基于叶面积指数估算植被总初级生产力

长时间序列的陆地碳通量数据在全球生态环境变化研究中具有重要意义。采用MODIS GPP(Gross Primary Productivity)算法,基于GIMMS LAI3g,MODIS15和Improved-MODIS15三种叶面积指数(LAI),估算了全球2000至2010年的植被总初级生产力(GPP)。该估算的GPP数值经过全球20个通量站点的验证,并结合MODIS17分析了它们在时空变化上的异同。结果表明:(1)4种GPP精度如下:GPP_(MOD17)GPP_(impro_MOD15)GPP_(LAI3g)GPP_(MOD15)。(2)4种GPP整体上具有一致的季节波动,冬季和夏季整体好于春季和秋季。GPP_(LAI3g)的4个季节精度较相近,而GPP_(MOD17)除了春秋季外其它季节都较好。(3)GPP_(LAI3g)在中等GPP值分布区的估值相对较高,其全球总GPP大体为(117±1.5)Pg C/a,GPP_(MOD17)和GPP_(impro_MOD15)相近且都低于该值。(4)GPP_(LAI3g)和GPP_(impro_MOD15)在大约63.29%的陆面上呈显著(P0.05)的正相关关系,它们和GPP_(MOD17)在LAI不确定性小的地区呈显著的正相关关系。GPP_(LAI3g)和GPP_(MOD15)正相关分布面积占比为40.61%。     

Satellite‐derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: implications for groundwater decline in a drying climate

There is increasing concern that widespread forest decline could occur in regions of the world where droughts are predicted to increase in frequency and severity as a result of climate change. The average annual leaf area index (LAI) is an indicator of canopy cover and the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. In this study, we analyzed satellite‐derived estimates of monthly LAI across forested coastal catchments of southwest Western Australia over a 12 year period (2000–2011) that included the driest year on record for the last 60 years. We observed that over the 12 year study period, the spatial pattern of average annual satellite‐derived LAI values was linearly related to mean annual rainfall. However, interannual changes to LAI in response to changes in annual rainfall were far less than expected from the long‐term LAI‐rainfall trend. This buffered response was investigated using a physiological growth model and attributed to availability of deep soil moisture and/or groundwater storage. The maintenance of high LAIs may be linked to a long‐term decline in areal average underground water storage and diminished summer flows, with an emerging trend toward more ephemeral flow regimes.     

基于PROSAIL混合反演模型的MODIS LAI产品改进及评估

叶面积指数（Leaf Area Index,LAI）是定量陆地生态系统中光合作用、呼吸作用、蒸腾、碳和养分循环等过程中物质与能量交换的重要结构参数。目前大、中尺度的气候和生态水文建模使用的LAI产品主要来源于中分辨率成像光谱仪（MODIS）,但由于其反演过程中的不确定性因素导致MODIS LAI产品在部分地区存在质量问题。以青海省复杂植被地区为研究区域,基于实地考察与采样验证了区域内MODIS LAI所存在的质量问题分布,并揭示了不确定因素的影响。与此同时,提出了一种基于PROSAIL模型与深度神经网络（DNN）的混合建模技术,针对MODIS LAI生成机制中地表分类数据、地表反射率数据和反演算法的不确定性进行改进,并基于青海省大范围实测LAI数据评估了改进前后产品的准确度,实测数据的验证结果发现：改进模型的LAI准确度（RMSE=0.48,R²=0.64）显著高于MODIS LAI （RMSE=0.71,R²=0.56）,预测结果与实测结果之间的偏差显著减少;区域尺度上,柴达木荒漠植被低覆盖典型区域、三江源高寒草甸中覆盖典型区域与青海湖牧场草地高覆盖典型区域的RMSE分别提高了0.19、0.10、0.54,改进方法有效解决了MODIS LAI产品中高覆盖植被饱和效应导致的高估以及低覆盖植被未检索导致低估的质量问题,改进结果分布连续,更符合真实植被状况。基于以上研究,充分证明了研究方法对MODIS LAI产品的改进具有可靠性,能够在缺少实测样本数据的情况下有效提高MODIS LAI的质量,为全球植被环境监测与生态建模提供重要的数据支持。     

Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long‐term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, ≥ 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome‐specific calibrations. Active fire locations are provided in near‐real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long‐term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter‐operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.     

