Aboveground Forest Carbon Research Articles

Comparison of Landsat-8 and Sentinel-2 spectral indices for estimating aboveground forest carbon in a community forest

This study aimed to estimate and map the Above Ground Forest Carbon stock in the Salbote Karele Community Forest in Ilam district. The research used field surveys and satellite data from Sentinel-2 and Landsat-8 to determine biomass and carbon stocks. A total of 30 inventory plots covering 283.56 hectares (ha) were surveyed, resulting in an estimated mean biomass of 156.48 ton ha-1 and carbon stock of 73.55 ton ha-1. The biomass was then correlated with five different Vegetation Indices (VIs), computed from Sentinel-2 and Landsat-8 image and used to develop various simple linear regression models. The study found that Sentinel-2 data showed the highest correlation with observed biomass (R2 = 0.76) and lowest prediction error (RMSE = 78.44 ton ha-1) compared to Landsat-8 data. The linear regression model developed from Sentinel-2’s NDVI (Normalized Difference Vegetation Index) data predicted a mean AGB of 129.82 ton ha-1 and AGC of 61.01 ton ha-1. This research highlights the importance of using Sentinel-2 data for estimating Above Ground Forest Carbon stocks due to its combination of spectral capabilities and broad applicability. It also shows that the Salbote Karele community forest is of great significance as it stores suitable amounts of carbon

Mapping of Forest Biomass in Shangri-La City Based on LiDAR Technology and Other Remote Sensing Data

Forest ecosystems can be regarded as huge carbon sinks. In order to effectively assess carbon balance in such ecosystems, rapid and accurate estimation of the aboveground biomass of a forest is critically needed. However, the current methods for biomass estimation and mapping are of limited spatial resolution and mostly depend on large numbers of measurements. In order to obtain better biomass estimation outcomes with higher spatial resolution, a rapid method is introduced for region-scale biomass estimation in alpine and canyon areas using space-borne light detection and ranging (LiDAR) data and optical remote-sensing images. Specifically, we explored alpine and canyon areas in Shangri-La City in China using space-borne LiDAR data from ICESAT-2 and optical remote-sensing images from Landsat8 OLI, Sentinel-2, and Microwave remote sensing Sentinel-1. An extrapolation model of the forest canopy heights in these areas was constructed with a 30-m resolution of continuous canopy height outputs. For continuously estimating the diameter at breast height (DBH) in Shangri-La City, a tree height-DBH growth model was constructed based on the LiDAR and remote-sensing measurements. Finally, based on the average DBH of the explored forests, a model was constructed for estimating and mapping the aboveground biomass and carbon storage in Shangri-La with a spatial resolution of 30 m. The results show that the forest canopy height in Shangri-La City is mainly in the range of 2.82–30.96 m, and that the estimation accuracy is verified by the LiDAR-based canopy height model (CHM) with a coefficient of determination of R2 = 0.7143. The inversion results were still largely affected by geospatial location factors (longitude, latitude), terrain factors (slope, elevation), and vegetation indices (NBR, NDGI, NDVI). Based on the relationship between the tree height and the DBH, the DBH of trees in Shangri-La City was estimated to be mainly in the range of 20 cm to 30 cm, and this estimate was verified by actual measurements with R2 greater than 0.7 all. Finally, the established model estimated the aboveground forest biomass and carbon storage of the study area of Shangri-La City in 2020 to be 1.28 × 108 t and 6.41 × 107 t, respectively. These estimates correspond to total accuracies of 92.28%, respectively.

Costs of forest carbon sequestration in the presence of climate change impacts

Forests play a critical role in mitigating climate change, and, at the same time, are predicted to experience large-scale impacts of climate change that will affect the efficiency of forests in mitigation efforts. Projections of future carbon sequestration potential typically do not account for the changing economic costs of timber and agricultural production and land use change. We integrated a dynamic forward-looking economic optimization model of global land use with results from a dynamic global vegetation model and meta-analysis of climate impacts on crop yields to project future carbon sequestration in forests. We find that the direct impacts of climate change on forests, represented by changes in dieback and forest growth, and indirect effects due to lost crop productivity, together result in a net gain of 17 Gt C in aboveground forest carbon storage from 2000 to 2100. Increases in climate-driven forest growth rates will result in an 81%–99% reduction in costs of reaching a range of global forest carbon stock targets in 2100, while the increases in dieback rates are projected to raise the costs by 57%–132%. When combined, these two direct impacts are expected to reduce the global costs of climate change mitigation in forests by more than 70%. Inclusion of the third, indirect impact of climate change on forests through reduction in crop yields, and the resulting expansion of cropland, raises the costs by 11%–38% and widens the uncertainty range. While we cannot rule out the possibility of climate change increasing mitigation costs, the central outcomes of the simultaneous impacts of climate change on forests and agriculture are 64%–86% reductions in the mitigation costs. Overall, the results suggest that concerns about climate driven dieback in forests should not inhibit the ambitions of policy makers in expanding forest-based climate solutions.

