scholarly journals Generation of High Resolution Vegetation Productivity from a Downscaling Method

2018 ◽  
Vol 10 (11) ◽  
pp. 1748 ◽  
Author(s):  
Tao Yu ◽  
Rui Sun ◽  
Zhiqiang Xiao ◽  
Qiang Zhang ◽  
Juanmin Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Primary Production ◽  
Land Surface ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Vegetation Productivity ◽  
Area Index ◽  
Multi Scale ◽  
Downscaling Method

Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP.

scholarly journals Leaf Area Index identified as a major source of variability in modelled CO<sub>2</sub> fertilization

2018 ◽  
Author(s):  
Qianyu Li ◽  
Xingjie Lu ◽  
Yingping Wang ◽  
Xin Huang ◽  
Peter M. Cox ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Vegetation Types ◽  
Cable Model ◽  
Co2 Fertilization ◽  
Area Index ◽  
Leaf Level

Abstract. The concentration-carbon feedback factor (β), also called the CO2 fertilization effect, is a key unknown in climate-carbon cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated CO2 is urgently needed to enable a more accurate prediction of future terrestrial carbon sink. We calculated CO2 fertilization effects at various hierarchical levels from leaf biochemical reaction, leaf photosynthesis, canopy gross primary production (GPP), net primary production (NPP), to ecosystem carbon storage (cpool), for seven C3 vegetation types in response to increasing CO2 under RCP 8.5 scenario, using the Community Atmosphere Biosphere Land Exchange model (CABLE). Our results show that coefficient of variation (CV) for the CABLE model among the seven vegetation types is 0.15–0.13 for the biochemical level β, 0.13–0.16 for the leaf-level β, 0.48 for the βGPP, 0.45 for the βNPP, and 0.58 for the βcpool. The low variation of the leaf-level β is consistent with a theoretical analysis that leaf photosynthetic sensitivity to increasing CO2 concentration is almost an invariant function. In CABLE, the major jump in CV of β values from leaf- to canopy- and ecosystem-levels results from divergence in modelled leaf area index (LAI) within and among the vegetation types. The correlations of βGPP, βNPP, or βcpool with βLAI are very high in CABLE. Overall, our results indicate that modelled LAI is a key factor causing the divergence in β values in CABLE model. It is therefore urgent to constrain processes that regulate LAI dynamics in order to better represent the response of ecosystem productivity to increasing CO2 in Earth System Models.

Spatial variability of aboveground net primary production for a forested landscape in northern Wisconsin

2003 ◽  
Vol 33 (10) ◽  
pp. 2007-2018 ◽  
Author(s):  
S N Burrows ◽  
S T Gower ◽  
J M Norman ◽  
G Diak ◽  
D S Mackay ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Vegetation Cover ◽  
Land Surface ◽  
Net Primary Production ◽  
Mixed Forest ◽  
Forested Wetlands ◽  
Area Index

Quantifying forest net primary production (NPP) is critical to understanding the global carbon cycle because forests are responsible for a large portion of the total terrestrial NPP. The objectives of this study were to measure above ground NPP (NPPA) for a land surface in northern Wisconsin, examine the spatial patterns of NPPA and its components, and correlate NPPA with vegetation cover types and leaf area index. Mean NPPA for aspen, hardwoods, mixed forest, upland conifers, nonforested wetlands, and forested wetlands was 7.8, 7.2, 5.7, 4.9, 5.0, and 4.5 t dry mass·ha–1·year–1, respectively. There were significant (p = 0.01) spatial patterns in wood, foliage, and understory NPP components and NPPA (p = 0.03) when the vegetation cover type was included in the model. The spatial range estimates for the three NPP components and NPPA differed significantly from each other, suggesting that different factors are influencing the components of NPP. NPPA was significantly correlated with leaf area index (p = 0.01) for the major vegetation cover types. The mean NPPA for the 3 km × 2 km site was 5.8 t dry mass·ha–1·year–1.

scholarly journals Reduction of Global Plant Production due to Droughts from 2001 to 2010: An Analysis with a Process-Based Global Terrestrial Ecosystem Model

2015 ◽  
Vol 19 (16) ◽  
pp. 1-21 ◽  
Author(s):  
Chang Liao ◽  
Qianlai Zhuang
Keyword(s):  
Primary Production ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Ecosystem Model ◽  
Plant Production ◽  
Model Parameters ◽  
Drought Impact ◽  
Terrestrial Ecosystem Model