植被年际变化对蒸散发影响的模拟研究

利用通用陆面模式(CLM3.0)及其植被动力学模式(DGVM)研究植被覆盖度(FC)和叶面积指数(LAI)的年际变化对全球蒸散发的影响。设计两套实验方案,其植被的FC和LAI的气候态相同,但一套实验中植被的FC和LAI有年际变化,而对照实验中则没有。结果表明:(1)在草、灌木、树占优势的地区植被FC年际变化依次减小;LAI年际变化较大的地区集中在草和灌木覆盖的地区,在落叶林地区,春秋两季植被LAI的年际变化也较大。(2)全球树占优势的大部分地区,植被的年际变化使得年平均蒸散发和地表蒸发增加、冠层蒸发和蒸腾减少;而在灌木和草覆盖区,变化则大致相反。(3)低纬度地区蒸散发季节循环变化比较明显,而北半球中纬度地区,蒸散发变化明显区随着纬度增加而在时间上向后推延。(4)FC和LAI年际变化较大时,蒸散发及地表蒸发降低,而蒸腾增加;这些差异随FC和LAI年际变化的增加而增加。单点分析进一步表明植被年际变化不仅改变蒸散发的多年平均值,同时改变其分量间的相对比例。     

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Aim We present the first global synthesis of plant canopy leaf area index (LAI) measurements from more than 1000 published estimates representing ～400 unique field sites. LAI is a key variable for regional and global models of biosphere‐atmosphere exchanges of energy, carbon dioxide, water vapour, and other materials. Location The location is global, geographically distributed. Results Biomes with LAI values well represented in the literature included croplands, forests and plantations. Biomes not well represented were deserts, shrublands, tundra and wetlands. Nearly 40% of the records in the database were published in the past 10 years (1991–2000), with a further 20% collected between 1981 and 1990. Mean (± SD) LAI, distributed between 15 biome classes, ranged from 1.3 ± 0.9 for deserts to 8.7 ± 4.3 for tree plantations, with temperate evergreen forests (needleleaf and broadleaf) displaying the highest average LAI (5.1–6.7) among the natural terrestrial vegetation classes. Following a statistical outlier analysis, the global mean (± SD) LAI decreased from 5.2 (4.1) to 4.5 (2.5), with a maximum LAI of 18. Biomes with the highest LAI values were plantations > temperate evergreen forests > wetlands. Those with the lowest LAI values were deserts < grasslands < tundra. Mean LAI values for all biomes did not differ statistically by the methodology employed. Direct and indirect measurement approaches produced similar LAI results. Mean LAI values for all biomes combined decreased significantly in the 1990s, a period of substantially more studies and improved methodologies. Main conclusions Applications of the LAI database span a wide range of ecological, biogeochemical, physical, and climate research areas. The data provide input to terrestrial ecosystem and land‐surface models, for evaluation of global remote sensing products, for comparisons to field studies, and other applications. Example uses of the database for global plant productivity, fractional energy absorption, and remote sensing studies are highlighted.     

