Forest Carbon Density Research Articles

Comparación de modelos de altura de la vegetación para estimación de biomasa en un bosque de manglar en el Caribe Colombiano

Este trabajo integra satélite-LIDAR para comparar la evaluación de stock de carbono en alta resolución en un bosque de manglar (Rincón Mosquito) en la zona costera del mar Caribe. Se comparó la altura de vegetación resultante de la extracción de un modelo de altura de la vegetación satelital EROS-B y un LIDAR aerotransportado vs datos en campo. Los satélites ofrecen una oportunidad de monitorear cambios en el carbono forestal causados por la deforestación y la degradación, igual que nuevos métodos aéreos, especialmente el método LIDAR, ofrecen una forma de estimar la densidad del carbono forestal, lo cual ayuda en el desarrollo de líneas base para inventarios de carbono. Luego de calcular la biomasa total desde los datos de satélite y LIDAR, se comparó la medida de biomasa tomada en campo. En términos de escalas detalladas se puede concluir que el sensor con mayor precisión es el LIDAR al tener un nivel de resolución espacial que permite gran detalle de la masa arbórea., El uso de estéreo pares satelitales es viable en grandes extensiones y no en pequeñas áreas, donde particularmente en los últimos años, los datos tomados con vehículos aéreos no tripulados parecen ser una alternativa más viable en disponibilidad y costo.

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

Stand carbon density drivers and changes under future climate scenarios across global forests

Climate and soil factors drive forest carbon (C) density. However, their impacts on the aboveground carbon density (ACD) and belowground carbon density (BCD) of global forest types have not been quantified in previous studies. In this study, we compiled global forest biomass data that included 8800 plots to better quantify the changes in total carbon density (TCD) of five forest types under current climate and future climate change scenarios (e.g., RCP2.6, RCP4.5 and RCP8.5) using the boosted regression tree method and TCD-stand age-climate models. Tropical forests have the largest amount of C density (130.0 ± 3.2 Mg C ha−1; 173.60 ± 17.24 Mg C ha−1), while boreal forests have the lowest TCDs (59.4 ± 0.9 Mg C ha−1; 62.34 ± 15.10 Mg C ha−1) in the current and future. For the current forest scenarios, the dryness index (DI) for tropical forests exhibited a negative mean change rate for the ACD (4.5 Mg C ha−1/0.1 DI) and BCD (0.9 Mg C ha−1/0.1 DI). The mean annual precipitation (MAP) had a positive mean change rate for the ACD (7.1 Mg C ha−1/100 mm) and BCD (1.3 Mg C ha−1/100 mm) when the MAP was ≤1100 mm in the temperate forests. The MAP was negative for the ACD (1.9 Mg C ha−1/100 mm) and BCD (0.4 Mg C ha−1/100 mm) when the MAP was >1100 mm. The mean annual temperature (MAT) in boreal forests had a positive mean change rate for the ACD (1.78 Mg C ha−1/1°C) and BCD (0.4 Mg C ha−1/1°C). We found that in contrast to 2050, there were positive impacts of the DI on the TCD change in tropical forests in 2070, and the DI was the most important driver for tropical forests. There were strong positive MAP impacts on the TCD change in temperate forests in both 2050 and 2070, where the MAP was the most important driver for temperate forests. There were negative MAT impacts on the TCD change in boreal forests under RCP4.5 (−0.22 ± 10.53 Mg C ha−1) and RCP8.5 (−4.75 ± 10.80 Mg C ha−1) in 2070, and the MAT was the most important driver in boreal forests. Our results could be used to improve the accuracy of C density estimates and assess climate change impacts on the C budgets of global forests.

Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level.

Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey’s height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.

