Forest Change Research Articles

Potential reduction in carbon fixation capacity under climate change in a Pinus koraiensis forest

There has been an increasing recognition of the crucial role of forests, responsible for sequestering atmospheric CO2, as a moral imperative for mitigating the pace of climate change. The complexity of evaluating climate change impacts on forest carbon and water dynamics lies in the diverse acclimations of forests to changing environments. In this study, we assessed two of the most common acclimation traits, namely leaf area index and the maximum rate of carboxylation (Vcmax), to explore the potential acclimation pathways of Pinus koraiensis under climate change. We used a mechanistic and process-based ecohydrological model applied to a P. koraiensis forest in Mt. Taehwa, South Korea. We conducted numerical investigations into the impacts of (i) Shared Socioeconomic Pathways 2–4.5 (SSP2-4.5) and 5–8.5 (SSP5-8.5), (ii) elevated atmospheric CO2 and temperature, and (iii) acclimations of leaf area index and Vcmax on the carbon and water dynamics of P. koraiensis. We found that there was a reduction in net primary productivity (NPP) under the SSP2-4.5 scenario, but not under SSP5-8.5, compared to the baseline, due to an imbalance between increases in atmospheric CO2 and temperature. A decrease in leaf area index and an increase in Vcmax of P. koraiensis were expected if acclimations were made to reduce its leaf temperature. Under such acclimation pathways, it would be expected that the well-known CO2 fertilizer effects on NPP would be attenuated.

The predictability of near‐term forest biomass change in boreal North America

AbstractClimate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

Deep Learning based Time Series Classification Model for Monitoring Land Cover Changes in Tropical High Forests

Deep Learning based Time Series Classification Model for Monitoring Land Cover Changes in Tropical High Forests

Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat

Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat

Quantifying the relative impacts of climate and human activities on vegetation change in Hunan Province between 2000 and 2015

Since1999, Hunan Province has embarked on a series of ecological projects to return farmland to forests across the province to explore the impacts of anthropogenic policies and actions on changes in vegetation cover between 2000 and 2015.It is important to understand the impacts of anthropogenic and climatic changes on regional vegetation cover change is an important guide for formulating reasonable environmental protection and restoration strategies in the region .this paper analysed the influence of climate and anthropogenic activities on the change of vegetation cover of Hunan Province during 2000-2015, which combined with an improved vegetation-climate residual regression model at the meta-scale. During 2000-2015, the contribution of anthropogenic activities to the increase of vegetation cover was 49%, 53%, 62% and 66% for unconverted forest land, grassland, unused land and water area respectively, while the contribution of climatic factors was 51%, 47%, 38% and 44% respectively. At the same time, all the land use types of contributions to vegetation cover change in Hunan Province is positive, and the land type without forest land change has the largest contribution to vegetation cover change in Hunan Province at 66.28%, while the land type where other land is converted to unused land has a lower contribution to vegetation cover change.

Drivers of spatial structure in thinned forests

BackgroundAs is widely known, an increasing number of forest areas were managed to preserve and enhance the health of forest ecosystems. However, previous research on forest management has often overlooked the importance of structure-based. AimsOur objectives were to define the direction of structure-based forest management. Subsequently, we investigated the relationships between forest structure and the regeneration, growth, and mortality of trees under different thinning treatments. Ultimately, the drivers of forest structural change were explored. MethodsOn the basis of 92 sites selected from northeastern China, with different recovery time (from 1 to 15 years) and different thinning intensities (0–59.9%) since the last thinning. Principal component analysis (PCA) identified relationships among factors determining forest spatial structure. The structural equation model (SEM) was used to analyze the driving factors behind the changes in forest spatial structure after thinning. ResultsLight thinning (0–20% trees removed) promoted forest regeneration, and heavy thinning (over 35% of trees removed) facilitated forest growth. However, only moderate thinning (20%–35% trees removed) created a reasonable spatial structure. While dead trees were clustered, and they were hardly affected by thinning intensity. Additionally, thinning intensity, recovery time, and altitude indirectly improve the spatial structure of the forest by influencing diameter at breast height (DBH) and canopy area. ConclusionCreating larger DBH and canopy area through thinning will promote the formation of complex forest structures, which cultivates healthy and stable forests.

