祁连山不同植被类型残体碳库贮量研究

采用野外调查测定、野外定位研究和室内分析相结合的方法,在植被类型变化较大林区,选择邻近相同海拔、坡向和土壤类型的天然林(青海云杉林、祁连圆柏林、高山灌丛林)、人工林(13 a华北落叶松)、牧坡草地和农田几种植被类型土壤为研究对象,研究了祁连山不同植被类型残体碳库贮量、组成与形成特征,结果表明:天然林残体碳总贮量为510.09~639.7 gC/m2,农田和草地分别为71.4 gC/m2和169.65 gC/m2,人工林为503.75 gC/m2。天然林地上残体碳年形成量为63.48~485 gC/(m2.a)、地下为267.8~314.3 gC/(m2.a),人工林地上为203.7gC/(m2.a)、地下为187.2 gC/(m2.a),农田地上为47.8 gC/(m2.a)、地下为106.4 gC/(m2.a),草地地上为98.3 gC/(m2.a)、地下为147.3 gC/(m2.a)。在不同植被类型中,从天然林到农田或草地,残体碳库贮量及形成量减小。     

基于改进土壤冻融水循环的Biome-BGC模型估算青藏高原草地NPP

Biome-BGC模型被广泛用于估算植被净初级生产力（Net Primary Productivity, NPP）,但是该模型未考虑冻土区土壤冻融水循环过程对植被生长的影响。本文基于Biome-BGC模型,改进冻土区活动层土壤冻融水循环,估算了2000—2018年青藏高原高寒草地NPP。通过比较原模型和改进后的模型,并对NPP模拟结果的时空特征进行了分析,结果表明：① 增加冻融循环提高了NPP估算精度,青藏高原草地NPP均值由114.68 gC/(m²·a)提高到128.02 gC/(m²·a)。② 原模型和改进后NPP的空间分布差异较大,时间变化趋势差异不明显。③ 青藏高原草地NPP总量为253.83 TgC/a,呈东南向西北递减的空间格局,年均增速为0.21gC/(m²·a)（P=0.023）,显著增加的占17.85%,主要分布在羌塘高寒草原地带的大部分地区和藏南山地灌木草原地带的西部。④ 该冻融水循环改进方法简单可靠,具有在其他多年冻土区推广的价值。     

黑河中游山前平原区退耕地土壤有机碳库特征

采用野外调查测定、野外定位研究和室内分析相结合的方法,在黑河中游山前平原区选择邻近相同海拔和土壤类型的退耕(退耕1 a、5 a、10 a)造林地为研究对象,研究了山前平原区退耕地不同退耕年限的土壤碳动态,结果表明:退耕1 a、退耕5 a、退耕10 a和退耕造林地的土壤有机碳含量变化分别为16.89 gC/kg,8.24 gC/kg,8.56gC/kg和9.98 gC/kg,平均土壤有机碳密度分别为8.05 kg/m2,4.78 kg/m2,5.02 kg/m2和6.52 kg/m2。平均土壤有机碳周转时间分别为23 a,25 a,26 a和33 a;不同植被类型土壤CO2通量依次为退耕1 a土壤530.8 gC/(m2.a);退耕5 a土壤316.9 gC/(m2.a);退耕10 a土壤266.1 gC/(m2.a);退耕地造林(杨树林)286.9 gC/(m2.a)。同一类型退耕地中,土壤有机碳含量和土壤碳密度随土壤深度增加而降低,而土壤有机碳周转时间则随深度增加而增大。     

内蒙古草地生态系统碳源/汇时空格局及其与气候因子的关系

草地净生态系统生产力(NEP)能够表征草地生态系统的固碳能力,直接定性定量地描述草地生态系统的碳源/汇性质和大小.因此,研究区域尺度草地生态系统NEP具有重要的实践意义.基于卫星遥感资料,地面气象观测资料及实地采样数据,结合光能利用率模型估算了2001-2012年内蒙古草地生态系统净初级生产力(NPP).同时,应用土壤呼吸模型估算了逐月平均土壤呼吸量(Rs),进而估算内蒙古草地净生态系统生产力(NEP).研究揭示了2001-2012年内蒙古草地生态系统NPP,NEP年际变化规律,气候因子的年际变化规律,以及草地NPP,NEP与主要气候因子的关系.结果表明:2001年以来,内蒙古草地生态系统整体发挥碳汇效应,净碳汇总量达到0.55 Pg C,年均固碳率约为0.046 Pg C/a;研究区大部分草地NPP,NEP与降水均呈正相关关系,与温度相关性不显著,内蒙古草地生态系统仍有巨大的固碳潜力.     

