The global carbon cycle is one of the most important bio-geochemical cycles. Through photosynthesis, green plants absorb CO₂ from the atmosphere to produce organic matters, such as sugars, and covert solar energy into chemical energy. The organic matters are then used by all other life forms including humans. When ecosystems and atmosphere are in dynamic equilibrium, the flow of CO₂ from the atmosphere into the biosphere because of photosynthesis should be equivalent to the flow of CO₂ released back into the atmosphere by respiration. However, during the past century atmospheric CO₂ concentration has increased substantially because of the burning of fossil fuels. It is highly likely that the atmospheric increase has resulted in global warming and sea level rise, as suggested by the Intergovernmental Panel on Climate Change (IPCC) .

The knowledge of carbon (C) stock and its dynamics is crucial for understanding the role of grassland ecosystems in China’s terrestrial C cycle. To date, a comprehensive assessment on C balance in China’s grasslands is still lacking. By reviewing pub-lished literature, this study aims to evaluate ecosystem C stocks (both vegetation biomass and soil organic C) and their changes in China’s grasslands. Our results are summarized as follows: (1) biomass C density (C stock per area) of China’s grasslands differed greatly among previous studies, ranging from 215.8 to 348.1 g C m⁻² with an average of 300.2 g C m⁻². Likewise, soil C density also varied greatly between 8.5 and 15.1 kg C m⁻². In total, ecosystem C stock in China’s grasslands was estimated at 29.1 Pg C. (2) Both the magnitude and direction of ecosystem C changes in China’s grasslands differed greatly among previous studies. According to recent reports, neither biomass nor soil C stock in China’s grasslands showed a significant change during the past 20 years, indicating that grassland ecosystems are C neutral. (3) Spatial patterns and temporal dynamics of grassland biomass were closely correlated with precipitation, while changes in soil C stocks exhibited close associations with soil moisture and soil texture. Human activities, such as livestock grazing and fencing could also affect ecosystem C dynamics in China’s grasslands.

The present study provides an overview of existing literature on changes in soil organic carbon (SOC) of various terrestrial ecosystems in China. Datasets from the literature suggest that SOC stocks in forest, grassland, shrubland and cropland increased between the early 1980s and the early 2000s, amounting to (71±19) Tg•a^-1. Conversion of marshland to cropland in the Sanjiang Plain of northeast China resulted in SOC loss of (6±2) Tg•a^-1 during the same period. Nevertheless, large uncertainties exist in these estimates, especially for the SOC changes in the forest, shrubland and grassland. To reduce uncertainty, we suggest that future research should focus on: (i) identifying land use changes throughout China with high spatiotemporal resolution, and measuring the SOC loss and sequestration due to land use change; (ii) estimating the changes in SOC of shrubland and non-forest trees (i.e., cash, shelter and landscape trees); (iii) quantifying the impacts of grassland management on the SOC pool; (iv) evaluating carbon changes in deep soil layers; (v) projecting SOC sequestration potential; and (vi) developing carbon budget models for better estimating the changes in SOC of terrestrial ecosystems in China.

China’s forests are characterized by young forest age, low carbon density and a large area of planted forests, and thus have high potential to act as carbon sinks in the future. Using China’s national forest inventory data during 1994–1998 and 1999–2003, and direct field measurements, we investigated the relationships between forest biomass density and forest age for 36 major forest types. Statistical approaches and the predicted future forest area from the national forestry development plan were applied to estimate the potential of forest biomass carbon storage in China during 2000–2050. Under an assumption of continuous natural forest growth, China’s existing forest biomass carbon (C) stock would increase from 5.86 Pg C (1 Pg=10¹⁵ g) in 1999–2003 to 10.23 Pg C in 2050, resulting in a total increase of 4.37 Pg C. Newly planted forests through afforestation and reforestation will sequestrate an additional 2.86 Pg C in biomass. Overall, China’s forests will potentially act as a carbon sink for 7.23 Pg C during the period 2000–2050, with an average carbon sink of 0.14 Pg C yr⁻¹. This suggests that China’s forests will be a significant carbon sink in the next 50 years.