CLM3.0-DGVM中植物叶面积指数与气候因子的时空关系

作为陆面模型里植被的特征量,叶面积值数（LAI）和植被覆盖度在陆地-大气相互作用的相关研究里被广泛应用。LAI的模拟是动态植被模式（DVM）的核心任务之一,需要对模拟的LAI与气候因子间的时空关系进行评估以更好的了解模式性能以及理解植被-大气反馈过程。用1950—1999年的气象数据驱动通用陆面模式的动态植被模式（CLM3.0-DGVM）模拟得到的全球潜在植被的LAI和2001—2003年MODIS观测资料衍生出的LAI数据进行对比,并在此基础上研究当前气候条件下不同植物功能型（PFT）的LAI与不同气候因子在年际尺度上的时空关系,包括运用Moran系数理论分析空间自相关性、运用逐步回归算法构建空间最优一阶线性回归方程、分析模式LAI与气候因子间的滞后相关性。研究表明：1）以MODIS衍生数据作参照,改进后的CLM3.0-DGVM能较好地模拟不同PFTs的LAI年最大值的空间分布型,但是在物候模拟即LAI的季节循环上存在不足;2）植物LAI的分布具有正的空间自相关性。对潜在植物LAI和气候因子进行拟合时不同气候因子对不同PFTs的方差贡献不一样,一般降水最大、风速最小。这反映了陆地生态系统和气候间复杂的相互关系;3）模式模拟的LAI和气候因子有显著的1～2年的滞后相关,其中光照、降水和LAI的滞后相关性波动较大,而温度、比湿的较小,风速的不明显。这些基于CLM3.0-DGVM的结论在自然界的植物–气候相互作用系统中具有普遍意义：不同地区不同植物受不同气候因子的影响不一样;找出不同PFT的主要气候影响因子和理解其中最关键的生物物理和生物化学过程是至关重要的。进一步工作需要用更精确和更高分辨率的气候数据以及局地观测的LAI对DGVM做评估,同时DGVM本身也需要继续改进（例如加入农作物和灌溉过程的模拟）。     

双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟

叶面积指数(LAI)是森林生态系统碳循环研究的重要观测数据,也是驱动森林生态系统模型模拟碳循环的重要参数.本文以毛竹林和雷竹林为研究对象,首先利用双集合卡尔曼滤波,同化两种竹林生态系统观测站点2014—2015年MODIS LAI时间序列数据,然后将同化的高质量毛竹LAI和雷竹LAI作为输入数据驱动BEPS模型,模拟两种竹林生态系统总初级生产力(GPP)、净生态系统碳交换量(NEE)和总生态系统呼吸(TER)等碳循环数据,并用通量站实际观测值评价模拟结果;另外,还对比不同质量LAI对碳循环模拟的影响.结果表明: 双集合卡尔曼滤波同化得到的毛竹林和雷竹林LAI与实测LAI之间的相关关系极为显著,R²分别为0.81和0.91,且均方根误差和绝对偏差均较小,极大地提高了MODIS LAI的产品精度;在同化得到的LAI驱动下,BEPS模型模拟的毛竹林GPP、NEE和TER与实际观测值之间的R²分别为0.66、0.47和0.64,雷竹林分别为0.66、0.45和0.73,模拟结果均好于三次样条帽盖算法平滑LAI模拟得到的GPP、NEE和TER,其中,毛竹林、雷竹林NEE的模拟精度提高幅度最大,分别为11.2%和11.8%.     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义.利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAIe)和真实LAI,构建了其遥感估算模型,生成了研究区LAIe和LAI的空间分布图.在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度.结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAIe的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30.LAIe和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点.LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R2=0.83),研究区森林生物量密度平均值为120.3 t/hm2,估算的总生物量为5.0×l06 t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R2=0.42,草地R2=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5％和24.4％.     

Temporal dynamics of marginal steppic vegetation over a 26‐year period of substantial environmental change

