省尺度森林植被碳储量空间分布模拟不确定性分析——以江西省为例（英文）

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2(AVIM2), Kriging, Satellite-data Based Approach(SBA), and a new method, High Accuracy Surface Modeling(HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m~2 and 94.32×109 m~2 simulated by HASM, respectively.     

中国东北地区林地面积变化的动态模拟

There is plenty of forests in Northeast China which contributes a lot to the conservation of water and land resources, produces timber products, and provides habitats for a huge number of wild animals and plants. With changes of socio-economic factors as well as the geophysical conditions, there are dramatic changes on the spatial patterns of forest area. In this sense, it is of great significance to shed light on the dynamics of forest area changes to find the underlining reasons for shaping the changing patterns of forest area in Northeast China. To explore the dynamics of forest area change in Northeast China, an econometric model is developed which is composed of three equations identifying forestry production, conversion from open forest to closed forest and conversion from other land uses to closed forest so as to explore the impacts on the forest area changes from demographic, social, economic, location and geophysical factors. On this basis, we employ the Dynamics of Land System (DLS) model to simulate land-use conversions between forest area and non-forest cover and the land-use conversions within the sub-classes of forest area for the period 2000–2020 under business as usual scenario, environmental protection scenario and economic growth scenario. The simulation results show that forest area will expand continuously and there exist various kinds of changing patterns for the sub-classes of forest area, for example, closed forest will expand continuously and open forest and shrub will decrease a little bit, while area of other forest will keep intact. The research results provide meaningful decision-making information for conserving and exploiting the forest resources and making out the planning for forestry production in the Northeast China region.     

中国森林、灌木林叶片碳含量及其空间格局与环境因子研究（英文）

Leaf carbon content(LCC) is widely used as an important parameter in estimating ecosystem carbon(C) storage,as well as for investigating the adaptation strategies of vegetation to their environment at a large scale.In this study,we used a dataset collected from forests(5119 plots) and shrublands(2564 plots) in China,2011–2015.The plots were sampled following a consistent protocol,and we used the data to explore the spatial patterns of LCC at three scales:plot scale,eco-region scale(n = 24),and eco-region scale(n = 8).The average LCC of forests and shrublands combined was 45.3%,with the LCC of forests(45.5%) being slightly higher than that of shrublands(44.9%).Forest LCC ranged from 40.2% to 51.2% throughout the 24 eco-regions,while that of shrublands ranged from 35% to 50.1%.Forest LCC decreased with increasing latitude and longitude,whereas shrubland LCC decreased with increasing latitude,but increased with increasing longitude.The LCC increased,to some extent,with increasing temperature and precipitation.These results demonstrate the spatial patterns of LCC in the forests and shrublands at different scales based on field-measured data,providing a reference(or standard) for estimating carbon storage in vegetation at a regional scale.     

1980-2017年中国碳强度关键影响因子变化特征

The Chinese government ratified the Paris Climate Agreement in 2016.Accordingly,China aims to reduce carbon dioxide emissions per unit of gross domestic product(carbon intensity)to 60%–65%of 2005 levels by 2030.However,since numerous factors influence carbon intensity in China,it is critical to assess their relative importance to determine the most important factors.As traditional methods are inadequate for identifying key factors from a range of factors acting in concert,machine learning was applied in this study.Specifically,random forest algorithm,which is based on decision tree theory,was employed because it is insensitive to multicollinearity,is robust to missing and unbalanced data,and provides reasonable predictive results.We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017.The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry,the proportion of fossil fuel-based energy,and technological progress.The Chinese economy developed rapidly between 1992 and 2007;during this time,the effects of the proportion of service industry,price of fossil fuel,and traditional residential consumption on carbon intensity increased.Subsequently,the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis;during this period,reductions in emissions and the availability of new energy types began to have effects on carbon intensity,and the importance of residential consumption increased.The results suggest that optimizing the energy and industrial structures,promoting technological advancement,increasing green consumption,and reducing emissions are keys to decreasing carbon intensity within China in the future.These approaches will help achieve the goal of reducing carbon intensity to 60%–65%of the 2005 level by 2030.     

