Aboveground Forest Carbon Stocks Research Articles

Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

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.

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.

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+.

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.

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.

Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran

A framework for estimating aboveground forest carbon stock (AFCS) is required for measurement, reporting, and verification (MRV) systems under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation (REDD) in Developing Countries. Recently, methods for estimating the spatial distribution of AFCS using remotely sensed datasets and multiple prediction techniques have been found to be useful, particularly given the prohibitive costs of acquiring the necessary sample sizes for sufficiently precise pure field-based estimation of this variable. The objective of the study was to assess and compare the capabilities of airborne laser scanning (ALS) data, L-band radar data and UltraCam images in combination with four commonly used prediction techniques for estimating mean AFCS per unit area: multiple linear regression (MLR) and the nonparametric k-Nearest Neighbors (k-NN), support vector regression (SVR), and random forest (RF) algorithms. Our study area was a part of Hyrcanian deciduous forests in two managed and unmanaged stands at Shast Kalateh forest. We used a systematic sample consisting of 308 circular field plots of 0.1 ha located at the intersections of a 150 × 200 m grid with a random starting point and remote sensing-derived metrics as auxiliary data. We used 67% of the sample plots for training purposes and the remaining 33% for validation. Also, we used the model-assisted estimators to statistically rigorously estimate mean AFCS per unit area and its standard error (SE).Among the remotely sensed datasets, considered singly, the ALS data with R2⁎ = 0.34, rRMSE = 47.42% and relative efficiency (RE) = 1.51 produced the greatest accuracy and precision for AFCS estimation. RE can be interpreted as the factor by which the sample size for the pure field-based estimator would have to be increased to obtain the same precision as for the model-assisted estimator using auxiliary data. Among the remotely sensed datasets considered in combination, the ALS and PALSAR dataset with R2⁎ = 0.41, rRMSE = 44.80% and RE = 1.70 produced the greatest prediction accuracy and precision and increased the proportion of variability explained relative to ALS and PALSAR separately by 7% and 36%, respectively. Contrary to our expectation, the combination of PALSAR and UltraCam data decreased the precision of AFCS estimates. All the remotely sensed datasets, singly and in combination, with the most accurate prediction techniques for each combination produced estimates of AFCS that were within the 95% confidence interval for the field-based estimate.

Carbon Dynamics in a Human-Modified Tropical Forest: A Case Study Using Multi-Temporal LiDAR Data

Tropical forests hold significant amounts of carbon and play a critical role on Earth´s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance (e.g., selective logging, understory fires, and fragmentation). Understanding the multi-temporal dynamics of carbon stocks over human-modified tropical forests (HMTF) is crucial to close the carbon cycle balance in the tropics. Here, we used multi-temporal and high-spatial resolution airborne LiDAR data to quantify rates of carbon dynamics over a large patch of HMTF in eastern Amazon, Brazil. We described a robust approach to monitor changes in aboveground forest carbon stocks between 2012 and 2018. Our results showed that this particular HMTF lost 0.57 m·yr−1 in mean forest canopy height and 1.38 Mg·C·ha−1·yr−1 of forest carbon between 2012 and 2018. LiDAR-based estimates of Aboveground Carbon Density (ACD) showed progressive loss through the years, from 77.9 Mg·C·ha−1 in 2012 to 53.1 Mg·C·ha−1 in 2018, thus a decrease of 31.8%. Rates of carbon stock changes were negative for all time intervals analyzed, yielding average annual carbon loss rates of −1.34 Mg·C·ha−1·yr−1. This suggests that this HMTF is acting more as a source of carbon than a sink, having great negative implications for carbon emission scenarios in tropical forests. Although more studies of forest dynamics in HMTFs are necessary to reduce the current remaining uncertainties in the carbon cycle, our results highlight the persistent effects of carbon losses for the study area. HMTFs are likely to expand across the Amazon in the near future. The resultant carbon source conditions, directly associated with disturbances, may be essential when considering climate projections and carbon accounting methods.

The future of Southeast Asia’s forests

While Southeast Asia’s forests play important roles in biodiversity conservation and global carbon (C) balance, the region is also a deforestation hotspot. Here, we consider the five shared socioeconomic pathways (SSPs) to portray a range of plausible futures for the region’s forests, employing a state-of-the-art land change modelling procedure and remotely sensed data. We find that by 2050 under the worst-case scenario, SSP 3 (regional rivalry/a rocky road), the region’s forests would shrink by 5.2 million ha. The region’s aboveground forest carbon stock (AFCS) would decrease by 790 Tg C, 21% of which would be due to old-growth forest loss. Conversely, under the best-case scenario, SSP 1 (sustainability/taking the green road), the region is projected to gain 19.6 million ha of forests and 1651 Tg C of AFCS. The choice of the pathway is thus critical for the future of the region’s forests and their ecosystem functions and services.

Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA

Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current forest cover and carbon stocks. For planning, the additional step of modeling is required to quantitatively estimate forest carbon sequestration potential in response to alternative land-use and management decisions. To be most useful and of decision-relevant quality, these model estimates must be at very high spatial resolution and with very high accuracy to capture important heterogeneity on the land surface and connect to monitoring efforts. Here, we present results from a new forest carbon monitoring and modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and project future forest carbon sequestration potential for the state of Maryland at 90 m resolution. Statewide, the contemporary above-ground carbon stock was estimated to be 110.8 Tg C (100.3–125.8 Tg C), with a corresponding mean above-ground biomass density of 103.7 Mg ha−1 which was within 2% of independent empirically-based estimates. The forest above-ground carbon sequestration potential for the state was estimated to be much larger at 314.8 Tg C, and the forest above-ground carbon sequestration potential gap (i.e. potential-current) was estimated to be 204.1 Tg C, nearly double the current stock. These results imply a large statewide potential for future carbon sequestration from afforestation and reforestation activities. The high spatial resolution of the model estimates underpinning these totals demonstrate important heterogeneity across the state and can inform prioritization of actual afforestation/reforestation opportunities. With this approach, it is now possible to quantify both the forest carbon stock and future carbon sequestration potential over large policy relevant areas with sufficient accuracy and spatial resolution to significantly advance planning.

Forest management in the Sierra Nevada provides limited carbon storage potential: an expert elicitation

AbstractAnalysis of long‐term trends in forest carbon stocks is challenged by interactions among climate change, wildfire and other disturbances, forest management actions, and heterogeneous vegetation responses. For such circumstances where complex interactions make it difficult to encompass the full range of processes in any one mode of analysis, expert elicitation is a well‐developed method for documenting judgments about uncertainty, based on available evidence, to inform ongoing decision‐making. Applying this method for the Sierra Nevada, we evaluate subjective probabilistic estimates of trends in aboveground forest carbon for different management scenarios toward the goal of maximizing carbon stored, while also considering implications for wildfire risk. The analysis examines the effects of four treatments in isolation (thinning, timber harvesting, prescribed burning, managed wildfire), as well as a user‐defined management portfolio allocating resources across five management practices (thinning, harvesting, prescribed burning, firefighting, and restoration). The expert elicitation suggests that aboveground forest carbon stocks will decline 8%, from 126 to 116 tC/ha, between 2030 and 2100 (median estimate across experts) assuming conventional forest management practices are continued. Out of all surveyed practices, the custom user‐defined management portfolio results in the highest carbon stock of 129 tC/ha which is 11% higher than conventional practice in 2100 at the 50th percentile. The expert elicitation indicates less beneficial carbon sequestration outcomes than recent modeling studies. Suggesting co‐benefits across objectives, 75 experts collectively estimate a 61% likelihood that managing for carbon also reduces wildfire risk. By contrast, decreases in carbon stocks are anticipated for large magnitudes of climate change or substantial decreases in forest management investments.

Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest Carbon Stocks

This study examines the potential of airborne PiSAR-L2 data for estimating forest carbon stocks in central Sumatra. Polarimetric interferometric synthetic aperture radar L-band-2 (PiSAR-L2) is a second-generation airborne sensor developed by JAXA. We acquired full-polarimetric data at a fine spatial resolution of 2.5 m during the PiSAR-L2 flight campaign in August 2012. A total of 59 field measurement plots for aboveground forest carbon stocks (AFCSs) were established in same year where AFCS ranged between 4.8 and $253.5\; \text{Mg}\; \text{C} \;\text{ha}^{-1}$ . The plots comprised natural and plantation forests. These plot-level field data were used for calibrating and validating AFCS estimation models with the SAR data. Various possibilities including direct sigma naught backscatters and their ratios and various types of textures obtained from HH, HV, and VV polarizations were examined by applying regression modeling. The main indicators used for the selection of best potential models in the calibration phase were $\text{R}^{2}$ , variable inflation factor (VIF), p-value, and root-mean-squared errors (RMSEs). The potential models were validated using the leave-one-out (LOO) method. The results indicated that a simple combination of backscatters and their ratios provides an AFCS estimate with an RMSE of $42.37\; \text{Mg}\; \text{C}\; \text{ha}^{-1}$ and an $\text{R}^{2}$ of 0.65. Inclusion of SAR textural parameters improved the AFCS estimates with an RMSE of $30.93\;\text{Mg}\; \text{C}\; \text{ha}^{-1}$ and an $\text{R}^{2}$ of 0.80. This indicates that the airborne PiSAR-L2 full-polarimetric data have the potential to estimate forest carbon stocks with an improved accuracy in the tropical region.