To predict changes in South Korean vegetation distribution, the Warmth Index (WI) and the Minimum Temperature of the Coldest Month Index (MTCI) were used. Historical climate data of the past 30 years, from 1971 to 2000, was obtained from the Korea Meteorological Administration. The Fifth-Generation National Center for Atmospheric Research (NCAR) /Penn State Mesoscale Model (MM5) was used as a source for future climatic data under the A1B scenario from the Special Report on Emission Scenario (SRES) of the Intergovernmental Panel on Climate Change (IPCC). To simulate future vegetation dis-tribution due to climate change, the optimal habitat ranges of Korean tree species were delimited by the thermal gradient indi-ces, such as WI and MTCI. To categorize the Thermal Analogy Groups (TAGs) for the tree species, the WI and MTCI were orthogonally plotted on a two-dimensional grid map. The TAGs were then designated by the analogue composition of tree spe-cies belonging to the optimal WI and MTCI ranges. As a result of the clustering process, 22 TAGs were generated to explain the forest vegetation distribution in Korea. The primary change in distribution for these TAGs will likely be in the shrinkage of areas for the TAGs related to Pinus densiflora and P. koraiensis, and in the expansion of the other TAG areas, mainly occupied by evergreen broad-leaved trees, such as Camellia japonica, Cyclobalanopsis glauca, and Schima superba. Using the TAGs to explain the effects of climate change on vegetation distribution on a more regional scale resulted in greater detail than previ-ously used global or continental scale vegetation models.

Biometric inventories for 25 years, from 1983 to 2005, indicated that the Jianfengling tropical mountain rain forest in Hainan, China, was either a source or a modest sink of carbon. Overall, this forest was a small carbon sink with an accumulation rate of (0.56±0.22) Mg C ha⁻¹yr⁻¹, integrated from the long-term measurement data of two plots (P9201 and P8302). These findings were similar to those for African and American rain forests ((0.62±0.23) Mg C ha⁻¹yr⁻¹). The carbon density varied between (201.43±29.38) Mg C ha⁻¹ and (229.16±39.2) Mg C ha⁻¹, and averaged (214.17±32.42) Mg C ha⁻¹ for plot P9201. Plot P8302, however, varied between (223.95±45.92) Mg C ha⁻¹ and (254.85±48.86) Mg C ha⁻¹, and averaged (243.35±47.64) Mg C ha⁻¹. Quadratic relationships were found between the strength of carbon sequestration and heavy rainstorms and dry months. Pre-cipitation and evapotranspiration are two major factors controlling carbon sequestration in the tropical mountain rain forest.

The biomass and productivity of Schima superba–Castanopsis carlesii forests in Tiantong, Zhejiang Province, were deter-mined using overlapping quadrants and stem analyses. The total community biomass was (225.3±30.1) t hm^–2, of which the aboveground parts accounted for 72.0% and the underground parts accounted for 28.0%. About 87.2% of biomass existed in the tree layer. The resprouting biomass was small, of which over 95.0% occurred in the shrub layer. The productivity of the aboveground parts of the community was (386.8±98.9) g m^–2a^–1, in which more than 96.0% was present at the tree level. The trunk’s contribution to productivity was the greatest, while that of leaves was the smallest. In China, the community biomass of subtropical evergreen broadleaved forests differs significantly with the age of the forest. The community biomass of the 52-year-old S. superba–C. carlesii forests in this study was lower than the average biomass of subtropical evergreen broad-leaved forests in China, and was lower than the biomass of other subtropical evergreen broadleaved forests elsewhere in the world. Moreover, its productivity was lower than the model estimate, indicating that without disturbance, this community has great developmental potential in terms of community biomass and productivity.

The carbon (C) and nitrogen (N) storage capabilities of Pinus densiflora in six different stand ages (10, 27, 30, 32, 44, and 71 years old) were investigated in Korea. Thirty sample trees were destructively harvested and 12 were excavated. Samples from the above and belowground tree components, coarse woody debris (CWD), forest floor, and mineral soil (0–30 cm) were collected. Tree biomass was highest in the 71-year-old stand (202.8 t ha^–1) and lowest in the 10-year-old stand (18.4 t ha^–1). C and N storage in the mineral soil was higher in the 71-year-old stand than in the other stands, mainly due to higher soil C and N concentrations. Consequently, the total ecosystem C and N storage (tree+forest floor+CWD+soil) was positively correlated with stand age: increasing from a minimum in the 10 year old stand (18.8 t C ha^–1 and 1.3 t N ha^–1) to a maximum in the 71-year-old stand (201.4 t C ha^–1 and 8.5 t N ha^–1). The total ecosystem C storage showed a similar sigmoidal pattern to that of tree C storage as a function of the age-sequence, while N storage in the CWD, forest floor and mineral soil showed no significant temporal trends. Our results provide important insights that will increase our understanding of C and N storage in P. densiflora stands and our ability to predict changes according to stand age in the region.

Quantifying forest carbon (C) storage and distribution is important for forest C cycling studies and terrestrial ecosystem mod-eling. Forest inventory and allometric approaches were used to measure C density and allocation in six representative temper-ate forests of similar stand age (42–59 years old) and growing under the same climate in northeastern China. The forests were an aspen-birch forest, a hardwood forest, a Korean pine plantation, a Dahurian larch plantation, a mixed deciduous forest, and a Mongolian oak forest. There were no significant differences in the C densities of ecosystem components (except for detritus) although the six forests had varying vegetation compositions and site conditions. However, the differences were significant when the C pools were normalized against stand basal area. The total ecosystem C density varied from 186.9 tC hm^-2 to 349.2 tC hm^-2 across the forests. The C densities of vegetation, detritus, and soil ranged from 86.3–122.7 tC hm^-2, 6.5–10.5 tC hm^-2, and 93.7–220.1 tC hm^-2, respectively, which accounted for 39.7%±7.1% (mean±SD), 3.3%±1.1%, and 57.0%±7.9% of the total C densities, respectively. The overstory C pool accounted for > 99% of the total vegetation C pool. The foliage bio-mass, small root (diameter < 5mm) biomass, root-shoot ratio, and small root to foliage biomass ratio varied from 2.08–4.72 tC hm^-2, 0.95–3.24 tC hm^-2, 22.0%–28.3%, and 34.5%–122.2%, respectively. The Korean pine plantation had the lowest foliage production efficiency (total biomass/foliage biomass: 22.6 g g^-1) among the six forests, while the Dahurian larch plantation had the highest small root production efficiency (total biomass/small root biomass: 124.7 g g^-1). The small root C density de-creased with soil depth for all forests except for the Mongolian oak forest, in which the small roots tended to be vertically dis-tributed downwards. The C density of coarse woody debris was significantly less in the two plantations than in the four natu-rally regenerated forests. The variability of C allocation patterns in a specific forest is jointly influenced by vegetation type, management history, and local water and nutrient availability. The study provides important data for developing and validating C cycling models for temperate forests.

Grassland covers approximately one-third of the area of China and plays an important role in the global terrestrial carbon (C) cycle. However, little is known about biomass C stocks and dynamics in these grasslands. During 2001–2005, we conducted five consecutive field sampling campaigns to investigate above- -and below-ground biomass for northern China’s grasslands. Using measurements obtained from 341 sampling sites, together with a NDVI (normalized difference vegetation index) time series dataset over 1982–2006, we examined changes in biomass C stock during the past 25 years. Our results showed that biomass C stock in northern China’s grasslands was estimated at 557.5 Tg C (1 Tg=10¹² g), with a mean density of 39.5 g C m^–2 for above-ground biomass and 244.6 g C m^–2 for below-ground biomass. An increasing rate of 0.2 Tg C yr^–1 has been observed over the past 25 years, but grassland biomass has not experienced a significant change since the late 1980s. Seasonal rainfall (January–July) was the dominant factor driving temporal dynamics in biomass C stock; however, the responses of grassland biomass to climate variables differed among various grassland types. Biomass in arid grasslands (i.e., desert steppe and typical steppe) was significantly associated with precipitation, while biomass in humid grasslands (i.e., alpine meadow) was positively correlated with mean January–July temperatures. These results suggest that different grassland ecosystems in China may show diverse responses to future climate changes.

Above- and belowground biomass allocation not only influences growth of individual plants, but also influences vegetation structures and functions, and consequently impacts soil carbon input as well as terrestrial ecosystem carbon cycling. However, due to sampling difficulties, a considerable amount of uncertainty remains about the root: shoot ratio (R/S), a key parameter for models of terrestrial ecosystem carbon cycling. We investigated biomass allocation patterns across a broad spatial scale. We collected data on individual plant biomass and systematically sampled along a transect across the temperate grasslands in Inner Mongolia as well as in the alpine grasslands on the Tibetan Plateau. Our results indicated that the median of R/S for herba-ceous species was 0.78 in China’s grasslands as a whole. R/S was significantly higher in temperate grasslands than in alpine grasslands (0.84 vs. 0.65). The slope of the allometric relationship between above- and belowground biomass was steeper for temperate grasslands than for alpine. Our results did not support the hypothesis that aboveground biomass scales isometrically with belowground biomass. The R/S in China’s grasslands was not significantly correlated with mean annual temperature (MAT) or mean annual precipitation (MAP). Moreover, comparisons of our results with previous findings indicated a large difference between R/S data from individual plants and communities. This might be mainly caused by the underestimation of R/S at the individual level as a result of an inevitable loss of fine roots and the overestimation of R/S in community-level sur-veys due to grazing and difficulties in identifying dead roots. Our findings suggest that root biomass in grasslands tended to have been overestimated in previous reports of R/S.

