Forest Carbon Stock Estimates Research Articles

Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric

Mapping aboveground carbon density (ACD) in tropical forests can enhance large-scale ecological studies and support CO2 emissions monitoring. Light Detection and Ranging (LiDAR) has proven useful for estimating carbon density patterns outside of field plot inventory networks. However, the accuracy and generality of calibrations between LiDAR-assisted ACD predictions (EACDLiDAR) and estimated ACD based on field inventory techniques (EACDfield) must be increased in order to make tropical forest carbon mapping more widely available. Using a network of 804 field inventory plots distributed across a wide range of tropical vegetation types, climates and successional states, we present a general conceptual and technical approach for linking tropical forest EACDfield to LiDAR top-of-canopy height (TCH) using regional-scale inputs of basal area and wood density. With this approach, we show that EACDLiDAR and EACDfield reach nearly 90% agreement at 1-ha resolution for a wide array of tropical vegetation types. We also show that Lorey's Height – a common metric used to calibrate LiDAR measurements to biomass – is severely flawed in open canopy forests that are common to the tropics. Our proposed approach can advance the use of airborne and space-based LiDAR measurements for estimation of tropical forest carbon stocks.

Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin

Policies to reduce emissions from deforestation and forest degradation largely depend on accurate estimates of tropical forest carbon stocks. Here we present the first field-based carbon stock data for the Central Congo Basin in Yangambi, Democratic Republic of Congo. We find an average aboveground carbon stock of 162 ± 20 Mg C ha(-1) for intact old-growth forest, which is significantly lower than stocks recorded in the outer regions of the Congo Basin. The best available tree height-diameter relationships derived for Central Africa do not render accurate canopy height estimates for our study area. Aboveground carbon stocks would be overestimated by 24% if these inaccurate relationships were used. The studied forests have a lower stature compared with forests in the outer regions of the basin, which confirms remotely sensed patterns. Additionally, we find an average soil carbon stock of 111 ± 24 Mg C ha(-1), slightly influenced by the current land-use change.

Quantifying the sampling error in tree census measurements by volunteers and its effect on carbon stock estimates

A typical way to quantify aboveground carbon in forests is to measure tree diameters and use species-specific allometric equations to estimate biomass and carbon stocks. Using "citizen scientists" to collect data that are usually time-consuming and labor-intensive can play a valuable role in ecological research. However, data validation, such as establishing the sampling error in volunteer measurements, is a crucial, but little studied, part of utilizing citizen science data. The aims of this study were to (1) evaluate the quality of tree diameter and height measurements carried out by volunteers compared to expert scientists and (2) estimate how sensitive carbon stock estimates are to these measurement sampling errors. Using all diameter data measured with a diameter tape, the volunteer mean sampling error (difference between repeated measurements of the same stem) was 9.9 mm, and the expert sampling error was 1.8 mm. Excluding those sampling errors > 1 cm, the mean sampling errors were 2.3 mm (volunteers) and 1.4 mm (experts) (this excluded 14% [volunteer] and 3% [expert] of the data). The sampling error in diameter measurements had a small effect on the biomass estimates of the plots: a volunteer (expert) diameter sampling error of 2.3 mm (1.4 mm) translated into 1.7% (0.9%) change in the biomass estimates calculated from species-specific allometric equations based upon diameter. Height sampling error had a dependent relationship with tree height. Including height measurements in biomass calculations compounded the sampling error markedly; the impact of volunteer sampling error on biomass estimates was +/- 15%, and the expert range was +/- 9%. Using dendrometer bands, used to measure growth rates, we calculated that the volunteer (vs. expert) sampling error was 0.6 mm (vs. 0.3 mm), which is equivalent to a difference in carbon storage of +/- 0.011 kg C/yr (vs. +/- 0.002 kg C/yr) per stem. Using a citizen science model for monitoring carbon stocks not only has benefits in educating and engaging the public in science, but as demonstrated here, can also provide accurate estimates of biomass or forest carbon stocks.

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.

Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage.