Effects of experimental fuel additions on fire intensity and severity: unexpected carbon resilience of a neotropical forest.

Global changes and associated droughts, heat waves, logging activities, and forest fragmentation may intensify fires in Amazonia by altering forest microclimate and fuel dynamics. To isolate the effects of fuel loads on fire behavior and fire-induced changes in forest carbon cycling, we manipulated fine fuel loads in a fire experiment located in southeast Amazonia. We predicted that a 50% increase in fine fuel loads would disproportionally increase fire intensity and severity (i.e., tree mortality and losses in carbon stocks) due to multiplicative effects of fine fuel loads on the rate of fire spread, fuel consumption, and burned area. The experiment followed a fully replicated randomized block design (N=6) comprised of unburned control plots and burned plots that were treated with and without fine fuel additions. The fuel addition treatment significantly increased burned area (+22%) and consequently canopy openness (+10%), fine fuel combustion (+5%), and mortality of individuals ≥5cm in diameter at breast height (dbh; +37%). Surprisingly, we observed nonsignificant effects of the fuel addition treatment on fireline intensity, and no significant differences among the three treatments for (i) mortality of large trees (≥30cm dbh), (ii) aboveground forest carbon stocks, and (iii) soil respiration. It was also surprising that postfire tree growth and wood increment were higher in the burned plots treated with fuels than in the unburned control. These results suggest that (i) fine fuel load accumulation increases the likelihood of larger understory fires and (ii) single, low-intensity fires weakly influence carbon cycling of this primary neotropical forest, although delayed postfire mortality of large trees may lower carbon stocks over the long term. Overall, our findings indicate that increased fine fuel loads alone are unlikely to create threshold conditions for high-intensity, catastrophic fires during nondrought years.

Change Analysis of Aboveground Forest Carbon Stocks According to the Land Cover Change Using Multi-Temporal Landsat TM Images and Machine Learning Algorithms

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

Time-series maps of aboveground carbon stocks in the forests of central Sumatra

BackgroundEfforts to reduce emissions from deforestation and forest degradation in tropical Asia require accurate high-resolution mapping of forest carbon stocks and predictions of their likely future variation. Here we combine radar and LiDAR with field measurements to create a high-resolution aboveground forest carbon stock (AFCS) map and use spatial modeling to present probable future AFCS changes for the Riau province of central Sumatra.ResultsOur map provides spatially explicit estimates of the AFCS with an accuracy of ±23.5 Mg C ha−1. According to this map, the natural forests in the province currently store 265 million Mg C, with a density of 72 Mg C ha−1, as aboveground biomass. Using a spatially explicit modeling technique we derived time-series AFCS maps up to the year 2030 under three forest policy scenarios: business as usual, conservation, and concession. The spatial patterns of AFCS and their trends under different scenarios vary on a local scale, and some areas are highlighted that are at eminent risk of carbon emission. Based on the business as usual scenario, the current AFCS could decrease by 75 %, which may lead to the release of 747 million Mg CO2. The other two scenarios, conservation and concession, suggest the risk reductions by 11 and 59 %, respectively.ConclusionThe time-series AFCS maps provide spatially explicit scenarios of changes in AFCS. These data may aid in planning Reducing Emissions from Deforestation and forest Degradation in developing countries projects in the study area, and stimulate the development of AFCS maps for other regions of tropical Asia.

Calibration of Aboveground Forest Carbon Stock Models for Major Tropical Forests in Central Sumatra Using Airborne LiDAR and Field Measurement Data

Despite substantial policy attention, tropical forests in Southeast Asian region are releasing large amount of carbon to the atmosphere due to accelerating deforestation. Accurately determining forest statistics and characterizing aboveground forest carbon stocks (AFCSs) are always challenging in the region. In order to develop more accurate estimates of AFCS, the present study collected airborne LiDAR and field measurements data and calibrated AFCS models to estimate carbon stock in the tropical forests in central Sumatra. The study region consists of natural forests, including peat swamp, dry moist, regrowth, and mangrove, and plantation forests, including rubber, acacia, oil palm, and coconut. To cover the different forest types, 60 field plots of 1 ha in size were inventoried. Eight transects crossing these field plots were acquired to calibrate the LiDAR to AFCS models. The AFCS values for the field plots ranged from 4 to 161 Mg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . General models were fitted without considering forest types, whereas a specific model was fitted for each specific forest type. Five alternative general models with different LiDAR metrics were calibrated with model performance expressed as R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ranging from 0.73 to 0.87 and root-meansquare error (RMSE) values ranging from 17.4 to 25.0 Mg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Seven forest-specific AFCS models were calibrated for different forest types, with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values ranging from 0.72 to 0.97 and RMSE values ranging from 1.4 to 10.7 Mg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . The performance of each model was cross-validated by iteratively removing one data point. While forest-specific models provide better AFCS estimates, the general models are still useful when forest types are ambiguous.