Spatial variation of carbon density in Pinus massoniana forest in Jiangxi Province, China

Carbon density is one of the important indicators for carbon sequestration capacity in forest ecosystems. The analysis of spatial variation in forest carbon density can provide scientific basis for management of forest carbon sink. We investigated the carbon density of Pinus massoniana forest in Jiangxi Province. Based on plot investigation and meaurement of sample carbon content, the spatial autocorrelation and distribution of carbon density were analyzed and explored respectively by using global Moran I, local Moran I and geostatistics. Results from the global Moran I showed that ecosystem carbon density had significant positive spatial autocorrelation and its autocorrelation decreased with the increase of distance. Ecosystem carbon density had no spatial autocorrelation when the distance exceeded 400 km. The spatial distribution of ecosystem carbon density changed from aggregated distribution to random distribution with the increase of distance. Results from the local Moran I showed that the local spatial distribution of ecosystem carbon density was not uniform, and the difference of local spatial distribution gradually increased with the increase of distance. The fitting results of semi-variogram models showed that the spherical model was the best fitting model for the estimation of ecosystem carbon density. The ratio of nugget to sill was 0.36, indicating mode-rate spatial correlation of carbon density. The ecosystem carbon density based on kriging spatial interpolation mainly concentrated in the range of 85.14-153.52 t·hm-2. The spatial distribution regularity was generally low in middle region and high in peripheral region, which was consistent with the terrain characteristics of the study area.

Managing for disturbance stabilizes forest carbon

Forest ecosystems sequester approximately 12% of anthropogenic carbon emissions, and efforts to increase forest carbon uptake are central to climate change mitigation policy (1). Managing forests to store carbon has focused on increasing forested area, decreasing area lost to logging and clearing, and increasing forest carbon density. Warming, drought, and wildfires challenge the stability of carbon stored in forests (2, 3). By contrast, natural cycles of low-intensity fires in dry forests can, over the long term, promote forest carbon storage by protecting carbon in soil and in large, old trees. The conundrum is how to balance immediate, disturbance-driven carbon loss with long-term, stable carbon storage and account for these risks in policies for forest carbon management (Fig. 1). Fig. 1. Carbon-management policies would do well to use disturbance ecology to factor in tree mortality risk. For wildfire and other impactful disturbances, researchers now have the capability to incorporate these risks into policy mechanisms that enhance forest carbon storage. Doing so would substantially improve global forest carbon policies aimed at climate change mitigation. Image credit: Shutterstock/Christian Roberts-Olsen. What has been missing is the explicit use of disturbance ecology to factor in tree mortality risk. For wildfire and other impactful disturbances, our understanding is now sufficient to incorporate these risks into policy mechanisms that enhance forest carbon storage. Doing so would substantially improve global forest carbon policies aimed at climate-change mitigation. Governments currently use our understanding of natural hazards and societal risk to inform building codes for earthquakes (4) and wildfires (5) and for national flood insurance (6). Underlying these policies are the quantification of the probability of a natural hazard occurring and an assessment of the societal impact. Current carbon policy and management need to use stability and risk accounting based on our understanding … [↵][1]1To whom correspondence should be addressed. Email: mhurteau{at}unm.edu. [1]: #xref-corresp-1-1

A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR.

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks.

Stand structure drives disparities in carbon storage in northern hardwood-conifer forests

Elements of forest structure are fundamentally associated with an array of ecosystem services and habitat characteristics. However, forest structure varies, in particular, through interactions with natural and human disturbances. Both variation in structural characteristics and associated relationships with ecosystem service outcomes have been poorly explored in mature, secondary forests redeveloped since 19th century agricultural abandonment in the eastern United States. Our study addressed this uncertainty focusing on carbon storage as an important climate regulation service. We conducted an inventory of 45 plots sharing similar land use history (i) to identify differences in forest structure, and (ii) to investigate links between stand structure and aboveground carbon storage. We derived 19 structural attributes and used these in Agglomerative Hierarchical Clustering (AHC) to categorize structurally different groups. Subsequently, we analyzed carbon density in each cluster and employed a random forest algorithm to derive partial effects of structural attributes on carbon storage.We found a distinctive disparity in forest structure inferred from two hardwood-dominated and one softwood-dominated clusters. Nine variables (cavity tree density, conifer ratio, foliage height index, gap area, live basal area, species diversity, variation in heights and diameters, and vertical shrub cover) explained significant differences between these clusters. Carbon storage varied markedly, and was highest in the softwood cluster. Structural complexity was overall positively associated with carbon storage, whereas this effect was more distinctive in hardwood compared to softwood-dominated forests. In particular, five variables exhibited a positive (conifer ratio, diameter variation, dead basal area, large live trees, and live basal area), one a negative (live tree density), and two (dead tree density and species diversity) a mixed relationship with carbon storage. Despite only moderate variation in climatic conditions across the investigated plots, we found a strong sensitivity of carbon storage to mean annual temperature. In contrast, annual precipitation and topography had no effect on carbon storage. The link between structural complexity and carbon storage suggests a high potential to actively increase forest carbon density. Based on our findings, a variety of options are available to enhance forest structure, and thus to improve carbon storage in managed forest ecosystems. These include, for instance, increasing the variability in tree dimensions and fostering large live trees which may improve niche complementarity. Increasing structural complexity in managed forest stands may thus improve buffering of potentially negative impacts of climate change on carbon stocks.