The U.S. has been providing national-scale estimates of forest carbon (C) stocks and stock change to meet United Nations Framework Convention on Climate Change (UNFCCC) reporting requirements for years. Although these currently are provided as national estimates by pool and year to meet greenhouse gas monitoring requirements, there is growing need to disaggregate these estimates to finer scales to enable strategic forest management and monitoring activities focused on various ecosystem services such as C storage enhancement. Through application of a nearest-neighbor imputation approach, spatially extant estimates of forest C density were developed for the conterminous U.S. using the U.S.’s annual forest inventory. Results suggest that an existing forest inventory plot imputation approach can be readily modified to provide raster maps of C density across a range of pools (e.g., live tree to soil organic carbon) and spatial scales (e.g., sub-county to biome). Comparisons among imputed maps indicate strong regional differences across C pools. The C density of pools closely related to detrital input (e.g., dead wood) is often highest in forests suffering from recent mortality events such as those in the northern Rocky Mountains (e.g., beetle infestations). In contrast, live tree carbon density is often highest on the highest quality forest sites such as those found in the Pacific Northwest. Validation results suggest strong agreement between the estimates produced from the forest inventory plots and those from the imputed maps, particularly when the C pool is closely associated with the imputation model (e.g., aboveground live biomass and live tree basal area), with weaker agreement for detrital pools (e.g., standing dead trees). Forest inventory imputed plot maps provide an efficient and flexible approach to monitoring diverse C pools at national (e.g., UNFCCC) and regional scales (e.g., Reducing Emissions from Deforestation and Forest Degradation projects) while allowing timely incorporation of empirical data (e.g., annual forest inventory).

Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features

Information on forest biomass is required for several purposes, including estimation of forest bioenergy resources and forest carbon stocks. Airborne laser scanning is today considered the most accurate remote sensing method for forest inventory. The three-dimensional nature of laser scanning data enables estimation of the volumes of the tree canopies. The dimensions of the tree canopies show high correlation with the amount of forest biomass. Optical aerial photographs are often used to complement laser data, for improved distinction between tree species. The paper reports on a study testing the estimation of forest biomass variables in two study areas in Southern Finland. The biomass variables were derived on the basis of tree-level field measurements, with biomass models used for pine, spruce, and birch. The sample-plot-level biomass components were derived on the basis of tree-level data and used as reference data for airborne-laser- and aerial‑photograph-based estimation. Results were slightly better for total biomass (RMSE 22.5% and 23.6% for the two study areas) than total volume (RMSE: 23.4% and 26.1%). Species-specific estimation errors were large in general but varied between the study areas, because of differences in their forest structures.

Using basal area to estimate aboveground carbon stocks in forests: La Primavera Biosphere's Reserve, Mexico

Increasing use of woody plants for greenhouse gas mitigation has led to demand for rapid, cost-effective estimation of forest carbon stocks. Bole diameter is readily measured and basal area can be correlated to biomass and carbon through application of allometric equations. We explore different forms of allometric equations and analyse the potential to use of equations for individual trees to derive stand-level equations, where the basal area and the average diameter are used as explanatory variables. To test the relationships derived from published allometric equations, we used data from a forest inventory in the oak–pine forests in La Primavera (Mexico). Results show that in two forests with the same species and basal area, there will be more carbon where trees are larger. Allometric equations for individual trees can be transformed into stand-level equations. The values of average diameter weighted by the basal area for these equations can be based on a small sample of large trees once the local relationship between tree size and tree density per hectare is known. This approach could considerably reduce field data requirements in comparison with inventory methods based on enumeration of all trees for estimation of biomass and carbon.

A CASE STUDY OF A FOREST CARBON STOCK MONITORING SYSTEM FOR REDD+ IN LAO P.D.R.