Questions: (1) Is climate a strong driver of vegetation dynamics, including interannual variation, in a range margin steppic community? (2) Are there long‐term trends in cover and species richness in this community, and are these consistent across species groups and species within groups? (3) Can long‐term trends in plant community data be related to variation in local climate over the last three decades? Location: A range margin steppic grassland community in central Germany. Methods: Cover, number and size of all individuals of all plant species present in three permanent 1‐m² plots were recorded in spring for 26 years (1980–2005). Climatic data for the study area were used to determine the best climatic predictor for each plant community, functional group and species variable (annual data and interannual variation) using best subsets regression. Results: April and autumn temperature showed the highest correlation with total cover and species richness and with interannual variations of cover and richness. However, key climate drivers differed between the five most abundant species. Similarly, total cover and number and cover of perennials significantly decreased over time, while no trend was found for the cover and number of annuals. However, within functional groups there were also contrasting species‐specific responses. Long‐term temperature increases and high interannual variability in both temperature and precipitation were strongly related to long‐term trends and interannual variations in plant community data. Conclusions: Temporal trends in vegetation were strongly associated with temporal trends in climate at the study site, with key roles for autumn and spring temperature and precipitation. Dynamics of functional groups and species within groups and their relationships to changes in temperature and precipitation reveal complex long‐term and interannual patterns that cannot be inferred from short‐term studies with only one or a few individual species. Our results also highlight that responses detected at the functional group level may mask contrasting responses within functional groups. We discuss the implications of these findings for attempts to predict the future response of biodiversity to climate change.     

基于临界慢化模型和长时间序列叶面积指数的植被及其恢复力遥感监测研究——以三峡库区为例

基于临界慢化模型,利用长时间序列叶面积指数(GLASS LAI)数据,进行时间序列分解后,计算了LAI及其时间自相关指数作为指标,对三峡库区植被及其恢复力进行监测,通过案例模型对临界慢化模型精度进行了验证,分析了三峡库区植被及其植被恢复力的时空分布特征,探索基于临界慢化模型的植被恢复力遥感定量估算方法的适用性。结果表明：(1)2000—2018年三峡库区LAI平均值为3.4,重庆段LAI较低,湖北段LAI较高;三峡库区LAI整体呈上升趋势,重庆段LAI呈现降低趋势,显著下降区域占重庆段面积的21.75%,湖北段LAI呈现升高趋势,显著上升区域占湖北段面积的21.22%;(2)2000—2018年三峡库区重庆市北碚区、大渡口区、渝北区植被恢复力较低,宜昌市兴山县、夷陵区、点军区植被恢复力较高;(3)模型精度方面,在两个地质灾害扰动事件中案例模型结果与临界慢化模型结果呈现较高的一致性。本文对三峡库区2000—2018年的植被恢复力进行了定量估算,同时通过案例模型对临界慢化模型在恢复力监测上的有效性进行了验证,为三峡库区制定相应生态环境管理决策提供理论基础,为保障西南地区生态安全提供决策依据...     

Autumn warming reduces the CO2 sink of a black spruce forest in interior Alaska based on a nine‐year eddy covariance measurement

Nine years (2003–2011) of carbon dioxide (CO₂) flux were measured at a black spruce forest in interior Alaska using the eddy covariance method. Seasonal and interannual variations in the gross primary productivity (GPP) and ecosystem respiration (RE) were associated primarily with air temperature: warmer conditions enhanced GPP and RE. Meanwhile, interannual variation in annual CO₂ balance was controlled predominantly by RE, and not GPP. During these 9 years of measurement, the annual CO₂ balance shifted from a CO₂ sink to a CO₂ source, with a 9‐year average near zero. The increase in autumn RE was associated with autumn warming and was mostly attributed to a shift in the annual CO₂ balance. The increase in autumn air temperature (0.22 °C yr^?1) during the 9 years of study was 15 times greater than the long‐term warming trend between 1905 and 2011 (0.015 °C yr^?1) due to decadal climate oscillation. This result indicates that most of the shifts in observed CO₂ fluxes were associated with decadal climate variability. Because the natural climate varies in a cycle of 10–30 years, a long‐term study covering at least one full cycle of decadal climate oscillation is important to quantify the CO₂ balance and its interaction with the climate.     

