Forest Gross Primary Production Research Articles

Gross primary production of global forest ecosystems has been overestimated.

Coverage rate, a critical variable for gridded forest area, has been neglected by previous studies in estimating the annual gross primary production (GPP) of global forest ecosystems. In this study, we investigated to what extent the coverage rate could impact forest GPP estimates from 1982 to 2011. Here we show that the traditional calculation without considering the coverage rate globally overestimated the forest gross carbon dioxide uptake by approximately 8.7%, with a value of 5.12 ± 0.23 Pg C yr−1, which is equivalent to 48% of the annual emissions from anthropogenic activities in 2012. Actually, the global annual GPP of forest ecosystems is approximately 53.71 ± 4.83 Pg C yr−1 for the past 30 years by taking the coverage rate into account. Accordingly, we argue that forest annual GPP calculated by previous studies has been overestimated due to the exaggerated forest area, and therefore, coverage rate may be a required factor to further quantify the global carbon cycle.

Retrieval and assessment of CO2 uptake by mediterranean ecosystems using remote sensing and

Retrieval and assessment of CO2 uptake by mediterranean ecosystems using remote sensing and

Bayesian optimization of a light use efficiency model for the estimation of daily gross primary productivity in a range of Italian forest ecosystems

In this study we applied a modified version of Prelued, a simple semi-empirical light use efficiency (LUE) model, to eight eddy-covariance Italian sites. Since this model has been successfully applied mainly to coniferous forests located at northern latitudes, in our study we aimed to test its generality, by comparing Prelued's outputs in coniferous, broadleaf forests and in a Mediterranean macchia, at different climatic and environmental conditions. The model was calibrated for daily gross primary production (GPP) observed over one year in each flux site and validated for another year. The model uncertainties on both GPP and model parameters were estimated, applying a Bayesian calibration based on a multiple chains Markov Chain Monte Carlo sampling.The accuracy of the model estimates of daily GPP over the entire period of simulation differed widely depending on the site considered, with generally good model performance when applied to evergreen and broadleaf forests and poor performances in the Mediterranean macchia. The values of the modifiers accounting for the response to climatic variables suggested the soil water content to be non-limiting in temperate mountain evergreen but limiting in Mediterranean forests. Model uncertainties were always smaller than data uncertainties, with variable magnitude depending on the site considered. Both modeled GPP and uncertainties were largely dependent also on uncertainties on the data, which made their calculation a key process in this modelling exercise.In conclusion, this semi-empirical model appears to be suitable for estimating daily and annual forest GPP in most of the considered sites, with the exception of Mediterranean macchias, and for supporting its application to a large range of ecosystems provided a site-specific calibration. The Bayesian calibration did not confer a clear advantage in terms of model performances in respect to other methods used in previous studies, but allowed us to estimate uncertainties on both parameter values and model estimates, which were useful to analyse more in detail the ecosystem response to environmental drivers of GPP.

Tree-ring δ13C tracks flux tower ecosystem productivity estimates in a NE temperate forest

We investigated relationships between tree-ring δ13C and growth, and flux tower estimates of gross primary productivity (GPP) at Harvard Forest from 1992 to 2010. Seasonal variations of derived photosynthetic isotope discrimination (Δ13C) and leaf intercellular CO2 concentration (ci) showed significant increasing trends for the dominant deciduous and coniferous species. Δ13C was positively correlated to growing-season GPP and is primarily controlled by precipitation and soil moisture indicating that site conditions maintained high stomatal conductance under increasing atmospheric CO2 levels. Increasing Δ13C over the 1992–2010 period is attributed to increasing annual and summer water availability identified at Harvard Forest and across the region. Higher Δ13C is coincident with an enhancement in growth and ecosystem-level net carbon uptake. This work suggests that tree-ring δ13C could serve as a measure of forest GPP and be used to improve the calibration and predictive skill of ecosystem and carbon cycle models.

Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product

Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500m reflectance data and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R2=0.45–0.86, Coniferous: R2=0.49–0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1km daytime LST (land surface temperature) (R2=0.55–0.81, 0.57–0.77) and between GPP and EVI2, PPFD and daytime LST in multiple linear regressions (R2=0.73–0.89, 0.65–0.93). One year of data was collected from all coniferous sites to derive a general empirical model for GPP versus (1) the product of EVI2 and PPFD (R2=0.70), (2) the product of EVI2 and daytime LST (R2=0.62) and (3) EVI2, PPFD and daytime LST (R2=0.72). These three models were then validated at six sites for the remaining years by linearly relating eddy covariance GPP to modeled GPP, which resulted in fairly strong to strong relationships for most sites (R2=0.49–0.91, RMSE=0.63–1.22gCm−2day−1, R2=0.53–0.73, RMSE=0.90–1.43gCm−2day−1, R2=0.56–0.87, RMSE=0.79–1.11gCm−2day−1). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1km GPP product (R2=0.59–0.88, RMSE=0.80–1.16gCm−2day−1). The main conclusion is that the suggested empirical models driven by MODIS 500m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1km GPP product.

Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes.

Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States

Gross primary production (GPP), the photosynthetic uptake of carbon, is an important variable in the global carbon cycle. Although continuous measurements of GPP are being collected from a network of micro-meteorological towers, each site represents a small area with records available for only a limited period. As a result, GPP is commonly modeled over forested landscapes as a function of climatic and soil variables, often supplemented with satellite-derived estimates of the vegetation's light-absorbing properties. Since the late 1990s, a number of models have been developed to provide seasonal and annual estimates of GPP across much of the Earth. Each model, however, contains different underlying assumptions and requires different amounts of data. As a result, predictions vary, sometimes significantly. In this paper we compare modeled estimates of GPP for forested areas across the U.S.A. derived from: NASA's MODIS Product (MOD17); the C-Fix model using SPOT-VGT satellite-derived vegetation data; and the Physiological Principles Predicting Growth from Satellites (3-PGS) model, a process-based model that requires information on both climate and soil properties. The models predicted average ecoregion values of forest GPP between 9.8 and 14.1 MgC ha − 1 y − 1 across the United States. 3-PGS predicted the lowest values while the C-Fix model, which included a CO 2 fertilization factor, produced the highest estimates. In the western part of the country, estimates of GPP within a given ecoregion varied by as much as 50%, whereas in the northeast, where topography and climate are less extreme, variation in GPP was less than 10%. Within ecoregions, 3PGS predicted the most variation, reflecting its sensitivity to variation in soil properties. We conclude that where model predictions disagree, an opportunity is presented to evaluate underlying assumptions through sensitivity analyses, additional data collection and where more detailed study is warranted.

Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems

This paper describes the development and testing of a procedure which combines remotely sensed and ancillary data to monitor forest productivity in Italy. The procedure is based on a straightforward parametric model (C-Fix) that uses the relationship between the fraction of photosynthetically active radiation absorbed by plant canopies (fAPAR) and relevant gross primary productivity (GPP). Estimates of forest fAPAR are derived from Spot-VGT NDVI images and are combined with spatially consistent data layers obtained by the elaboration of ground meteorological measurements. The original version of C-Fix is first applied to estimate monthly GPP of Italian forests during eight years (1999–2006). Next, a modification of the model is proposed in order to simulate the short-term effect of summer water stress more efficiently. The accuracy of the original and modified C-Fix versions is evaluated by comparison with GPP data taken at eight Italian eddy covariance flux tower sites. The experimental results confirm the capacity of C-Fix to monitor national forest GPP patterns and indicate the utility of considering the short-term effect of water stress during Mediterranean dry months.

Estimation of the gross primary production of an old‐growth temperate mixed forest using eddy covariance and remote sensing

