Aboveground Forest Research Articles

Soil Microbial Resource Limitations and Community Assembly Along a Camellia oleifera Plantation Chronosequence.

Understanding soil microbial element limitation and its relation with the microbial community can help in elucidating the soil fertility status and improving nutrient management of planted forest ecosystems. The stand age of a planted forest determines the aboveground forest biomass and structure and underground microbial function and diversity. In this study, we investigated 30 plantations of Camellia oleifera distributed across the subtropical region of China that we classified into four stand ages (planted <9 years, 9–20 years, 21–60 years, and >60 years age). Enzymatic stoichiometry analysis showed that microbial metabolism in the forests was mainly limited by C and P. P limitation significantly decreased and C limitation slightly increased along the stand age gradient. The alpha diversity of the soil microbiota remained steady along stand age, while microbial communities gradually converged from scattered to clustered, which was accompanied by a decrease in network complexity. The soil bacterial community assembly shifted from stochastic to deterministic processes, which probably contributed to a decrease in soil pH along stand age. Our findings emphasize that the stand age regulated the soil microbial metabolism limitation and community assembly, which provides new insight into the improvement of C and P management in subtropical planted forest.

Annual Tropical‐Rainforest Productivity Through Two Decades: Complex Responses to Climatic Factors, [CO2] and Storm Damage

AbstractYearly changes in tropical carbon‐cycling have been a major biotic determinant of the interannual variation in the rise of atmospheric carbon dioxide ([CO2]). The environmental responses underlying these changes remain poorly understood. A 21‐year field study (1997–2018) in a Costa Rican rainforest has produced the first multi‐decade record of landscape‐scale annual aboveground productivity for a tropical ecosystem. While none of the four production components (wood production; leaf‐, twig‐, and reproductive litterfall) increased over two decades, all significantly varied among years. Multi‐factor environmental models explained a large fraction of the inter‐year changes in two production components. Annual changes in three climatic factors and [CO2] jointly accounted for 84% of the variation in annual wood production. While the strongest response of wood production was to decline in years of slightly warmer nights, it also significantly declined in years with more dry‐season hours hotter than 28°C. The yearly changes in reproductive litterfall were largely (52%) determined by the combined effects of increasing [CO2] and declining annual radiation. Twig litterfall declined over the 21 years. Annual leaf litterfall, the largest component of field‐assessed aboveground production, showed no significant environmental responses. An extreme storm in the study's final year disrupted the environmental controls on production that prevailed through the prior 20 years. While important uncertainties are introduced by some of the field methods for assessing aboveground forest production, the findings of this first long‐term field study of annual landscape‐scale productivity raise deep concerns for the future of tropical forests as global warming proceeds.

Time matters: Resilience of a post-disturbance forest landscape

Present-day disturbances are transforming European forest landscapes, and their legacies determine the vulnerability and resilience of the emergent forest generation. To understand these legacy effects, we investigated the resilience of the aboveground forest biomass (Babg) to a sequence of disturbances affecting the forest in different recovery phases from the initial large-scale impact. We used the model iLand to simulate windthrows that affected 13–24% of the Babg in a Central European forest landscape. An additional wind event was simulated 20, 40, 60, or 80 years after the initial impact (i.e., sequences of two windthrows were defined). Each windthrow triggered an outbreak of bark beetles that interacted with the recovery processes. We evaluated the resistance of the Babg to and recovery after the impact. Random Forest models were used to identify factors influencing resilience. We found that Babg resistance was the lowest 20 years after the initial impact when the increased proportion of emergent wind-exposed forest edges prevailed the disturbance-dampening effect of reduced biomass levels and increased landscape heterogeneity. This forest had a remarkably high recovery rate and reached the pre-disturbance Babg within 28 years. The forest exhibited a higher resistance and a slower recovery rate in the more advanced recovery phases, reaching the pre-disturbance Babg within 60–80 years. The recovery was enhanced by higher levels of alpha and beta diversity. Under elevated air temperature, the bark beetle outbreak triggered by windthrow delayed the recovery. However, the positive effect of increased temperature on forest productivity caused the recovery rate to be higher under the warming scenario than under the reference climate.We conclude that resilience is not a static property, but its magnitude and drivers vary in time, depending on vegetation feedbacks, interactions between disturbances, and climate. Understanding these mechanisms is an essential step towards the operationalization of resilience-oriented stewardship.