Divergent climatic effects on forest greenness modulated by temperature and precipitation gradients in the Yellow River Basin, China

How climatic effects on forest greenness vary between different temperature and precipitation gradients in climate–sensitive and ecologically fragile areas, such as the Yellow River Basin (YRB) in China, is not well understood. Here we used meteorological data and remotely sensed leaf area index (LAI) data to investigate climatic effects on forest LAI changes along temperature–precipitation gradients in the YRB from 2001 to 2020. We found that changing precipitation thresholds divided forests into higher and lower LAI areas. The precipitation thresholds increased from 400 mm mean annual precipitation (MAP) in areas with a mean annual temperature (MAT) below 4 °C to 500 mm MAP in areas with a MAT above 4 °C. Forest LAI increased significantly from 2001 to 2020. The MAT and MAP thresholds changed when dividing forests into different areas with different LAI values and climatic effects. Forests greening was mainly driven by temperature in areas with LAI values between 3 and 6 m2 m−2 but was mainly driven by precipitation in areas with LAI values less than 3 m2 m−2. The threshold of MAP isopleth increased from 500 mm to 700 mm and then decreased to 500 mm, while the MAT increased from less than 0 °C to 5 °C and then increased to 12 °C. These divergent climatic effects were caused by the combined effects of water resource limitations and increasing evapotranspiration. Identifying MAT and MAP thresholds is helpful for decision–maker and managers to optimize forest restoration more accurately in future ecological protection projects facing the challenge of limited water resources in the YRB.

The effects of long-term drainage and wildfires on water table level and water chemistry of the Great Vasyugan Mire

In this paper, we assessed long-term changes in water table level of the Great Vasyugan Mire, water chemistry and the removal of mineral and organic substances in the mire-river system in the taiga zone of Western Siberia under the conditions of forest reclamation, pyrogenic factor and climate change. In the drained area of the Great Vasyugan Mire, a significant decrease in water table levels and an increase in the amplitude of their fluctuations by 1.6 times were noted. The effect of the pyrogenic factor is expressed, on the contrary, in an increase in water table levels due to surface burnout. In the water of the drained part of the Great Vasyugan Mire, an increase in the concentrations of Са2+, Mg2+, Fetotal, Cl-, К+, Na+, Cl-, SО42–, NO3-, DOC was noted. An increase in the content of NH+4, Feобщ, Cl-, SO42-, NO-3, and DOC is observed in river waters. An indicator of the effect of the pyrogenic factor on the chemical composition is an increase in the pH value and the content of K+, Na+, SО42–, NО–3, HCО3– ions, as well as Са2+, Mg2+, Feобщ. In connection with the overgrowth of the ditches, the process of self-healing of the mire is observed, and backwater zones are formed near the ditches and the amplitude of level fluctuations increases, as a result, the chemical composition of the waters of the drained and natural areas of the Great Vasyugan Mire in certain periods has minimal differences. Studies have shown that the removal of mineral and organic substances from the drained area of the great Vasyugan Mire prevailed in the high-water year 2018 and amounted to 2054 tons, of which the removal of DOC was equal to 47% or 969 tons. In the future, climate change, an increase in air temperature and precipitation in the region are likely, will contribute to the removal of biogenic elements and organic substances from mires and increase their concentration in river waters.

Patterns and impacts of an unprecedented outbreak of bark beetles in Central Europe: A glimpse into the future?

Natural disturbances have significantly intensified across European forests, with bark beetle outbreaks being the most rapidly escalating disturbance type. Since 2018, the Czech Republic (Central Europe) has become a Europe's disturbance epicentre due to the unprecedented outbreak of spruce bark beetle Ips typographus in the forests dominated by Norway spruce Picea abies. Here we provide novel insights into the impacts and dynamics of this disturbance from 2016 to 2022. The investigation is based on annual forest change maps developed by the classification of optical and Synthetic Aperture Radar satellite imagery. We identified seven major outbreak foci across the country, where the outbreaks culminated between 2018 and 2021. Most of the outbreak waves exhibited a symmetric shape, characterized by a three-year build-up phase, a single culmination year, and the subsequent decline. The substantial proportion of spruce remaining in the outbreak areas after the culmination point implies that resource depletion is an improbable cause for the outbreak's retreat. In the year of retreat, the proportion of spruce in the forest ranged between 26% and 36% in most of the outbreak areas. The disturbance dynamics manifested a transition from the emergence of new tree mortality spots in the early outbreak phase to their short-range expansion, suggesting density-dependent changes in bark beetle dispersal during the studied period. The core disturbance zone, pinpointed in 2022, covered an area of 9,000 ​km2 and experienced a 38% loss in forest cover. Within this area, forest fragmentation increased significantly, leading to a greater forest patch complexity and reduced connectivity among the patches. The presented findings can serve as a glimpse into the future for other European regions, revealing the potential impacts of natural disturbances amplified by climate change.

Effects of Payment for Ecosystem Services and Tourism on Conservation and Development: Trade-Off or Synergistic?

