Growing Season Leaf Area Index Research Articles

Disentangling the roles of land-use-related drivers on vegetation greenness across China

Quantifying the drivers of terrestrial vegetation dynamics is critical for monitoring ecosystem carbon sequestration and bioenergy production. Large scale vegetation dynamics can be observed using the leaf area index (LAI) derived from satellite data as a measure of ‘greenness’. Previous studies have quantified the effects of climate change and carbon dioxide (CO2) fertilization on vegetation greenness. In contrast, the specific roles of land-use-related drivers (LURDs) on vegetation greenness have not been characterized. Here, we combined the Interior-Point Method-optimized ecosystem model and the Bayesian model averaging statistical method to disentangle the roles of LURDs on vegetation greenness in China from 2000 to 2014. Results showed a significant increase in growing season LAI (greening) over 35% of the land area of China, whereas less than 6% of it exhibited a significantly decreasing trend (browning). The overall impact of LURDs on vegetation greenness over the whole country was comparatively low. However, the local effects of LURDs on the greenness trends of some specified areas were considerable due to afforestation and urbanization. Southern Coastal China had the greatest area fractions (35.82% of its corresponding area) of the LURDs effects on greening, following by Southwest China. It was because of these economic regions with great afforestation programs. Afforestation effects could explain 27% of the observed greening trends in the forest area. In contrast, the browning impact caused by urbanization was approximately three times of the greening effects of both climate change and CO2 fertilization on the urban area. And they made the urban area had a 50% decrease in LAI. The effects of residual LURDs only accounted for less than 8% of the corresponding observed greenness changes. Such divergent roles would be valuable for understanding changes in local ecosystem functions and services under global environmental changes.

Optimization of Multi-Ecosystem Model Ensembles to Simulate Vegetation Growth at the Global Scale

Process-based ecosystem models are increasingly used to simulate the effects of a changing environment on vegetation growth in the past, present, and future. To improve the simulation, the multimodel ensemble mean (MME) and ensemble Bayesian model averaging (EBMA) methods are often used in optimizing the integration of ecosystem model ensemble. These two methods were compared with four other optimization techniques, including genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and interior-point method (IPM), to evaluate their efficiency in this article. Here, we focused on eight commonly used ecosystem models to simulate vegetation growth, represented by the growing season leaf area index (LAIgs), collected globally from 2000 to 2014. The performances of the multimodel ensembles and individual models were compared using the satellite-observed LAI products as the reference. Generally, ensemble simulations provide more accurate estimates than individual models. There were significant performance differences among the six tested methods. The IPM ensemble model simulated LAIgs more accurately than the other tested models, as the reduction in the root-mean-square error was 84.99% higher than the MME results and 61.50% higher than the EBMA results. Thus, IPM optimization can reproduce LAIgs trends accurately for 91.62% of the global vegetated area, which is double the area of the results from MME. Furthermore, the contributions and uncertainties of the individual models in the final simulated IPM LAIgs changes indicated that the best individual model (CABLE) showed the greatest area fraction for the maximum IPM weight (32.49%), especially in the low-lalitude to midlatitude areas.

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

AbstractA reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4‐Crop and CLM3.5‐CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn‐soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4‐Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5‐CornSoy‐simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4‐Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long‐wave radiation bias. CLM4‐Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes.

Validating a Dynamic Global Vegetation Model with Remotely Sensed Vegetation Index

The present study aims to evaluate the ability of IBIS model to capture the difference in vegetation characteristics among six major biomes in the Northeast China Transect and to calibrate the simulated LAI by IBIS, using the product of MODIS LAI (Leaf Area Index). The results showed that IBIS simulated a little lower growing season LAI over temperate evergreen conifer forest and boreal evergreen forest, while it overestimated LAI relative to MODIS in non-growing season. IBIS performed poorly on LAI over savanna, grassland and shrub land, compared with MODIS and it nearly simulated higher LAI throughout the year. Based on regression analysis, the simulating LAI by IBIS (Integrated Biosphere Simulator) presented a significant linear correlation with that from MODIS over temperate evergreen conifer forest in spring and winter, boreal evergreen forest throughout the year and grassland from summer to early autumn. Therefore, it was help to adjust the model parameters over these plant functional types to calibrate the estimated LAI in a large spatial scale.