Carbon storage dynamics of subtropical forests estimated with multi-period forest inventories at a regional scale: the case of Jiangxi forests

Temperate and high-latitude forests are carbon sinks and play pivotal roles in offsetting greenhouse gas emissions of CO2. However, uncertainty still exists for subtropical forests, especially in monsoon-prevalent eastern Asia. Earlier studies have depended on remote sensing, ecosystem modeling, carbon fluxes, or single period forest surveys to estimate carbon sequestration capacities, and the results vary significantly. This study was designed to utilize multi-period forest survey data to explore spatial-dynamics of biomass storage in subtropical forests of China. Jiangxi province, a region with over 60% subtropical forest cover, was selected as the case study site and is located in central east China. Based on forest inventory data 1984–2013, and the stock-difference and biomass expansion factor methods, the carbon storage and density, of arboreal forests, economic forests, bamboo forests, woodlands and shrubberies were estimated. The results show that carbon storage increased from 159.1 Tg C in 1988 to 276.1 TgC in 2013, making up 3.1–3.8% of carbon stored throughout China. Among the four types of forests, the amount of carbon stored was as follows: arboreal forest > economic forest > bamboo forest > woodland and shrubbery. Arboreal forests accounted for 64.0–79.4% of the total. Forest carbon density increased from 21.2 Mg C ha−1 in 1984 to 26.2 Mg C ha−1 in 2013, equal to 61.2–70.2% of the average carbon density of China’s forests in the same period. Forest carbon storage in Jiangxi will reach 355.5 Tg C and 535.8 Tg C in 2020 and 2030, respectively, and forest carbon density is predicted to be 31.9 Mg C ha−1 and 46.4 Mg C ha−1, respectively. As one of the few studies using multi-period data tracking biomass dynamics in Jiangxi province, the findings of this study may be used as a reference for other research. Using Jiangxi as a case study underlies the fact that subtropical forests in China have great carbon sequestration potential and have fundamental significance to offset global environmental change effects.

Recovery of forest carbon density and carbon storage in a soil-degraded landscape in southeastern China

Adaptive managements on susceptible forested regions not only benefit forest recovery, but also help to improve the stability of soil-degraded area and carbon (C) sequestration. The gain of afforestation on C sink has not been addressed clearly especially in tropical and subtropical regions that will be critical in an era of global warming. Based on the Landsat satellite images, in situ forest survey and archives, we analyzed the spatiotemporal patterns of C density and storage in a typical soil–water conservation area in Changting county in southeastern China, dominated by the pioneer tree Pinus massoniana forest. The results showed that the C density (storage) increased significantly from 20.14 Mg C ha−1 (0.38 Tg C) in 1981 to 43.57 Mg C ha−1 (1.98 Tg C) in 2015 (p < 0.05) and proved the success of ecological management adopted considering the interactions between local residential livelihoods and the features of local forest ecosystem. In addition, the differences of C density and storage across elevational and slope gradients also narrowed over the past 35 years. The landscape also gradually shifted from lower C density to higher C density conditions based on the landscape metrics. The ecological-dominated approaches should be put in a high priority for the issue of C sequestration especially in a changing climate.

The opportunity cost of preserving the Brazilian Amazon forest

AbstractWe estimate the trade‐off between forest preservation and agricultural production for the Legal Amazon region, using census and deforestation data for municipalities in 2006. We use a directional distance function to represent the production possibility frontier, and then calculate the shadow price of reducing deforestation in terms of agricultural income foregone. Results indicate that, on average, to preserve 1 ha of forest, $797 in annual agricultural GDP must be foregone. Using a discount rate of 10% and average forest carbon density of 132 tons per hectare, these results imply an average shadow price of $16 per ton of CO2 permanently sequestered.

