西北干旱区植被净初级生产力年际变化及其时空和气候因子的贡献——以河西走廊为例

干旱半干旱区主导着全球植被净初级生产力(net primary productivity,NPP)的年际变化(inter-annual variability,IAV),但该区域NPP-IAV及其驱动机制并不清楚。本文以河西走廊为研究区,使用CASA模型估算2002—2017年的NPP,基于距平通量公式计算了该地区NPP-IAV,并定量估算了时间、空间以及气候因子对NPP-IAV的贡献。结果表明:2002—2017年河西走廊NPP呈上升趋势,植被生长状况良好; NPP-IAV呈现先负后正的变化规律;河西走廊NPP-IAV受暖干和冷湿气候影响,前者控制的面积最大(51.01%),但对NPP-IAV的贡献仅占15.38%;后者对NPP-IAV的贡献率最大(48.68%),但面积仅占23.67%;暖干气候主导着河西走廊NPP的分布格局,冷湿气候主导着NPP年际变化; 7月NPP对IAV贡献最大,3月最小;草地主导着河西走廊NPP-IAV;荒漠NPP总量最低,但贡献率却高于森林。据此预测,随着中国西北干旱区气候变暖,该地区植被NPP-IAV将进一步加剧。     

长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子

长江流域是我国重要的工农业生产区和生态安全屏障。深入开展长江流域陆地植被总初级生产力（GPP）时空变化特征和驱动因子研究,对了解变化环境下区域植被生长状况和生物固碳能力、掌握生态环境质量具有重要意义。基于MODIS GPP遥感数据产品、土地利用和气象观测数据,采用趋势分析和偏相关分析法,系统研究了2000-2015年间长江流域陆地植被GPP时空变化特征,探讨了不同二级水资源区内气候因子对GPP变化影响的空间差异,揭示了不同土地利用类型GPP变化特征以及气候因子作用。结果表明：1）长江流域陆地植被覆盖区GPP在0.3-2765 gC m^-2 a^-1之间,均值约990.46 gC m^-2 a^-1,多年平均GPP总量为1.735 P gC;2）近年来,长江流域GPP呈不显著上升趋势,趋势率为2.39 gC m^-2 a^-1。空间上,GPP上升区和下降区分别占总流域面积的68%和32%。各二级水资源区内,除了洞庭湖流域和太湖流域GPP呈下降趋势外,其他区GPP均呈上升趋势;3）不同土地利用类型GPP均值在198.50-1276.90 gC m^-2 a^-1之间。各土地利用类型中除水田GPP呈微弱下降外,其他均呈上升趋势,尤其是高、中、低覆盖度草地GPP上升趋势较为显著;4）不同气候因子对植被GPP变化的影响程度在不同二级水资源区、不同土地利用类型间均存在一定差异,但就长江流域整体而言,GPP年际变化受温度影响显著,其次是蒸发,而降水等其他气候因子的影响不大。     

长白山自然保护区总初级生产力时空变化特征及其影响因素

长白山自然保护区是典型的温带原始森林生态系统,其植被总初级生产力(GPP)与地形和气候变化密切相关,开展长白山自然保护区GPP的时空变化特征以及影响因子研究,对于了解植被生长状况和生态环境质量具有重要意义。本研究采用光能利用率模型(VPM)对长白山自然保护区GPP进行估算,并分析坡度、海拔、气温、降水、总辐射对其的影响。结果表明：2000—2020年,长白山自然保护区GPP多年平均值为63～1706 g C·m^-2·a^-1,GPP随海拔的增加而减小。气温是影响长白山自然保护区植被GPP空间分布差异的主要因素,其与GPP之间存在显著正相关性。研究期间,长白山自然保护区年GPP整体呈极显著增加趋势,年均增加值为13 g C·m^-2·a^-1。年GPP增加的区域占总区域的79.9%,且各植物功能型年GPP增加面积的比例存在差异。年降水与长白山自然保护区43.2%区域的GPP呈显著负相关,而年均温和年总辐射分别与长白山自然保护区47.2%和82.4%区域的GPP呈显著正相关。在未来全球气候变暖的背景下,长白山...     

