Estimates Of Forest Carbon Storage Research Articles

The distribution of tree biomass carbon within the Pacific Coastal Temperate Rainforest, a disproportionally carbon dense forest

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.

Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning

Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation.

Allodb: An R package for biomass estimation at globally distributed extratropical forest plots

Abstract Allometric equations for calculation of tree above‐ground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built‐in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types.

A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System

The multi-baseline synthetic aperture radar (SAR) tomography (TomoSAR) system is employed in such applications as disaster remote sensing, urban 3-D reconstruction, and forest carbon storage estimation. This is because of its 3-D imaging capability in a single-pass platform. However, a high 3-D resolution of TomoSAR is based on the premise that the channel imbalance and antenna phase center (APC) position are precisely known. If this is not the case, the 3-D resolution performance will be seriously degraded. In this paper, a unified algorithm for channel imbalance and APC position calibration of a single-pass multi-baseline TomoSAR system is proposed. Based on the maximum likelihood method, as well as the least squares and the damped Newton method, we can calibrate the channel imbalance and APC position. The algorithm is suitable for near-field conditions, and no phase unwrapping operation is required. The effectiveness of the proposed algorithm has been verified by simulation and experimental results.

Characteristics of Carbon Storage and Density in Different Layers of Forest Ecosystems

Characteristics of carbon storage and density in different layers of forest ecosystems should be studied comprehensively and in more detail. Using forest inventory data in combination with field survey data, we explored the characteristics of carbon storage and density in different layers of forest ecosystems in Liaoning Province of China. Results showed that total carbon storage was 813.034 Tg C. The carbon storage of soil layer accounted for 81.0% of the total storage with 658.783 Tg C, followed by those of arbor, litter and shrub layers with 128.403 Tg C (15.8%), 22.723 Tg C (2.8%) and 3.125 Tg C (0.4%), respectively. The average carbon density for the forest ecosystems were 183.571 Mg C ha–1, with soil layer (148.744 Mg C ha–1) being the highest one, followed by arbor layer (28.992 Mg C ha–1), litter layer (5.131 Mg C ha–1) and shrub-grass layer (0.706 Mg C ha–1). Carbon storage in different forest ecosystems varied from 1.595 to 319.161 Tg C, while C density ranged from 165.067 to 235.947Mg C ha–1, with the highest and lowest values being observed in soil layer and shrub-grass layers, respectively, implying that soil is the main body of forest carbon storage. Young-aged forests accounted for a greater proportion of forests in the Province than forests in other age classes; and proper management of forests could increase the carbon sequestration in the forest ecosystems. The comparison with previous estimations of carbon storage for forest ecosystem implied that methods and data used for forest carbon storage estimation affected the results of estimates obviously.

Correcting the overestimate of forest biomass carbon on the national scale

SummaryFor decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error.The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively.Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation.The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades.This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

The impact of urban residential development patterns on forest carbon density: An integration of LiDAR, aerial photography and field mensuration

Urban development continues to reshape forest landscapes and influence the carbon storage capacity of trees. To date, the impact of urban patterns on forest carbon density remains to be systematically evaluated. A major challenge is the lack of accurate and spatially explicit estimates of forest carbon storage over the entire urbanized area. In this study, we first developed an integrated approach that synergizes remote sensing LiDAR (light detection and ranging) and aerial photography to efficiently model landscape-level forest carbon storage in an urban environment at a fine resolution of 20m. Using a case study in the Charlotte Metropolitan Region, USA, we were able to determine the total amount of carbon stored in the local forests to be 3.8 million tonnes ($298 million value), with an average carbon density of 53.6t/ha. We further applied statistical analysis to investigate the relationship between urban developed patterns (i.e., landscape metrics) and forest carbon density in four types of residential neighborhoods (categorized by percent built-up ranging from low, medium-low, medium-high to high density). Results indicate a decrease of forest carbon density with an increase of carbon variance in neighborhoods where the intensity of development became higher. Residential neighborhoods with a higher built-up density were more likely to be affected by a larger number of landscape metrics. This indicates that a proper design of the neighborhood level urban spatial patterns (especially in high density neighborhoods) is essential to maximizing forest carbon storage at the landscape level.

Estimates of forest biomass carbon storage inLiaoning Province of Northeast China: a review and assessment.