Abstract. Various technical studies for building forest monitoring system for MRV system of REDD+ has been implemented utilizing satellite remote sensing technology and ground survey upon configuring two pilot study areas, at whole Louangphabang (LPB) province (approximately 20,000 km2) and in Bolikhmxai(BLK) province (approximately 4,400 km2) in Lao PDR. Multi-temporal land use/cover data were prepared for making analyses of deforestation and forest degradation caused by various driving factors, and to establish reference scenario for REDD+. In addition to ordinary method of forest carbon stock estimation based on the forest plot surveys, land use/cover maps and IPCC's emission factors (GOFC-GOLD, 2010), improved methods were studied introducing a concept of biomass classing derived from multispectral data and tree height measurement utilizing ALOS/PRIS stereo images, in order to reduce difficulty of field surveys at high altitude and steep mountain forest, especially in natural forest areas. First, multi-temporal land use/cover maps were prepared for two pilot study areas for analyzing deforestation and forest degradation of the subjected area. Then, the biomass level of "Current Forest" area of the land use/cover maps were classified into three classes as high, medium, and low applying spectral analyses of LANDSAT/TM and SPOT images, and based on visual interpretation results of pan-sharpened ALOS/AVNIR2 images in addition to limited number of field surveys as references. Matching accuracies were around 60% at this stage of study (This number improved to 85% at the later stage). Based on the field survey data, the forest carbon stock vs. tree height model was established, and furthermore it was related to the forest biomass classes. ALOS/PRISM images were used to measure heights at about 1,500 forest stands selected at 2 – 4 km grid intervals. Accuracy analyses showed that the standard deviation of the tree height measurement errors was approximately 4 – 5 m, but the mean value of the measured data were within 1– 2 m comparing to the field survey data. Finally, wall-to-wall, above-ground forest carbon stock estimation maps which would be useful for forest management and REDD+ were prepared. As a conclusion, it can be said that 3D measurement, in addition to multi-spectral information, of the forest provides useful information for monitoring forest carbon stock for REDD+ although further refinement of technologies is to be needed. And, the results and experiences obtained from the studies will provide useful data for establishing actual MRVsystem.

Landscape-scale variation in structure and biomass of Amazonian seasonally flooded and unflooded forests

Accurate estimates of current forest carbon stocks are required for efforts to reduce emissions from tropical deforestation and forest degradation. The relative contributions of different vegetation types to carbon stocks and potential emissions are poorly understood in highly heterogeneous forest mosaics, and further field-based measurements are necessary from severely undersampled regions and forest types to improve regional scale extrapolations based on remote sensing. We assessed the aboveground biomass (AGB) of two contiguous western Brazilian Amazonian protected areas totalling 886,176ha, which contain vast expanses of seasonally flooded várzea (VZ) forest along the floodplain of the Juruá river and adjacent terra firme (TF) forest farther inland. Estimates were based on equations incorporating wood specific gravity (WSG) and tree height in addition to DBH, and derived from a network of 200 forest plots of 0.1ha (=20ha) sampled across adjacent areas of flooded and unflooded forest. A large number of small plots stratified by forest type allowed a more representative sample, encompassing the considerable variation in forest structure and composition both within and between forest types. Mean basal area per plot was higher in várzea forest plots than in terra firme plots (VZ: 37.6±1.2m2ha−1; TF: 32.4±0.9m2ha−1) but AGB was lower in várzea (VZ: 281.9±12.0Mgha−1; TF: 358.4±14.4Mgha−1) due to lower WSG and tree height. Linear mixed effects models showed the overriding effect of forest type on AGB, and the roles of water stress and a historical signature of selective logging pressure, particularly within várzea forests. ALOS ScanSAR generated categories of flood duration provided a more relevant description of water stress than SRTM elevation data; AGB within várzea forest was higher in plots subjected to longer flood duration. Várzea forests store significant levels of forest carbon despite their lighter-wooded trees and lower canopy stature, and yet are heavily settled by rural Amazonians, and are increasingly vulnerable to deforestation and logging. This study helps understand how baseline environmental gradients and human disturbances in these unique forests affect their carbon storage value, and highlights their importance both within and outside existing protected areas.

Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates

Forest ecosystems are the largest terrestrial carbon sink on earth and their management has been recognized as a relatively cost-effective strategy for offsetting greenhouse gas emissions. Forest carbon stocks in the U.S. are estimated using data from the USDA Forest Service, Forest Inventory and Analysis (FIA) program. In an attempt to balance accuracy with consistency, the FIA program recently developed the component ratio method which utilizes regional volume models to replace the existing set of generalized allometric regression models used to estimate biomass and carbon stocks. This study describes the impact of the transition from the generalized allometric regression models to the component ratio method on the National Greenhouse Gas Inventory estimates by comparing estimates of carbon stocks from both approaches by common tree species and varying spatial scales (e.g., tree to national scale). Results for the 20 most abundant tree species in the 48 conterminous states of the U.S. suggest there is a significant difference in estimates of carbon stocks at the plot and national scales for the two estimation approaches. The component ratio method decreased estimates of national carbon stocks by an average of 16% for the species in the study. The observed reductions in carbon estimates can be attributed to incorporation of tree height as a predictor variable into species-specific volume models used to estimate tree biomass and carbon stocks. While the transition from the generalized allometric regression models to the component ratio method is procedural in nature, it may have important implications for national and global forest carbon sink estimates and the perception of the role forests play in mitigating the effects of atmospheric carbon dioxide. By combining regional accuracy with a nationally consistent approach, the component ratio method reflects a critical first step in aligning estimates of forest carbon stocks in the U.S.’s National Greenhouse Gas Inventory with estimates of tree volume in the FIA database.

Historic emissions from deforestation and forest degradation in Mato Grosso, Brazil: 1) source data uncertainties

BackgroundHistoric carbon emissions are an important foundation for proposed efforts to Reduce Emissions from Deforestation and forest Degradation and enhance forest carbon stocks through conservation and sustainable forest management (REDD+). The level of uncertainty in historic carbon emissions estimates is also critical for REDD+, since high uncertainties could limit climate benefits from credited mitigation actions. Here, we analyzed source data uncertainties based on the range of available deforestation, forest degradation, and forest carbon stock estimates for the Brazilian state of Mato Grosso during 1990-2008.ResultsDeforestation estimates showed good agreement for multi-year periods of increasing and decreasing deforestation during the study period. However, annual deforestation rates differed by > 20% in more than half of the years between 1997-2008, even for products based on similar input data. Tier 2 estimates of average forest carbon stocks varied between 99-192 Mg C ha-1, with greatest differences in northwest Mato Grosso. Carbon stocks in deforested areas increased over the study period, yet this increasing trend in deforested biomass was smaller than the difference among carbon stock datasets for these areas.ConclusionsEstimates of source data uncertainties are essential for REDD+. Patterns of spatial and temporal disagreement among available data products provide a roadmap for future efforts to reduce source data uncertainties for estimates of historic forest carbon emissions. Specifically, regions with large discrepancies in available estimates of both deforestation and forest carbon stocks are priority areas for evaluating and improving existing estimates. Full carbon accounting for REDD+ will also require filling data gaps, including forest degradation and secondary forest, with annual data on all forest transitions.

Accounting for density reduction and structural loss in standing dead trees: Implications for forest biomass and carbon stock estimates in the United States

BackgroundStanding dead trees are one component of forest ecosystem dead wood carbon (C) pools, whose national stock is estimated by the U.S. as required by the United Nations Framework Convention on Climate Change. Historically, standing dead tree C has been estimated as a function of live tree growing stock volume in the U.S.'s National Greenhouse Gas Inventory. Initiated in 1998, the USDA Forest Service's Forest Inventory and Analysis program (responsible for compiling the Nation's forest C estimates) began consistent nationwide sampling of standing dead trees, which may now supplant previous purely model-based approaches to standing dead biomass and C stock estimation. A substantial hurdle to estimating standing dead tree biomass and C attributes is that traditional estimation procedures are based on merchantability paradigms that may not reflect density reductions or structural loss due to decomposition common in standing dead trees. The goal of this study was to incorporate standing dead tree adjustments into the current estimation procedures and assess how biomass and C stocks change at multiple spatial scales.ResultsAccounting for decay and structural loss in standing dead trees significantly decreased tree- and plot-level C stock estimates (and subsequent C stocks) by decay class and tree component. At a regional scale, incorporating adjustment factors decreased standing dead quaking aspen biomass estimates by almost 50 percent in the Lake States and Douglas-fir estimates by more than 36 percent in the Pacific Northwest.ConclusionsSubstantial overestimates of standing dead tree biomass and C stocks occur when one does not account for density reductions or structural loss. Forest inventory estimation procedures that are descended from merchantability standards may need to be revised toward a more holistic approach to determining standing dead tree biomass and C attributes (i.e., attributes of tree biomass outside of sawlog portions). Incorporating density reductions and structural loss adjustments reduces uncertainty associated with standing dead tree biomass and C while improving consistency with field methods and documentation.