热带森林覆被的时空动态评价——以印度Malkangiri district of Orissa为例

Tropical forests have been recognized as having global conservation importance. However,they are being rapidly destroyed in many regions of the world. Regular monitoring of forests is necessary for an adaptive management approach and the successful implementation of ecosystem management. The present study analyses the temporal changes in forest ecosystem structure in tribal dominated Malkangiri district of Orissa,India,during 1973-2004 period based on digitized forest cover maps using geographic information system (GIS) and interpretation of satellite data. Three satellite images Landsat MSS (1973),Landsat TM (1990) and IRS P6 LISS III (2004) were used to determine changes. Six land cover types were delineated which includes dense forest,open forest,scrub land,agriculture,barren land and water body. Different forest types were also demarcated within forest class for better understanding the degradation pattern in each forest types. The results showed that there was a net decrease of 475.7 km2 forest cover (rate of deforestation = 2.34) from 1973 to 1990 and 402.3 km2 (rate of deforestation = 2.27) from 1990 to 2004. Forest cover has changed over time depending on a few factors such as large-scale deforestation,shifting cultivation,dam and road construction,unregulated management actions,and social pressure. A significant increase of 1222.8 km2 agriculture area (1973-2004) clearly indicated the conversion of forest cover to agricultural land. These alterations had resulted in significant environmental consequences,including decline in forest cover,soil erosion,and loss of biodiversity. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. Particular attention must be paid to tropical forests,which are rapidly being deforested.     

中国天气发生器的降水模拟

A stochastic model for daily precipitation simulation in China was developed based on the framework of a ‘Richardson-type‘ weather generator that is an important tool in studying impacts of weather/climate on a variety of systems including ecosystem and risk assessment. The purpose of this work is to develop a weather generator for applications in China. The focus is on precipitation simulation since determination of other weather variables such as temperature is dependent on precipitation simulation. A framework of first order Markov Chain with Gamma Distribution for daily precipitation is adopted in this work. Based on this framework, four parameters of precipitation simulation for each month at 672 stations all over China were determined using daily precipitation data from 1961 to 2000. Compared with previous works, our estimation for the parameters was made for more stations and longer observations, which makes the weather generator more applicable and reliable. Spatial distributions of the four parameters are analyzed in a regional climate context. The seasonal variations of these parameters at five stations representing regional differences are discussed.Based on the estimated monthly parameters at 672 stations, daily precipitations for any period can be simulated. A 30-year simulation was made and compared with observations during 1971-2000 in terms of annual and monthly statistics. The results are satisfactory, which demonstrates the usefulness of the weather generator.     

Spatial patterns of Pinus tabulaeformis and Pinus massoniana forests in Qinling-Daba Mountains and the boundary of subtropical and warm temperate zones

The Qinling Mountains is not only the geographical boundary between North and South China,but also the boundary between subtropical and warm temperate zones.It plays an important role in the geo-ecological pattern of China.However,there is controversy about the specific location of this geographical boundary in academic community due to the complexity,transition and heterogeneity of the transitional zone,as well as the differences in the delimitation indicators and research purposes.To further reveal the characteristics of the North-South transitional zone and clarify the specific location of the geo-ecological boundary between North and South China,combined with SRTM topographic data,temperature and precipitation data,Pinus massoniana forest and Pinus tabulaeformis forest,which represent subtropical coniferous forest in South China and temperate coniferous forest in North China respectively,were chosen to analyze their spatial distributions in the Qinling-Daba Mountains and the climatic conditions at their boundary with the climatic indexes of annual precipitation,the coldest month(January) average temperature,the warmest month(July) average temperature and the annual average temperature.The results show that:(1) Pinus massoniana and Pinus tabulaeformis forests and the climate indicators of their boundary can be used as one of the vegetation-climate indexes for the delimitation of subtropical and warm temperate zones.The boundary between the subtropical coniferous forest(Pinus massoniana forest) and temperate coniferous forest(Pinus tabulaeformis forest) is located along the south slope of Funiu Mountain to the north edge of Hanzhong Basin(the south slope of Qinling Mountains) at an altitude of 1000–1200 m,where the climatic indictors are stable:the annual precipitation is about 750–1000 mm,the annual average temperature is about 12–14℃,the coldest monthly average temperature is 0–4℃,and the warmest monthly average temperature is about 22–26℃.(2) It can be more scientifically to delimitate the boundary of subtropical and warm temperate zones in China by comprehensively considering the vegetation-climate indicators.Additionally,the boundary between subtropical and warm temperate zones in Qinling-Daba Mountains should be a transitional zone consisting of the boundaries of coniferous forests,broad-leaved forests and shrubs between subtropical and warm temperate zones.The results provide a scientific basis for the selection of delimitation index of subtropical and warm temperate zones.     

