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Background: It is always a challenging job to compare forest resources as there is not a standardized spatial unit with location information. Google Plus Code, the newest alphanumeric geocoding system, uses 20 specifically selected letters and numbers to assign a unique global ID to every cell at different levels of a hierarchical grid system which is established based on latitude and longitude. It can be used as a standardized, unique global geospatial unit to segment, locate, quantitate, evaluate, and compare natural resources with area, boundary, and location information embedded. Results: For this proof-of-concept case study, forest inventory data from 1987, 2002, and 2019 for the Zijin Mountain National Forest Park in Jiangsu Province, China was analyzed based on Google Plus Code grid/cell. This enabled the quantification of carbon storage at each cell allowing for the comparison of estimated carbon storage at same or different locations over time. Conclusions: This methodology is used to quantify the impacts of changing forest conditions and forest management activities on carbon storage with high spatial accuracy through the 32-year study period. Furthermore, this technique could be used for providing technical support and validation of carbon credit quantification and management. [ABSTRACT FROM AUTHOR]

Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. In this study, we chose Chongming eco-island in the mega-city of Shanghai, a national experimental carbon neutral construction plot in China, as the study object. Remote sensing estimation models (simple regression models vs. machine learning models) of forest carbon density were constructed across different classification scales (all forests, different forest types, and dominant tree species) based on high-resolution aerial photographs and Sentinel-2A remote sensing images, and a large number of field surveys and optimal models were screened by ten-fold cross-validation. The results showed that (1) in early 2020, the total forest area and carbon storage of Chongming eco-island were 307.8 km2 and 573,123.6 t, respectively, among which the areal ratios and total carbon storage ratios of evergreen broad-leaved forest, deciduous broad-leaved forest, and warm coniferous forest were 51.4% and 53.3%, 33.5% and 32.8%, and 15.1% and 13.9%, respectively. (2) The average forest carbon density of Chongming eco-island was 18.6 t/ha, among which no differences were detected among the three forest types (i.e., 17.2–19.2 t/ha), opposite to what was observed among the dominant tree species (i.e., 14.6–23.7 t/ha). (3) Compared to simple regression models, machine learning models showed an improvement in accuracy performance across all three classification scales, with average rRMSE and rBias values decreasing by 29.4% and 53.1%, respectively; compared to the all-forests classification scale, the average rRMSE and rBias across the algorithms decreased by 25.0% and 45.2% at the forest-type classification scale and by 28.6% and 44.3% at the tree species classification scale, respectively. We concluded that refining the forest classification, combined with advanced prediction procedures, could improve the accuracy of carbon storage estimates for urban forests. [ABSTRACT FROM AUTHOR]

Forests provide a huge carbon pool, a substantial portion of which is stored in above‐ground biomass (AGB). Deciduous broadleaf forests in China are an essential component of global deciduous broadleaf forests, yet the impacts of climate and forest attributes on their AGB are not well understood.Using a comprehensive forest inventory database from 772 plots distributed across temperate and subtropical deciduous broadleaf forests in China (23.51°–42.53°N and 104.24°–128.27°E), we applied variance partitioning analysis, model selection analysis and structural equation models to explore how climate and forest attributes (species diversity, community‐level functional traits and stand structures) affect AGB in different climatic forests (semi‐arid forests, semi‐humid forests and humid forests).Community‐level functional traits and stand structures together explained a great portion of the variance in AGB. The effect of community‐level functional traits was greater than that of stand structures in semi‐arid forests and semi‐humid forests, but smaller in humid forests. Further analyses showed that community‐level maximum tree height, stem density and tree size inequality were important explanatory variables. Although climate and species diversity had minor effects, the direct positive effect of mean annual precipitation (MAP) was still important, especially in semi‐arid forests.Synthesis. Community‐level functional traits but not species diversity were key drivers of AGB, indicating that tree species diversity loss may not impair AGB substantially in deciduous broadleaf forests in China. Moreover, stand structures also had strong effects on AGB in both semi‐arid forests and humid forests, highlighting the importance of structural complexity. In addition, MAP had a direct positive effect on AGB in semi‐arid forests and semi‐humid forests, and a future increase in drought might potentially reduce carbon storage in these forests. [ABSTRACT FROM AUTHOR]