Abstract Droughts dramatically affect plant production of global terrestrial ecosystems. To date, quantification of this impact remains a challenge because of the complex plant physiological and biochemical processes associated with drought. Here, this study incorporates a drought index into an existing process-based terrestrial ecosystem model to estimate the drought impact on global plant production for the period 2001–10. Global Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data products are used to constrain model parameters and verify the model algorithms. The verified model is then applied to evaluate the drought impact. The study indicates that droughts will reduce GPP by 9.8 g C m−2 month−1 during the study period. On average, drought reduces GPP by 10% globally. As a result, the global GPP decreased from 106.4 to 95.9 Pg C yr−1 while the global net primary production (NPP) decreased from 54.9 to 49.9 Pg C yr−1. This study revises the estimation of the global NPP and suggests that the future quantification of the global carbon budget of terrestrial ecosystems should take the drought impact into account.

scholarly journals Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

2018 ◽  
Vol 10 (9) ◽  
pp. 1329 ◽  
Author(s):  
Shangrong Lin ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Alfredo Huete ◽  
Longhui Li
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Forest Canopy ◽  
Gross Primary Production ◽  
Model Performance ◽  
Single Layer ◽  
Environmental Parameters ◽  
Terrestrial Ecosystems ◽  
Vertical Stratification ◽  
Area Index

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

scholarly journals Moderate forest disturbance as a stringent test for gap and big-leaf models

2015 ◽  
Vol 12 (2) ◽  
pp. 513-526 ◽  
Author(s):  
B. Bond-Lamberty ◽  
J. P. Fisk ◽  
J. A. Holm ◽  
V. Bailey ◽  
G. Bohrer ◽  
...  
Keyword(s):  
Primary Production ◽  
Tree Mortality ◽  
Net Primary Production ◽  
Forest Disturbance ◽  
Gross Primary Production ◽  
Structural Complexity ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Area Index ◽  
C Cycle

Abstract. Disturbance-induced tree mortality is a key factor regulating the carbon balance of a forest, but tree mortality and its subsequent effects are poorly represented processes in terrestrial ecosystem models. It is thus unclear whether models can robustly simulate moderate (non-catastrophic) disturbances, which tend to increase biological and structural complexity and are increasingly common in aging US forests. We tested whether three forest ecosystem models – Biome-BGC (BioGeochemical Cycles), a classic big-leaf model, and the ZELIG and ED (Ecosystem Demography) gap-oriented models – could reproduce the resilience to moderate disturbance observed in an experimentally manipulated forest (the Forest Accelerated Succession Experiment in northern Michigan, USA, in which 38% of canopy dominants were stem girdled and compared to control plots). Each model was parameterized, spun up, and disturbed following similar protocols and run for 5 years post-disturbance. The models replicated observed declines in aboveground biomass well. Biome-BGC captured the timing and rebound of observed leaf area index (LAI), while ZELIG and ED correctly estimated the magnitude of LAI decline. None of the models fully captured the observed post-disturbance C fluxes, in particular gross primary production or net primary production (NPP). Biome-BGC NPP was correctly resilient but for the wrong reasons, and could not match the absolute observational values. ZELIG and ED, in contrast, exhibited large, unobserved drops in NPP and net ecosystem production. The biological mechanisms proposed to explain the observed rapid resilience of the C cycle are typically not incorporated by these or other models. It is thus an open question whether most ecosystem models will simulate correctly the gradual and less extensive tree mortality characteristic of moderate disturbances.

scholarly journals Effects of carbon turnover time on terrestrial ecosystem carbon storage

2017 ◽  
Vol 14 (23) ◽  
pp. 5441-5454 ◽  
Author(s):  
Yaner Yan ◽  
Xuhui Zhou ◽  
Lifeng Jiang ◽  
Yiqi Luo
Keyword(s):  
Primary Production ◽  
Spatial Variation ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Turnover Time ◽  
Soil C ◽  
C Storage ◽  
Key Factor

Abstract. Carbon (C) turnover time is a key factor in determining C storage capacity in various plant and soil pools as well as terrestrial C sink in a changing climate. However, the effects of C turnover time on ecosystem C storage have not been well explored. In this study, we compared mean C turnover times (MTTs) of ecosystem and soil, examined their variability to climate, and then quantified the spatial variation in ecosystem C storage over time from changes in C turnover time and/or net primary production (NPP). Our results showed that mean ecosystem MTT based on gross primary production (GPP; MTTEC_GPP =  Cpool/GPP, 25.0 ± 2.7 years) was shorter than soil MTT (MTTsoil =  Csoil/NPP, 35.5 ± 1.2 years) and NPP-based ecosystem MTT (MTTEC_NPP =  Cpool/NPP, 50.8 ± 3 years; Cpool and Csoil referred to ecosystem or soil C storage, respectively). On the biome scale, temperature is the best predictor for MTTEC (R2 =  0.77, p < 0.001) and MTTsoil (R2 =  0.68, p < 0.001), while the inclusion of precipitation in the model did not improve the performance of MTTEC (R2 =  0.76, p < 0.001). Ecosystem MTT decreased by approximately 4 years from 1901 to 2011 when only temperature was considered, resulting in a large C release from terrestrial ecosystems. The resultant terrestrial C release caused by the decrease in MTT only accounted for about 13.5 % of that due to the change in NPP uptake (159.3 ± 1.45 vs. 1215.4 ± 11.0 Pg C). However, the larger uncertainties in the spatial variation of MTT than temporal changes could lead to a greater impact on ecosystem C storage, which deserves further study in the future.