Accurate estimates of forest carbon storage and changes in storage capacity are critical for scientific assessment of the effects of forest management on the role of forests as carbon sinks. Up to now, several studies reported forest biomass carbon (FBC) in Liaoning Province based on data from China's Continuous Forest Inventory, however, their accuracy were still not known. This study compared estimates of FBC in Liaoning Province derived from different methods. We found substantial variation in estimates of FBC storage for young and middle-age forests. For provincial forests with high proportions in these age classes, the continuous biomass expansion factor method (CBM) by forest type with age class is more accurate and therefore more appropriate for estimating forest biomass. Based on the above approach designed for this study, forests in Liaoning Province were found to be a carbon sink, with carbon stocks increasing from 63.0 TgC in 1980 to 120.9 TgC in 2010, reflecting an annual increase of 1.9 TgC. The average carbon density of forest biomass in the province has increased from 26.2 Mg ha−1 in 1980 to 31.0 Mg ha−1 in 2010. While the largest FBC occurred in middle-age forests, the average carbon density decreased in this age class during these three decades. The increase in forest carbon density resulted primarily from the increased area and carbon storage of mature forests. The relatively long age interval in each age class for slow-growing forest types increased the uncertainty of FBC estimates by CBM-forest type with age class, and further studies should devote more attention to the time span of age classes in establishing biomass expansion factors for use in CBM calculations.

Multi-year convergence of biometric and meteorological estimates of forest carbon storage

We measured annual C storage, or net ecosystem production (NEP), from 1999 to 2003 in an aspen-dominated, mixed-deciduous forest in Michigan, USA. Measurements of the annual production of above- and below-ground live and dead mass, foliar herbivory, and soil respiration were used to develop biometric estimates of NEP (NEP B). Eddy-covariance measurements made above the canopy were used to construct meteorological estimates of NEP (NEP M). Over the 5-year study period, C stored annually in live mass (above- and below-ground wood) averaged 2.13 Mg C ha −1 year −1, while annual detritus production (fine roots, leaf litter, woody debris) averaged 4.35 Mg C ha −1 year −1. Fine root inputs were the largest component (41%) of annual net primary production. The forest was a consistent C sink, with annual NEP ranging from 0.80 to 1.98 Mg C ha −1 year −1, comparable in magnitude to other eastern North American forests. When annual NEP B and NEP M were compared in a given year, they differed from each other by 13–148%. However, when compared over 5 years, these independent estimates of C storage converged to within 1% of each other. Differences between same-year biometric and meteorological NEP estimates were explained in part by a lag between late-season net canopy photosynthesis and C allocated to tree growth the following spring. Weekly assessments of bole radial growth in 2001 and 2002 indicated that woody mass growth began prior to positive net canopy photosynthesis in the spring and that >25% of annual photosynthetic C assimilation occurred after growth had stopped in the autumn. Thus, while the temporal separation between photosynthesis and growth reduced agreement between annual NEP B and NEP M estimates, these metrics converged over several years. These results suggest that the allocation of recent photosynthate to storage carbohydrates, rather than to immediate growth, may obscure the relationship between shorter term, annual biometric and meteorological C storage estimates. Moreover, the 5-year convergence of biometric and meteorological NEP estimates serves as an important cross-validation, demonstrating that both approaches can yield accurate forest C storage assessments.

Biometric and eddy-covariance based estimates of annual carbon storage in five eastern North American deciduous forests

Quantifying net carbon (C) storage by forests is a necessary step in the validation of carbon sequestration estimates and in assessing the possible role of these ecosystems in offsetting fossil fuel emissions. In eastern North America, five sites were established in deciduous forests to provide measurements of net ecosystem CO 2 exchange (NEE) using micro-meteorological methods, and measures of major carbon pools and fluxes, using a combination of forest mensurational, eco-physiological, and other biometric methods. The five study sites, part of the AmeriFlux network, ranged across 10° of latitude and 18° of longitude, but were all of similar age, canopy height, and stand basal area. Here we present a cross-site synthesis of annual carbon storage estimates, comparing meteorological and biometric approaches, and also comparing biometric estimates based on analyses of autotrophic carbon pools and heterotrophic carbon fluxes (net ecosystem production, NEP) versus those based on measurements of change in two major carbon pools (Δ C). Annual above-ground net primary production (ANPP) varied nearly two-fold among sites and was strongly correlated with average annual temperature and with annual soil nitrogen mineralization ( N min). Estimates of NEP ranged from 0.7 Mg C per hectare per year in northern lower Michigan to 3.5 Mg C per hectare per year in central Indiana, and were also well correlated with N min. There was less variation among sites in estimates of Δ C (range, 1.8–3.2 Mg C per hectare per year). In general, Δ C more closely matched NEE than did NEP, but there was no systematic pattern among sites in over- versus under-estimation of the biometric compared to the meteorologically based measures. Root and soil carbon dynamics were significant sources of uncertainty in our biometric measures and represent a prerequisite area of study needed for accurate estimates of forest carbon storage.