Partitioning of evapotranspiration and its controls in four grassland ecosystems: Application of a two-source model

Quantifying the partitioning of evapotranspiration (ET) and its controls are particularly important for accurate prediction of the climatic response of ecosystem carbon, water, and energy budgets. In this study, we employed the Shuttleworth–Wallace model to partition ET into soil water evaporation ( E) and vegetation transpiration ( T) at four grassland ecosystems in China. Two to three years (2003–2005) of continuous measurements of ET with the eddy covariance technique were used to test the long-term performance of the model. Monte Carlo simulations were performed to estimate the key parameters in the model and to evaluate the accuracy in model partitioning (i.e. E/ET). Results indicated that the simulated ET at the four ecosystems was in good agreement with the measurements at both the diurnal and seasonal timescales, but the model tended to underestimate ET by 3–11% on rainy days, probably due to the lack of model representation of rainfall interception. In general, E accounted for a large proportion of ET at these grasslands. The monthly E/ET ranged from 12% to 56% in the peak growing seasons and the annual E/ET ranged from 51% to 67% across the four ecosystems. Canopy stomatal conductance controlled E/ET at the diurnal timescale, and the variations and magnitude of leaf area index (LAI) explained most of the seasonal, annual, and site-to-site variations in E/ET. A simple linear relationship between growing season LAI and E/ET explained ca. 80% of the variation observed at the four sites for the 10 modeled site-years. Our work indicated that the daily E/ET decreased to a minimum value of ca. 10% for values of LAI greater than 3 m 2 m −2 at the ecosystem with a dense canopy. The sensitivities of E/ET to changes in LAI increased with the decline in water and vegetation conditions at both the seasonal and the annual time scales, i.e., the variations in LAI could cause stronger effects on E/ET in the sparse-canopy ecosystems than in the dense-canopy ecosystems. It implies that the hydrological processes and vegetation productivity for ecosystems in arid environments might be more vulnerable to projected climate change than those in humid environments.

Evaluating a terrestrial ecosystem model with satellite information of greenness

Dynamic global vegetation models (DGVMs) simulate ecosystem responses to multiple environmental factors. Traditionally, these models were evaluated with sparse ground measurements, but satellite observations now allow evaluation at model grid cell scales with near‐global coverage. Agro‐IBIS is the first DGVM to explicitly simulate the major crop types of the United States. We evaluate these simulated agroecosystems using satellite information of greenness for the first time at the regional scale. We compare Agro‐IBIS‐simulated leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) of crops, forests, and grasses with AVHRR (1982–2000) and MODIS (2000–2002) LAI and FPAR data sets across the central and eastern United States. Compared with MODIS data, Agro‐IBIS overestimates growing season LAI over broadleaf crops and simulates reasonable but lower growing season LAI over deciduous forest, with an early onset bias in the southern region. Agro‐IBIS underestimates needleleaf forest LAI and substantially overestimates the magnitude of grassland growing season LAI, with an early onset bias of one month. Similar bias patterns in FPAR occur in all biomes. While we do not trust the LAI/FPAR magnitudes from AVHRR data, we do suggest that AVHRR data may be used to evaluate the timing of onset and offset of the growing season of broadleaf crops and grasses. Evaluation of broadleaf crop peak LAI from Agro‐IBIS and MODIS with ground measurements shows significant discrepancies. Continuing improvements in DGVMs (especially grass algorithms in Agro‐IBIS) and validation of satellite data sets (especially over crops in MODIS) are needed to understand regional‐scale terrestrial ecosystem processes.

