Classification Of Plant Functional Types Research Articles

Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach

AbstractThe long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr−1, with an upward trend of 0.21 Pg C yr−2 (p < 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP.

Mapping Phenological Functional Types (PhFT) in the Indian Eastern Himalayas using machine learning algorithm in Google Earth Engine

Phenological studies involve capturing information on dates of recurrent and seasonal biological events in plants and animals. In plants, phenological events of leaf flushing, full bloom, autumn discolouration, leaf fall etc. can be used to distinguish vegetation into separate classes. We refer here such phenological distinction as Phenological Functional Types (PhFT). The PhFT can be considered as the precursor of Plant Functional Types (PFTs) where PFT uses additional traits (e.g. leaf area, tree height, leaf structure, rate of evapotranspiration and photosynthesis, etc.) to classify vegetation. The PFT classification is essentially needed for developing and running dynamic global vegetation models (DGVMs) used in studying impacts of climate change on vegetation. We used archived long time series satellite remote sensing data, Landsat 5,7 and 8 (1985–2019), for classifying a landscape into appropriate PhFT. Normalized Difference Vegetation Index (NDVI) was calculated for the given period in the Google Earth Engine (GEE). The GEE is a cloud-based platform developed for the retrieval and processing of remotely sensed images and other data. The monthly median values of NDVI for the mentioned period was used to label each pixel into appropriate PhFT classes using Random Forest (RF) algorithm in GEE to obtain four distinct classes of evergreen forest, deciduous forest, agriculture and non-forest. The comparison of PhFT map was done with reference maps of global MCD12Q1 and the forest type map of India; with an overall moderate agreement of 68.55% and 66.22%, respectively. MCD12Q1 has an accuracy of 73% for the land cover map and the forest type map of India has 75% accuracy, whereas, we achieved an overall accuracy of 78%. The PhFT classification accuracy can further be improved using additional indices and topographic variables. The methodology demonstrated in this study can be adopted for classifying a landscape into distinct PhFT/PFT classes.

Probabilistic Mapping and Spatial Pattern Analysis of Grazing Lawns in Southern African Savannahs Using WorldView-3 Imagery and Machine Learning Techniques

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73±0.004%, OA = 94.16±0.004%), SVM (F1 = 95.64±0.002%, OA = 94.02±0.002%) and MLP (F1 = 95.71±0.003%, OA = 94.27±0.003%) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74±0.006%, OA = 90.93±0.003%). Grazing lawn detection accuracy followed a similar trend within the Lower Sabie landscape, with RF, SVM, MLP and CART achieving F-scores of 0.89, 0.93, 0.94 and 0.81, respectively. Transferring models to the Satara landscape however resulted in relatively lower but high grazing lawn detection accuracies across models (RF = 0.87, SVM = 0.88, MLP = 0.85 and CART = 0.75). Results from spatial pattern analysis revealed a relatively higher proportion of grazing lawn cover under semiarid savannah conditions (Satara) compared to the mesic savannah landscape (Lower Sabie). Additionally, the results show strong negative correlation between grazing lawn spatial structure (fractional cover, patch size and connectivity) and distance from waterbodies, with larger and contiguous grazing lawn patches occurring in close proximity to waterbodies in both landscapes. The proposed machine learning approach provides a novel and robust workflow for accurate and consistent landscape-scale monitoring of grazing lawns, while our findings and research outputs provide timely information critical for understanding habitat heterogeneity in southern African savannahs.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth’s system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine

ABSTRACT Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions.

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

AbstractAdvances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. In this study, satellite and ground‐based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground‐based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground‐based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

Evaluation of leaf-level optical properties employed in land surface models

Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.