Forest phenological patterns of Northeas China inferred from MODIS data

The role of remote sensing in phenological studies is increasingly regarded as a key to understand large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for forest phenological patterns. The forest phenological phase of Northeast China (NE China) and its spatial characteristics were inferred using 1-km 10-day MODIS normalized difference vegetation index (NDVI) datasets of 2002. The threshold-based method was used to estimate three key forest phenological variables, which are the start of growing season (SOS), the end of growing season (EOS) and growing season length (GSL).Then the spatial patterns of forest phenological variables of NE China were mapped and analyzed. The derived phenological variables were validated by the field observed data from published papers in the same study area. Results indicate that forest phenological phase from MODIS data is comparable with the observed data. As the derived forest phenological pattern is related to forest type distribution, it is helpful to discriminate between forest types.     

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.     

内蒙古农牧交错带土地利用变化对土壤碳储量的影响研究（英文）

Soil organic carbon(SOC) stocks in terrestrial ecosystems vary considerably with land use types. Grassland, forest, and cropland coexist in the agro-pastoral ecotone of Inner Mongolia, China. Using SOC data compiled from literature and field investigations, this study compared SOC stocks and their vertical distributions among three types of ecosystems. The results indicate that grassland had the largest SOC stock, which was 1.5-and 1.8-folds more than stocks in forest and cropland, respectively. Relative to the stock in 0–100 cm depth, grassland held more than 40% of its SOC stock in the upper 20 cm soil layer; forest and cropland both held over 30% of their respective SOC stocks in the upper 20 cm soil layer. SOC stocks in grazed grasslands were remarkably promoted after ≥20 years of grazing exclusion. Conservational cultivation substantially increased the SOC stocks in cropland, especially in the 0–40 cm depth. Stand ages, tree species, and forest types did not have obvious impacts on forest SOC stocks in the study area likely due to the younger stand ages. Our study implies that soil carbon loss should be taken into account during the implementation of ecological projects, such as reclamation and afforestation, in the arid and semi-arid regions of China.     

基于HASM的中国森林植被碳储量空间分布模拟

当前区域尺度上森林碳储量估算主要依据森林资源清查数据,整个过程不仅消耗大量人力、物力,而且十分耗时,严重影响了森林碳储量估算的时效性。针对这一问题,本文提出了基于HASM的森林植被碳储量模拟方法,该方法以全球植被动态模型LPJ-Guess 输出的植被碳储量为驱动场,以森林清查样地数据为精度控制点,模拟生成中国陆地森林碳储量分布情况。研究以第7 次中国森林资源清查数据作为精度控制点数据源,同时作为本文模拟方法的精度验证。结果表明,中国森林碳储量为9.2405 Pg,考虑到森林资源清查是基于一定的郁闭度进行的,因此HASM模拟的结果与根据森林资源清查结果计算得出的7.8115 Pg 相比更符合实际情况,西南山区和东北林区是中国森林最主要的碳库,其碳储量分别占中国森林植被碳储量的39.82%和20.46%。同时与之前(1975-1995 年) 相比具有较大幅度的增长,表明近几十年来中国坚持大规模植树造林的碳汇效果显著。同时也表明基于HASM的森林植被碳储量空间分布模拟方法是有效的,模拟结果合理且精度较高,表明该方法在全球尺度上森林植被碳储量模拟及其它生态系统中碳储量模拟中具有应用潜力。     

基于阈值分割的黑龙江省森林类型遥感识别

全球变化背景下,准确获取森林覆盖是监测森林资源动态、实现林业可持续发展的重要基础。为将省级尺度森林资源清查面积资料空间化,以黑龙江省为例,利用1999-2003年该省森林资源清查面积数据,结合2000年500 m分辨率的MODIS数据,构建了基于阈值分割的森林类型遥感识别方法。该方法利用不同地表覆被类型归一化植被指数时间序列的季节分异特征,以森林资源清查面积为标准,设定森林类型的划分阈值,识别了黑龙江省森林类型的空间分布。最后,基于分层随机抽样和精度评价方法,表明森林类型识别结果与地面参考数据具有较高的一致性,总体分类精度为78.1%;特别是季节特征明显的落叶林,精度可达80%以上。本文所构建的方法可将森林清查统计数据进行准确的空间定位,同时结合多期森林资源连续清查资料和遥感信息,可为识别并量化区域生态系统生物量和碳库变化等提供科技支撑。     