2000-2020年祁连山植被净初级生产力时空变化及其驱动因素

植被净初级生产力(NPP)是评价植被生长的重要参数,也是评估陆地生态系统质量与功能的重要指标。基于MODIS NPP、数字高程模型(DEM)、气象水文及人类活动数据,采用空间分析、趋势分析,分别从像元尺度和县域尺度识别了2000-2020年以来祁连山NPP时空变化特征,采用偏相关分析研究了NPP对年均温和年降水的响应,并借助地理探测器模型揭示了NPP变化的驱动因素,最后采用Hurst指数预测了NPP未来变化趋势。结果表明:2000-2020年祁连山平均NPP呈波动增加趋势,年均增加2.38 g C/m²,其中栽培植被和阔叶林增长最为明显。近20年,像元尺度上有75.37%的区域NPP增加,主要位于东南部;县域尺度上,古浪、平安、化隆和永登县NPP增速较快,而祁连、海西、德令哈和门源县增速较慢。祁连山NPP空间分布具有明显的集聚性,高值集聚区主要位于东南部,而低值集聚区主要位于西北部。年均温和降水量的增加均促进了NPP的增加,但不同区域NPP对气温和降水的响应有明显差异。降水量、饱和水气压差和蒸散发是NPP变化的主要驱动因子,驱动因子之间对植被NPP变化存在交互作用,分为双因子增强和非线性增强效应。未来祁连山NPP变化以增加非持续性为主,说明植被变化面临较大不确定性。研究结果有助于揭示全球气候变化背景下区域植被NPP对气候变化及人类活动的非线性响应机制,亦可为祁连山生态保护与可持续发展提供理论依据。     

2000—2019年石羊河流域植被总初级生产力变化及其气候特征

石羊河流域是我国典型的内陆河流域,生态特征敏感脆弱,是了解干旱地区陆地生态系统总初级生产力(gross primary productivity, GPP)对气候变化响应及反馈的典型区域。本研究通过卫星数据和地面观测数据建立光能利用率模型,模拟估算了石羊河流域2000—2019年植被GPP,分析了气候影响下的不同植被类型GPP的空间分布以及年际变化。结果表明：石羊河流域GPP的平均值为256.52 g C·m^-2;落叶阔叶林、常绿针叶林、灌木林、耕地、草原、湿地和荒漠植被GPP分别为676.38、609.96、144.42、404.49、314.07、75.15和110.21 g C·m^-2,表现为南部祁连山区的落叶阔叶林GPP最高,北部荒漠区的湿地GPP最低;GPP的变化呈上升趋势,年际变化存在波动,趋势增加的面积为92%,平均速率为6.99 g C·m^-2·a^-1;流域内不同植被类型GPP增加速度从大到小顺序为落叶阔叶林>常绿针叶林>草地>耕地>灌木林>荒漠>...     

2001—2018年中国总初级生产力时空变化的遥感研究

总初级生产力（GPP）是绿色植被吸收大气中CO₂进行光合作用生产的有机质,是陆地生态系统碳循环研究的一个关键参数。利用遥感数据和气象数据驱动的双叶光能利用率DTEC模型计算了2001-2018年中国逐月GPP,并结合日光诱导叶绿素荧光（SIF）反演的GOSIF GPP数据集,分析了中国陆地生态系统2001-2018年GPP的时空变化特征。结果表明：（1） GOSIF和DTEC模拟的中国多年GPP平均值分别为7.23 Pg C和6.93 Pg C,在空间分布上呈现东南部高西北部低的特征;（2）2001-2018年,中国GPP呈显著增长（P<0.01）,年增长幅度分别为0.094 PgC/a （GOSIF）和0.073 PgC/a （DTEC）。而已有研究估计的中国GPP年增长幅度约为0.02-0.057 PgC/a,低估了GPP增长趋势。（3）在中国通量网6个通量站的GPP验证表明,两种模型精度高、表现好,都能较好地模拟观测站的GPP季节变化。（4） GOSIF GPP的精度优于DTEC GPP模型,这可能是由于SIF与GPP存在直接机理联系。GOSIF GPP算法能客观地反映植被生产力状况,而DTEC模型更适合自然条件下植被生产力的模拟。     