Canopy structure in savannas along a moisture gradient on Kalahari sands

Measurements of tree canopy architecture were made at six savanna sites on deep, sandy soils, along a gradient of increasing aridity. There was substantial variation in the leaf area estimated within each site, using the same sample frame, but different measurement techniques. The trends in canopy properties in relation to the aridity gradient were consistent, regardless of the technique used for estimating the properties. The effective plant area index for the tree canopy (the sum of the stem area index and the leaf area index (LAI)) declined from around 2 to around 0.8 m² m^?2 over a gradient of mean annual rainfall from 1000 to 350 mm. Stems contributed 2–5% of the tree canopy plant area index. Since the tree canopy cover decreased from 50% to 20% over this aridity range, the leaf area index within the area covered by tree canopies remained fairly constant at 3–4 m² m^?2. Tree leaves tended from a horizontal orientation to a more random orientation as the aridity increased. On the same gradient, the leaf minor axis dimension decreased from around 30 mm to around 3 mm, and the mean specific leaf area decreased from 14 to 5 m² kgha^?1. There was good agreement between LAI observed in the field using a line ceptometer and the LAI inferred by the MODIS sensor on the Terra satellite platform, 2 months later in the same season.     

Carbon dioxide exchange above a Mediterranean C3/C4 grassland during two climatologically contrasting years

Eddy‐covariance measurements of net ecosystem carbon exchange (NEE) were carried out above a grazed Mediterranean C3/C4 grassland in southern Portugal, during two hydrological years, 2004–2005 and 2005–2006, of contrasting rainfall. Here, we examine the seasonal and interannual variation in NEE and its major components, gross primary production (GPP) and ecosystem respiration (R_eco), in terms of the relevant biophysical controls. The first hydrological year was dry, with total precipitation 45% below the long‐term mean (669 mm) and the second was normal, with total precipitation only 12% above the long‐term mean. The drought conditions during the winter and early spring of the dry year limited grass production and the leaf area index (LAI) was very low. Hence, during the peak of the growth period, the maximum daily rate of NEE and the light‐use and water‐use efficiencies were approximately half of those observed in the normal year. In the summer of 2006, the warm‐season C4 grass, Cynodon dactylon L., exerted an evident positive effect on NEE by converting the ecosystem into a carbon sink after strong rain events and extending the carbon sequestration for several days, after the end of senescence of the C3 grasses. On an annual basis, the GPP and NEE were 524 and 49 g C m^?2, respectively, for the dry year, and 1261 and ?190 g C m^?2 for the normal year. Therefore, the grassland was a moderate net source of carbon to the atmosphere, in the dry year, and a considerable net carbon sink, in the normal year. In these 2 years of experiment the total amount of precipitation was the main factor determining the interannual variation in NEE. In terms of relevant controls, GPP and NEE were strongly related to incident photosynthetic photon flux density on short‐term time scales. Changes in LAI explained 84% and 77% of the variation found in GPP and NEE, respectively. Variations in R_eco were mainly controlled by canopy photosynthesis. After each grazing event, the reduction in LAI affected negatively the NEE.     

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.     

Multiscale model intercomparisons of CO2 and H2O exchange rates in a maturing southeastern US pine forest

We compared four existing process‐based stand‐level models of varying complexity (physiological principles in predicting growth, photosynthesis and evapotranspiration, biogeochemical cycles, and stand to ecosystem carbon and evapotranspiration simulator) and a new nested model with 4 years of eddy‐covariance‐measured water vapor (LE) and CO₂ (Fc) fluxes at a maturing loblolly pine forest. The nested model resolves the ‘fast’ CO₂ and H₂O exchange processes using canopy turbulence theories and radiative transfer principles whereas slowly evolving processes were resolved using standard carbon allocation methods modified to improve leaf phenology. This model captured most of the intraannual variations in leaf area index (LAI), net ecosystem exchange (NEE), and LE for this stand in which maximum LAI was not at a steady state. The model comparisons suggest strong linkages between carbon production and LAI variability, especially at seasonal time scales. This linkage necessitates the use of multilayer models to reproduce the seasonal dynamics of LAI, NEE, and LE. However, our findings suggest that increasing model complexity, often justified for resolving faster processes, does not necessarily translate into improved predictive skills at all time scales. Additionally, none of the models tested here adequately captured drought effects on water and CO₂ fluxes. Furthermore, the good performance of some models in capturing flux variability on interannual time scales appears to stem from erroneous LAI dynamics and from sensitivity to droughts that injects unrealistic flux variability at longer time scales.     