Continuous flux data from CO2 flux sites can be used to improve our understanding of leaf phenology and validate the algorithms of satellite‐ based carbon cycling models. In this study, we conducted a simulation of the Vegetation Photosynthesis Model (VPM) using the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI) derived from the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product, as well as site‐specific air temperature, biological temperature, and photosynthetically active radiation (PAR) data. Gross primary production (GPP) estimates derived from the VPM were compared with field observations of a flux tower in an old temperate mixed forest in northeastern China during 2003–2005. Time series data for the EVI have a stronger exponential relationship with the GPP (R 2 = 0.74, n = 67, p<0.01) than those for the Normalized Difference Vegetation Index (NDVI) (R 2 = 0.62, n = 67, p<0.01), indicating a different light use efficiency during the different stages of foliage development. In comparison to the flux tower GPP, the VPM‐predicted GPP captured the onset of the growing season well, and their seasonal dynamics were generally consistent in terms of phase in the peak growing season, while the end date of the growing season was 8–16 days earlier than that of field measurements. The annual forest GPP estimated from the flux tower observations varied from 1312 g C m−2 (grams of carbon per metre squared) to 1490 g C m−2 in the three observation years from 2003 to 2005, which is less 10% different from the VPM‐based annual GPP. These results demonstrate the potential of the satellite‐driven VPM for scaling up the GPP of forests at the CO2 flux tower site, a key issue for the study of the carbon budget at regional scales.

Assessment of forest GPP variations in central Italy

A debate is in progress concerning the possible effects of climate changes on the primary production of both natural and artificial ecosystems. The current investigation builds on the hypothesis that trends of increasing air temperature observed in several Italian regions should positively affect productivity of mountain forest ecosystems. Temperature rise in the Mugello valley (central Italy) in the period 1986–2001 was first confirmed by the analysis of data from a local station. The effects of this rise on the productivity of deciduous forest ecosystems (dominated by beech, Fagus sylvatica L.) were then analysed through estimates of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from the US National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite normalized difference vegetation index (NDVI) data. The use of a simplified parametric model (C-Fix) then allowed the combination of these FAPAR estimates with meteorological data (temperature and radiation) to produce annual values of forest gross primary productivity (GPP). Finally, validation of these GPP estimates was carried out by a comparison with dendrochronological measurements taken in the study forests. Because tree measurements were affected by external factors not exclusively related to forest GPP (stand aging, management practices, etc.), the comparison gave positive results only after applying a detrending operation to both series of annual GPP estimates and dendrochronological data. These results are a first indication that the rise in temperature that has occurred in Italy in the last decades has positively affected the productivity of mountain forest ecosystems.

CO2 balance of boreal, temperate, and tropical forests derived from a global database

AbstractTerrestrial ecosystems sequester 2.1 Pg of atmospheric carbon annually. A large amount of the terrestrial sink is realized by forests. However, considerable uncertainties remain regarding the fate of this carbon over both short and long timescales. Relevant data to address these uncertainties are being collected at many sites around the world, but syntheses of these data are still sparse. To facilitate future synthesis activities, we have assembled a comprehensive global database for forest ecosystems, which includes carbon budget variables (fluxes and stocks), ecosystem traits (e.g. leaf area index, age), as well as ancillary site information such as management regime, climate, and soil characteristics. This publicly available database can be used to quantify global, regional or biome‐specific carbon budgets; to re‐examine established relationships; to test emerging hypotheses about ecosystem functioning [e.g. a constant net ecosystem production (NEP) to gross primary production (GPP) ratio]; and as benchmarks for model evaluations. In this paper, we present the first analysis of this database. We discuss the climatic influences on GPP, net primary production (NPP) and NEP and present the CO2 balances for boreal, temperate, and tropical forest biomes based on micrometeorological, ecophysiological, and biometric flux and inventory estimates. Globally, GPP of forests benefited from higher temperatures and precipitation whereas NPP saturated above either a threshold of 1500 mm precipitation or a mean annual temperature of 10 °C. The global pattern in NEP was insensitive to climate and is hypothesized to be mainly determined by nonclimatic conditions such as successional stage, management, site history, and site disturbance. In all biomes, closing the CO2 balance required the introduction of substantial biome‐specific closure terms. Nonclosure was taken as an indication that respiratory processes, advection, and non‐CO2 carbon fluxes are not presently being adequately accounted for.