北部湾南流江流域植被净初级生产力时空分布及其驱动因素

分析北部湾南流江流域净初级生产力时空动态变化特征及其驱动机制,为该区域生态环境保护及应对气候变化提供科学依据。基于光能利用率模型（CASA）,利用遥感数据、气象数据和植被类型数据估算了研究区2000-2015年流域的净初级生产力（NPP）,借助于Theil-Sen趋势、Mann-Kendall检验以及Hurst指数等数理统计方法对研究区NPP的时空变化特征、未来趋势及其驱动因素进行了定量化分析。结果表明：①时间尺度上,流域NPP总体上呈现出波动上升趋势,增速为44.03 g C m^-2（10 a）^-1,快于广西自治区,上游和下游地区NPP快于全区,而中游地区慢于全区;②空间尺度上,流域NPP的分布规律表现出明显的地域分异,中游最高（1098.99 g C/m²）,下游次之（1041.71 g C/m²）,而上游最小（1013.22 g C/m²）。NPP的Sen趋势度介于-77.10-74.80 g C m^-2 a^-1之间,在空间上呈现出增加的趋势。③空间波动性上,流域NPP的变异系数较大,其值介于0.01-0.71,其中洪潮江水库、小江水库周边以及玉林市的城乡建设用地扩张区域处于高波动状态,而流域中部的六万大山以及五皇山地带则处于低波动状态;④未来变化趋势上,流域NPP的Hurst指数范围介于0-0.99之间,平均值为0.70,呈现单峰右偏分布,预示着流域NPP未来处于持续性增加的趋势;⑤驱动机制上,流域NPP与多年平均气温呈正相关关系,与年均降水量呈负相关关系,气温是该流域植被NPP的主控因子。由耕地转化为建设用地所导致的NPP损失值最大,其值达到4715.62 t/a,而草地转换为建设用地导致NPP损失值最小,其值仅为184.63 t/a。     

2000—2015年秦巴山区植被净初级生产力时空变化及其驱动因子

基于2000—2015年MOD17A3数据及各驱动因素数据,辅以地统计学理论,利用趋势分析、Hurst指数及相关分析等方法,剖析秦巴山区近16年植被净初级生产力(NPP)时空变化特征,研究气候变化、土壤类型、地形因子、植被类型及人类活动等对植被NPP的影响.结果表明: 秦巴山区植被NPP空间上呈西南高、东北低的分布格局;时间上,近16年呈西北增、东北减的变化特征,在未来呈北部持续性、南部反持续性的发展态势.秦巴山区植被NPP与降雨量和气温呈正相关;暗棕壤、黄壤、紫色土和水稻土4种土壤类型的NPP均值明显高于其他土壤类型;不同植被类型的NPP存在空间分布和变化趋势的差异;NPP的高值主要分布在坡度25°～50°、海拔500～1000 m 以及大于2500 m的区域内;人类活动对NPP具有双重扰动性,表现为正向促进,反向抑制.     

青海省植被净初级生产力(NPP)时空格局变化及其驱动因素

植被净初级生产力(NPP)作为陆地生态过程的关键参数,不仅用以估算地球支持能力和评价陆地生态系统的可持续发展,也是全球碳循环的重要组成部分和关键环节。基于2000—2014年MOD17A3年均NPP数据和气象站点气温、降水资料,采用简单差值、趋势分析、相关性分析和Hurst指数等方法,分析了青海省NPP的时空变化特征及其与气候因子的关系。结果表明:①青海省植被年均NPP在2000—2014年间整体分布呈现由南到北、由东到西递减的趋势,各生态区的空间存在显著差异,表现为Ⅱ区>Ⅰ区>Ⅲ区>Ⅳ区>Ⅴ区。②2000—2014年,青海省NPP变化趋势由北到南、由西到东呈现逐渐增加趋势,平均趋势系数为0.61,NPP值增加的区域占总面积的15%,其中显著增加区域为2.8%,轻度增加区域为12.2%。③青海省NPP值的Hurst的值域范围为0—0.39,均值为0.12,除了河流湖泊,建筑用地和未利用土地,青海省NPP变化特征为反持续性特征。④气候因子(年平均降水量和年均气温)对年均NPP的分布有影响,海拔的高低造成气温、降水和土壤的差异,间接影响植被NPP,15年土地利用/覆...     