基于森林清查资料的河南省森林植被碳储量动态变化

PDF HTML阅读 XML下载 导出引用 引用提醒 基于森林清查资料的河南省森林植被碳储量动态变化 DOI: 10.5846/stxb201807081487 作者: 作者单位: 河南科技大学,河南科技大学,河南科技大学,河南科技大学,河南科技大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31700367）；河南省高等学校青年骨干教师培养计划项目（2016GGJS-062）；河南科技大学学科提升振兴A计划项目（13660001，13660002）；河南科技大学博士科研启动基金（4024-13480081） Dynamic of forest vegetation carbon storage in He'nan Province based on forest inventory data Author: Affiliation: Henan University of Science and Technology,Hean University of Science and Technology,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用1994-1998年、1999-2003年、2004-2008年、2009-2013年河南省4期森林资源清查数据，运用生物量转换因子连续函数法和平均生物量法，估算了1998-2013年河南省森林植被的碳储量和碳密度变化。研究结果表明，河南省森林植被碳储量由1998年的45.57 Tg增加到2013年的107.98 Tg，年均碳汇量为4.16 Tg/a。乔木林碳储量和碳密度分别由1998年的33.54 Tg和22.39 Mg/hm2增加到2013年的97.11 Tg和31.80 Mg/hm2。乔木林碳储量在所有植被类型中占主体，4个森林清查时期乔木林碳储量占森林植被总碳储量的比例分别为73.60%、79.22%、85.63%和89.93%。2013年森林清查时，乔木林中杨树和栎类碳储量最大，分别占总碳储量的37.61%和25.22%，各龄组乔木林碳密度大小顺序依次为成熟林 > 近熟林 > 中龄林 > 过熟林 > 幼龄林。阔叶林面积、碳储量、碳密度均高于针叶林，阔叶林是河南省森林碳汇的主要贡献者。人工林面积、碳储量、碳密度增加幅度都要高于天然林，人工林碳储量由1998年的9.62 Tg增加到2013年的55.67 Tg，占乔木林碳储量总增量的77.15%，人工林碳密度由1998年的17.86 Mg/hm2提高到2013年的32.01 Mg/hm2，人工林在河南省森林碳汇中逐步发挥重要的作用，逐渐成为河南省森林碳汇的主体，随着人工林生长为具有较高碳密度的成熟林，河南省乔木林将具有较大的碳汇潜力。 Abstract:Based on the forest resources inventory data of 1994-1998, 1999-2003, 2004-2008, and 2009-2013 in He'nan Province, the dynamic changes in forest vegetation carbon storage and density between 1998 and 2013 were estimated by using the continuous biomass expansion factor method and average biomass method. The results showed that the carbon storage of forest vegetation in the He'nan Province increased from 45.57 Tg in 1998 to 107.98 Tg in 2013, with an annual carbon sink of 4.16 Tg year-1. The carbon storage and density of arbor forests increased from 33.54 Tg and 22.39 Mg hm-2 in 1998 to 97.11 Tg and 31.80 Mg hm-2 in 2013, respectively. The carbon storage of arbor forests comprised the largest proportion of storage for all vegetation types; the proportion of carbon storage of arbor forests to total carbon storage of forest vegetation in the four forest inventory periods was 73.60%, 79.22%, 85.63%, and 89.93%, respectively. In 2013, Populus and Quercus had the largest carbon storage, accounting for 37.61% and 25.22% of the total carbon storage in arbor forests, respectively. The carbon density of arbor forests in each age group was ranked in the following order:mature forest > premature forest > middle-aged forest > over-mature forest > young-aged forest. The area, carbon storage and carbon density of broad-leaved forests were higher than those of coniferous forests, and broad-leaved forests were the main contributor to the forest carbon sink in He'nan Province. The area, carbon storage and carbon density of the planted forests increased more than the natural forests. The carbon storage of planted forests increased from 9.62 Tg in 1998 to 55.67 Tg in 2013, accounting for 77.15% of the total increment of carbon storage in arbor forests. The carbon density of planted forests increased from 17.86 Mg hm-2 in 1998 to 32.01 Mg hm-2 in 2013. The importance of the role of planted forests in the forest carbon sink of He'nan Province is gradually emerging. As the planted forests grow into mature forest with a high carbon density, the arbor forests in He'nan Province will have greater carbon sequestration potential. 参考文献 相似文献 引证文献