基于Biome-BGC模型的青藏高原五道梁地区NPP变化及情景模拟

以“气候变暖”为标志的全球气候变化对青藏高原生态系统产生强烈影响,利用参数本地化的生物地球化学模型（Biome-BGC）对五道梁地区草地生态系统进行模拟,研究了该区域1961~2015年净初级生产力（net primary productivity,NPP）的变化,并进行了情景模拟。结果表明:五道梁地区近55 a草地年均NPP为67.94 g/(m ²·a),呈显著上升趋势,主要是由生长季延长以及9月份生物量快速增长造成。在该地区,温度是草地NPP的主导因子,降水变化在40%以内对生产力影响不显著;温度和降水交互影响NPP,对单一影响有放大作用,暖湿条件下NPP对气候变化响应更加明显。     

中国南方红壤丘陵区植被净初级生产力空间分布及其与气候因子的关系——以江西省泰和县为例

植被净初级生产力(NPP)对气候变化的响应研究是全球变化研究的核心内容之一。在区域尺度上研究NPP年际间的空间变化规律,探究气候因子与植被生长的关系,是应对气候变化区域响应、探讨区域生态过程的科学基础。基于SPOT VEGETATION NDVI植被指数数据、气候和植被分类数据,利用光能利用率模型(CASA)估算了中国南方红壤丘陵区泰和县1998-2012年植被NPP,分析了NPP时空分布特征及其与气候因子的相关性。结果表明:1 1998-2012年泰和县植被年均NPP为762 g C/m2·a,不同植被类型差异明显,空间上表现出东西高、中间低的分布特征;2 1998-2012年泰和县植被NPP总体呈增长趋势,年际波动较大,平均值为2.21×106g C/a;3研究区NPP与年降水量呈不显著正相关关系,与年均气温呈显著负相关关系,表明温度是影响该地区植被NPP的主要气候因子。     

黑河流域植被净初级生产力的遥感估算

利用光能利用率模型C-FIX,高时空分辨率的SPOT/VEGETATION遥感数据,全球格网化气象再分析资料以及黑河流域土地利用图,估算了1998—2002年黑河流域不同生态系统净初级生产力(NPP)的年总量和平均生产力,输出了黑河流域NPP年累积量空间分布格局图、NPP季节动态图,分析了近5a来黑河流域NPP时空变化特征,评价了不同土地利用类型下的生态系统生产力水平差异。其结果表明,黑河流域陆地生态系统NPP空间分布及其季相变化特征是流域自然环境、地貌、气候以及人类生产活动长期共同作用和影响的结果,其中水分条件是控制黑河流域陆地生态系统NPP空间格局的决定因子。1998—2002年黑河流域山区高覆盖度草地和下游荒漠区净初级生产力持续下降,反映出这些地区生态环境恶化严重。这些研究结果可以为黑河流域的水-生态-经济系统的合理设计与有效实施提供科学数据集。     

CASA模型在金沙江流域(云南部分)NPP研究中的应用

净初级生产力是陆地生态系统碳循环的重要组成部分,对于控制大气CO2的上升有着重要的作用,是生态系统中物质与能量流动研究的基础。采用气象数据、MODIS NDVI数据,运用改进的CASA模型对金沙江流域(云南部分)的净初级生产力进行研究,并分析了其分布格局及影响因素。结果表明:流域初级生产力最大值达到1 382.39 g/m2,平均为719.54 g/m2;在空间分布上,中游[751.21g/(m2·a)]上游[714.42 g/(m2·a)]下游[693.00 g/(m2·a)];流域的净初级生产力有明显的季节变化,表现为干湿季分明,雨季[612.26 g/(m2·a)]的生产力明显高于干季[107.08 g/(m2·a)],秋季又较春季为高;净初级生产力受植被类型、温度、降水等因素的影响明显。用遥感的方法对区域的NPP产量测算已得到了普遍的认可,其研究结果为NPP动态研究和评价生态系统服务功能提供了有效的基础数据。     