不同数据集的1982—2017年中国总初级生产力的时空动态

了解陆地生态系统总初级生产力(GPP)时空变化及其与气候因子的关系,可以为植被恢复及保护提供重要依据。基于气象数据和3套公开的GPP数据集(EC-LUE GPP、GLASS GPP和NIRv GPP),本研究系统分析了中国1982—2017年GPP的时空变化特征及其对气候变化的响应。结果表明: 3套GPP数据均表明,1982—2017年,中国年和季节的GPP呈逐年上升趋势,1998和2002年明显高于研究期间的平均水平,1989和1992年明显低于多年平均GPP。1982—2017年,中国大部分地区GPP呈显著上升趋势,3套GPP数据显著增加的区域分别占整个研究区的75.7%、73.0%和69.6%。年GPP与降水和温度均存在显著正相关关系,但空间异质性较强。其中GPP与温度呈正相关的区域主要分布在西北和华中地区,GPP与降水呈正相关的区域主要分布在华北地区。不同季节的GPP受气温、降水影响的区域存在明显的时空异质性。温度是春、秋和冬季GPP的限制因子,而夏季GPP主要受降水影响。     

Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data‐model comparison

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons.     

Temporal dynamics of estuarine phytoplankton: A case study of San Francisco Bay

Detailed surveys throughout San Francisco Bay over an annual cycle (1980) show that seasonal variations of phytoplankton biomass, community composition, and productivity can differ markedly among estuarine habitat types. For example, in the river-dominated northern reach (Suisun Bay) phytoplankton seasonality is characterized by a prolonged summer bloom of netplanktonic diatoms that results from the accumulation of suspended particulates at the convergence of nontidal currents (i.e. where residence time is long). Here turbidity is persistently high such that phytoplankton growth and productivity are severely limited by light availability, the phytoplankton population turns over slowly, and biological processes appear to be less important mechanisms of temporal change than physical processes associated with freshwater inflow and turbulent mixing. The South Bay, in contrast, is a lagoon-type estuary less directly coupled to the influence of river discharge. Residence time is long (months) in this estuary, turbidity is lower and estimated rates of population growth are high (up to 1–2 doublings d^–1), but the rapid production of phytoplankton biomass is presumably balanced by grazing losses to benthic herbivores. Exceptions occur for brief intervals (days to weeks) during spring when the water column stratifies so that algae retained in the surface layer are uncoupled from benthic grazing, and phytoplankton blooms develop. The degree of stratification varies over the neap-spring tidal cycle, so the South Bay represents an estuary where (1) biological processes (growth, grazing) and a physical process (vertical mixing) interact to cause temporal variability of phytoplankton biomass, and (2) temporal variability is highly dynamic because of the short-term variability of tides. Other mechanisms of temporal variability in estuarine phytoplankton include: zooplankton grazing, exchanges of microalgae between the sediment and water column, and horizontal dispersion which transports phytoplankton from regions of high productivity (shallows) to regions of low productivity (deep channels).Multi-year records of phytoplankton biomass show that large deviations from the typical annual cycles observed in 1980 can occur, and that interannual variability is driven by variability of annual precipitation and river discharge. Here, too, the nature of this variability differs among estuary types. Blooms occur only in the northern reach when river discharge falls within a narrow range, and the summer biomass increase was absent during years of extreme drought (1977) or years of exceptionally high discharge (1982). In South Bay, however, there is a direct relationship between phytoplankton biomass and river discharge. As discharge increases so does the buoyancy input required for density stratification, and wet years are characterized by persistent and intense spring blooms.     

Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years

The uncertainties of China's gross primary productivity (GPP) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year² from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.     

The phenology of leaf quality and its within‐canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per‐area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO₂ assimilation. We developed a two‐fraction leaf (sun and shade), two‐layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leaf quantity, quality, and within‐canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground‐based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two‐fraction leaf, two‐layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance‐derived CO₂ assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO₂ assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.     