원격탐사와 GIS 기반의 산림탄소저장량 추정에 관한 주요국 연구동향 개관

토지이용변화에 따른 산림탄소저장량 변화는 기후변화협약에서 요구하는 주요 자료 중 하나이다. IPCC 우수실행지침(intergovernmental panel on climate change good practice guidance, IPCC GPG) 수준 3에 근거하여 공간적으로 명확한 산림탄소저장량을 추정하게 되면 높은 신뢰도를 확보할 수 있다. 그러나 기존의 추정 방법은 표본점(sample plot) 단위의 국가산림자원조사 (national forest inventory, NFI) 자료만을 이용하여 행정구역별 평균을 집계하는 것으로 폴리곤 혹은 셀 단위의 상세한 탄소저장량을 파악할 수 없었다. 이를 보완하기 위해 유럽, 북미 등에서는 NFI 자료, 원격탐사 및 GIS 기술을 결합하여 산림탄소저장량을 추정하기 위한 노력이 활발히 이루어져왔다. 주요국의 연구 동향을 활용 기법에 따라 분류해보면 원격탐사, GIS, 지구통계 및 환경변수 모델링 등 크게 4가지 범주로 나눌 수 있다. 이 중 가장 손쉽게 국내 적용을 고려해 볼 수 있는 방법은 NFI 자료와 임상도를 결합하는 GIS 기반의 방법이다. 특히, 복잡한 수종 구성을 가지고 있는 국내 산림 환경 특성을 고려할 때 국지적 변이 추정에 유용한 지구통계 기법의 활용성이 기대된다. 아울러, 고해상도 영상의 활용은 산림탄소저장량 추정의 검증 및 탄소배출권 확보를 위한 CDM(clean development mechanism, 청정개발체제) 사업 적지 선정 등에 유용할 것으로 판단된다. 국내 관련 연구는 아직 초기 단계로 최근린 기법(k-nearest neighbor, k-NN)의 적용성을 검토하는 연구가 주를 이루고 있으나 국내 산림 환경에 적합한 방법론의 선정을 위해서는 보다 다양한 공간 자료와 알고리즘의 적용성이 검토되고 방법론 간의 비교 연구가 필요하다. Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique

This article explores a non-linear partial least square (NLPLS) regression method for bamboo forest carbon stock estimation based on Landsat Thematic Mapper (TM) data. Two schemes, leave-one-out (LOO) cross validation (scheme 1) and split sample validation (scheme 2), are used to build models. For each scheme, the NLPLS model is compared to a linear partial least square (LPLS) regression model and multivariant linear model based on ordinary least square (LOLS). This research indicates that an optimized NLPLS regression mode can substantially improve the estimation accuracy of Moso bamboo (Phyllostachys heterocycla var. pubescens) carbon stock, and it provides a new method for estimating biophysical variables by using remotely sensed data.