Harvesting Intensity and Aridity Are More Important Than Climate Change in Affecting Future Carbon Stocks of Douglas-Fir Forests

Improved forest management may offer climate mitigation needed to hold warming to below 2°C. However, uncertainties persist about the effects of harvesting intensity on forest carbon sequestration, especially when considering interactions with regional climate and climate change. Here, we investigated the combined effects of harvesting intensity, climatic aridity, and climate change on carbon stocks in Douglas-fir [Pseudotsuga menziesii Mirb. (Franco)] stands. We used the Carbon Budget Model of the Canadian Forest Sector to simulate the harvest and regrowth of seven Douglas-fir stand types covering a 900 km-long climate gradient across British Columbia, Canada. In particular, we simulated stand growth under three regimes (+17%, −17% and historical growth increment) and used three temperature regimes [historical, representative concentration pathways (RCP) 2.6 and RCP 8.5]. Increasing harvesting intensity led to significant losses in total ecosystem carbon stocks 50 years post-harvest. Specifically, forests that underwent clearcutting were projected to stock about 36% less carbon by 2,069 than forests that were left untouched. Belowground carbon stocks 50 years into the future were less sensitive to harvesting intensity than aboveground carbon stocks and carbon losses were greater in arid interior Douglas-fir forests than in humid, more productive forests. In addition, growth multipliers and decay due to the RCP’s had little effect on total ecosystem carbon, but aboveground carbon declined by 7% (95% confidence interval [−10.98, −1.81]) in the high emissions (RCP8.5) scenario. We call attention to the implementation of low intensity harvesting systems to preserve aboveground forest carbon stocks until we have a more complete understanding of the impacts of climate change on British Columbia’s forests.

Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes

ABSTRACT Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the `signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman’s ) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful `signal’ than extraneous `noise’ in these cases.

Size‐dependent intraspecific variation in wood traits has little impact on aboveground carbon estimates in a tropical forest landscape

Abstract There is increasing evidence that intraspecific trait variation plays a role in governing rates of ecosystem functioning. While wood traits such as wood specific gravity (WSG) and wood carbon concentration (WCC) are key drivers of forest aboveground carbon (AGC) stocks, the sources of intraspecific variation in these wood traits and the consequences of this variation on AGC are poorly known, especially in the tropics. Here, we investigated intraspecific variation in wood specific gravity (WSG) and wood carbon concentration (WCC) from 556 individual trees belonging to 15 species that well characterize different successional stages of seasonal evergreen forests in Southeast Asia. Specifically, we tested the contribution of individual or species characteristics (tree size, growth rate and regeneration guilds) and local environmental conditions (topographic wetness index and successional stages) to intraspecific variation in WSG and WCC, and assessed the consequences of intraspecific variation in these wood traits on AGC estimates in 14 permanent forest plots established along a successional gradient in Khao Yai National park, Thailand. We found that tree size was the main driver of intraspecific variation in WSG and WCC as tree sizes increased from 10–100 cm in diameter, WSG increased by 7.3%, while WCC increased by 2.4% in heartwood, 1.6% and 2.7% in sapwood without and with volatile carbon included. There was no effect of the topographic wetness and other local environment condition in wood traits led to a slight overestimation of AGC in young secondary forests (+0.09% to +1.29%) and a small underestimation in older forests (−0.86% to −2.87%), but overall AGC estimates (13 of 14 forest plots) remained within error margins (the 95% interval). Our study provides evidence that tree size variation translates into intraspecific variability in wood traits, whereas local environmental conditions related to topography successional stages had no effect on wood trait variability. While size‐dependent variation in WSG and WCC have largely been undocumented and thus ignored in forest carbon assessment approaches, we highlight that it has a limited impact on AGC estimates, indicating that it does not invalidate current forest carbon stock estimation approaches. Read the free Plain Language Summary for this article on the Journal blog.

