habitat loss (2030) is expected to occur. After that, the management steps that can be taken for each management zone were suggested. Our results show the impact of climate change, especially change in Bio1 (annual mean temperature) and Bio13 (precipitation of wettest month), on species distribution patterns and have potential applicability in biological resource management. We have specified the suitable point of time, area, and direction of management in this study, which will contribute to climate change management planning and policy-making. By doing so, we hope that when a management policy on biological resources is applied, by dividing the four management zones, policymakers will be able to apply a cost-efficient policy. Full article

(This article belongs to the Special Issue Advances in Resilient Adaptation to Climate Change in the Forest Sector)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Advances in Resilient Adaptation to Climate Change in the Forest Sector"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI