Spatio-temporal changes in biomass carbon sinks in China’s forests from 1977 to 2008

Guo, Zhaodi; Hu, HuiFeng; Пин, Ли; Li, Nuyun; Fang, Jingyun

doi:10.1007/s11427-013-4492-2

Cited by 130 publications

(107 citation statements)

References 31 publications

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“…According to the latest national forest inventory, China's forest area increased by about 3. [42]. A long-term investigation of vegetation dynamics in the TNSFP region based on the AVHRR NDVI3g dataset showed that the overall state of vegetation in the study region has improved over the past three decades [43].…”

Section: Role Of Ecological Restoration In Vegetation Dynamicsmentioning

confidence: 99%

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Liu

Zhu

et al. 2015

Remote Sensing

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Abstract:In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001-2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grassforb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. OPEN ACCESSRemote Sens. 2015, 7 15518Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade.

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Section: Role Of Ecological Restoration In Vegetation Dynamicsmentioning

confidence: 99%

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Liu

Zhu

et al. 2015

Remote Sensing

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“…Forests in China show a highly significant and linear relationship between biomass density (Mg·ha −1 ) and stock volume density (m 3 ·ha −1 ), with a correlation coefficient of 0.98 at the level of provincial forests [47]. The forest stock volume density increases in a nonlinear manner with the increase in age and height [48,49].…”

Section: Distribution Map Of Forest Stock Volumementioning

confidence: 99%

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Luo

Zhou

et al. 2018

Forests

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Abstract:Revealing forest drought response characteristics and the potential impact factors is quite an important scientific issue against the background of global climate change, which is the foundation to reliably evaluate and predict the effects of future drought. Due to the high spatial heterogeneity of forest properties such as biomass, forest age, and height, and the distinct differences in drought stress in terms of frequency, intensity, and duration, current studies still contain many uncertainties. In this research, we used the forests in Yunnan Province in Southwest China as an example and aimed to reveal the potential impacts of forest properties (i.e., stock volume) on drought response characteristics. Specifically, we divided the forest into five groups of stock volume density values and then analyzed their drought response differences. To depict forest response to drought intensity, the standardized precipitation evapotranspiration index (SPEI) was chosen as the explanatory variable, and the change in remote sensing-based enhanced vegetation index (deficit of MODIS-EVI, dEVI) was chosen as the response variable of drought stress. Given that the SPEI has different time scales, we first analyzed the statistical dependency of SPEIs with different time scales (1 to 36 months) to the response variable (i.e., dEVI). The optimal time scale of SPEI (SPEI opt ) to interpret the maximum variation of dEVI (R-square) was then chosen to build the ultimate statistical models for the five groups of stock volume density. The main findings were as follows: (1) the impacts of drought showed hysteresis and cumulative effects, and the length of the hysteresis increased with stock volume densities; (2) forests with high stock volume densities required more soil water and were therefore more sensitive to the changes in water deficit; (3) compared with the optimal time scale of SPEI (SPEI opt ), the SPEI with the commonly used time scale (e.g., 1, 6, and 12 months) could not well reflect the impacts of drought on forests and the simulation error of dEVI increased with stock volume densities; and (4) forests with higher stock volume densities were likely to experience a greater risk of degradation following higher atmospheric concentrations of greenhouse gases (Representative Concentration Pathway (RCP) 8.5). As a result, both the time scale of the meteorological drought index and the spatial difference in forest stock volumes should be considered when evaluating forest drought responses at regional and global scales.

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“…A significant difference exists in the results of this sequestration coefficient ( Table 1). [37] 0.50 1.83 Guo et al [38] 0.39 1.42 Piao [39] 0.15 0.55…”

Section: Figurementioning

confidence: 99%

“…For instance, Fang [19] used a biomass method to estimate forest carbon stocks in five East Asian countries between the 1970s and 2000s. Guo [20] explored the spatial-temporal changes in forest biomass carbon stocks in China between 1977 and 2008. Most previous studies on carbon emissions in China considered only one aspect of the carbon emission accounting and focused on the national level.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

Deng

Liu

2015

Energies

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This study calculated the provincial carbon dioxide (CO2) emissions in China, analyzed the temporal and spatial variations in emissions, and determined the emission intensity from 2005 to 2015. The total emissions control was forecasted in 2015, and the reduction pressure of the 30 provinces in China was assessed based on historical emissions and the 12th five-year (2011-2015) reduction plan. Results indicate that CO2 emissions eventually increased and gradually decreased from east to west, whereas the emission intensity ultimately decreased and gradually increased from south to north. By the end of 2015, the total control of provincial emissions will increase significantly compared to the 2010 level, whereas the emission intensity will decrease. The provinces in the North, East, and South Coast regions will maintain the highest emission levels. The provinces in the Southwest and Northwest regions will experience a rapid growth rate of emissions. However, the national emission reduction target will nearly be achieved if all provinces can implement reduction targets as planned. Pressure indices show that the South Coast and Northwest regions are confronted with a greater reduction pressure of emission intensity. Finally, policy implications are provided for CO2 reductions in China.

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Spatio-temporal changes in biomass carbon sinks in China’s forests from 1977 to 2008

Cited by 130 publications

References 31 publications

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Temporal and Spatial Variations in Provincial CO2 Emissions in China from 2005 to 2015 and Assessment of a Reduction Plan

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