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

Assessing intraspecific wood density variations of Syzgium sp. in tropical forest of Southwest Sabah

Wood density (WD) is a critical determinant of estimating forest above-ground biomass (AGB) and carbon stock. Thus, heterogeneity in WD on individuals within species trees needs to be scrutinized, and acquisition of fixed WD value is essential to estimate carbon stock with confidence. This study investigated intraspecific variation in WD of Syzgium sp., also known as “Jambu” or “Kelat”. It is the most occurring species in study areas, and is regarded as an economically important species. Firstly, one half-diameter drilling from bark-to-pith measurement was taken per tree using Rinntech Resistograph R650-ED at breast height. Meanwhile, 5.15 mm-diameter core was sampled at 1.30 m above-ground, with DeWalt DCF899HP2 20V impact wrench 950 Nm and Haglöf increment borer. WD was estimated for each core sample using a dimensional method. Drilling resistance (DR) profiles were processed using DECOM 2.38m1 Scientific (c), and several independent variables were extracted from the resistogram. All resistogram-derived variables were positively correlated with field WD (R: 0.2 – 0.70). In addition, variability on WD in Syzgium sp. population is predominantly explained by the Resistograph amplitude, expressed as mean raw scale of adjusted DR (DR adj.RawSC) in a regression model. Given that intraspecific variation in WD is a crucial conjecture in forest AGB estimation, it is recommended to analyze with larger samples, and in-depth exploration on Resistograph-based variables is deemed to improve the accuracy of WD prediction models.

Novel coupled permafrost–forest model (LAVESI–CryoGrid v1.0) revealing the interplay between permafrost, vegetation, and climate across eastern Siberia

Abstract. Boreal forests of Siberia play a relevant role in the global carbon cycle. However, global warming threatens the existence of summergreen larch-dominated ecosystems, likely enabling a transition to evergreen tree taxa with deeper active layers. Complex permafrost–vegetation interactions make it uncertain whether these ecosystems could develop into a carbon source rather than continuing atmospheric carbon sequestration under global warming. Consequently, shedding light on the role of current and future active layer dynamics and the feedbacks with the apparent tree species is crucial to predict boreal forest transition dynamics and thus for aboveground forest biomass and carbon stock developments. Hence, we established a coupled model version amalgamating a one-dimensional permafrost multilayer forest land-surface model (CryoGrid) with LAVESI, an individual-based and spatially explicit forest model for larch species (Larix Mill.), extended for this study by including other relevant Siberian forest species and explicit terrain. Following parameterization, we ran simulations with the coupled version to the near future to 2030 with a mild climate-warming scenario. We focus on three regions covering a gradient of summergreen forests in the east at Spasskaya Pad, mixed summergreen–evergreen forests close to Nyurba, and the warmest area at Lake Khamra in the southeast of Yakutia, Russia. Coupled simulations were run with the newly implemented boreal forest species and compared to runs allowing only one species at a time, as well as to simulations using just LAVESI. Results reveal that the coupled version corrects for overestimation of active layer thickness (ALT) and soil moisture, and large differences in established forests are simulated. We conclude that the coupled version can simulate the complex environment of eastern Siberia by reproducing vegetation patterns, making it an excellent tool to disentangle processes driving boreal forest dynamics.

Combining Sample Plot Stratification and Machine Learning Algorithms to Improve Forest Aboveground Carbon Density Estimation in Northeast China Using Airborne LiDAR Data

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R2 = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBoost algorithm can effectively improve the estimation accuracy of forest AGC. Integrating this strategy with national forest inventory could help improve the monitoring of forest carbon stock over large areas.

Topography, Diversity, and Forest Structure Attributes Drive Aboveground Carbon Storage in Different Forest Types in Northeast China

Forests regulate air quality and respond to climate change by storing carbon. Assessing the driving factors of forest aboveground carbon (AGC) storage is of great importance for forest management. We assumed that different forest types would affect the relationship between species richness, stand density, individual tree size variation, and AGC. In order to test and verify it, we analyzed the inventory data of 206 fixed plots (20 m × 20 m) of Jingouling Forest Farm, taking advantage of the piecewise structural equation model (pSEM) to explore the effects of species diversity, stand structure attributes, and topography on the AGC storage in the Wangqing Forest in Jilin Province. In addition, in this study, we aimed to investigate whether the fixed factors (species diversity, stand structure attributes, and topography) influenced AGC storage more significantly than the random factor (forest type). According to the results of pSEM, the selected factors jointly explain the impact on 33% of AGC storage. The relationship between stand density and AGC is positive, and the impact of individual tree size variation on AGC storage is negative. Species richness has direct and indirect impacts on AGC storage, and the indirect impact is more significant through individual tree size variation. Both elevation and slope are significantly negatively associated with AGC storage. Forest type explains the impact on 12% of AGC storage, which means the relationship between AGC and predictors varies across forest types. The results provide a scientific basis for the protection and management decision of natural forests in northeastern China.

Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa\u2019s rainforests

Spatially explicit monitoring of tropical forest aboveground carbon is an important prerequisite for better targeting and assessing forest conservation efforts and more transparent reporting of carbon losses. Here, we combine near-real-time forest disturbance alerts based on all-weather radar data with aboveground carbon stocks to provide carbon loss estimates at high spatial and temporal resolution for the rainforests of Africa. We identified spatial and temporal hotspots of carbon loss for 2019 and 2020 for the 23 countries analyzed, led by different drivers of forest disturbance. We found that 75.7% of total annual carbon loss in the Central African Republic happened within the first three months of 2020, while 89% of the annual carbon loss in Madagascar occurred within the last five months of 2020. Our detailed spatiotemporal mapping of carbon loss creates opportunities for much more transparent, timely, and efficient assessments of forest carbon changes both at the level of specific activities, for national-level GHG reporting, and large area comparative analysis.

Aboveground Forest Carbon Stock in Protected Area: A Case Study of Bukit Tigapuluh National Park, Indonesia

The role of protected areas has been expanded into climate change mitigation, specifically on Reducing Emissions from Deforestation and Forest Degradation (REDD+). A reliable and practical method for measuring, reporting and verifying carbon stock is an essential component for REDD+. This study aims to recognize the characteristic and estimate aboveground forest carbon (AGC) stock in the tropical protected tropical area using a combination of terrestrial forest inventory and spatial data. A 168 cluster plots totaling 33.6 hectares were taken proportionally based on the percentage of forest cover types (dryland primary natural forest/DPF and dryland secondary natural forest/DSF) using a traditional forest inventory method (more than 5 cm dbh). Results showed that Bukit Tigapuluh National Park secured a significant AGC stock which has been estimated to be 269.2 [247.07; 291.43] tC/ha or 35,823,639 [32,872,312; 38,774,966] tC in total, being stored in approximately 133,051 hectares of the tropical rain forest. This result was higher than other studies in non-protected areas but slightly lower than other studies within protected areas. This finding supported the argument that protected areas possess a higher figure of AGC stock than other forest management units. The high amount of forest carbon biomass in the protected areas shall be very important assets for conducting the role of conservation for REDD+.

GIS in the jungle: Experiential Environmental Education (EEE) in Panama.

A University of Redlands travel course to Panama builds on Monty Hempel’s legacy in environmental studies, Experiential Environmental Education, and geographic information systems (GIS) instruction at the University of Redlands. It follows his experience developing social relationships in Panama, and nearly 20 years leading an EEE travel course to Palau. This Panama course, nicknamed “GIS in the Jungle,” tasks students with activities such as trail mapping, gathering original data with unmanned aerial vehicles (UAV), landcover mapping of pastures and forests, and visualizing the distribution of above-ground forest carbon content. These GIS activities help students construct an understanding of rainforest conservation that integrates their experiences living, hiking, and collecting original data in that environment. UAV and GIS also provided a platform for interacting with indigenous peoples struggling to defend their rainforest territory from colonist deforestation. As Monty Hempel observed about this course, students use GIS and UAV to integrate their direct experience with ground-level fieldwork in the rainforest with a birds-eye view of the interlocking ecosystems and human impacts. Combined with direct rainforest experiences, GIS projects help students develop an understanding of nature’s interlocking systems and the interdependence of life on Earth.

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.

Do functional identity and divergence promote aboveground carbon differently in tropical semi‐arid forests and savannas?