Potential of high-resolution ALOS–PALSAR mosaic texture for aboveground forest carbon tracking in tropical region

Estimating accurate aboveground forest carbon stocks (AFCS) is always challenging in tropical regions due to the complex mosaic of forest structure and species diversity. This study evaluates the potential of high-resolution ALOS/PALSAR mosaics data in the tropical forests of central Sumatra to improve AFCS estimates. The study region has an average AFCS (47% of the aboveground biomass) of 66Mg C ha−1 with a range of 1 to 334Mg C ha−1 and consists of natural forests including peat swamp, dry moist, regrowth, and mangrove, and plantation forests including rubber, acacia, oil palm, and coconut. Field measurements of AFCS were carried out in 87 (ha−1) plots, where half of them were from plantation forests. Various possibilities including direct gamma naught backscatters and their ratios and various types of textures of dual polarized mosaics from the years 2009 and 2010 were examined applying regression modeling in a five step framework. R2, variable inflation factor (VIF), p-value, and root mean square errors (RMSE) were the major indicators considered for selection of best model in the calibration process. The potential models selected were cross validated by the leave-one-out (LOO) method where R2, RMSE, mean deviation (MD), and Nash–Sutcliffe Efficiency (NSE, model performance indicator) were examined. The results indicate that a simple combination of backscatters and their ratios provides an AFCS estimate with a RMSE of 45Mg C ha−1, more efficient than the average of field measured AFCS (NSE of 0.54), and R2 of 0.63. Inclusion of appropriate texture parameters derived from the high-resolution PALSAR mosaics further increases the potential for AFCS estimation by increasing R2 and model performance (NSE) to 0.84 and 0.83, respectively and decreasing the uncertainty to 28Mg C ha−1. This SAR based method offers the low cost wall-to-wall forest carbon mapping with a high level of accuracy in the dense tropical forest regions of Southeast Asia where other methods are still rare.

Spatial and temporal dynamics of forest aboveground carbon stocks in response to climate and environmental changes

Climate change, especially global warming due to rising atmospheric carbon dioxide (CO2) concentration, has attracted much attention in the past century. Increasing efforts have been made to find solutions to mitigate the CO2 emission and sequester the existing CO2 in the atmosphere into land-based ecosystems. Forest ecosystems are the best effective way to fix the atmospheric CO2 by photosynthesis and allocate to tree biomass and into soils. Meanwhile, trees or forests will also respond to gradually increasing CO2 concentration and environment changes. It is important to quantify the dynamic interaction between the changing environment and activities of carbon (C) accumulation by forests with a proper method and also assess the status of the forest C stocks in response to climate and environmental changes. Estimation of forest aboveground C stock still experiences much uncertainty, even for the same forest ecosystem such as in the tropics, due to the different methods used. Most of the work has been based on inventory data and allometric equations to estimate biomass and calculate C stock by multiplying a C content coefficient. Great uncertainties exist because of the representativeness of the allometric equations, the differences in C content for different tree species, and the spatial heterogeneous nature of C distribution in the forest ecosystems. The development of remote sensing has stimulated applications of the technology in estimating forest aboveground C stocks at a larger scale. Remote sensing can reduce the uncertainty of spatial variations caused by extrapolation with the inventory methods, but it has the limitation of lacking the ability to express the processes involved in C accumulation and their responses to the changing environment. Tree growth and climate change information embedded in tree rings can be a good supplement to interpret the results acquired by the remote sensing technique. The application of remote sensing techniques offers a practical method for C stock estimates in forest ecosystems in the context of spatial variations. However, the long-term responses of forest C accumulation to the gradually changing environment and climate are still not well understood. Integrated study of combining remote sensing and ecological research techniques in forest ecosystems is necessary for future study to explore the mechanisms of interaction between forest development and the gradual changing environment and also to assess the C sequestration status and potential of forest ecosystems under climate change.

Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm

This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data.