Topsoil soil organic carbon (SOC) data were collected from long-term Chinese agro-ecosystem experiments presented in 76 reports with measurements over 1977 and 2006. The data set comprised 481 observations (135 rice paddies and 346 dry croplands) of SOC under different fertilization schemes at 70 experimental sites (28 rice paddies and 42 dry croplands). The data set covered 16 dominant soil types found in croplands across 23 provinces of mainland China. The fertilization schemes were grouped into six categories: N (inorganic nitrogen fertilizer only), NP (compound inorganic nitrogen and phosphorus fertilizers), NPK (compound inorganic nitrogen, phosphorus and potassium fertilizers), O (organic fertilizers only), OF (combined inorganic/organic fertilization) and Others (other unbalanced fertilizations such as P only, K only, P plus K and N plus K). Relative change in SOC content was analyzed, and rice paddies and dry croplands soils were compared. There was an overall temporal increase in topsoil SOC content, and relative annual change (RAC, g kg⁻¹ yr⁻¹) ranged −0.14–0.60 (0.13 on average) for dry cropland soils and −0.12–0.70 (0.19 on average) for rice paddies. SOC content increase was higher in rice paddies than in dry croplands. SOC increased across experimental sites, but was higher under organic fertilization and combined organic/inorganic fertilizations than chemical fertilizations. SOC increase was higher under balanced chemical fertilizations with compound N, P and K fertilizers than unbalanced fertilizations such as N only, N plus P, and N plus K. The effects of specific rational fertilizations on SOC increase persisted for 15 years in dry croplands and 20 years in rice paddies, although RAC values decreased generally as the experiment duration increased. Therefore, the extension of rational fertilization in China’s croplands may offer a technical option to enhance C sequestration potential and to sustain long-term crop productivity.

Agroecosystems have a critical role in the terrestrial carbon cycling process. Soil organic carbon (SOC) in cropland is of great importance for mitigating atmospheric carbon dioxide increases and for global food security. With an understanding of soil carbon saturation, we analyzed the datasets from 95 global long-term agricultural experiments distributed across a vast area spanning wide ranges of temperate, subtropical and tropical climates. We then developed a statistical model for estimating SOC sequestration potential in cropland. The model is driven by air temperature, precipitation, soil clay content and pH, and explains 58% of the variation in the observed soil carbon saturation (n=76). Model validation using independent data observed in China yielded a correlation coefficient R² of 0.74 (n=19, P<0.001). Model sensitivity analysis suggested that soils with high clay content and low pH in the cold, humid regions possess a larger carbon sequestration potential than other soils. As a case study, we estimated the SOC sequestration potential by applying the model in Henan Province. Model estimations suggested that carbon (C) density at the saturation state would reach an average of 32 t C ha^-1 in the top 0–20 cm soil depth. Using SOC density in the 1990s as a reference, cropland soils in Henan Province are expected to sequester an additional 100 Tg C in the future.

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

Light Detection and Ranging (LiDAR) systems can be used to estimate both the vertical and horizontal forest structure. including wWoody components, the leaves of trees and the understory can be assesseddescribed with high precision, using georegistered 3D-points. Based on this concept, the Effective Plant Area Indices (PAIe) of for areas of Korean Pine (Pinus koraiensis), Japanese Larch (Larix leptolepis) and Oak (Quercus spp.) were estimated producedestimated by calculating the ratio of intercepted and incident LIDAR laser rays, for the canopies of the three forest types canopies. Initially, the canopy gap fraction (GLiDAR) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAIe was then estimated by using GLIDAR in with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAIe revealed the coefficients of the determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences in the correlation between field-based and LIDAR-based PAIe for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, suggests in the case of both Larch and Oak, meant thatthat the LiDAR pulses might bewere only reflected fromon the branches only. The probability that the LiDAR pulses are reflected fromon bare branches is quite low when as compared to the reflection from branches with a high leaf density. This is because the size of the branch iwas smaller than the resolution across and along the 1 meter track LIDAR laser trackresolution (1m). Therefore, a better predictive accuracy is would be expected for the model if the study iswould be performed repeated in late spring when the shoots and leaves of the deciduous trees begin to appear.