中国森林生态系统的植物碳贮量及其影响因子分析

利用中国第四次(1989~1993年)森林资源清查资料,指出中国森林植被的总碳贮量和碳密度分别为 3 778.1Tg(1Tg = 10¹² g)和41.321 Mg/hm²(1 Mg= 10⁶ g),其分布很不均衡,东北和西南各省的碳贮量和碳密度较大。中国森林碳贮量约占世界的1.1%,森林碳密度低于世界平均水平,但中国森林以中、幼龄林为主,占80%以上,表明中国森林植被具有巨大的固碳潜力,对全球碳循环具有重要作用。同时,采用多元线性回归模型、标准系数法定量分析了气候因子对森林植被碳贮量的影响程度,指出气温对森林植被碳贮量的贡献大于降水。     

1980s-2010s中国陆地生态系统土壤碳储量的变化

土壤作为陆地生态系统有机碳库的主体,在全球碳循环中起着重要作用。然而,当前区域土壤有机碳储量的变化情况及其碳源/汇功能仍然不清楚。利用中国1980s （1979-1985年）第二次土壤普查数据,同时收集整理2010s（2004-2014年）已发表的有关中国土壤有机碳储量（0~20 cm和0~100 cm）的文献数据,综合评估了1980s-2010s中国土壤有机碳储量的变化情况,并分析森林、草地、农田和湿地等生态系统土壤碳源/汇功能;同时结合现有的中国植被碳储量变化研究,进一步探讨了1980s-2010s中国陆地生态系统的碳源/汇效应。研究发现：① 1980s-2010s中国土壤（0~100 cm）有机碳储量净增长3.04±1.65 Pg C,增长速率为0.101±0.055 Pg C yr^-1,其中表层土壤（0~20 cm）的碳汇效应明显;② 森林土壤是固碳主体,净增长2.52±0.77 Pg C,而草地和农田土壤增长有限,分别为0.40±0.78和0.07±0.31 Pg C;③ 湿地有机碳储量净减少0.76±0.29 Pg C;④ 中国陆地生态系统的碳汇效应较强,总碳汇量相当于同期（1980-2009年）化石燃料和水泥生产排放CO₂总量的14.85%~27.79%。随着中国森林和草地生态系统植被和土壤的进一步保护、恢复和重建,中国陆地生态系统具有较大的碳汇潜力,在未来全球碳平衡中将发挥更大的作用。     

2005-2013年中国新增造林植被生物量碳库固碳潜力分析

本文利用2005-2013年林业统计年鉴中每个省市新造林面积和遥感分类提取得到的2010年土地覆被类型,结合公开发表的各类森林生长方程和各个时期的森林存活率,估算了中国新造林在2005-2100年生物量碳库变化及其固碳潜力。结果表明：2005-2013年中国新造林面积达到4394×10⁴ hm²,在自然生长状况下,到2020年新造林蓄积量增加16.8亿m³,生物量增加1.6 Pg,生物量碳库0.76 Pg C;新造林生物量碳库在2005-2100年中将增加2.11 Pg C,相当于目前现有森林生物量碳库的25%,约是过去20年森林总碳汇的1.5倍;新造林生物量碳密度逐年增加,最高达到48.1 Mg/hm²。整合林业统计年鉴以及遥感解译的森林类型对新造林生物量固碳潜力分析,研究表明新造林具有较大的碳汇潜力,对中国现有森林碳汇平衡有重要贡献。     

Spatial patterns and environmental factors influencing leaf carbon content in the forests and shrublands of China

Leaf carbon content (LCC) is widely used as an important parameter in estimating ecosystem carbon (C) storage, as well as for investigating the adaptation strategies of vegetation to their environment at a large scale. In this study, we used a dataset collected from forests (5119 plots) and shrublands (2564 plots) in China, 2011–2015. The plots were sampled following a consistent protocol, and we used the data to explore the spatial patterns of LCC at three scales: plot scale, eco-region scale (n = 24), and eco-region scale (n = 8). The average LCC of forests and shrublands combined was 45.3%, with the LCC of forests (45.5%) being slightly higher than that of shrublands (44.9%). Forest LCC ranged from 40.2% to 51.2% throughout the 24 eco-regions, while that of shrublands ranged from 35% to 50.1%. Forest LCC decreased with increasing latitude and longitude, whereas shrubland LCC decreased with increasing latitude, but increased with increasing longitude. The LCC increased, to some extent, with increasing temperature and precipitation. These results demonstrate the spatial patterns of LCC in the forests and shrublands at different scales based on field-measured data, providing a reference (or standard) for estimating carbon storage in vegetation at a regional scale.     