Potential future changes of the terrestrial ecosystem based on climate projections by eight general circulation models

A number of previous modeling studies have assessed the implications of projected CO2‐induced climate change for future terrestrial ecosystems. However, although current understanding of possible long‐term response of vegetation to elevated CO2 and CO2‐induced climate change in some geographical areas (e.g., the high‐latitude regions) has been strengthened by dint of accumulating evidence from these past studies, it is still weak in others. This study examines the responses of global potential natural vegetation distribution, net primary production (NPP), and fire emissions to future changes in atmospheric CO2 concentration and climate using the National Center for Atmospheric Research Community Land Model's dynamic global vegetation model. The model is run to vegetative equilibrium (i.e., with respect to leaf area index (LAI) and vegetation coverage) driven with preindustrial climate and future climate near 2100, respectively, simulated by eight general circulation models (GCMs). The simulated potential vegetation under the preindustrial control mean climate (CO2 concentration held at 275 ppm) is compared with that under the SRESA1B 2100 mean climate (CO2 concentration stabilizes at 720 ppm beyond 2100). Simulated vegetation response ranges from mild changes of the fractional coverage of different plant functional types to the rather dramatic changes of dominant plant functional types. Although such response differs significantly across different GCM climate projections, a quite consistent spatial pattern emerges, characterized by a considerable poleward spread or shift of temperate and boreal forests in the Northern Hemisphere high latitudes, and a substantial degradation of vegetation type in the tropics (e.g., increase of drought deciduous trees coverage at the expense of evergreen trees) especially in portions of West and southern Africa and South America. Despite the widespread degradation of vegetation type in the tropics, NPP, and growing season LAI are predicted to increase under most GCM scenarios over most of the globe. Carbon fluxes to the atmosphere due to fire generally increase too across the globe. Such responses of NPP and fire occurrence result from the synergistic effects of CO2 concentration changes, climate changes, and vegetation changes. In the HadCM‐driven simulation, however, extreme responses are shown in some regions: Deciduous forest is replaced by grasses in large areas in the middle latitudes, and substantial areas in northern South America and southern Africa predominantly covered by evergreen forest are replaced with grasses while NPP and fire emissions reduce drastically (by more than 100%). A future paper will examine how the biosphere response documented here influences the impact of climate change on surface hydrological conditions.

Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest

This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian conditional simulation (SGCS). Deterministic methods such as RMA and KED provide a single predicted map with either aspatial (e.g., standard error, in regression techniques) or limited spatial (e.g., KED variance) assessments of errors, respectively. In contrast, SGCS takes a probabilistic approach, where simulated values are conditional on the sample values and preserve the sample statistics. In this application, canonical indices were used to maximize the ability of Landsat ETM+ spectral data to account for LAI variability measured in the field through a spatially nested sampling design. As expected based on theory, SGCS did the best job preserving the distribution of measured LAI values. In terms of spatial pattern, SGCS preserved the anisotropy observed in semivariograms of measured LAI, while KED reduced anisotropy and lowered global variance (i.e., lower sill), also consistent with theory. The conditional variance of multiple SGCS realizations provided a useful visual and quantitative measure of spatial uncertainty. For applications requiring spatial prediction methods, we concluded KED is more useful if local accuracy is important, but SGCS is better for indicating global pattern. Predicting LAI from satellite data using geostatistical methods requires a distribution and density of primary, reference LAI measurements that are impractical to obtain. For regional NPP modeling with coarse resolution inputs, the aspatial RMA regression method is the most practical option.