Benchmark map of forest carbon stocks in tropical regions across three continents

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ± 6% to ± 53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ± 5% and ca. ± 1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Significance of carbon stock uncertainties on emission reductions from deforestation and forest degradation in developing countries

A historical agreement was reached in Bali under the United Nations Framework Convention on Climate Change, encouraging countries to initiate actions to reduce emissions from deforestation and forest degradation in developing countries (REDD). In this context, we use a Panama‐based example to show the impacts of the current levels of uncertainty in forest carbon density estimates on GHG baseline estimation and estimations of emission reductions. Using five aboveground tree carbon stock estimates for Moist Tropical forest in a simulation study, we found a difference in terms of annual CO2 emissions of more than 100% between the lowest and the highest estimates. We analyze the economic significance to show that when comparing the income generated for the different forest carbon density estimates to the cost of 10% reduced deforestation, the break‐even point differs from US$6.74 to US$16.58 per ton of CO2e between the highest and the lowest estimate. We argue that for a country such as Panama, improving the quality of forest carbon stock estimates would make economic sense since the highest forest carbon density estimates were developed nationally while the lowest estimate is the global default value. REDD could result in a huge incentive for forest protection and improved forest management, in consequence, we highlight that progress on the incorporation of uncertainty analysis and on the mitigation of the main sources of error in forest carbon density estimates merit further methodological guidance.

Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests

Self-organizing maps (SOMs) are an advanced neural networks application. SOMs were applied for the spatially explicit estimation of forest carbon stocks for a test region in Thuringia (Germany). The approach utilizes in situ national forest inventory data and satellite remote sensing data (Landsat 7 ETM+) and provides maps showing a high-resolution spatial distribution of forest carbon stocks. The generated maps are compared to alternative estimates obtained by the k-nearest neighbour (kNN) method—a remote sensing based carbon assessment. Beside maps the SOM- and kNN-approaches were utilized to calculate statistical estimates of carbon stock and growing stock. The statistical estimates were validated by calculating bias and mean square errors with reference to in situ assessments. SOM- and kNN-approaches have been tested in a forested region in Central Germany. The results show that SOMs are an approach that has the ability to reproduce the spatial pattern of forest carbon stocks. SOMs are—with some restrictions—comparable to spatially explicit estimates generated by the kNN-method.

Implications of differing input data sources and approaches upon forest carbon stock estimation.

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p < 0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within +/-1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p < 0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p = 0.288). Overall, the relationship between the two biomass estimates was strong (R(2) = 0.92, p < 0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.

Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation

Monitoring and transparent reporting of forest carbon sinks are currently needed under the Climate Convention. From 2005 onwards, national GHG inventories should also provide uncertainty estimates of the reported emissions and removals. Comprehensive uncertainty analysis and key category analysis of the carbon inventory can provide guidance for prioritizing efforts in further development of the inventory. In this study, the estimates of the forest carbon stock and carbon sink were obtained by combining forest inventory data, models of biomass and turnover, and a dynamic decomposition model for SOM and litter, Yasso. To study the decisive factors affecting uncertainties of forest carbon sink and stock estimates, we conducted a Monte Carlo analysis for the calculation of the forest carbon budget of Finnish forests for the period 1989–2004. Uncertainty of the vegetation carbon sink was affected mostly by input data on growth variation and drain. Uncertainty of the soil carbon sink was dominated by the soil model initialization, but the effect decreased with time. After few years, the effect of initialization leveled with the effect of temperature and drain, both of which were given as input data to the system and which varied inter-annually. The contribution of these variables was less important to uncertainty of stocks in vegetation and soil than the contribution of model parameters. The most influential parameters for vegetation C stock were carbon density and conversion factors for tree and ground vegetation biomass, and for soil C stock, they were soil model parameters, and biomass conversion factors and turnover rates of fine roots and ground vegetation. After short initialization period for soil C, uncertainty of soil sink can be reduced by improving the precision of input data (harvests on upland soils, annual temperature). Precision of vegetation sink can be improved mainly by improving the quality of input data on growth variation and harvests. There is an unknown error source related to inter-annual variability of the forest ecosystems, which cannot be represented with the system. Vegetation sink was compiled with biomass models that are based on long-term averages and they do not support year-to-year variations which may occur in forest ecosystems. Averaged biomass models with averaged turnover models, produce highly auto-correlated series of litter input, which result in relatively precise annual soil sink estimates. Due to these reasons, the current inventory-based approach is more justified for the estimation of average sinks for longer periods than 1 year.