Sustained Biomass Carbon Sequestration by China’s Forests from 2010 to 2050

China’s forests have functioned as important carbon sinks. They are expected to have substantial future potential for biomass carbon sequestration (BCS) resulting from afforestation and reforestation. However, previous estimates of forest BCS have included large uncertainties due to the limitations of sample size, multiple data sources, and inconsistent methodologies. This study refined the BCS estimation of China’s forests from 2010 to 2050 using the national forest inventory data (FID) of 2009−2013, as well as the relationships between forest biomass and stand age retrieved from field observations for major forest types in different regions of China. The results showed that biomass–age relationships were well-fitted using field data, with respective R2 values more than 0.70 (p &lt; 0.01) for most forest types, indicating the applicability of these relationships developed for BCS estimation in China. National BCS would increase from 130.90 to 159.94 Tg C year−1 during the period of 2010−2050 because of increases in forest area and biomass carbon density, with a maximum of 230.15 Tg C year−1 around 2030. BCS for young and middle-aged forests would increase by 65.35 and 15.38 Tg C year−1, respectively. 187.8% of this increase would be offset by premature, mature, and overmature forests. During the study period, forest BCS would increase in all but the northern region. The largest contributor to the increment would be the southern region (52.5%), followed by the southwest, northeast, northwest, and east regions. Their BCS would be primarily driven by the area expansion and forest growth of young and middle-aged forests as a result of afforestation and reforestation. In the northern region, BCS reduction would occur mainly in the Inner Mongolia province (6.38 Tg C year−1) and be caused predominantly by a slowdown in the increases of forest area and biomass carbon density for different age–class forests. Our findings are in broader agreement with other studies, which provide valuable references for the validation and parameterization of carbon models and climate-change mitigation policies in China.

Similar carbon density of natural and planted forests in the Lüliang Mountains, China

The carbon density was not different between natural and planted forests, while the biomass carbon density was greater in natural forests than in planted forests. The difference is due primarily to the larger carbon density in the standing trees in natural forests compared to planted forests (at an average age of 50.6 and 15.7 years, respectively). Afforestation and reforestation programs might have noticeable effect on carbon stock. An integrated assessment of the forest carbon density in mountain regions is vital to evaluate the contribution of planted forests to carbon sequestration. We compared the carbon densities and carbon stocks between natural and planted forests in the Luliang Mountains region where large-scale afforestation and reforestation programs have been implemented. The introduced peashrubs (Caragana spp.), poplars (Populus spp.), black locust (Robinia pseudoacacia), and native Chinese pine (Pinus tabulaeformis) were the four most common species in planted forests. In contrast, the deciduous oaks (Quercus spp.), Asia white birch (Betula platyphylla), wild poplar (Populus davidiana), and Chinese pine (Pinus tabulaeformis) dominated in natural forests. Based on the forest inventory data of 3768 sample plots, we estimated the values of carbon densities and carbon stocks of natural and planted forests, and analyzed the spatial patterns of carbon densities and the effects of various factors on carbon densities using semivariogram analysis and nested analysis of variance (nested ANOVA), respectively. The carbon density was 123.7 and 119.7 Mg ha−1 for natural and planted forests respectively. Natural and planted forests accounted for 54.8% and 45.2% of the total carbon stock over the whole region, respectively. The biomass carbon density (the above- and belowground biomass plus dead wood and litter biomass carbon density) was greater in natural forests than in planted forests (22.5 versus 13.2 Mg ha−1). The higher (lower) spatial carbon density variability of natural (planted) forests was featured with a much smaller (larger) range value of 32.7 km (102.0 km) within which a strong (moderate) spatial autocorrelation could be observed. Stand age, stand density, annual mean temperature, and annual precipitation had statistically significant effects on the carbon density of all forests in the region. No significant difference was detected in the carbon densities between natural and planted forests, and planted forests have made a substantial contribution to the total carbon stock of the region due to the implementation of large-scale afforestation and reforestation programs. The spatial patterns of carbon densities were clearly different between natural and planted forests. Stand age, stand density, temperature, and precipitation were important factors influencing forest carbon density over the mountain region.

Trade-offs between carbon stocks and timber recovery in tropical forests are mediated by logging intensity.

Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land-use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced-impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16trees/ha). Our census data span 20years postlogging and 17years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out-of-sample R2 values >90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks.

Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR

Remote sensing data have increasingly been employed in combination with field plot data to estimate aboveground carbon (C) stocks across heterogeneous forested landscapes around the world. The Forest Inventory and Analysis (FIA) program of the US Forest Service offers a gridded network of field plots which potentially can be linked to airborne Light Detection and Ranging (LiDAR) data to estimate forest aboveground carbon density (ACD; units of Mg C ha−1). Here we utilized FIA plot and airborne LiDAR data sets collected across two contrasting landscapes known as Laupahoehoe and Pu‘u Wa‘awa‘a on Hawai’i Island to explore strengths and weaknesses of linking those two data sets to estimate ACD. We varied FIA plot sample designs with respect to sampling density (i.e., the number of plots across landscape) and intensity (i.e., the structural detail within inventory plots) to test the capability of the mapping approach. Results indicated that Laupahoehoe and Pu‘u Wa‘awa‘a landscapes supported an estimated 545 Gg C and 157 Gg C aboveground, respectively, and mean ACD values of the wet windward Laupahoehoe landscape (109 Mg ha−1) were an order of magnitude greater than those of the leeward dry Pu‘u Wa‘awa‘a landscape (9.7 Mg ha−1). Patterns of ACD were largely determined by combined factors of precipitation, lava substrate, prior land use, and presence of non-native, often invasive, species. Results demonstrated the relative importance of sample plot density over sample plot intensity, and showed that FIA inventory plots, even at their lowest sample intensity design, can be linked with LiDAR data to accurately estimate ACD across spatially heterogeneous landscapes. We also developed and applied a straightforward, statistically-robust approach to provide error estimates for the 100 million pixels that characterize the Laupahoehoe and Pu‘u Wa‘awa‘a landscapes as well as for any sub-units of those landscapes. We contend that augmenting existing FIA forest plot data with airborne LiDAR coverage, even if that requires an increase in plot density somewhat above the FIA standard 1X or 2X approaches, is a feasible, cost-effective, scientifically sound approach from which to obtain accurate landscape- to regional-scale ACD measures across the extensive and heterogeneous forests of the United States.

National REDD+ Implications for Tenured Indigenous Communities in Guyana, and Communities’ Impact on Forest Carbon Stocks

Early project-level initiatives of ‘reducing emissions from deforestation and forest degradation’ (REDD+) have left a negative impression among many forest-dependent peoples (FDP) across the tropics. As countries move towards national-level implementation and results-based payments, it is timely to analyze the effects of ‘national REDD+’ on FDP. We use Guyana’s technically approved United Nations Forest Reference Emission Level (FREL) submission and Opt-In Mechanism to assess how fifteen indigenous communities with tenured forestland may financially benefit from national REDD+, and evaluate whether, and to what extent, Guyana forms a best-case scenario. In addition, we provide a first-time assessment whether field estimates of the average carbon density of mature forests managed by fifteen forest-dependent communities (beyond rotational farming lands) equals that of nearby unmanaged mature forest, as this could affect REDD+ payment levels. We conclude that, notwithstanding some pending issues, Guyana’s national REDD+ program could be very beneficial for FDP, even under a modest United States (US) $5 unit carbon price. We present economic evidence to support forest governance change domestically in sovereign developing countries that may ease FDP tenure and national REDD+ implementation. The average carbon density was locally substantially less in FDP-managed forest, but had little effect on the overall carbon stock of the titled forest area, and is considered modest when incorporating ecological and socioeconomic attributes. Partnerships with FDP when combined with advances in remote sensing could have potential for economic monitoring of forest emissions across the tropics.

Distribution characteristics and factors influencing the ecosystem in western Hubei