Research progresses in improving dynamic global vegetation models (DGVMs) with plant functional traits

Dynamic global vegetation models (DGVMs) are key components of earth system models (ESMs), which aim to understand ecosystem processes and their interactions with the atmosphere. DGVMs are designed to simulate the structural and functional responses of global vegetation to changes in climate and atmospheric CO2 concentration with the coexistence of plant functional types (PFTs). PFTs are groups of plant species with similar functions based on morphological, physiological, biochemical, reproductive, and demographic characteristics. DGVMs typically assign each PFT with fixed parameters representing the above characteristics, consequently ignoring their variations within PFT and their responses to environmental changes. This parameterization scheme can simplify plant species represented in DGVMs but inevitably result in large uncertainties in model predictions on ecosystem processes. Therefore, a new generation of DGVMs are urgently needed to overcome the limitations of PFTs by replacing the PFTs parameterization scheme with continuous variation in plant functional traits. Plant functional traits (FTs) are defined as morphological, physiological, and phenological characteristics that affect plants via their effects on the growth, reproduction, and survival. Plant FTs that mediate the structure and function of ecosystems can be implemented into DGVMs as variables rather than PFT-specific parameters with fixed values. The plant FT scheme not only provides a widely applicable approach for forecasting ecosystem shifts and changes in ecosystem structure, but can also be linked with ecosystem functions under a changing climate. This holds great potentials for predicting possible responses of terrestrial ecosystems to environmental changes. This review first introduces the state-of-the-art DGVMs based on PFTs. PFTs represent most of the worlds vegetation types and characteristics through their functional behaviors and attributes. Although PFT-based DGVMs play a pivotal role in simulating atmosphere-land interactions by quantifying the processes of global carbon, nitrogen, and water cycles, uncertainties arise from inadequate PFT parameters and incomplete PFT classification. For example, many traits vary more within PFTs than between PFTs. Moreover, the values assigned to different PFTs often do not differ much—so little is gained (and much uncertainty is added) compared to a generic model where all plants behave identically. This paper also reviews the recent progress in plant FTs for developing new generations of DGVMs, which includes: (1) descripting adaption strategies of plants to the changing environment, (2) revealing the mechanisms of coexistence between plant species, (3) highlighting the close relationship with the structures and processes of ecosystems, and (4) summarizing their application in the parameterization of vegetation models. We also summarize the main approaches to improve current or build new DGVMs based on FTs, such as building a transitional framework for a PFT-FT hybrid DGVM and building a completely FT-based DGVM. Based on those discussions, several future research directions are recommended for developing a new-generation DGVMs, such as improving the prediction power of traits within the PFTs, building the mechanism expressions between plant adaptations and environments, and standardizing of the data sharing for FTs. We argue that constructing next generation of DGVMs is not simply a matter of incorporating trait-climate relationships, but more importantly to combine optimality concepts and classical vegetation dynamic theories making vegetation modelling more reliable and robust. Botanists, geographers, vegetation modelers, and other relevant scientists should cooperate and share the trait data to elucidate the huge potential of plant FTs in constructing the next generation of more reliable, robust and realistic DGVMs. We hope this review will promote the developments and applications of new generation DGVMs in China.

Constraining plant functional types in a semi-arid ecosystem with waveform lidar

Accurate classification of plant functional types (PFTs) reduces the uncertainty in global biomass and carbon estimates. Airborne small-footprint waveform lidar data are increasingly used for vegetation classification and above-ground carbon estimates at a range of spatial scales in woody or homogeneous grass and savanna ecosystems. However, a gap remains in understanding how waveform features represent and ultimately can be used to constrain the PFTs in heterogeneous semi-arid ecosystems. This study evaluates lidar waveform features and classification performance of six major PFTs, including shrubs and trees, along with bare ground in the Reynolds Creek Experimental Watershed, Idaho, USA. Waveform lidar data were obtained with the NASA Airborne Snow Observatory (ASO). From these data we derived waveform features at two spatial scales (1 m and 10 m rasters) by applying a Gaussian decomposition and a frequency-domain deconvolution. An ensemble random forest algorithm was used to assess classification performance and to select the most important waveform features. Classification models developed with the 10 m waveform features outperformed those at 1 m (Kappa (κ) = 0.81–0.86 vs. 0.60–0.70, respectively). At 1 m resolution, lidar height features improved the PFT classification accuracy by 10% compared to the analysis without these features. However, at 10 m resolution, the inclusion of lidar derived heights with other waveform features decreased the PFT classification performance by 4%. Pulse width, rise time, percent energy, differential target cross section, and radiometrically calibrated backscatter coefficient were the most important waveform features at both spatial scales. A significant finding is that bare ground was clearly differentiated from shrubs using pulse width. Though the overall accuracy ranges between 0.72 and 0.89 across spatial scales, the two shrub PFTs showed 0.45–0.87 individual classification success at 1 m, while bare ground and tree PFTs showed high (0.72–1.0) classification accuracy at 10 m. We conclude that small-footprint waveform features can be used to characterize the heterogeneous vegetation in this and similar semi-arid ecosystems at high spatial resolution. Furthermore, waveform features such as pulse width can be used to constrain the uncertainty of terrain modeling in environments where vegetation and bare ground lidar returns are close in time and space. The dependency on spatial resolution plays a critical role in classification performance in tree-shrub co-dominant ecosystems.