AbstractVarious studies have shown that plant species richness can promote ecosystem functions such as biomass storage. However, it is less well known whether this is mostly driven by the dominance of a few species and their associated traits (functional identity), or by complementarity among species that highly vary in their traits (functional diversity). The relative contribution of functional diversity and functional identity on biomass and carbon storage may in part depend on the type of functional traits that are considered, and on ecosystem type. Here, we used forest inventory data from West African semi‐arid environments, and functional traits (wood density and tree maximum height) to examine the effects of functional trait identity (FI or community weighted mean; CWM) and diversity (FD or single functional divergence; FDvar) on aboveground carbon (AGC) storage in both forests and savannas. We fitted simple linear and structural equation models to test the direct and indirect effects of functional traits on AGC, while accounting for potential effects of vegetation stand structure such as stand density and basal area. When evaluated independently, CWM of tree maximum height and FDvar of wood density correlated positively with AGC, in both forests and savannas, whereas species richness was unrelated to AGC. However, structural equation models indicated different mechanisms by which these biodiversity components drove AGC in forests and savannas. In forests, species richness had an indirect, positive effect on AGC via basal area, but also an indirect, negative effect, through a reduction in CWM of maximum height. In savannas, species richness had a direct, negative effect on AGC, while both CWM of maximum height (through an increase in basal area) and FDvar of wood density had positive effects. Our study suggests that integrative models are crucial for understanding the effects of species richness, functional trait diversity, and identity on AGC across forests. Furthermore, our study shows that relationships between biodiversity and AGC differ among ecosystem types. In both forests and savannas, FI played an important role, as AGC was maximized in communities dominated by species with a high maximum height. However, only in savannas a high FD additionally promoted AGC.

A new remote sensing-based carbon sequestration potential index (CSPI): A tool to support land carbon management

Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.

High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA

The inclusion of forest carbon in climate change mitigation planning requires the development of models able to project potential future carbon stocks—a step beyond traditional monitoring, reporting and verification frameworks. Here, we updated and expanded a high-resolution forest carbon modelling approach previously developed for the state of Maryland to 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain, which includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. In this study, we employ an updated version of the Ecosystem Demography (ED) model, an improved lidar initialization strategy, and an expanded calibration/validation approach. High resolution (90 m) wall-to-wall maps of present aboveground carbon, aboveground carbon sequestration potential, aboveground carbon sequestration potential gap (CSPG), and time to reach sequestration potential were produced over the RGGI domain where airborne lidar data were available, including 100% of eight states, 62% of Maine, 12% of New Jersey, and 0.65% of New York. For the eight states with complete data, an area of 228 552 km2, the contemporary forest aboveground carbon stock is estimated to be 1134 Tg C, and the forest aboveground CSPG is estimated to be larger at >1770 Tg C. Importantly, these estimates of the potential for added aboveground carbon sequestration in forests are spatially resolved, are further partitioned between continued growth of existing trees and new afforested/reforested areas, and include time estimates for realization. They are also assessed for sensitivity to potential changes in vegetation productivity and disturbance rate in response to climate change. The results from this study are intended as input into regional, state, and local planning efforts that consider future climate mitigation in forests along with other land-use considerations.

Carbon balance of tropical peat forests at different fire history and implications for carbon emissions.

Accurate assessment of tropical peat forest carbon stocks and impact of fires on carbon pools is required to determine the magnitude of emissions to the atmosphere and to support emissions reduction policies. We assessed total aboveground carbon (AGC) in biomass pools including trees, shrubs, deadwood, litter and char, and peat carbon to develop empirical estimates of peat swamp forest carbon stocks in response to fire and disturbance. In contrast to the common assumption that peat fires combust all AGC, we observed that about half of undisturbed forest AGC, equivalent to about 70Mg C ha-1, remains after one or two recent fires - mainly in dead trees, woody debris and pyrogenic carbon. Both recently burnt and repeatedly burnt peat forests store similar amounts of carbon in the top 10cm of peat when compared with undisturbed forests (70Mg C ha-1), mainly due to increased peat bulk density after fires that compensates for their lower peat C%. The proportion of fuel mass consumed in fire, or combustion factor (CF), is required to make accurate estimates of peat fire emissions for both AGC and peat carbon. This study estimated a CF for AGC (CFAGC) of 0.56, comparable to the default value of the Intergovernmental Panel on Climate Change (IPCC). This study estimated a varying CF for peat (CFPEAT) that ranged from 0.4 to 0.68 as depth of burn increased. This revised CFPEAT is one third to one half of the IPCC default value of 1.0. The current assumption of complete combustion of peat (CF=1.0) is widely acknowledged in the literature as oversimplification and is not supported by our field observations or data. This study provides novel empirical data to improve estimates of peat forests carbon stocks and emissions from tropical peat fires.

High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA’s Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r 2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha−1), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r 2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha−1) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA’s spaceborne lidar missions.