毛竹林总初级生产力年际变化及其驱动因素——以安吉县为例

森林生态系统碳通量的年际变化及其驱动因素分析是了解森林碳收支动态变化以及预测未来气候变化对森林碳收支影响的重要理论基础,对评估森林应对气候变化的贡献具有重要意义。结合MODIS叶面积指数(LAI)和归一化植被指数(NDVI)产品、MERRA气象数据和通量塔观测数据,采用光能利用率模型模拟2004—2011年安吉县毛竹林生态系统总初级生产力(GPP)空间分布,并分析GPP年际变化及其驱动因素。结果表明:(1)小年毛竹林GPP稍高于大年GPP;(2)2004—2011年安吉县毛竹林年日均GPP呈下降趋势,东部、西部和整个安吉县毛竹林年日均GPP变化速率分别为-0.064、-0.033和-0.045g C m-2W-1,年均温度持续下降是主要驱动因素;(3)LAI年际变化是GPP年际变化的主要驱动因素,主要原因是毛竹林大小年交替规律引起了有效LAI年际间差异;(4)西部GPP年际变化幅度大于东部,环境和生物因素对GPP年际变化的作用方向决定了毛竹林GPP年际变化的幅度。     

Introduced annual grass increases regional fire activity across the arid western USA (1980–2009)

Non‐native, invasive grasses have been linked to altered grass‐fire cycles worldwide. Although a few studies have quantified resulting changes in fire activity at local scales, and many have speculated about larger scales, regional alterations to fire regimes remain poorly documented. We assessed the influence of large‐scale Bromus tectorum (hereafter cheatgrass) invasion on fire size, duration, spread rate, and interannual variability in comparison to other prominent land cover classes across the Great Basin, USA. We compared regional land cover maps to burned area measured using the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2000–2009 and to fire extents recorded by the USGS registry of fires from 1980 to 2009. Cheatgrass dominates at least 6% of the central Great Basin (650 000 km²). MODIS records show that 13% of these cheatgrass‐dominated lands burned, resulting in a fire return interval of 78 years for any given location within cheatgrass. This proportion was more than double the amount burned across all other vegetation types (range: 0.5–6% burned). During the 1990s, this difference was even more extreme, with cheatgrass burning nearly four times more frequently than any native vegetation type (16% of cheatgrass burned compared to 1–5% of native vegetation). Cheatgrass was also disproportionately represented in the largest fires, comprising 24% of the land area of the 50 largest fires recorded by MODIS during the 2000s. Furthermore, multi‐date fires that burned across multiple vegetation types were significantly more likely to have started in cheatgrass. Finally, cheatgrass fires showed a strong interannual response to wet years, a trend only weakly observed in native vegetation types. These results demonstrate that cheatgrass invasion has substantially altered the regional fire regime. Although this result has been suspected by managers for decades, this study is the first to document recent cheatgrass‐driven fire regimes at a regional scale.     

1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演

叶面积指数（Leaf Area Index,LAI）高度综合了植被水平覆盖状况和垂直结构,以及枯枝落叶层厚薄和地下生物量多少,是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此,及时准确地掌握区域尺度上长时间序列的植被LAI,对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像,基于反向传播（Back Propagation,BP）神经网络构建LAI反演模型,进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值,验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高,2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174,0.2962和0.2581,0.7713和0.6844,各项精度评价指标均较好;（2）统计分析去除耕地后全市LAI变化,低植被覆盖（LAI<2）面积不断增加,高植被覆盖区（LAI>3）面积先减少后增加,耕地面积不断减少,符合南京市的发展变化规律;（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致,反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的,为区域尺度土壤侵蚀定量遥感监测提供新途径。     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.