Classification of plant functional types based on the nutrition traits: a case study on alpine meadow community in the Zoigê Plateau

The ecological concept of Plant Functional Types (PFTs), which refers to the assemblage of plants with certain functional traits, has been introduced for the study of plant responses to the environment change and human disturbance. Taking the alpine meadow community in the Zoige Plateau as a study case, this paper classified PFTs in terms of plant nutrition traits. The sequential results are as follows. (1) The main herbages in the Zoige Plateau included 16 species in 5 families. Among the five families, Cyperaceae vegetation accounted for 81.37% of herbage area in total, while the remaining 4 families occupied less than 20%. As for the species, Kobresia setchwanensis Hand.-Maizz. was dominant, accounting for 48.74% of the total area; while the remaining 51.26% was comprised of Polygonum viviparum L., Anaphalis fiavescens Hand.-Mazz., Stipa aliena Keng and other species. (2) By using the Principal Component Analysis (PCA), the assessment of herbages nutrition was carried out based on the comprehensive multi-index evaluation model. Polygonum viviparum L. had the highest nutritional value score (1.43), and Stipa aliena Keng had the lowest (-1.40). Nutritional value of herbage species had a significantly positive correlation with altitude (P<0.01) in the Zoige Plateau. (3) Based on the nutritional values, herbages in the Zoige Plateau could be grouped into 3 nutrition PFTs (high, medium and low) by using the Natural Breaks (Jenks) method.

Middle Miocene climate and vegetation models and their validation with proxy data

The Miocene is a relatively recent epoch of the Earth's history with warmer climate than today, particularly during the middle Miocene Climatic Optimum (MMCO, approximately 17–15Ma). Although the cause of the warming is probably not only attributable to CO2, but also to changes in orography and configuration of ocean gateways, this time interval represents an ideal case study to test the ability of climate models to simulate warm climates comparable to those that the Earth may experience in the near future. However, even with higher than present-day CO2 concentrations, the MMCO warming inferred from terrestrial proxy data has been difficult to reproduce in climate models.Since fossil flora do not provide direct information on climate, but on flora and vegetation, climate model results are generally compared to climate reconstructions obtained from the fossil flora. In this study, we apply an alternative method by simulating palaeovegetation from the outputs of the climate model, using a dynamic vegetation model. Model vegetation reconstruction can then be compared to the vegetation cover indicated by the fossil flora record at the various localities, provided that a common classification of plant functional types (PFTs) is used for the data and the model. Here, we reconstruct the vegetation of the middle Miocene with the global dynamic vegetation model CARAIB, using the climatologies derived from five atmospheric general circulation models. The reliability of the simulations is examined on a presence/absence basis of PFTs by comparison of vegetation reconstructions to palaeoflora data recorded in the Northern Hemisphere and the Tropics.This comparison provides an overall agreement around 60% between model and data, when all sites and tree types are considered. Three model simulations out of five show to be better at predicting the absence than the presence. The presence of warm-temperate mixed forests in the middle latitudes, dominated by broadleaved deciduous warm temperate and subtropical trees is generally well reproduced in CARAIB simulations. However, poor agreement is obtained for the presence of tropical PFTs out of the Tropics and for warm PFTs at latitudes northward of 50°N, where climate models remain too cold to produce assemblages of trees consistent with the data. Nevertheless, the model–data comparison performed here highlights several mismatches that could result not only from missing feedbacks in the climate simulations, but also from the data. The results of the likelihood analysis on presence/absence of PFTs illustrate the uncertainties in the PFT classification of the Neogene floral records. The coexistence of some PFTs in the palaeovegetation data is impossible to reproduce in the vegetation model simulations because of the climatic definition of the modern PFTs. This result indicates either a bias in the identification of modern analogues for fossil plant taxa, or a possible evolution of environmental requirements of certain plants.

Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology.