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km2 area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region.

Past and Future of Temperate Forests State under Climate Change Effects in the Romanian Southern Carpathians

Research Highlights: Carpathian forests hold high ecological and economic value while generating conservation concerns, with some of these forests being among the few remaining temperate virgin forests in Europe. Carpathian forests partially lost their original integrity due to their management. Climate change has also gradually contributed to forest changes due to its modification of the environmental conditions. Background and Objectives: Understanding trees’ responses to past climates and forms of management is critical in foreseeing the responses of forests to future conditions. This study aims (1) to determine the sensitivity of Carpathian forests to past climates using dendrochronological records and (2) to describe the effects that climate change and management will have on the attributes of Carpathian forests, with a particular focus on the different response of pure and mixed forests. Materials and Methods: To this end, we first analysed the past climate-induced growth change in a dendrochronological reference series generated for virgin forests in the Romanian Curvature Carpathians and then used the obtained information to calibrate spatially explicit forest Landis-II models for the same region. The model was used to project forest change under four climate change scenarios, from mild to extreme. Results: The dendrochronological analysis revealed a climate-driven increase in forest growth over time. Landis-II model simulations also indicate that the amount of aboveground forest biomass will tend to increase with climate change. Conclusions: There are differences in the response of pure and mixed forests. Therefore, suitable forest management is required when forests change with the climate.

A 17 year successional enrichment plantation of tree recruitment and restoration in a tropical rainforest forest

The Enrichment Plantation of Akure Forest Reserve is one of the forests currently experiencing a17-year-long post-disturbance following deforestation and fragmentation in the country. To better understand the contribution of enrichment planting on forest regeneration and restoration, the Enrichment Plantation after 17 years of post-disturbance was examined. We studied the recruitment drive of aboveground and underbrush stands of an Enrichment plantation in a tropical forest. We assess the trees diversity, species compositions, species richness, and growth forms of the vegetations. A total of 3 (50 m × 50 m) plots were sampled with a total of 47 aboveground tree species and 45 underbrush stands from Enrichment plantation were identified. The result shows an increase in the diversity and an even distribution of the species of the aboveground forest trees, compared to the underbrush stands. Conversely, the aboveground forest trees have lower species richness as compared to the level of underbrush stands. The sapling density was significantly higher than the aboveground tree of the forest (one-way ANOVA: P < 0.05 and P = 0.000). The mean ± standard deviation of 2.41 ± 1.55 aboveground tree species was extremely low to what was obtained of tree sapling (17.55 ± 8.06) in the forest structure. It was also observed that the aboveground forest trees and underbrush stands are somewhat similar in species compositions, which implies that sapling recruitment is a key determinant of the tree species composition of the forest. It is then concluded that the method adopted for restoration encouraged species diversity in this successional forest among the aboveground trees species and underbrush.

Forest carbon trajectories: Consequences of alternative land-use scenarios in New England

The extent to which forest carbon sequestration can help mitigate climate change will be determined in large part by future land use. Here we quantify the impacts of five divergent future land-use scenarios on aboveground forest carbon stocks and fluxes throughout New England. These scenarios, four co-designed with stakeholders from throughout the region and the fifth a continuation of recent trends in land use, were simulated by coupling a land-cover change model with a mechanistic forest growth model to produce estimates of aboveground carbon over 50 years. We tracked the fate of forest carbon removed through harvesting and development using a standard carbon accounting methodology, modified to fit our modeling framework. Of the simulated changes in land use, changes in harvesting had the most profound and immediate impacts on carbon stocks and fluxes. In one land-use scenario that included a rapid expansion of harvesting for biomass energy, New England’s forests stopped serving as a net carbon sink and became a net carbon source by 2060. In an alternative scenario, relatively small reductions in harvest intensities (i.e., ~10% less biomass removed), coupled with an increased percent of wood going into longer-term storage, led to substantial reductions in net carbon emissions (909 MMtCO2eq) as compared to a continuation of recent trends in land use. However, these projected gains in carbon storage and reduction in emissions from less intense harvesting regimes can only be realized if they are paired with a reduction in the consumption of the timber products, and their replacements, that otherwise would result in additional emissions from leakage and substitution.