2000—2017年内蒙古荒漠草原植被物候变化及对净初级生产力的影响

荒漠草原分布于干旱区和半干旱区,对气候变化的响应极为敏感,但目前学术界对于荒漠草原物候与生产力变化的研究仍较为薄弱。有鉴于此,论文采用2000—2017年MODIS NDVI数据和气象数据,利用通用数量化方法提取内蒙古荒漠草原植被的生长季始期(start of season, SOS)和生长季末期(end of season, EOS);基于Carnegie-Ames-Stanford Approach (CASA)模型估算了植被净初级生产力(NPP),并分析了植被物候和净初级生产力之间的关系。研究结果表明：① 2000—2017年内蒙古荒漠草原SOS呈显著提前趋势(0.88 d/a,P<0.05),EOS不显著提前(0.13 d/a,P>0.05),生长季长度(length of season, LOS)呈显著延长趋势(0.76 d/a)。81.53%像元的SOS与2—4月平均气温呈负相关(8.21%显著相关,P<0.05),60.80%像元的SOS与4月降水量呈负相关关系(6.12%显著相关,P<0.05);65.16%像元的EOS与9月平均气温呈负相关(5.03%显著相关,P<0.05),78.61%像元的EOS 与7—9月降水量呈正相关关系(10.12%显著相关,P<0.05)。② 内蒙古荒漠草原多年平均NPP为104.71 gC/(m ²·a),有自东向西逐渐降低的区域差异;在研究时段内,春、夏季和生长季的NPP均呈不显著增加趋势,秋季NPP有不显著减少趋势;生长季降水量增加有利于生长季NPP的积累。③ 春季NPP与SOS呈不显著负相关,秋季NPP与EOS呈显著正相关。LOS的延长促进了NPP的累积,其中生长季NPP与EOS的推迟关系更为密切。研究结果揭示气候变化对内蒙古荒漠草原植被物候和生产力有显著影响,对区域生态系统管理和生态建设具有重要参考意义。     

基于CASA模型的云南省植被净初级生产力遥感估算

植被净初级生产力能够反映植被的生产能力、当地的生态资源及生态资源潜力。采用多时间序列MODIS的NDVI遥感数据和气象数据,利用CASA光能利用率模型在空间分析技术支持下反演估算云南省2015年植被净初级生产力(NPP)。结果表明:(1)云南省2015年平均NPP值为804.904 2 gC/(m~2·a~(-1)),总值为0.317 212 745 PgC;(2)2015年云南省NPP的平均值1月至2月、5月至9月、10月至12月处于下降趋势,2月至4月、9月至10月处于上升趋势,一年中的最高值在5月份,最低值在9月份;(3)植被净初级生产力的高值位于云南省的南部和西部。     

2001—2017 年三江源区典型草地群落碳源/ 汇模拟及动态变化分析

碳源/汇是解释地球大气碳循环过程的重要指标,探究三江源的碳源/汇特征对于理解该地 区植被对全球气候变化的响应具有重要意义。三江源以脆弱的草地生态系统为主,且对全球气候 变化非常敏感。该地区生态环境极其脆弱,大部分地区条件恶劣导致实测数据稀缺,很难对该地 区的碳源/汇时空格局进行完整剖析。因此通过以三江源 5 种典型草地群落（金露梅、紫花针茅、风 毛菊、小蒿草、及青藏薹草群落）为研究对象,基于 BIOME-BGC 模型,利用地理数据、气象数据和植 被生理参数等数据,得出 2001—2017 年三江源草地群落的净初级生产力（NPP）、净生态系统生产 力（NEP）模拟值,并对草地群落 NPP、NEP 变化特征与气温、降水相关性以及碳利用效率变化等特 征进行了综合分析。结果表明：三江源区 NPP、NEP 在空间格局上,表现为由东南向西北数值逐渐 递减趋势;5 种典型草地群落多年 NPP 均呈现逐年增高趋势,其平均值为 196.06 g C·m ^-2·a ^-1。其 中,金露梅群落 NPP 平均值最高为 342.00 g C·m^-2·a^-1,青藏薹草群落 NPP 平均值最低为 55.93g C·m^-2·a^-1;5 种草地群落 NEP 的多年平均值为 49.02 g C·m^-2·a^-1,金露梅、紫花针茅及青藏薹草 3 种植 被群落的 NEP 值呈缓慢的上升趋势,风毛菊和小蒿草群落呈缓慢下降趋势。研究发现三江源草地 生态系统具有显著的碳汇作用,且不同群落 NPP、NEP 对气温和降水的响应程度有所差异,5 种群 落 NPP 与气温均呈显著正相关,但 NPP、NEP 与降水量的相关性较低;5 种群落均具有较强固碳潜 力,除金露梅外其余植被群落的碳利用率均在 0.625 以上。     

Spatial and temporal variations of vegetation in Qinghai Province based on satellite data

This paper used five years (2001-2006) time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classi-fication approach for different land cover types with special emphasis on vegetation, espe-cially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dy-namic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km2 in 2001 to 374,576 km2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest (67.92%) among the five grade elevation belts in Qinghai Prov-ince. The second largest vegetation cover ratio is in middle mountain belt (61.80%). Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt (38.98%), low mountain belt (25.55%) and flat region belt (15.46%). The area of middle density grassland in high mountain belt is the biggest (94,003 km2), and vegetation cover ratio of dense grassland in middle mountain belt is the highest (32.62%), and the in-creased area of dense grassland in high mountain belt is the greatest (1280 km2). In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km2.     

近5年青海省植被覆盖变化的遥感监测

This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover types with special emphasis on vegetation, especially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dynamic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km^2 in 2001 to 374,576 km^2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest （67.92%） among the five grade elevation belts in Qinghai Province. The second largest vegetation cover ratio is in middle mountain belt （61.80%）. Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt （38.98%）, low mountain belt （25.55%） and flat region belt （15.46%）. The area of middle density grassland in high mountain belt is the biggest （94,003 km^2）, and vegetation cover ratio of dense grassland in middle mountain belt is the highest （32.62%）, and the increased area of dense grassland in high mountain belt is the greatest （1280 km^2）. In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km^2.     

Simulating net primary production of grasslands in northeastern Asia using MODIS data from 2000 to 2005

The net primary production (NPP) of grasslands in northeastern Asia was estimated using improved CASA model with MODIS data distributed from 2000 and ground data as driving variables from 2000 to 2005. Average annual NPP was 146.05 g C m?2 yr?1 and average annual total NPP was 0.32 Pg C yr?1 in all grasslands during the period. It was shown that average annual grassland NPP in the whole northeastern Asia changed dramatically from 2000 to 2005, with the highest value of 174.80 g C m?2 yr?1 in 2005 and the lowest value of 125.65 g C m?2 yr?1 in 2001. On regional scale, average annual grassland NPP of 179.71 g C m?2 yr?1 in southeastern Russia was the highest among the three main grassland regions in the six years. Grasslands in northern China exhibited the highest average annual total NPP of 0.16 Pg C yr?1 and contributed 51.42% of the average annual total grassland NPP in northeastern Asia. Grassland NPP in northeastern Asia also showed a clear seasonal pattern with the highest NPP occurred in July every year. Average monthly grassland NPP in southeastern Russia was the highest from May to August while average monthly grassland NPP in northern China showed the highest NPP before May and after August. The change rate distribution of grassland NPP between the former three years and the latter three years showed grassland NPP changed slightly between the two stages in most regions, and that NPP change rate in 80.98% of northeastern Asia grasslands was between –0.2 and 0.2. Grassland NPP had close correlation with precipitation and temperature, that indicates climate change will influence the grassland NPP and thus have a great impact on domestic livestock in this region in future.     