Plant functional types (PFTs) aggregate the variety of plant species into a small number of functionally different classes. We examined to what extent plant traits, which reflect species' functional adaptations, can capture functional differences between predefined PFTs and which traits optimally describe these differences. We applied Gaussian kernel density estimation to determine probability density functions for individual PFTs in an n-dimensional trait space and compared predicted PFTs with observed PFTs. All possible combinations of 1-6 traits from a database with 18 different traits (total of 18 287 species) were tested. A variety of trait sets had approximately similar performance, and 4-5 traits were sufficient to classify up to 85% of the species into PFTs correctly, whereas this was 80% for a bioclimatically defined tree PFT classification. Well-performing trait sets included combinations of correlated traits that are considered functionally redundant within a single plant strategy. This analysis quantitatively demonstrates how structural differences between PFTs are reflected in functional differences described by particular traits. Differentiation between PFTs is possible despite large overlap in plant strategies and traits, showing that PFTs are differently positioned in multidimensional trait space. This study therefore provides the foundation for important applications for predictive ecology.

Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

山西植物功能型划分及其空间格局

PDF HTML阅读 XML下载 导出引用 引用提醒 山西植物功能型划分及其空间格局 DOI: 10.5846/stxb201312042886 作者: 作者单位: 山西大学黄土高原研究所,山西大学生命科学学院 山西大学黄土高原研究所,山西大学生命科学学院;山西大学黄土高原研究所 作者简介: 通讯作者: 中图分类号: Q948 基金项目: 科技部科技基础性工作专项(2011FY110300); 山西省自然科学基金项目(2013011037-1); 山西省回国留学人员科研资助项目(20100012) Classification of plant functional types and spatial pattern in Shanxi Province Author: Affiliation: Institute of Loess Plateau,Shanxi University;Institute of Loess Plateau,Shanxi University,,School of Life Science,Shanxi University;Institute of Loess Plateau,Shanxi University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着全球气候变化的加剧,作为沟通陆地生态系统与气候变化的桥梁,植物功能型(Plant Functional Types, PFTs)越来越受到生态学家的关注。PFTs不仅是简化生态系统复杂性的有效工具,而且可将植物的生理生态过程、生物物理特征及物候变化等引入到动态植被模型中,研究气候变化下的植被反应及其反馈机制。为了在区域尺度上研究气候变化和植被反应,基于"生态-外貌"原则,依据植物特征(如生长型、叶的性状)及其对水分、温度的需求,结合区域的气候与地理条件,对山西植被进行植物功能型的划分,并在此基础上对其空间格局进行分析。结果表明:(1)山西植被可划分为19类植物功能型(其中包括4类栽培作物功能型),分别是:寒温性常绿针叶林、温性常绿针叶林、寒温性落叶针叶林、温性落叶阔叶林、高寒落叶灌丛、温性落叶灌丛、多年生禾草草原、多年生禾草草丛、多年生禾草草甸、多年生莎草草甸、多年生杂类草草原、多年生杂类草草丛、多年生杂类草草甸。1年生杂类草草甸、多年生豆科草原、果树、一年一熟栽培作物、一年二熟栽培作物和二年三熟栽培作物。植物功能型的划分和分布与山西植被区划有较好的一致性,基本反映了植物固有特征及其对水热条件的需求。(2)农作物在山西占有较大比重,占植被类型面积的53.15%,森林类型以温性常绿针叶林和温性落叶阔叶林为主,灌丛类型以温性落叶灌丛为主,草本类型中多年生禾草草丛占较大比例,占草本类型面积的50.98%。(3)由于水热条件及地理条件的差异,植物功能型(不考虑栽培作物)在各区域表现出较大差异,如多年生杂类草草原主要分布于北部地区,在南部并不存在这种植物功能型;森林类型的功能型主要分布于中、南部地区,且结构复杂、类型多样。(4)除栽培作物表现出较好的整体性和连通性,其他植物功能型均表现出不同程度的破碎化和离散化。(5)山西植物功能型整体上表现出较高的多样性,其中中部地区比其他地区的多样性和破碎化程度高,斑块类型更加趋向于离散的小斑块状,北部地区则以一年一熟栽培作物占明显优势,表现出较强的优势度,而南部地区并没有表现出很强的破碎度或优势度。 Abstract:Plant Functional Types (PFTs) have been paid increasing attention last decades as the linkage between terrestrial ecosystem and climate change with the intensification of global climate change. PFTs are not only an effective tool to simplify the complexity of ecosystem, but also helpful when exploring the response and feedback mechanism of plant to the climate changes by introducing plant physiological, ecological process, biophysical characteristics and phenological change into the vegetation dynamic model. In order to investigate climate change and response to vegetation at regional scale, PFTs classification system was proposed under the "eco-physiognomy" principle based on plant characteristics (such as vegetative form, leaf traits), thermal characteristics and water availability, combined with the regional climate and geographical conditions in Shanxi. Furthermore, the spatial pattern of PFTs was discussed in this paper. The results showed that: (1) Nineteen PFTs, including four PFTs of cultivated crops were identified, namely, cold warm-temperate evergreen coniferous forest, warm-temperate evergreen coniferous forest, cold warm-temperate deciduous coniferous forest, warm-temperate deciduous broadleaf forest, cold warm-temperate deciduous shrub, warm-temperate deciduous shrub, perennial grass steppe, perennial grass community, perennial grass meadow, perennial sedge meadow, perennial forbs steppe, perennial forbs community, perennial forbs meadow, annual forbs meadow, perennial Legume steppe, fruit tree, one crop per annual, two crop per annual, and three crop two annual. The classification and distribution of plant functional types reflect the inherent characteristics and demand for water and thermal of plants, being consistent with vegetation regionalization of Shanxi Province. (2) Crops are dominant in Shanxi, accounting for 53.15% of the total vegetation type area. Forest and shrubs types are dominated by warm-temperate evergreen coniferous forest, warm-temperate deciduous broadleaf forest and warm deciduous shrubs, respectively. Perennial grass community occupies a large proportion, accounting for 50.98% of the total area of the herbaceous types. (3) PFTs (except for crop cultivation) show bigger difference in different regions due to hydrothermal and geographical conditions. For example, perennial forbs steppe mainly distributed in north Shanxi, rather than in the south Shanxi. Compared with north Shanxi, the functional types of forest mainly distributed in the central and south Shanxi, and its structure and types were complex and diverse. (4) The cultivation crops show good integrity and connectivity, while the other PFTs show the fragmentation and discretization. (5) PFTs of Shanxi show integrally high diversity. Compared with the other two parts, more fragmentation and higher diversity are found in the central Shanxi. One crop per annual mainly distributed in North Shanxi, while south Shanxi did not exhibit significant fragmentation and dominance. 参考文献 相似文献 引证文献