Contribution of meandering rivers to natural carbon fluxes: Evidence from the Ucayali River, Peruvian Amazonia

Better understanding the fate of the atmospheric carbon (C) captured by plant photosynthesis is essential to improve natural C flux modelling. Soils are considered as the major terrestrial bioreactor and repository of plant C, whereas channel networks of floodplain rivers collect and transport, throughout the aquatic continuum, a significant part of plant primary production until its export through outgassing or sequestration in marine sediments. Here, we show that river meandering in forested floodplains is a crucial and widely overlooked Earth surface process promoting C fluxes from the atmosphere to the aquatic continuum, via the floodplain vegetation. Over a recent period of 35 years (1984–2019), we quantified those C fluxes in one of the most active meandering rivers on Earth, the Ucayali River, Peru, South America. We used map time series combined with above-ground forest C data to derive the amount of C that is annually captured by the growing floodplain vegetation within the active meander belt, as well as exported to the aquatic continuum by lateral channel erosion. We found that the annual building and erosion of forested floodplain areas was nearly balanced over time with 19.0±7.7×103 ha−1 yr−1 and 19.8±6.7×103 ha−1 yr−1, respectively. While growing forests within the active meander belt annually captured 0.01±0.05×106 Mg C yr−1, lateral channel erosion provided the nearly 100-fold amount of C to the river channel and its streamflow, i.e. 0.9±0.4×106 Mg C yr−1. Our findings revealed that the migration of the Ucayali River channel provided nearly 10-times more lignified C per unit area to the aquatic continuum (44.7±21.4 Mg C ha−1 yr−1) than non-meandering central Amazonian floodplains do. Together, these findings point to the importance of quantifying the overall contribution of meandering rivers to natural C fluxes worldwide.

A forest loss report card for the world's protected areas.

Protected areas are a key tool in the conservation of global biodiversity and carbon stores. We conducted a global test of the degree to which more than 18,000 terrestrial protected areas (totalling 5,293,217 km2) reduce deforestation in relation to unprotected areas. We also derived indices that quantify how well countries' forests are protected, both in terms of forested area protected and effectiveness of protected areas at reducing deforestation, in relation to vertebrate species richness, aboveground forest carbon biomass and background deforestation rates. Overall, protected areas did not eliminate deforestation, but reduced deforestation rates by 41%. Protected area deforestation rates were lowest in small reserves with low background deforestation rates. Critically, we found that after adjusting for effectiveness, only 6.5%-rather than 15.7%-of the world's forests are protected, well below the Aichi Convention on Biological Diversity's 2020 Target of 17%. We propose that global targets for protected areas should include quantitative goals for effectiveness in addition to spatial extent.

Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest.