内蒙古草地生态系统碳源/汇时空格局及其与气候因子的关系（英文）

Global climate change has become a major concern worldwide. The spatio-temporal characteristics of net ecosystem productivity(NEP), which represents carbon sequestration capacity and directly describes the qualitative and quantitative characteristics of carbon sources/sinks(C sources/sinks), are crucial for increasing C sinks and reducing C sources. In this study, field sampling data, remote sensing data, and ground meteorological observation data were used to estimate the net primary productivity(NPP) in the Inner Mongolia grassland ecosystem(IMGE) from 2001 to 2012 using a light use efficiency model. The spatio-temporal distribution of the NEP in the IMGE was then determined by estimating the NPP and soil respiration from 2001 to 2012. This research also investigated the response of the NPP and NEP to the main climatic variables at the spatial and temporal scales from 2001 to 2012. The results showed that most of the grassland area in Inner Mongolia has functioned as a C sink since 2001 and that the annual carbon sequestration rate amounts to 0.046 Pg C/a. The total net C sink of the IMGE over the 12-year research period reached 0.557 Pg C. The carbon sink area accounted for 60.28% of the total grassland area and the sequestered 0.692 Pg C, whereas the C source area accounted for 39.72% of the total grassland area and released 0.135 Pg C. The NPP and NEP of the IMGE were more significantly correlated with precipitation than with temperature, showing great potential for C sequestration.     

Analysis of spatio-temporal features of a carbon source/sink and its relationship to climatic factors in the Inner Mongolia grassland ecosystem

Global climate change has become a major concern worldwide. The spatio-temporal characteristics of net ecosystem productivity (NEP), which represents carbon sequestration capacity and directly describes the qualitative and quantitative characteristics of carbon sources/sinks (C sources/sinks), are crucial for increasing C sinks and reducing C sources. In this study, field sampling data, remote sensing data, and ground meteorological observation data were used to estimate the net primary productivity (NPP) in the Inner Mongolia grassland ecosystem (IMGE) from 2001 to 2012 using a light use efficiency model. The spatio-temporal distribution of the NEP in the IMGE was then determined by estimating the NPP and soil respiration from 2001 to 2012. This research also investigated the response of the NPP and NEP to the main climatic variables at the spatial and temporal scales from 2001 to 2012. The results showed that most of the grassland area in Inner Mongolia has functioned as a C sink since 2001 and that the annual carbon sequestration rate amounts to 0.046 Pg C/a. The total net C sink of the IMGE over the 12-year research period reached 0.557 Pg C. The carbon sink area accounted for 60.28% of the total grassland area and the sequestered 0.692 Pg C, whereas the C source area accounted for 39.72% of the total grassland area and released 0.135 Pg C. The NPP and NEP of the IMGE were more significantly correlated with precipitation than with temperature, showing great potential for C sequestration.     

基于MODIS的青海草地产草量变化遥感分析

青海省属于全国四大牧区之一,及时监测草地植被长势、准确估算牧草产量对青海牧区可持续发展与生态保护具有重要意义。草地产草量遥感估算主要基于植被指数与地面实测数据的统计关系,但是估算涉及植被指数、统计模型和建模指标等因素,不同组合建立的估算模型的精度不同。本文基于青海省MODIS数据与地面实测产草量数据,选择了6种植被指数（NDVI、EVI、RVI、DVI、RDVI、MSAVI）、5种统计模型（简单线性模型、二次多项式模型、幂函数模型、指数函数模型、对数函数模型）以及3种建模指标（植被指数年度最大值VI_max、植被指数生长季累积值VI_season-cum、植被指数年度累积值VI_annual-cum）,研究不同组合下估算模型的精度差异,并从中选出最优产草量估算模型,用于估算青海省2015年和2016年的产草量。结果表明：（1）6种植被指数中,基于NDVI的产草量估算精度最高;非线性模型的估算精度高于线性模型,尤其是指数模型,适用于大多数草地类型产草量的估算;基于NDVI年度最大值的估算模型对大多数草地类型都具有最高的决定系数（R²）。（2）从干重来看,高产草量区（>1 200 kg·hm^-2）主要位于青海东部的高寒草原,中等产草量区（600~1 200 kg·hm^-2）位于青海南部和东部的高寒草原和禾草草原,低产草量区（<600 kg·hm^-2）位于青海西部和北部的高寒草甸、高寒草原、高寒荒漠和盐生草甸。（3）与2015年相比,2016年青海省干草总产量减少31.60×10⁴ t,减幅为1.36%。其中,禾草草原和高寒草甸的减产幅度最大,而荒漠草原和盐生草甸的产量则有所增加。本文可为草地产草量遥感估算的研究和实践提供参考。