Linking plant and ecosystem functional biogeography.

Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere-atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.

Examination of Fire-Related Succession Within the Dry Mixed-Grass Subregion of Alberta With the Use of MODIS and Landsat

Fire is an important disturbance process historically present across the northern Great Plains. Previous research from northern dry mixed-grass prairie suggests that C4 (warm season) grasses replace C3 (cool season) climax species with increasing fire, particularly in the spring. This hypothesis was tested at a landscape scale at Canadian Forces Base Suffield Alberta, by exploring the relationship between ecosystem states (C3 dominant, C3/C4 codominant, C4 dominant) inferred from a MODIS multitemporal plant functional type classification (pseudo R2: 0.598, overall accuracy: 0.74) and interyear fire history digitized from the Landsat archive (1972–2007). Probit regression showed that succession processes were different between range sites, where C4-dominant pixels were positively related to fire (P < 0.001, pseudo R2 = 1) and completely replaced C3-dominant pixels on loamy range sites after 14 fires in 36 yr. In contrast, C3- and C3/C4-codominant pixels were related with fire on Blowouts range sites (P < 0.001, pseudo R2 = 1), where C3/C4-codominant pixels replaced C3-dominant pixels with increasing fire. Finally, there were no statistically significant relationships between ecosystem states and fire for Sands range sites. Analysis of recovery showed that after loamy pixels experienced three to six fires in 18 yr followed by 18 yr of rest, C3-dominant pixels were reduced by over 30% compared to unburned pixels. Finally, intrayear fire timing (2001–2009) was explored with the use of Wilcoxon signed-rank tests between the spatial extent of spring and summer fires, with the use of MODIS burned-area data. Results indicate that fires are not limited to the spring season (P < 0.05), but occur across the entire growing season. Although fire timing does not appear to play a role in driving succession, this ecosystem is generally sensitive to repeated fire, with recovery of C3 climax species taking decades.