The study of China's carbon emissions and carbon sequestration potentiat io of great practisil significance to the formulation o* carbon neutralita strateoies and methods ol' China. Carbon emission is an inevitabie outcome of tie initiat state of economia development especialiy tie oght oO developing counties existenca and developmeni. The carbon emission ol' China has been maintained at a low level for a long time and reached the top ol' the world in 2005. Howeveo, peo capita carbon emission was stili oniy 46. 28% ol' the United States in 2016. China's totat CO2 emissions are expected to reach 17 - 19 Gt/a by 2030. To achieve the goat ol' carbon neutrality in 2060, the main problems are the low technicat capacita °l' emission reduction and the unreasonabOe structure ol' energy consumption. Therefore, replacing coat with gas is one ol' the most effective ways ol' emission reduction. By 2060, the carbon sequestration capacita ol' forest veaetation wili reach oo exceed 759. 14 Mt/a and the CO2 sequestration capacita wilf reach 2 783.5 Mt/a. According ta that a China's carbon intensity must reducc by 95. 39% on tha basis ol' the carbon intensity in 2017, reaching 640 t/100 million yuan. The carbon sequestration capacity of terrestial veaetation (forest, grassland) will reach oo exceed 1 380.3 Mt/a and the CO2 sequestration capacita will reach 5 069. 3 Mt/a. According te that, China's carbon intensity must reducc by 91.07% on the basis of the carbon intensity in 2017, reach 1 152 t C per 100 moooon yuan. [ABSTRACT FROM AUTHOR]

Biomass estimation studies of subtropical forests in China are often based on small plots ranging from 400 to 900 m². However, what is the appropriate sample area remains unknown. In the present study, aboveground biomass(AGB)and its spatial distribution pattern of woody plants with diameter at breast height(DBH)≥1 cm in three subtropical secondary forests in Jiulong Mountain National Nature Reserve, Zhejiang Province, East China were analyzed, and the appropriate sample areas for the AGB survey of the three secondary forests were explored by moving window method. The results were as follows:(1)The AGB of woody plants in the three secondary forests were 63.75(Dayanqian site), 84.70(Batongling site)and 128.20(Piguku site)Mg·hm-2, respectively. AGB was concentrated in plants with large DBH but with a small individual number. The spatial variation degree of secondary forest AGB at the Piguku site was higher than those at the Dayanqian and Batongling sites.(2)The appropriate sample areas of the three secondary forests based on AGB data were 2 025(Dayanqian site), 2 500(Batongling site)and 3 600(Piguku site)m², respectively. The higher the forest AGB and its spatial variation degree were, the larger sample area was required. The present study provides evidence for sample area setting of AGB survey in subtropical forests, and provides basic data for regional forest biomass and carbon storage estimations. [ABSTRACT FROM AUTHOR]

Plantation forests play a critical role in forest management due to their high productivity and large contribution to carbon sequestration (CSE). The purpose of this study was to assess the CSE of plantations containing four important conifer species distributed across Taiwan, namely, the China fir (Cunninghamia lanceolata), Japanese cedar (Cryptomeria japonica), Taiwania (Taiwania cryptomerioides) and Taiwan red cypress (Chamaecyparis formosensis). Data regarding the plantations were obtained from a survey of permanent sample plots (PSPs). We used these data to calculate the CSE in each PSP and adopted CSEmean and CSEperiod as indicators to assess the CSE of the four conifers. According to the CSEmean obtained from analysis of variance and the least significant difference method, two groups were identified among these four conifers: the Japanese cedar (4.03 Mg ha−1 yr−1) and Taiwania (3.52 Mg ha−1 yr−1) yielded higher CSEmean values and the China fir (1.79 Mg ha−1 yr−1) and Taiwan red cypress (2.36 Mg ha−1 yr−1) yielded lower CSEmean values. The same patterns were observed in the CSEperiod values; however, no significant difference in CSEperiod was observed between Taiwan red cypress and either of the two groups. Therefore, Japanese cedar and Taiwania have high CSE potential among conifers. [ABSTRACT FROM AUTHOR]

Understanding spatio-temporal dynamics of UF and its above-ground carbon storage (CS) is important for mitigating urban CO 2 emissions under China's rapid urban expansion and greening. In our study, vegetation index (VI) data obtained from TM image and CS derived from field-based surveys were amalgamated to develop a regression model to predict spatio-temporal patterns of CS. VI correction model was established by normalizing previous imagery (1984, 1995, and 2005) to 2014 image data. NDVI is better than other VIs for predicting urban forest CS. Both UF area and its CS increased gradually from 1984 to 2014, especially in outer rings of the city. CS showed a definite decreasing trend from outer rings to downtown. Due to urban greening policies, landscape patches of UF or CS recently became larger and more aggregated. The CS by UF class distribution was skewed toward low values in all the years, but the skew gradually decreased over time. It was estimated that the average annual increase of CS by UF could offset 3.9% of the average annual increase in urban carbon emissions. Our study proposes that spatio-temporal changes in UF patterns dramatically affected the amount of CS and carbon capture. The increase in UF coverage and quality would mitigate more urban carbon emissions, especially in outer rings. • The linear normalization models were established to correct multi-date imagery. • Vegetation index from TM image and carbon storage(CS) from field surveys were used to develop a predicting model for spatio-temporal CS. • Both urban forest(UF) area and CS increased gradually from 1984 to 2014, especially in outer rings of the city. • The average annual increase of CS by UF could offset 3.9% of the average annual increase in urban carbon emissions. [ABSTRACT FROM AUTHOR]

