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

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.

SummaryEstimates of carbon stocks and their annual change for extensive Australian sub-tropical forests are based on indirect estimates or on data derived from temperate forests. We estimated live above-ground tree carbon (LAC) stocks at landscape level from 355 000 measurements of 94 127 tree stems from 604 permanently monitored plots representing 2.6 million ha of managed uneven-aged mixed-species native forests in sub-tropical Queensland. These plots were established between 1936 and 1998 and re-measured every 2 to 10 years up to 2011. Landscapes were represented by 16 broad vegetation groups growing across a mean annual rainfall range of 500 to 2000 mm. Landscape-mean LAC stocks varied from 20.8 ± 4.3 t C ha−1 in inland eucalypt woodlands to 146.4 ± 11.1 t C ha−1 in coastal wet tall open forests. Landscape maximum LAC stock, defined as the mean of maximum LAC stocks over the entire measurement history for a specified landscape under prevailing environmental conditions and disturbance regimes, including sustainable forest management, ranged from 34.0 ± 7.2 t C ha−1 in inland eucalypt woodlands to 154.9 ± 19.4 t C ha−1 in coastal wet tall open forests. Annual live above-ground net carbon flux (C-flux) across all forests types ranged from 0.46 to 2.92 t C ha−1 y−1 with an overall mean of 0.95 t C ha−1 y−1 (n = 2067). Comparison of our results with Intergovernmental Panel on Climate Change (IPCC) estimates shows that in all cases, except for the sub-tropical steppe, the IPCC over-estimated stocks by between 13% and 34%. Conversely, the IPCC estimated C-fluxes were between 14% and 40% less than the Queensland estimates. These results extend statistically valid estimates of landscape LAC stocks and fluxes to the sub-tropical regions of Australia.

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

Spatial pattern of different component carbon in varied grasslands of northern China

Forests have a vital role in maintaining global climate stability by removing greenhouse gases like carbon dioxide from environment. Estimation of carbon stock is crucial in quantifying the amount of carbon that is present in the forest. The estimation of forest biomass and carbon stock through field measurements is a challenging and timeconsuming task. Here in this scenario, our study aims to estimate carbon stock in a forest area using the hybrid technique i.e., aerial survey and ground survey. We used low-altitude remote sensing data acquired by UAV to estimate biomass and carbon stock in an efficient way compared to the traditional techniques. We developed an orthomosaic from the collected aerial imageries and manually delineated tree crowns to obtain crown projection area (CPA) for the entire study area using GIS tools. Our study area contained a mixed species with Pinus Wallichiana to be the dominant species while other species are negligible. Using field-measured tree height and diameter at breast height (DBH) as input, we estimated above-ground biomass (AGB) with an allometric equation and then used a factor value to estimate carbon stock or aboveground carbon (AGC) for six sample plots. Next, we developed a relationship between CPA and carbon stock and validated it by comparing the carbon stock values obtained from the allometric equation for the remaining four sample plots. Among the various developed model, 4th order Polynomial model was chosen due to its highest coefficient of correlation. After the model validation was done the AGC of whole study area was obtained by using the CPA delineated manually from the orthomosaic image. The total AGC and AGB obtained for our study area which was about 7 hectare was 210.7480 tons and 448.4 tons respectively.

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

Identifying and addressing knowledge gaps for improving greenhouse gas emissions estimates from tropical peat forest fires

Глубина прогорания торфа и потери углерода при лесном подземном пожаре

It is necessary to estimate carbon (C) stored in urban vegetation for the purpose of carbon accounting and trading. This study aims to develop a refined method for reliably estimating above-ground carbon (AGC) stock of urban vegetation from integrated WorldView-2 imagery and Light Detection And Ranging (LiDAR) data in Auckland, New Zealand. Also assessed in this study is the impact of image resolution on regional AGC estimates by vegetation type. The integration of WorldView-2 imagery with a 2-m digital surface model produced from LiDAR data enables urban vegetation to be mapped into trees (101.5 km2), shrubs (64.9 km2), and grasses (172.2 km2) at a producer’s accuracy over 95.9%. The AGC stock of trees, shrubs and grasses is estimated at 1,134,287, 207,606, and 127,427 Mg C, respectively, from the vegetation map. Overall, the total AGC of all types of vegetation does not vary significantly with image spatial resolution over the range of 5 to 30 m if estimated using the same model. This is because high AGC densities are generalised at a coarser resolution, but the larger pixel size compensates for the decrease. Although the spatial resolution does not affect the most significant spectral predicators of plot-level AGC noticeably, it has an obvious effect on both model accuracy and complexity. Thus, the impact of image resolution on AGC would be pronounced if it were estimated using different models that were the best at a given resolution. Of the three vegetation types, the AGC of shrubs is the most variable with spatial resolution, followed by trees. Thus, the AGC of relatively small but more spatially fragmented vegetation parcels is more susceptible to change in image spatial resolution. The estimation model based on spectral features of vegetation has the lowest room-mean-square-error at 15 m. More research is needed to confirm whether it is true in other natural environments in future studies.

Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image

SummaryForests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high‐fidelity mapping of carbon stocks at regional scales.We develop a tree‐centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region‐growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown‐width estimate. From that point on, we use well‐established approaches developed for field‐based inventories: above‐ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density.We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field‐ and ALS‐based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect.An advantage of the tree‐centric approach over existing area‐based methods is that it can produce maps at any scale and is fundamentally based on field‐based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.

The development of country-specific emission factors in relation to the Agriculture, Forestry, and Other Land Use (AFOLU) sector has the potential to improve national greenhouse gas inventory systems. Forests are carbon sinks in the AFOLU that can play an important role in mitigating global climate change. According to the United Nations Framework Convention on Climate Change (UNFCCC), signatory countries must report forest carbon stocks, and the changes within them, using emission factors from the Intergovernmental Panel on Climate Change (IPCC) or from country-specific values. This study was conducted to estimate forests carbon stocks and to complement and improve the accuracy of national greenhouse gas inventory reporting in South Korea. We developed country-specific emissions factors and estimated carbon stocks and their changes using the different approaches and methods described by the IPCC (IPCCEF: IPCC default emission factors, CSFT: country-specific emission factors by forest type, and CSSP: country-specific emission factors by species). CSFT returned a result for carbon stocks that was 1.2 times higher than the value using IPCCEF. Using CSSP, CO2 removal was estimated to be 60,648 Gg CO2 per year with an uncertainty of 22%. Despite a reduction in total forest area, forests continued to store carbon and absorb CO2, owing to differences in the carbon storage capacities of different forest types and tree species. The results of this study will aid estimations of carbon stock changes and CO2 removal by forest type or species, and help to improve the completeness and accuracy of the national greenhouse gas inventory. Furthermore, our results provide important information for developing countries implementing Tier 2, the level national greenhouse gas inventory systems recommended by the IPCC.

To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon

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.

Aboveground biomass allometric models for large trees in southwestern Amazonia