VEGETATION CHANGE IN SIBERIA AND THE NORTHEAST OF RUSSIA DURING THE CENOZOIC COOLING: A STUDY BASED ON DIVERSITY OF PLANT FUNCTIONAL TYPES

Cenozoic vegetation change in Western Siberia and northeastern Russia is quantified based on the analysis of diversity of plant functional types (PFTs). Carpofloras (148 total) compiled from published sources are assigned to a total of nine time slices spanning the period from the middle Eocene to the late Pliocene. Comparisons among four defined key regions allow study of spatial diversity gradients and their evolution. Our novel PFT classification scheme, designed for use in biome modeling, comprises 26 herbaceous to arboreal PFTs based on physiognomic characters and bioclimatic tolerances of plants, completed by an aquatic PFT. Using multivariate statistics, localities with similar PFT spectra are grouped and interpreted in terms of biomes. The results are visualized on paleovegetation maps and in PFT diversity records documenting vegetation evolution by key region. In northeastern Russia, mixed and conifer forest biomes existed during the Neogene. Diversity spectra indicate that even in the late Neogene, warm and humid conditions prevailed in this region, probably related to coeval intensification of the East Asian monsoon system. In Western Siberia, mesophytic forests with higher proportions of broadleaved evergreens dominated during the earlier Paleogene. Subsequent vegetation change is mainly expressed by a steadily increasing diversity of herbaceous PFTs. In the southern part of Western Siberia, distinct opening of the vegetation occurred in the late Miocene, connected to drying. The coeval declining trend of thermophilous PFTs in the north coincides with intensified cooling of the high latitudes.

ASSESSMENT OF PLANT FUNCTIONAL TYPES IN TROPICAL ARID AND SEMI-ARID ECOSYSTEMS OF INDIA USING REMOTE SENSING DATA AND GIS

Abstract. Tropical ecosystems undergo changes caused by season, climate or multiple anthropogenic impacts. Such changes may cause gradual or rapid shifts from one state to another. There has been a focus on functional classifications of plants to find tools for monitoring and assessing species status in changing environments. It has been recognised that plant biological characteristics can be related to their response to predominant environmental factors and interactions between other organisms. These findings have resulted in a search for plant functional types (PFTs) that are user-defined groups of species with similar response to environmental resources and disturbance associated to common biological traits. Now, identification of plant functional types is priority area in the climate change research. Satellite Earth observation data is an important tool in providing considerable information on extracting PFT information at global and regional levels. From the modelling perspective, some of the current needs are the refinement of processes that govern community assembly, such as natural and anthropogenic disturbances. PFTs used in large-scale models are insufficient to represent the diversity of responses in natural plant communities. The currently available MODIS PFT map was generated by re-labeling the IGBP land cover type classes. However, the error magnitudes of the MODIS PFT product and their spatial and temporal distributions have not been fully characterized. Remotely sensed derived information of the phenology, community composition and vegetation structure are the key inputs to integrate with the variability in precipitation and temperature to map the spatial distribution of Plant functional types. PFTs allows accurate representation of the land surface by separately specifying the composition and structure of PFTs within a grid cell. Very little research efforts are discernible in India that explicitly address the PFTs. In the present study five natural vegetation systems were studied along arid, semi-arid and sub-humid regions of Indian Desert and Aravallis of Rajasthan, India, with a distance of 600 km. The annual precipitation decreases from as high as 1600 mm in the south to 700 mm in the north-east and 100 mm in the west. The study is based on the integrated approach of remote sensing, GIS, and phytosociology. In the step 1, vegetation type map was prepared using multi-season IRS P6 LISS III data. Screening of plant traits was done based on field observations and literature. Phytosociological data pertains to 500 sample plots was used to identify plant functional traits of 900 species at morphological level. The vegetation classification scheme at regional level identified thorn forest, dry deciduous forest, broad leaved forest, woodland, shrubland and grasslands. Five plant traits selected in the study were significant for tropical environments. Attributes such as leaf size, leaf texture, spinescence, stem diameter and bark consistency were categorized systematically. Ordination analysis was carried out across the environmental gradient. Plant functional traits were measured on 20 individuals per species at each site. Environmental information was integrated to identify plant trait response. The spatial trends in PFTs were analysed and compared across the vegetation types, along the gradient of land surface temperature, climate, elevation and time integrated NDVI. Results established the occurrence of recurrent patterns of association among selected plant traits. Functional response groups were identified by summarizing results and relating them to individual species. Finally phytoclimatic map was prepared to represent spatial distribution of PFTs. The species with of functional traits of representing microphylls, sclerophyll, rough bark, spinescence and therophyte are demarcated as drought tolerant traits. Drought Intolerant PFTs are represented by macrophyll, malacophyll, smooth Bark, non spinescent stems and leaves. Biological spectrum of Desert national park, Jaisalmer and and Ajmer represents therophytic climate (below 600mm rainfall) while Sariska and Mt. Abu are of therophanerophytic (&gt;600mm rainfall). The higher proportion of microphylls and therophytes are distributed within tropical arid environments. The dominance of the drought tolerant PFTs increased towards west while the dominance of the drought intolerant PFTs increased in southern and northern parts of Rajasthan. Detecting the change of spatial characteristics of plant species under precipitation gradients would be helpful to study species dynamics in a large region under environmental gradient. On the basis of easily-measured plant traits and phytosociological surveys, the present approach summarized the complexity of tropical vegetation into a relatively few PFTs. The present study open up new possibilities for understanding plant trait and environment relationship and developing PFT classification in tropical regions.