Abstract The effects of various strategies aimed at simultaneously promoting environmental conservation and human development are closely related to sustainable development regionally and globally. However, although the effects of many such strategies have been evaluated by ecologists and sociologists separately, their ability to simultaneously meet these two anticipated goals (i.e., environmental conservation and human development) at the fine spatial scale remains unclear. To answer this fundamental but crucial question, incorporating household and forest change data, we concurrently estimated the ecological and socioeconomic effects of two world-renowned Payment for Ecosystem Services (PES) programs (i.e., the Nature Forest Conservation Program, the Grain to Green Program) and nature-based tourism in 30 protected areas across 8 provinces in China. Here we showed a trade-off between the ecological and economic effects of two PES programs, while synergistic effects exist in the ecological and economic benefits of tourism. Attributes of household and protected areas significantly influenced economic and environmental benefits as well. Our research provides new insights into the complex effects of PES programs and tourism, and crucial information to support their adequate and sustainable implementation in China and the rest of the world. Significance Statement This work answers a fundamental but crucial question, that is, whether the policies commonly advocated to incorporate environmental conservation and human development can yield positive effects both for conservation and economic development. Our evaluation is also timely to inform some shortness (i.e., negligible economic effects, or the lack of expected positive economic benefits) and provides new insights (e.g., the implication of households and protected-areas attributes in conservation and economic outcomes) of Payment for Ecosystem Services (PES) programs and the complex effects of instruments in the context of multiple policies, particularly given the upcoming 2030 deadline for achieving the Sustainable Development goals (SDGs). We expected that implications in this study can provide important lessons for these two instruments, other PES programs, and other conservation and development instruments to support their adequate and sustainable implementation in China and beyond and to contribute to the achievement of relevant SDGs in the remaining years.

Forest Change Detection and Prediction Using the Crow Chicken Swarm Optimization-Based Deep LSTM Classifier

Forest Change Detection and Prediction Using the Crow Chicken Swarm Optimization-Based Deep LSTM Classifier

Functional responses in American marten habitat selection indicate cumulative effects of progressive habitat change

AbstractShifts in habitat selection may serve as behavioral indicators of changing habitat quality and can provide an insight into the effects of chronic disturbance on wildlife populations before rates of occurrence and other metrics related to population viability are affected. Long‐term studies can provide unique opportunities to understand animals' behavioral responses to the cumulative effects of habitat disturbance that may be missed by short‐term studies or space‐for‐time substitution. Using the American marten (Martes americana) as a focal species, we evaluated the effects of progressive habitat disturbance on an animal population by identifying changes in habitat availability and selection over time. Marten are strongly associated with mature forest conditions and are thus sensitive to forest harvesting, which is a chronic and prevalent form of disturbance across the species' range. We developed resource selection functions that characterized both temporally consistent patterns and functional responses in the relative selection of habitats by marten at the patch scale. We used a combination of marten location data collected during three periods over 30 years of extensive forest change in northern Maine and a habitat classification scheme informed by species' behavior and designed to capture habitat changes over time. Marten increased their selection of tall (>12 m mean tree height) mature forest, including uncut forest and regenerated clearcuts, in response to decreased availability. Marten also increased avoidance of early‐successional forest (<9 m mean tree height) in response to increased availability. These functional responses in habitat selection appear to enable marten to maintain access to required resources in mature forest and limit exposure to risky or resource‐poor habitat within home ranges. However, a declining trend in the occurrence of resident adult females may indicate a limit to the functional response strategies for individuals unable to incur the energetic or prey‐related costs that may be associated with strong functional responses. Our results suggest that, despite adaptive habitat selection strategies, there are minimum thresholds of mature forest availability below which resident adults, particularly females, will struggle to persist. We recommend forest management strategies targeted at minimizing the strength of functional responses displayed by marten within occupied landscapes.

Time-Series Assessment of Vegetation Structure and Compositional Changes of Wawa-Zange Forest, Gombe, Nigeria

Time-Series Assessment of Vegetation Structure and Compositional Changes of Wawa-Zange Forest, Gombe, Nigeria

Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

This study presents an innovative approach for monitoring forest canopy cover changes in Antalya, Turkey, a region characterized by rich biodiversity, recurrent forest fires, and rapid rehabilitation efforts. Employing advanced remote sensing techniques, the research integrates ICESat-2 ATL08 segment data, Landsat satellite imagery, and Esri Sentinel-2 Land Cover datasets to generate forest canopy cover (FCC) maps for the years 2019 and 2022. The study area was chosen due to its high population, significant tourism industry, and frequent forest fires, making it a critical region for biodiversity and forest resources in Turkey. The Canopy Cover Estimation Method (CCEM) was employed, achieving accuracy percentages ranging from 77% to 86%, demonstrating its efficacy in estimating FCC without the need for fieldwork. The integration of annual updates from the Esri Sentinel-2 Land Cover dataset as auxiliary data greatly enhanced the reliability and accuracy of the analyses. Despite challenges posed by the study area and differences in spatial resolution among the datasets, the study successfully demonstrated the utility of integrating ICESat-2 segment data and Landsat imagery for understanding the dynamics of FCC in a complex environment. The results contribute to more informed decision-making in forest management and conservation efforts. Future research should focus on refining the methodologies to minimize spectral misclassification, improving the spatial resolution of the FCC maps, and leveraging advancements in remote sensing technology to obtain more accurate and reliable results.

Automatic Separation of Photosynthetic Components in a LiDAR Point Cloud Data Collected from a Canadian Boreal Forest

Terrestrial LiDAR has emerged as a promising technology for accurate forest assessment. LiDAR can provide a 3D image composed of a cloud of points using a rotary laser scanner. The point cloud data (PCD) contain information on the (x, y, z) coordinates of every single scanned point and a raw intensity parameter. This study introduces an algorithm for the automatic and accurate separation of the photosynthetic features of a PCD. It is shown that the recorded raw intensity is not a suitable parameter for the separation of photosynthetic features. Instead, for the first time, the absorption intensity is developed for every point based on its raw intensity and distance from the scanner, using proper scaling functions. Then, the absorption intensity is utilized as the only criterion for the classification of the points between photosynthetic and non-photosynthetic features. The proposed method is applied to the scans from a Canadian Boreal Forest and successfully extracted the photosynthetic features with minimal average type I and type II error rates of 5.7% and 4.8%. The extracted photosynthetic PCD can be readily used for calculating important forest parameters such as the leaf area index (LAI) and the green biomass. In addition, it can be used for estimating forest carbon storage and monitoring temporal changes in vegetation structure and ecosystem health.

Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests

AbstractAimDespite mounting empirical evidence regarding the positive effects of forest structural diversity (STRDIV) on forest functioning, the underlying biotic mechanisms and controlling abiotic factors remain poorly understood. This study provides the first assessment of the interactive effects of STRDIV and diversity in species and functional traits on aboveground biomass (AGB) in natural forests in West and East Africa.LocationWest and East Africa.Time period2014–2020.Major taxa studiedWoody plants.MethodsUsing data from 276 plots and 7993 trees of 207 species distributed across various types of natural forests and major climatic zones of Africa, linear mixed‐effects and structural equation models, we have evaluated how alternative causal relationships between STRDIV and taxonomic and functional diversity attributes influence AGB, while accounting for the effects of environmental covariates. We also assessed the consistency of these relationships across floristically and environmentally homogenous forest types.ResultsWe found that the positive effects of STRDIV on AGB were underpinned by both the community‐weighted mean (CWM) of trait values (selection effects) and species richness (niche complementarity), but the relative importance of these effects varied depending on forest types. Across the forest types, STRDIV primarily mediated the effects of CWM of traits and species richness on AGB. We also found that STRDIV–AGB relationships were constrained by resource (water and nutrient) availability.Main conclusionsOur findings provide novel insights into the role of functional traits as key determinants of the effects of STRDIV on AGB in tropical forests. We suggest that forest management and climate change mitigation strategies aimed at conserving biodiversity, and fostering biomass storage through increased STRDIV should focus on maintaining high levels of functionally dominant species while also increasing tree species diversity.

Streamflow responses to forest and climate change in the boreal Da Hinggan Mountains, Northeastern China

AbstractBoreal forests cover vast stretches of land across all continents and represent a principal source area of clean water in the northern hemisphere. Increasingly, studies are conducted on the impact of changes in boreal forest cover on water yield; however, much remains unknown concerning the effects of forest structure changes on stream discharge over the course of multi‐decadal forest harvest cycles. In this study, we analysed long‐term hydrometeorological and forest dynamics data spanning from 1990 to 2016 from a typical boreal forest watershed in the Da Hinggan Mountains in northern China. Our objective was to quantify how changes in forest age and tree species composition affect mean annual streamflow and flow regimes in the context of a changing climate. To distinguish the effects of forest and climate changes on annual streamflow from one another, we employed a combination of a sensitivity‐based method and a temporal trend analysis. Further, we evaluated the impact of forest changes on flow regimes using four indicators: magnitude, duration, frequency, and variability. The results indicated that mean annual streamflow increased by 55.8 mm, with forest changes contributing +61.4 mm compared to −5.6 mm due to climate change (negative effect). This increase occurred when approximately 20% of mature coniferous forests transitioned to mid‐age broad‐leaved forests, accompanied by a 10% increase in total stock volume during the later period. Finally, the effect of changes in forest structure on flow regime were not significant. Our results underscore that variations in forest structure affect streamflow differently depending on stand age and species proportions. Therefore, dynamic forest structure management can benefit not only carbon sequestration but also water supply capacity in boreal forested watersheds.