Western Hubei is the most concentrated area of forest resources in Hubei Province, and the knowledge of the distribution characteristics of ecosystem carbon density is important to understand the regional characteristics of carbon density and its mechanism of formation. Carbon density and factors influencing different layers in the ecosystem were studied by using field data. The average carbon density of ecosystems in western Hubei was 159.05t/hm2; the carbon density of different forest types in descending order was Abies fargesii forests (362.25t/hm2), mixed broadleaf-conifer forests (154.13t/hm2), broad-leaved forests (146.09t/hm2), and coniferous forests (135.76t/hm2), and ecosystem carbon density increased with increasing age. The carbon density of the arborous layer, shrub layer, and soil layer of A. fargesii forests was significant higher than that of the other forests (P<0.05), indicating the carbon storage per unit area of A. fargesii forests, which grow at higher elevations, was the greatest. The carbon density in arborous layers of broad-leaved forests, mixed broadleaf-conifer forests, and coniferous forests was 39.29t/hm2, 48.99t/hm2, and 48.39t/hm2, respectively. Those of the soil layer were 102.96t/hm2, 100.97t/hm2, and 82.37t/hm2, respectively, and there were no significant differences among them. Among the three forest types, carbon density in the litter layer was greater than that of the shrub layer, which indicated the litter layer plays an important role in carbon storage. The carbon density of mixed broadleaf-conifer forests was greatest, excluding A. fargesii forests, in medium (58.71t/hm2) and mature forests (79.66t/hm2). Thus, the carbon sink of mixed broadleaf-conifer forests had more potential than the others at the medium and mature forest stage. The soil layer carbon density in different forests constituted 60.67—70.48% of the entire ecosystem, and was 1.70—2.62 times greater than that of the arborous layer. There are many factors influencing ecosystem carbon density, which result from the interaction of environmental and topographical factors. The main explanatory variables of carbon density of the region were altitude, precipitation, and canopy density. The vegetation and soil layer carbon density increased as altitude increased, and the rate of change for every vertical 100m was 1.3t/hm2 and 1.9t/hm2, respectively (P<0.05). Although the annual average precipitation only affected the carbon density of the vegetation, it increased to 4t/hm2 (P<0.01) when average precipitation was >100mm.

Realizing Mitigation Efficiency of European Commercial Forests by Climate Smart Forestry

European temperate and boreal forests sequester up to 12% of Europe’s annual carbon emissions. Forest carbon density can be manipulated through management to maximize its climate mitigation potential, and fast-growing tree species may contribute the most to Climate Smart Forestry (CSF) compared to slow-growing hardwoods. This type of CSF takes into account not only forest resource potentials in sequestering carbon, but also the economic impact of regional forest products and discounts both variables over time. We used the process-based forest model 4 C to simulate European commercial forests’ growth conditions and coupled it with an optimization algorithm to simulate the implementation of CSF for 18 European countries encompassing 68.3 million ha of forest (42.4% of total EU-28 forest area). We found a European CSF policy that could sequester 7.3–11.1 billion tons of carbon, projected to be worth 103 to 141 billion euros in the 21st century. An efficient CSF policy would allocate carbon sequestration to European countries with a lower wood price, lower labor costs, high harvest costs, or a mixture thereof to increase its economic efficiency. This policy prioritized the allocation of mitigation efforts to northern, eastern and central European countries and favored fast growing conifers Picea abies and Pinus sylvestris to broadleaves Fagus sylvatica and Quercus species.

Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests

Forest carbon (C) estimates are the key inputs to the understanding of the global C cycle. We report the estimates of forest carbon pool and its spatial distribution in the Indian forests for the years 1994 and 2010 at 5km grid level. This study improves upon earlier spatial estimates of Indian forest biomass carbon by using data from a robustly designed National Forest Inventory (NFI). The realized sampling intensity has addressed the large heterogeneity of the Indian forest types and allowed the computation of 5km grid level forest C, yielding a realistic estimate of forest biomass C in Indian forests. Forest cover density maps were intersected with 5km mesh and estimates of forest area, forest carbon density for each Agro-ecological sub region and forest carbon pools were linked to the 5km grid coverage of India. National forest carbon estimates for the years 1994 and 2010 are 3911.78 and 4368.03TgC respectively, and these estimates showed a net increase of 456.25TgC in 16years. Uncertainty of the estimates has been addressed spatially. Mean forest carbon density increased from 61.14Mgha−1 in 1994 to 64.08Mgha−1 in 2010. C densities for dense and open forest in 1994 estimated as 77.08 and 38.47Mgha−1 with total C pools of 2895.28TgC and 1016.50TgC which has increased to 80.24Mgha−1 and 41.69Mgha−1 with total C pools of 3176.48TgC and 1191.55TgC in 2010. This study provides the first 5km level C analysis for Indian forests. Spatial distribution of C shows large differences in C density over Indian forests indicating that estimates of the spatial distribution of C are even more important than the total C pool estimates of the country.