Forest wildfires consume and redistribute carbon within forest carbon pools. Because the incidence of wildfires is unpredictable, quantifying wildfire effects is challenging due to the lack of prefire data or controls from experiments over a large landscape. We explored a quasi-experimental method, propensity score matching, to estimate wildfire effects on aboveground forest woody carbon mass in Washington and Oregon, United States. Observational data, including national forest inventory plot measurements and satellite imagery metrics, were utilized to obtain a control set of unburned plots that are comparable to burned plots in terms of environmental conditions as well as spatial locations. Three matching methods were implemented: propensity score matching (PSM), spatial matching (SM), and distance-adjusted propensity score matching (DAPSM). We investigated if propensity score matching with and without spatial adjustment led to different outcomes in terms of (1) balance in covariate distributions between burned and control plots, (2) mean carbon mass obtained from the selected control plots compared to burned and all unburned plots, and (3) estimates of wildfire effects by burn severity. We found that PSM and SM, which use only the environmental covariate set or the spatial distance for estimating propensity scores, respectively, did not appear to produce a comparable set of control plots in terms of the estimated propensity scores and the outcomes of mean carbon mass. DAPSM was the preferred method both in balancing the observed covariates and in dealing with unobservable confounding variables through spatial adjustment. The average wildfire effects estimated by DAPSM showed clear evidence of redistribution of carbon among aboveground woody pools, from live to dead trees, but the consumption of total woody carbon by wildfire was not substantial. Only moderate burn severity led to significant reduction of total woody carbon mass across Washington and Oregon forests (64% of control plots remained on average). This study provides an applied example of a quasi-experimental approach to quantify the effects of a natural disturbance for which experimental settings are unavailable. The study results suggest that incorporating spatial information in addition to environmental covariates would yield a comparable set of control plots for wildfire effects quantification.

南方红壤典型水土流失区马尾松林地上林木碳储量的遥感监测——以长汀县河田镇为例

森林碳储量动态变化对揭示区域水土流失治理成效具有重要指示意义。以长汀县河田镇为例，2017年随机设置34个马尾松林样本作为建模集，分别与同期Landsat影像的原始波段、植被指数及主成分因子进行回归分析，构建马尾松（（Pinus massoniana））林地上林木碳储量的最佳反演模型，基于伪不变特征原理的线性归一化法实现该模型在2003、2010年影像上的适用性校正转换，实现研究区2003、2010、2017年马尾松林地上林木碳储量的反演及时空分异特征的研究。结果表明：研究区2017年马尾松林地上林木碳储量最佳遥感反演模型是以绿色植被指数（GNDVI）为自变量构建的指数模型：C₂₀₁₇=0.006e^{14.357GNDVI₂₀₁₇}，该模型拟合的决定系数为0.57，平均相对精度为82.19%；2003年、2010年马尾松林地上林木碳储量遥感估测模型为：C₂₀₀₃=0.006e^{（16.4086GNDVI₂₀₀₃+1.1428）}、C₂₀₁₀=0.006e^{（15.1677GNDVI₂₀₁₀+1.5821）}，两期校正模型的决定系数均在0.85以上；2003、2010及2017年碳储量分别为8.24 t/hm²、11.34t/hm²、16.14 t/hm²，整体呈上升趋势；地上林木碳储量随海拔、坡度的升高而增加，向阳坡地上林木碳储量高于背阴坡；碳储量增长率随海拔、坡度的升高而降低，背阴坡碳储量增长率高于向阳坡。;The dynamic changes of forest carbon storage have important indications for revealing the effectiveness of regional soil erosion control. Take Hetian Town, Changting County as an example. In 2017, 34 Masson pine (Pinus massoniana) forests samples were randomly set as the modeling set, and the original waveband, vegetation index and principal component factor regression analysis of the Landsat images of the same period were used to construct the best inversion model for the aboveground forest carbon storage of Masson pine forests. The best inversion model, the linear normalization method based on the principle of pseudo invariant feature (PIF), realizes the applicability correction conversion of the model on the images of 2003 and 2010, and realizes the inversion of the carbon storage of the Masson pine forests in the study area in 2003, 2010 and 2017. Research on spatio-temporal differentiation characteristics. The results show that the best remote sensing inversion model for the aboveground forest carbon storage of Masson pine forests in the study area in 2017 is an index model constructed with the Green Normalized Vegetation Index (GNDVI) as the independent variable:C₂₀₁₇=0.006e^{14.357GNDVI₂₀₁₇},the fitting coefficient of determination of this model is 0.57, and the average relative accuracy is 82.19%; In 2003 and 2010, the remote sensing estimation models of the aboveground forest carbon storage of Masson pine forests are: C₂₀₀₃=0.006e^{(16.4086GNDVI₂₀₀₃+1.1428)},C₂₀₁₀=0.006e^{(15.1677GNDVI₂₀₁₀+1.5821)}, The coefficient of determination of the two-period calibration model is above 0.85; Carbon storage in 2003, 2010 and 2017 were 8.24 t/hm², 11.34 t/hm² and 16.14 t/hm², respectively, showing an overall upward trend; the aboveground forest carbon storage increases with the elevation and slope, and the aboveground forest carbon storage on the sunny slope is higher than that on the shady slope; The growth rate of carbon storage decreases with the increase of altitude and slope, and the growth rate of carbon storage on the shady slope is higher than that on the sunny slope.

Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi’an, China

Investigating the spatial distribution of urban forest biomass and its potential influencing factors would provide useful insights for configuring urban greenspace. Although China is experiencing an unprecedented scale of urbanization, the spatial pattern of the urban forest biomass distribution as a critical component in the urban landscape has not been fully examined. Using the geographic detector method, this research examines the impacts of four geographical factors (GFs)—dominant tree species, forest categories, land types, and age groups—on the aboveground biomass distribution of urban forests in 1480 plots in Xi’an, China. The results indicate that (1) the aboveground biomass and four GFs show obvious heterogeneity regarding their spatial distribution in Xi’an; (2) the dominant tree species and age group which impacts the patterns of aboveground biomass are the primary GFs, with the independent q value (a statistic metric used to quantify the impacts of GFs in this study) reaching 0.595 and 0.202, respectively, while the forest category and land type were weakly linked to the spatial variation of aboveground biomass, with a q value of 0.087 and 0.076, respectively; and (3) the interactions among these four GFs also tend to contribute to the distribution pattern of aboveground biomass. The interactions between GFs achieved a larger impact than the sum of impacts that were independently obtained from the factors. Our results showed that the method of using a geographical detector is a useful tool in the urban area, and can reveal the driver pattern of aboveground biomass and provide a reference for city planning and management.

Context-dependency of tree species diversity, trait composition and stand structural attributes regulate temperate forest multifunctionality

High species diversity is generally thought to be a requirement for sustaining forest multifunctionality. However, the degree to which the relationship between species-, structural-, and trait-diversity of forests and multifunctionality depend on the context (such as stand age or abiotic conditions) is not well studied. Here, we hypothesized that context-dependency of tree species diversity, functional trait composition and stand structural attributes promote temperate forest multifunctionality including above- and below-ground multiple and single functions. To do so, we used repeated forest inventory data, from temperate mixed forests of northeast China, to quantify two above-ground (i.e. coarse woody productivity and wild edible plant biomass), five below-ground (i.e. soil organic carbon, total soil nitrogen, potassium, phosphorus and sulfur) functions, tree species diversity, individual tree size variation (CVDBH) and functional trait composition of specific leaf area (CWMSLA) as well as stand age and abiotic conditions. We found that tree species diversity increased forest multifunctionality and most of the single functions. Below-ground single and multifunctionality were better explained by tree species diversity. In contrast, above-ground single and multifunctionality were better explained by CVDBH. However, CWMSLA was also an additional important driver for maintaining above- and below-ground forest multifunctionality through opposing plant functional strategies. Stand age markedly reduced forest multifunctionality, tree species diversity and CWMSLA but substantially increased CVDBH. Below-ground forest multifunctionality and tree species diversity decreased while above-ground forest multifunctionality increased on steep slopes. These results highlight that context-dependency of forest diversity attributes might regulate forest multifunctionality but may not have a consistent effect on above-ground and below-ground forest multifunctionality due to the fact that those functions were driven by varied functional strategies of different plant species. We argue that maximizing forest complexity could act as a viable strategy to maximizing forest multifunctionality, while also promoting biodiversity conservation to mitigate climate change effects.

Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass

We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

Contribution of species abundance and frequency to aboveground forest biomass along an Andean elevation gradient

AimsTo determine whether species that contribute most to a plot’s biomass are the most abundant (high local abundance at plot scale) or the most frequent (occur the most across plots at landscape scale), or both. In the tropical Andes, these patterns may change with elevation. This study assesses the contribution to plot’s above-ground biomass (AGB) of the plant community abundance pattern –the prevalence of within-plot dominant species– and the over-occurrence of regionally frequent species, in an elevation gradient. MethodsWe considered all trees ≥2.5 cm DBH from 446 0.1 ha plots in an Amazonia-Andes 260–4350 m elevation cline in N Bolivia. Plot AGB was calculated as the sum of AGBs for all stems contained. We grouped plots into four bins segregated by elevation and ran a bootstrap analysis over subsets of 58 random plots per bin with 100 iterations. Simpson evenness index (ED) for all species in each plot was used as a measure for its species abundance. Values for each plot’s species frequency was calculated as the mean of all species’ in the plot mean frequencies across the bin (i.e. the fraction of plots where each species occurs). We used linear models to correlate plot AGB with (1) elevation and mean annual precipitation (MAP), and (2) ED, plot species frequency and elevation. We performed all analyses at the species, genus and family levels. ResultsPlot AGB was related negatively with elevation, and thus positively with MAP, and also negatively with plot ED and plot species frequency, all significant. Plot species abundance therefore contributes positively to explain the relationship with AGB along elevational gradients, while plot species frequency does so negatively (i.e. less frequent species contribute more to a plot’s AGB across elevation). AGB, for both generic and familial levels was also significantly and negatively correlated with ED, but not related with plot species frequency biomass at these taxonomic levels. ConclusionsPlot AGB was mainly associated with elevation and floristic composition where species, genera and families tended to be abundant at the local (plot) scale. Species that were less frequent at the regional scale contributed with more AGB regionally, while frequency at generic and familial scales did little to explain AGB patterns. This association seems stronger at lower elevations for all taxonomic levels while decreases toward higher elevation. Our study reveals a relationship between plot structural features like C stocks –influenced by species local abundances– and the distribution of taxa across the landscape.

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

Carbon Stocks in Chir Pine (Pinus roxburghii) Forests on Two Different Aspects in the Mahabharat Region of Makawanpur, Nepal

Despite the significant contribution of forests in climate change mitigation, studies to establish the potential of sub-tropical forest ecosystems at different aspects in enhancing soil health indicators are only partly known. The study was carried out to quantify vegetation and soil carbon stocks of a natural Chir Pine (Pinus roxburghii) forest at two different aspects (northern and southern) of a typical sub-tropical environment in Nepal. Stratified random sampling was used for forest inventory and soil sample collection. Aboveground forest biomass was calculated using standard allometric models. Soil was sampled up to 60 cm depth and at 20 cm intervals. Walkey and Black method was used to determine soil organic carbon. Total aboveground plant biomass carbon in southern aspect (140.20 t ha-1) was higher compared to that on the northern aspect (115.34 t ha-1). Similarly, soil carbon stock on southern aspect (46.65 t ha-1) was higher than that of northern aspect (42.14 t ha-1). This resulted to total carbon stock on southern and northern aspect of P. roxburghii forest of 186.85 t ha-1 and 157.48 t ha-1 respectively. The total carbon stock of P. roxburghii forest is significantly higher on southern aspect than on northern aspect with p value 0.001 (p&lt;0.05). Hence, we conclude that the southern aspect of the Mahabharat range favour the growth of P. roxburghii forest compared to the northern aspect. However, the contribution of the entire Chir pine forest ecosystem to carbon sequestration and global climate warming mitigation can’t be neglected.

Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

The aboveground forest biomass plays a key role in the global carbon cycle and is considered a large and constant carbon reservoir. Hence, exploring the future potential changes in forest-cover pattern can help to estimate the trend of forest biomass and therefore, carbon stock in a certain area. As a result, the present paper attempts to model the potential changes in aboveground forest carbon stock based on the forest-cover pattern scenario simulated for 2050. Specifically, the resulting aboveground forest biomass, estimated for 2015 using the allometric equation based on diameter at breast height and the estimated forest density, was used as baseline data in the present approach. These spatial data were integrated into the forest-cover pattern scenario, predicted by using a spatially explicit model, i.e., the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), in order to estimate the potential variation of aboveground forest carbon stock. Our results suggest an overall increase by approximately 4% in the aboveground forest carbon stock until 2050 in Romania. However, important differences in the forest-cover pattern change were predicted on the regional scale, thus highlighting that the rates of carbon accumulation will change significantly in large areas. This study may increase the knowledge of aboveground forest biomass and the future trend of carbon stock in the European countries. Furthermore, due to their predictive character, the results may provide a background for further studies, in order to investigate the potential ecological, socio-economic and forest management responses to the changes in the aboveground forest carbon stock. However, in view of the uncertainties associated with the data accuracy and methodology used, it is presumed that the results include several spatial errors related to the estimation of aboveground forest biomass and simulation of future forest-cover pattern change and therefore, represent an uncertainty for the practical management of applications and decisions.

Massive loss of aboveground biomass and its effect on sediment organic carbon concentration: Less mangrove, more carbon?

Mangroves play an important role in the nutrient cycle of coastal areas, contributing to the health of adjacent ecosystems by retaining carbon, nitrogen and phosphorous. Different studies have shown that sediment organic carbon (SOC) stock decrease with the loss of above-ground forest biomass. Our objective was to assess the effect of deforestation on the ability of mangroves to store carbon and retain nutrients in the sediment. We hypothesized that conserved forests contain the highest values of SOC compared to sites where the vegetal cover was completely lost. The sampling was carried out in a highly altered ecosystem in the South Caribbean, where a freshwater diversion project had been implemented between 1993 and 1998 to rehabilitate 290 km2 of a deforested mangrove area after hypersalination of the sediment. Based on our results, we reject our initial hypothesis; SOC stores in areas where massive mangrove mortality occurred were between 258 ± 2 and 339 ± 19 Mg C ha−1, i.e., two times higher than those in conserved mangrove forest areas (156 ± 16 Mg C ha−1). Nitrogen (N) followed a trend similar to that of SOC, in which sites with degraded mangroves had greater N stores (between 15.5 ± 0.4 and 19.1 ± 1.0 Mg N ha−1) than conserved site (11.7 ± 0.8 Mg N ha−1). In contrast, phosphorus (P) presented higher values at the conserved site (3.2 ± 0.3 Mg P ha−1) than at degraded sites (between 0.8 ± 0.2 and 2.0 ± 0.2 Mg P ha−1). The higher concentration of SOC in highly degraded sites may be driven by the balance between the large accumulation of dead plant material due to hypersalination and the low decay rates due to anoxia sediment conditions.

Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States

Forest resources have a high economic value in the State of Georgia (USA) and the landscape is frequently disturbed as a part of forest management activities, such as plantation forest management activities. Thus, tracking the stand-clearing disturbance history in a spatially referenced manner might be pivotal in discussions of forest resource sustainability within the State. The two major objectives of this research are (i) to develop and test a reliable methodology for statewide tracking of forest disturbances in Georgia, (ii) to consider and discuss the use and implications of the information derived from the forest disturbance map. Two primary disturbance detection methods, a threshold algorithm and a statistical boundary method, were combined to develop a robust estimation of recent forest disturbance history. The developed model was used to create a forest disturbance record for the years 1987–2016, through the use of all available Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The final product was a raster database, where each pixel was assigned a value corresponding to the last disturbance year. The overall accuracy of the forest disturbance map was 87%, and it indicated that 4,503,253 ha, equivalent to 29.2% of the total land area in Georgia, experienced disturbances between 1987 and 2016. The estimated disturbed area in each year was highly variable and ranged between 84,651 ha (±36,354 ha) to 211,780 ha (±49,504 ha). By combining the use of the disturbance map along with the 2016 database from the National Land Cover Database (NLCD), we also analyzed the regional variation in the disturbance history. This analysis indicated that disturbed forests in urban areas were more likely to be converted to other land-uses. The forest disturbance record created in this research provides the necessary spatial data and address forest resource sustainability in Georgia. Additionally, the methodology used has application in the analysis of other resources, such as the estimation of the aboveground forest biomass.