Forest ecosystems are an integral component of the global carbon cycle as they take up and release large amounts of C over short time periods (C flux) or accumulate it over longer time periods (C stock). However, there remains uncertainty about whether and in which direction C fluxes and in particular C stocks may differ between forests of high versus low species richness. Based on a comprehensive dataset derived from field-based measurements, we tested the effect of species richness (3-20 tree species) and stand age (22-116 years) on six compartments of above- and below-ground C stocks and four components of C fluxes in subtropical forests in southeast China. Across forest stands, total C stock was 149+12 Mg ha-1 with richness explaining 28.5% and age explaining 29.4% of variation in this measure. Species-rich stands had higher C stocks and fluxes than stands with low richness; and, in addition, old stands had higher C stocks than young ones. Overall, for each additional tree species, the total C stock increased by 6.4%. Our results provide comprehensive evidence for diversity-mediated above- and below-ground C sequestration in species-rich subtropical forests in southeast China. Therefore, afforestation policies in this region and elsewhere should consider a change from the current focus on monocultures to multi-species plantations to increase C fixation and thus slow increasing atmospheric CO2 concentrations and global warming. [ABSTRACT FROM AUTHOR]

Abstract: The Grain for Green Program (GGP) was the most all‐embracing program of ecological reconstruction implemented in China. To estimate carbon storages and carbon sequestration potentials of the GGP forests, the study presented in the paper collected data spanning from 1999 to 2010, such as tree species, tree planting area relevant to the GGP, empirical growth curves suitable for different planted tree species in China, as well as wood density (WD), biomass expansion factor (BEF), carbon fraction (CF) of different trees species, and estimated the carbon storages of the biomasses of GGP forests from 1999 to 2050. It showed that the total carbon storage of the biomass of GGP forests was 320.29 Tg upon the GGP completion in 2010; the total carbon sequestration is higher during the early GGP‐implementation stage than at the late GGP‐implementation stage, and the annual mean carbon sequestration of GGP forests was 26.69 Tg/year. The potential of GGP forests as carbon sink presented an increasing increment. In China, the potential increments of GGP forests as carbon sinks were estimated to be 397.34, 604.00, 725.53, and 808.90 Tg in 2020, 2030, 2040, and 2050, respectively, and the carbon sequestration rates were 1.72, 0.89, 0.52, and 0.36 Mg ha−1 year−1, respectively, corresponding to 2010s, 2020s, 2030s, and 2040s. Therefore, the GGP forests had bigger carbon sequestration capacities and potentials in China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Carbon (C) storage for all the components, especially dead mass and soil organic carbon, was rarely reported and remained uncertainty in China's forest ecosystems. This study used field-measured data published between 2004 and 2014 to estimate C storage by three forest type classifications and three spatial interpolations and assessed the uncertainty in C storage resulting from different integrative methods in China's forest ecosystems. The results showed that C storage in China's forest ecosystems ranged from 30.99 to 34.96 Pg C by the six integrative methods. We detected 5.0% variation (coefficient of variation, CV, %) among the six methods, which was influenced mainly by soil C estimates. Soil C density and storage in the 0-100 cm soil layer were estimated to be 136.11-153.16 Mg C·ha−1 and 20.63-23.21 Pg C, respectively. Dead mass C density and storage were estimated to be 3.66-5.41 Mg C·ha−1 and 0.68-0.82 Pg C, respectively. Mean C storage in China's forest ecosystems estimated by the six integrative methods was 8.557 Pg C (25.8%) for aboveground biomass, 1.950 Pg C (5.9%) for belowground biomass, 0.697 Pg C (2.1%) for dead mass, and 21.958 Pg C (66.2%) for soil organic C in the 0-100 cm soil layer. The R:S ratio was 0.23, and C storage in the soil was 2.1 times greater than in the vegetation. Carbon storage estimates with respect to forest type classification (38 forest subtypes) were closer to the average value than those calculated using the spatial interpolation methods. Variance among different methods and data sources may partially explain the high uncertainty of C storage detected by different studies. This study demonstrates the importance of using multimethodological approaches to estimate C storage accurately in the large-scale forest ecosystems. [ABSTRACT FROM AUTHOR]

Evergreen broad-leaved forest is an important forest type in China. This paper analyzes the allocation characteristics of vegetation and soil carbon pool of evergreen broad-leaved forest, to understand the current status of research on the carbon storage of evergreen broad-leaved forest as well as shortcomings. In the context of global climate change, it is necessary to carry out the long-term research of evergreen broad-leaved forest, in order to grasp the formation mechanism of evergreen broad-leaved forest productivity, and the mpact of climate change on the carbon sequestration function of evergreen broad-leaved forest ecosystem. [ABSTRACT FROM AUTHOR]