scholarly journals MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

2012 ◽  
Vol 56 (1) ◽  
pp. 56-78
Author(s):  
Tiit Nilson ◽  
Mattias Rennel ◽  
Andres Luhamaa ◽  
Maris Hordo ◽  
Aire Olesk ◽  
...  
Keyword(s):  
Primary Production ◽  
Tree Ring ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Weather Prediction ◽  
Type Model ◽  
Limited Area ◽  
Area Index ◽  
Tree Ring Data ◽  
Resolution Imaging

Abstract. A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather prediction model. The land cover map of Estonia needed for the model was derived using DMCii (Disaster Monitoring Constellation International Imaging) SLIM-6-22 (Surrey Linear Imager - 6 channel - 22 m resolution) images and ancillary information. The EST_PP model was run for the period from years 2003 to 2011. The results of GPP and NPP simulation are compared with the available global MODIS (Moderate Resolution Imaging Spectroradiometer) GPP/NPP product and with the Estonian statistical data on yearly volume increment in forests and on yield of agricultural crops. The NPP simulation results on coniferous and deciduous forests are compared with the data from tree ring analyses from different counties. These comparisons show us that the simulated country average yearly NPP values for Estonian forests agree reasonably well with the indirect estimates from other sources, taking into account the rather high uncertainty of the model predictions, uncertainty of forest inventory-based estimates and limited representativity of existing tree ring data. However, problems arise with the ability of present versions of EST_PP and MODIS NPP models to adequately simulate the regional differences of productivity and of variability of productivity in different years. The model needs some modification and the basic LUE principles to be tested in Estonia. Nevertheless, the MODIS NPP and EST_PP models offer additional possibilities to map yearly productivity and carbon sequestration by Estonian vegetation. There is a perspective to add the model-simulated NPP values into the national inventory datasets.

scholarly journals Importance of succession in estimating biomass loss: Combining remote sensing and individual-based forest models

2020 ◽  
Author(s):  
Ulrike Hiltner ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tree Mortality ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Mortality Rates ◽  
Area Index ◽  
Forest Height ◽  
Biomass Loss

Abstract. Disturbances can have strong impacts on the dynamics and structure of tropical forests. They often lead to increased tree mortality and affect their behaviour as carbon sinks. In the future, the intensity of disturbances, such as extreme weather events, fires, floods, and biotic agents, will probably even increase, with more serious consequences for tropical forests than we have already observed. However, impacts of altering disturbances on rates of forest biomass loss through tree mortality (hereinafter: biomass mortality) have been little described yet. This complicates progress in quantifying the effects of climate change on forests globally. This study aims to analyse the consequences of elevated tree mortality on forest dynamics and to provide a methodology that can reduce uncertainties in estimating biomass mortality rates at local and country level. We achieved this by linking benefits of individual-based forest model-ling, statistical linear regression, and remote sensing. We applied an individual-based forest model to investigate the impact of varying disturbance regimes on the succession dynamic of a humid Terra Firma forest at the Paracou study site in French Guiana. By simulating increased tree mortality rates, we were able to investigate their influence on several forest attributes, namely biomass, leaf area index, forest height, gross primary production, net primary production, and biomass mortality. Based on simulations of leaf area index and forest height, we developed a linear multivariate regression model to project biomass mortality. Our findings demonstrate that severe disturbances altered the succession pattern of the forests in favour of fast-growing species, which changed gross primary production, but net primary production remained stable. We also observed a strong influence on biomass mortality rates as well as observed complex relationships between these rates and single forest attributes (leaf area index, forest height, and biomass). By combining leaf area index and forest height we obtained relationships that allow an estimation of the biomass mortality. Based on these findings, we mapped the biomass mortality for whole French Guiana. We found a nation-wide biomass mortality of 3 % per year (standard deviation = 1.4 % per year). The approach we describe here, provides a novel methodology for quantifying the spatial-temporal distribution of biomass loss, which has recently been identified as particularly critical for monitoring mortality hot spots. Quantifying biomass mortality rates may help reducing uncertainties in the terrestrial component of the global carbon cycle.

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