Measuring the Canopy Architecture of Young Vegetation Using the Fastrak Polhemus 3D Digitizer.

In the context of climate change conditions, addressing the shifting composition of forest stands and changes in traditional forest management practices are necessary. For this purpose, understanding the biomass allocation directly influenced by crown architecture is crucial. In this paper, we want to demonstrate the possibility of 3D mensuration of canopy architecture with the digitizer sensor Fastrak Polhemus and demonstrate its capability for assessing important structural information for forest purposes. Scots pine trees were chosen for this purpose, as it is the most widespread tree species in Europe, which, paradoxically, is very negatively affected by climate change. In our study, we examined young trees since the architecture of young trees influences their growth potential. In order to get the most accurate measurement of tree architecture, we evaluated the use of the Fastrak Polhemus magnetic digitizer to create a 3D model of individual trees and perform a subsequent statistical analysis of the data obtained. It was found that the stand density affects the number of branches in different orders and the heights of the trees in the process of natural regeneration. Regarding the branches, in our case, the highest number of branch orders was found in the clear-cut areas (density = 0.0), whereas the lowest branching was on-site with mature stands (density = 0.8). The results showed that the intensity of branching (assessed as the number of third-order branches) depends on the total number of branches of the tree of different branch orders but also on stand density where the tree is growing. An important finding in this study was the negative correlation between the tree branching and the tree height. The growth in height is lower when the branching expansion is higher. Similar data could be obtained with Lidar sensors. However, the occlusion due to the complexity of the tree crown would impede the information from being complete when using the magnetic digitizer. These results provide vital information for the creation of structural-functional models, which can be used to predict and estimate future tree growth and carbon fixation.

Modelling diameter at breast height distribution of jack pine and black spruce natural stands in eastern Canada

Individual tree diameter at breast height (DBH) distribution is an important information for forest management planning. Forest managers obtain the DBH data either by field measurements or estimations using predictive models. However, probability distribution models are still lacking or need improvement. Therefore, we aimed to construct and fit diameter distribution models that reflect forest structure and composition change. We evaluated gamma, log-normal, and Weibull probability distribution functions (PDFs) for two commercially important tree species, black spruce ( Picea mariana (Mill) B.S.P.) and jack pine ( Pinus banksiana Lamb), grown in natural stands across Ontario, Canada. We modelled the parameters of the distributions as a function of stand-level variables for these species. We used DBH data from 735 permanent sample plots. Our results showed that all three evaluated PDFs reflected observed DBH distribution. We demonstrated that the moment-based recovered parameters could represent the maximum likelihood-estimated parameters precisely, and parameters of the PDFs can be modelled as a function of stand-level dynamic covariates. The models unbiasedly predicted the PDF parameters DBH means and DBH classes. The R2 of the model fit ranged between 0.35 and 0.98 for the predicted parameters and 0.90 and 0.97 for the predicted DBH.

An assessment of seasonal changes in forests using the segmentation and cluster analysis of Landsat space images

For the purposes of monitoring the state of forest ecosystems, it is most effective to use the capabilities of remote methods. On multispectral Landsat satellite images (a time series of summer and autumn images for 2000, 2001, 2013, 2014) of the territory of the Olekminsky State Nature Reserve, fragments of forests dominated by Gmelin larch (Larix gmelinii Rupr.) with an area of 250 km2 (scale 1:5000). Then the polygons were saved at three levels of segmentation (scaling) – 4, 16, 64 with scales of 1:2500, 1:1250, 1:625. During decryption, uncontrolled classification of polygons using the ISODATA (Iterative Self-Organizing Data Analysis Technigue) method into 2,4,10 classes was carried out. Classification into two classes was used to calculate the forest cover index. Seasonal changes were determined by the difference in the forest cover index values of the polygons in summer and autumn. It has been shown that the greater the difference, the greater the proportion of larch in mixed forest stands. Distribution curves of forest cover index values were constructed for polygons of the 3rd scaling level. Based on the results of classification into 4, 10 classes, statistical processing was carried out with the calculation of indicators of difference and similarity of polygons - dispersion of the general population and Fisher's test (F-test). The results of changes in population dispersion and F-test at different levels of segmentation and in different years are considered.