Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

Influence of Spring and Autumn Phenological Transitions on Forest Ecosystem Productivity

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ¿extra¿ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.JRC.H.5-Land Resources Managemen

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

Regional forest biomass estimation using ICESat/GLAS spaceborne LiDAR over Borneo

Background: We aimed to clarify the potential of spaceborne light detection and ranging (LiDAR) to meet the increased demand for large-scale monitoring of forest resources.Results: We developed empirical models to estimate aboveground biomass (AGB) and canopy height in Borneo from Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) data, and obtained root-mean-square errors of 38.7 Mg ha(-1) and 4.0 m, respectively. GLAS-estimated AGB averaged 191.8 Mg ha(-1). From 2004 to 2007, AGB decreased by an average of 33.1 Mg ha(-1), and the rate of forest loss was 2.4% year(-1). The total AGB in Borneo was estimated as 10.34 Gt.Conclusions: The results demonstrate the potential of spaceborne LiDAR for monitoring forest resources, and its potential to play an important role in REDD+ implementations

Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH₄ Emissions in China

Bottom-up CH4 emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH4 bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH4 emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH4 emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector

Functional Consequences of Differences in Canopy Phenology for the Carbon Budgets of Two Cool-Temperate Forest Types : Simulations Using the NCAR/LSM Model and Validation Using Tower Flux and Biometric Data

We quantified the sensitivity of estimated carbon budgets in Japanese evergreen coniferous and deciduous broad-leaved forests using NCAR/LSM simulations under two climatic conditions: the relatively warm end of the cool-temperate zone (i.e., 800 m a.s.l., annual average temperature of 9.4℃, annual average precipitation of 1700 mm), and the relatively cold end of this zone (i.e., 1420 m a.s.l., 7.2℃, and 2400 mm). To improve the model's performance for both forests, we modified parameters such as biomass and plant area index (PAI) based on measured values and calibrated the model using field-measured tower flux and biometric data at two AsiaFlux sites near Takayama City, Japan. The seasonal patterns and annual cumulative values of gross primary production (GPP), ecosystem respiration (RE), and net ecosystem production (NEP) predicted by the model agreed well with field measurements at the two sites. Our sensitivity analysis of the impact of growing period length on the carbon budget in the deciduous broad-leaved forest showed that GPP and NEP increased by 12.7% and 48.0%, respectively, when we considered the temperature dependency of the growing period length. In simulations under both climatic conditions, NEP peaked between April and June in the evergreen coniferous forest, and between July and September in the deciduous broad-leaved forest. The different seasonal patterns of NEP between the two forest types were determined primarily by differences in GPP that resulted from differences in PAI from April to June. The annual values of GPP, RE, and light-use efficiency were clearly greater in the evergreen coniferous forest than in the deciduous broad-leaved forest. Our simulation results suggest that the evergreen coniferous forest has higher metabolic activity than the deciduous broad-leaved forest in this region due to its larger biomass