Global patterns and predictors of stem CO2 efflux in forest ecosystems

Stem CO₂ efflux (E_S) plays an important role in the carbon balance of forest ecosystems. However, its primary controls at the global scale are poorly understood and observation‐based global estimates are lacking. We synthesized data from 121 published studies across global forest ecosystems and examined the relationships between annual E_S and biotic and abiotic factors at individual, biome, and global scales, and developed a global gridded estimate of annual E_S. We tested the following hypotheses: (1) Leaf area index (LAI) will be highly correlated with annual E_S at biome and global scales; (2) there will be parallel patterns in stem and root CO₂ effluxes (R_A) in all forests; (3) annual E_S will decline with forest age; and (4) LAI coupled with mean annual temperature (MAT) and mean annual precipitation (MAP) will be sufficient to predict annual E_S across forests in different regions. Positive linear relationships were found between E_S and LAI, as well as gross primary production (GPP), net primary production (NPP), wood NPP, soil CO₂ efflux (R_S), and R_A. Annual E_S was correlated with R_A in temperate forests after controlling for GPP and MAT, suggesting other additional factors contributed to the relationship. Annual E_S tended to decrease with stand age. Leaf area index, MAT and MAP, predicted 74% of variation in E_S at global scales. Our statistical model estimated a global annual E_S of 6.7 ± 1.1 Pg C yr⁻¹ over the period of 2000–2012 with little interannual variability. Modeled mean annual E_S was 71 ± 43, 270 ± 103, and 420 ± 134 g C m² yr⁻¹ for boreal, temperate, and tropical forests, respectively. We recommend that future studies report E_S at a standardized constant temperature, incorporate more manipulative treatments, such as fertilization and drought, and whenever possible, simultaneously measure both aboveground and belowground CO₂ fluxes.     

基于MODIS和CASA模型的伊春市森林植被NPP变化特征及其影响因子分析

植被净初级生产力(net primary productivity,NPP)是判定生态系统质量状况和碳汇的重要因子,反映了植被群落的生产能力和生态过程,对调节全球碳平衡、增强生态服务功能具有重要的意义.本文基于MODIS卫星遥感资料和改进的CASA模型,利用2000-2019年MOD17A3HGF的NPP年际数据和气象...     

Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data

Biomass partitioning is an important variable in terrestrial ecosystem carbon modeling. However, geographical and interannual variability in f(BNPP), defined as the fraction of belowground net primary productivity (BNPP) to total NPP, and its relationship with climatic variables, have not been explored. Here we addressed these issues by synthesizing 94 site-year field biomass data at 12 grassland sites around the world from a global NPP database and from the literature. Results showed that f(BNPP) varied from 0.40 to 0.86 across 12 sites. In general, savanna and humid savanna ecosystems had smaller f(BNPP) but larger interannual variability in f(BNPP), and cold desert steppes had larger f(BNPP) but smaller interannual variability. While mean f(BNPP) at a site decreased significantly with increasing mean annual temperature and precipitation across sites, no consistent temporal response of f(BNPP) with annual temperature and precipitation was found within sites. Based on these results, both geographical variability in f(BNPP) and the divergent responses of f(BNPP) with climatic variables at geographical and temporal scales should be considered in global C modeling.     

华北平原玉米田生态系统光合作用特征及影响因素

采用涡度相关法对华北平原夏玉米田进行了连续4a(2003-2006年)的碳通量观测,结果表明:夏玉米田生态系统初始量子效率(α)、最大光合速率(P_max)、暗呼吸速率(R_d)和总初级生产力(GPP)随作物生长发育而变化。在夏玉米生育前期和后期,α、P_max、R_d和GPP都比较小,其最大值出现在抽穗期/灌浆期。2003-2006年,夏玉米生长季平均α、P_max、R_d的范围分别为0.054-0.124 μmol/μmol、1.72-2.93 mg CO₂ · m^-2 · s^-1、0.23-0.38 mg CO₂ · m^-2 · s^-1。α、P_max和R_d均随叶面积指数(LAI)增加呈指数增长。2003-2006年夏玉米生长季GPP总量分别为806.2、741.5、703.0、817.4 g C/m²,年际差异较大。玉米田生态系统GPP随温度升高呈指数增长。在玉米营养生长阶段,GPP随LAI增加而增大,两者之间的关系可用直角双曲线方程来表示;生殖生长阶段,GPP随LAI降低而下降.相同LAI下,生殖生长阶段的GPP明显低于营养生长阶段。     

锡林郭勒盟净初级生产力时空变化及其气候影响

净初级生产力(NPP)是表征生态系统质量与功能的核心指标,监测生态工程区NPP的时空变化是生态建设成效评估的重要内容.本文利用2000-2015年时序遥感数据与光能利用效率模型(CASA),分析了锡林郭勒盟NPP的时空变化以及气温与降水的影响.结果 表明:2000-2015年锡林郭勒盟的NPP为108.66～359.7...