Net ecosystem production of a Douglas‐fir stand for 3 years following clearcut harvesting

AbstractTo investigate the variations in annual and seasonal net ecosystem production (FNEP) during the development of a young forest, 3 years of continuous eddy covariance measurements of carbon dioxide (CO2) fluxes were collected following clearcut harvesting and replanting of a coastal Douglas‐fir stand on the east coast of Vancouver Island, BC, Canada. The impact of changing weather and stand structure onFNEPwas examined by developing relationships betweenFNEPand variables such as light, temperature, soil moisture, and leaf area index (LAI). In all 3 years, the stand was a large source of CO2(620, 520, and 600 g C m−2 yr−1in the first, second, and third years, respectively). During this period, the growth of pioneer and understory species resulted in an increase in maximum growing season LAI from 0.2 in the year the seedlings were planted to 2.5 in the third year. The associated increase in annual gross ecosystem production (P=FNEP−Re, whereReis ecosystem respiration) from 220 g C m−2 yr−1in the first year to 640 g C m−2 yr−1in the third year was exceeded by an increase in annualRefrom 840 to 1240 g C m−2 yr−1. Seasonal and interannual variations in daytimeFNEPandPwere well described by variations in photosynthetically active radiation, temperature, and changes in LAI. Night‐time measurements ofReexponentially increased with 2 cm soil temperature with an averageQ10of 2 (relative increase inRefor a 10°C increase in temperature) andR10(Reat 10°C) that increased from 2.1 in the first year to 2.5 in the second year to 3.2 μmol m−2 s−1in the third year. Although the re‐establishment of vegetation in this stand had a major impact on bothPandRe, interannual variations in weather also affected annualFNEP. Drought, in the summer of the third year, resulted in early senescence and reduced bothPandRe. This resulted in more C being lost from the stand in the third year after harvesting than in the second year.

Trends in vegetation activity and their climatic correlates: China 1982 to 1998

We combined a satellite-derived Leaf Area Index (LAI) dataset and a gridded climate dataset to analyse trends in vegetation activity and their correlation with climate variability in China between 1982 and 1998. Vegetation activity over the growing season increased 11.03% in China during the 17-year period, which is broadly consistent with the greening trend in the northern high latitudes in Eurasia and North America shown in previous studies. Approximately 99×106 ha of croplands and 35×106 ha of forest exhibited significant upward trends in growing season LAI, and accounted for 53% and 19% of the total vegetated area with greening trends, respectively. Temperature was the leading climatic factor controlling greening patterns. However, trends in agricultural practices, such as increased use of high-yield crops and application of chemical fertilizers, along with land-use changes such as afforestation and reforestation probably made a greater contribution to the greening trend than temperature. Increased vegetation activity in forests suggests an increasing carbon stock in forest biomass in China, which supports previous studies based on satellite sensor data and forest inventory data.

Structural Phenology of the Leaf Community in the Canopy of a Liriodendron tulipifera L. Forest in Maryland, USA

Abstract We measured the vertical dynamics of leaf area in a tall, tulip poplar (Liriodendron tulipifera)-dominated, deciduous forest on the Maryland coastal plain over three growing seasons (one intensively) using nondestructive, ground-based observations. Leaf area index (LAI) and leaf area density (LAD) were calculated by vertical level, time, and species; the time-integrated duration of leaf display (leaf area extent, LAE) was estimated by species and vertical level. In this stand LAI rises rapidly in the spring, declines slightly during the growing season, and then rapidly in the fall. In the intensively studied year, total LAI peaked at 7.1 m2m-2 LAE was 1259 m2m-2day, and the length of the canopy season (time from half-maximum LAI at leaf-out to the half-maximum at decline) was 191 days. The maximum growing season LAI, the date of leaf emergence, and the LAE differed among years, ranging 0.61 m2m-2, 14 days, and 261 m2m-2day, respectively. However, the timing of leaf fall was more consistent, ranging only 6 days. Each species differs in its height distribution, density, and timing of foliar display. In the canopy, leaf number, area, and duration are the bases of species importance; these differ substantially by species from typical importance values derived from stem-based attributes (density, basal area, biomass). FOR. SCI. 50(3):387-397.