A new time series vegetation–water index of phenological–hydrological trait across species and functional types for Poyang Lake wetland ecosystem

Despite the long history of vegetation mapping with remote sensing, challenges remain in effectively linking remote sensing observations with plant traits and environmental disturbance across species and functional types. This study proposes a classification of aquatic and wetland vegetation plant functional types (PFTs) using time series remotely sensed data applicable to wetland ecosystems with large annual water level changes like Poyang Lake, the largest freshwater lake–wetland of China. We first developed the aquatic and wetland PFT classification scheme which included perennial C4 grasses, perennial C3 reed, C3 (sedges and taller forbs), short C3 forbs, floating aquatic macrophytes, and submerged aquatic macrophytes. Using this scheme, time series normalized difference vegetation index (time series NDVI) images and time series vegetation–water index (time series VWI) images extracted from 32-m spatial resolution Beijing-1 microsatellite data were applied to perform two PFT classifications with a support vector machine (SVM) algorithm. We found that the time series NDVI-based SVM classification method mapped aquatic and wetland PFT distribution with an overall accuracy of 81.3%. However, when the information on water level fluctuation was combined with time series NDVI into a new time series index (referred to as time series VWI), the overall accuracy increased to 90.4%. Results suggest that time series VWI has a stronger capability in distinguishing wetland PFTs with different submersion times and flood tolerances than time series NDVI, and thus may be considered as a key input dataset for future PFT classifications in seasonally dynamic wetlands.

A global reanalysis of vegetation phenology

[1] Simulations of the global water and carbon cycle are sensitive to the model representation of vegetation phenology. Current phenology models are empirical, and few predict both phenological timing and leaf state. Our previous study demonstrated how satellite data assimilation employing an Ensemble Kalman Filter yields realistic phenological model parameters for several ecosystem types. In this study the data assimilation framework is extended to global scales using a subgrid-scale representation of plant functional types (PFTs) and elevation classes. A reanalysis of vegetation phenology for 256 globally distributed regions is performed using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of photosynthetically active radiation (FPAR) absorbed by vegetation and leaf area index (LAI) data. The 9 · 108 quality screened observations (corresponding to <1% of the globally available MODIS data) successfully constrain a posterior PFT-dependent phenological parameter set. It reduces the global FPAR and LAI prediction error to 20.6% and 14.8%, respectively, compared to the prior prediction error. A 50 year long (1960–2009) daily 1° × 1° global phenology data set with a mean FPAR and LAI prediction error of 0.065 (−) and 0.34 (m2 m−2) is generated. Temperate phenology is best explained by a combination of light and temperature. Tropical evergreen phenology is found to be largely insensitive to moisture and light variations. Boreal phenology can be accurately predicted from local to global scales, while temperate and mediterranean landscapes might benefit from a better subgrid-scale PFT classification or from a more complex canopy radiative transfer model.