Comparison of MODIS gross primary production estimates for forests across the U.S.A. with those generated by a simple process model, 3-PGS

Since 2000, NASA's Moderate Resolution Imaging Spectro-radiometer (MODIS) has provided 1 × 1 km estimates of 8-day gross primary production (GPP). The MODIS algorithm computes GPP as a simple function of absorbed photosynthetically active radiation and a regionally assigned light-use conversion efficiency (LUE) that is reduced if temperature or atmospheric vapor pressure deficits are suboptimal. We compared MODIS-derived GPP estimates for forested areas across the United States of America (U.S.A.) with those generated by the 3-PGS (Physiological Principles Predicting Growth using Satellite data) model, the latter of which considers spatial variation in available soil water storage capacity (ASWC) and nitrogen content. We expected seasonal and annual MODIS GPP values to be in close agreement with those derived from the 3-PGS model in regions with adequate precipitation, soil water storage, and moderately fertile soils. 3-PGS was initially run with STATSGO-derived soils information provided by the Oak Ridge National Laboratory. The analysis was expanded to include sensitivity analyses with ASWC set at 50, 100, 300, and 400 mm to identify areas within nine major ecoregions where drought might prove to be a major limitation on GPP. The majority of forests across the U.S.A. were relatively insensitive to large variations in ASW storage. In areas where ASWC was assumed < 200 mm and average annual rainfall was < 100 mm yr − 1 , GPP was predicted to be reduced by > 60%. There was generally good agreement (within 20%) between MODIS and 3-PGS estimates of forest GPP across the U.S.A. GPP predicted by the MODIS model was generally higher in ecoregions with substantial drought and with relatively low soil fertility. The latter, which influences LUE, was more than twice as important as soil drought.

Toward Synthesis of Relationships among Leaf Longevity, Instantaneous Photosynthetic Rate, Lifetime Leaf Carbon Gain, and the Gross Primary Production of Forests

The assimilation of carbon by plant communities (gross primary production [GPP]) is a central concern in plant ecology as well as for our understanding of global climate change. As an alternative to traditional methods involving destructive harvests or time-consuming measurements, we present a simple, general model for GPP as the product of the lifetime carbon gain by a single leaf, the daily leaf production rate, and the length of the favorable period for photosynthesis. To test the model, we estimated leaf lifetime carbon gain for 26 species using the concept of mean labor time for leaves (the part of each day the leaf functions to full capacity), average potential photosynthetic capacity over the leaf lifetime, and functional leaf longevity (leaf longevity discounted for periods within a year wholly unfavorable for photosynthesis). We found that the lifetime carbon gain of leaves was rather constant across species. Moreover, when foliar biomass was regressed against functional leaf longevity, aseasonal and seasonal forests fell on a single line, suggesting that the leaf production rate during favorable periods is not substantially different among forests in the world. The gross production of forest ecosystems then can be predicted to a first approximation simply by the annual duration of the period favorable for photosynthetic activity in any given region.

Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy

The current paper describes the development and testing of a procedure which can use widely available remotely sensed and ancillary data to assess large-scale patterns of forest productivity in Italy. To reach this objective a straightforward model (C-Fix) was applied which is based on the relationship between photosynthetically active radiation absorbed by plant canopies and relevant gross primary productivity (GPP). The original C-Fix methodology was improved by using more abundant ancillary information and more efficient techniques for NDVI data processing. In particular, two extraction methods were applied to NDVI data, derived from two sensors (NOAA-AVHRR and SPOT-VGT) to feed C-Fix. The accuracy of the model outputs was assessed through comparison with annual and monthly values of forest GPP derived from eight eddy covariance flux towers. The results obtained indicated the superiority of SPOT-VGT over NOAA-AVHRR data and a higher efficiency of the more advanced NDVI extraction method. Globally, the procedure was proved to be of easy and objective implementation and allowed the evaluation of mean productivity levels of existing forests on the national scale.