中国森林生态系统植被碳储量 时空动态变化研究

森林是陆地生态系统的主体, 在全球碳循环中起着十分重要的作用。本文利用20 世纪70 年代以来的六次森林清查资料, 结合森林生物量实测数据, 采用分树种、分龄组的生物量—蓄积 拟合关系, 估算了中国20 世纪70 年代以来森林植被碳储量的动态变化。结果表明: 我国六次森 林资源清查中森林的植被总碳储量分别为3.8488PgC、3.6960PgC、3.759PgC、4.1138PgC、 4.6563PgC 和5.5064PgC, 虽然存在一定的波动现象, 但总体增长趋势明显, 尤其是80 年代以来, 植被碳储量净增加1.8104PgC, 平均每年以0.0823PgC 的速率增加, 这表明80 年代以来我国森林 植被一直起着明显的CO₂ 汇的作用。从碳密度的变化看, 70 年代以来我国森林植被平均碳密度 增长了3.001Mgha ^-1, 其中幼龄林与中龄林碳密度分别增长5.2871Mgha ^-1 和0.6022 Mgha ^{-1<,sup>, 而成 熟林碳密度却降低了0.7581Mgha -1, 可见中国森林植被的碳汇功能主要来自于人工林的贡献, 而 且随着幼龄林、中龄林碳储量和碳密度的增长, 中国森林植被的碳汇功能将进一步增强。我国森 林植被碳储量和碳密度空间差异显著, 森林植被碳库主要集中于东北和西南地区, 平均碳密度以 西南、东北以及西北地区为大, 中国森林植被碳储量和碳密度的这种空间分布规律与人类活动对 森林的干扰强度密切相关。}     

全球森林固碳潜力巨大(英文)

森林是重要的陆地生态系统碳汇。1990–2007年间全球森林平均每年从大气中吸收固定2.4±0.4PgC,但对全球森林未来固碳量的评价多是基于气候因素的过程模型的模拟结果,很少有基于森林调查样地数据评价全球森林固碳潜力的研究。我们收集整理野外调查和已发表的成熟林生物量数据728条,建立全球成熟林生物量数据库。根据成熟林地上生物量碳储量空间插值,得到全球森林地上生物量碳容量,进而评估全球森林地上生物量的固碳潜力。结果显示:(1)全球成熟林地上生物量自赤道向两极整体呈递减趋势,但最大值出现在中纬度区;(2)气温和降水是影响成熟林地上生物量的重要因素;(3)全球森林地上生物量碳容量约为586.2±49.3PgC,其地上生物量固碳潜力为313.4PgC。因此,充分发挥现有森林的碳吸存能力,减少对现有森林碳库的干扰,是土地利用变化之外减缓温室气体排放的又一可选途径。     

中国陆地生态系统的碳储量及其空间格局(英文)

地球系统的碳库变化和碳循环过程机制是气候变化成因分析、变化趋势预测、减缓和适应对策的科学基础,受到科技界和国际社会的广泛关注。从20世纪80年代中后期开始,中国学者就开展了陆地生态系统碳循环研究工作,并且在许多研究领域都取得了可喜的进展。本文在回顾中国的陆地生态系统碳循环研究发展历程基础上,重点评述陆地生态系统的碳储量及其空间格局方面的研究成果,评价有关研究的不确定性以及亟待解决的重要科学问题。分析表明,中国的陆地生态系统碳循环研究经过了陆地生态系统碳循环的前期研究、区域尺度生态系统碳循环综合研究、生态系统碳循环过程对环境变化适应性实验研究,以及生态系统碳-氮-水耦合循环及其区域调控管理研究4个主要发展阶段。大多的研究表明,中国陆地生态系统碳储量及其空间格局是温度和降水控制的。全国土壤、森林和草地植被储存约为97.95 —118.93 Pg C;自20世纪70年代中期以来,我国的植树造林和林业管理、草地保护、农作制度改革和保护性耕作等措施发挥了重要的固碳功能,但是各种方法评估的结果仍然存在较大不确定性。今后的研究重点工作是建立天地空一体化碳储量和碳汇动态监测体系、开展生态系统碳-氮-水耦合循环及其区域调控管理的前瞻性研究,定量评价中国区域生态系统的碳收支状况和增汇潜力,评估各种典型生态系统增汇技术的经济效益,为国家尺度的温室气体管理和碳交易机制与政策体系的建立提供可报告、可度量和可核查的科学数据和技术支持。