Toward Synthesis of Relationships among Leaf Longevity, Instantaneous Photosynthetic Rate, Lifetime Leaf Carbon Gain, and the Gross Primary Production of Forests

The assimilation of carbon by plant communities (gross primary production [GPP]) is a central concern in plant ecology as well as for our understanding of global climate change. As an alternative to traditional methods involving destructive harvests or time‐consuming measurements, we present a simple, general model for GPP as the product of the lifetime carbon gain by a single leaf, the daily leaf production rate, and the length of the favorable period for photosynthesis. To test the model, we estimated leaf lifetime carbon gain for 26 species using the concept of mean labor time for leaves (the part of each day the leaf functions to full capacity), average potential photosynthetic capacity over the leaf lifetime, and functional leaf longevity (leaf longevity discounted for periods within a year wholly unfavorable for photosynthesis). We found that the lifetime carbon gain of leaves was rather constant across species. Moreover, when foliar biomass was regressed against functional leaf longevity, aseasonal and seasonal forests fell on a single line, suggesting that the leaf production rate during favorable periods is not substantially different among forests in the world. The gross production of forest ecosystems then can be predicted to a first approximation simply by the annual duration of the period favorable for photosynthetic activity in any given region.

Modelling carbon budget of Mediterranean forests using ground and remote sensing measurements

The current paper illustrates a method to operationally apply the model FOREST-BGC for the estimation of forest carbon fluxes in Mediterranean environments. The work was carried out in a pine forest stand within the coastal area of San Rossore (Central Italy) using both conventionally collected and remotely sensed data. The calibration of the model was performed using estimates of net primary productivity (NPP) derived from the carbon accumulated in the forest stems during the last four decades. Such estimates were obtained by transforming dendrochronological measurements collected in the stand into annual increments of woody biomass and carbon matter. Next, the model performance was validated against values of net ecosystem exchange (NEE) and gross primary productivity (GPP) collected during four years (1999–2002) by an eddy covariance flux tower. A method based on deriving fraction of photosynthetically active radiation (FAPAR) from remotely sensed normalised difference vegetation index (NDVI) data was also calibrated and validated in order to more directly assess forest GPP. The results achieved indicate that the multi-year calibration against past carbon accumulation was essential in properly configuring the model in terms of respiration and allocation functions. Due to the importance of these functions, only the calibrated model was in fact able to correctly simulate the forest carbon fluxes, giving monthly estimates of both NEE and GPP quite close to those measured by the flux tower. These estimates were further improved by the proper integration of remotely sensed GPP evaluation and model carbon partitioning, which could be particularly useful for operational monitoring applications on a regional scale.

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO 2, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO 2 eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998–2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO 2 flux data from a CO 2 eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998–2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO 2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

Isotopic constraints on the transpiration, evaporation, energy, and gross primary production Budgets of a large boreal watershed: Ottawa River Basin, Canada

The residual signals of the physical and chemical processes undergone in a river basin are stored in the isotopic composition of river water and are used here to isolate and quantify fluxes of water, energy and carbon for a large boreal river basin. The integrated nature of the river signal is exploited to provide meaningful basin‐wide annual averages for fluxes difficult to quantify and extrapolate by studying highly variable interface exchanges at discrete locations. The slope of the linear regression of deuterium (δD) and oxygen (δ18O) isotopes for the Ottawa River is ∼6.0, considerably less than the slope of the local meteoric water line (7.7). This discrepancy is a consequence of evaporative loss from open water bodies and soils and, through a new method, is calculated to be 8.1% of annual precipitation. As well, on the basis of thirty years of daily meteorological and discharge data, annual evapotranspiration for the Ottawa River basin is calculated to be 53.1%. Combining the evaporation and evapotranspiration calculations apportions 45% of the water losses to transpiration. The energy required to drive these cycles is calculated to be 8% of annual solar radiation for total evapotranspiration and 13% of growing season solar radiation for transpiration. These energies are transformed into latent heat. The water use efficiency ratio is used to estimate total fixation of carbon (gross primary production (GPP)) for the basin at 15.6 mol C m−2 yr−1. This rate is substantially greater than the export of carbon via rivers plus rates estimated for carbon respiration in the literature, indicating that the boreal forest is a plausible component of the postulated “missing” carbon sink. Comparison of accumulation rates of C in peatlands and the rates required to account for the missing sink suggest that peat accumulation rates are ∼20 times too slow to account for the missing sink flux. Speculatively, the living biomass of the boreal forest is the dominant sink. Accepting this, the respiration rate needed for a steady state balance between the calculated boreal forest GPP and the missing global carbon sink is found to be around 5.6 mol C m−2 yr−1.