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 The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

2021 ◽  
Vol 13 (12) ◽  
pp. 2363
Author(s):  
Xiangfen Cheng ◽  
Yu Zhou ◽  
Meijun Hu ◽  
Feng Wang ◽  
Hui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Forest Canopy ◽  
Meteorological Factors ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Near Surface ◽  
The Relationship

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.

Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China

2015 ◽  
Vol 17 (4) ◽  
pp. 753-762
Author(s):  
Mingquan Wu ◽  
Shakir Muhammad ◽  
Fang Chen ◽  
Zheng Niu ◽  
Changyao Wang
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production ◽  
Model Performance ◽  
Wetland Ecosystem ◽  
Wetland Ecosystems ◽  
New Model ◽  
Estuarine Wetland ◽  
Better Than

A new model performance better than the MODIS GPP product for wetland ecosystems was proposed and validated.

scholarly journals Low sensitivity of gross primary production to elevated CO<sub>2</sub> in a mature Eucalypt woodland

2019 ◽  
Author(s):  
Jinyan Yang ◽  
Belinda E. Medlyn ◽  
Martin G. De Kauwe ◽  
Remko A. Duursma ◽  
Mingkai Jiang ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Primary Production ◽  
Forest Canopy ◽  
Gross Primary Production ◽  
Carbon Dioxide Concentration ◽  
Photosynthetic Acclimation ◽  
Leaf Photosynthesis ◽  
Ambient Conditions ◽  
Area Index ◽  
Leaf Level

Abstract. The response of mature forest ecosystems to rising atmospheric carbon dioxide concentration (Ca) is a major uncertainty in projecting the future trajectory of the Earth’s climate. Although leaf-level net photosynthesis is typically stimulated by exposure to elevated Ca (eCa), it is unclear how this stimulation translates into carbon cycle responses at whole-ecosystem scale. Here we estimate a key component of the carbon cycle, the gross primary productivity (GPP), of a mature native Eucalypt forest exposed to Free Air CO2 Enrichment (the EucFACE experiment). In this experiment, light-saturated leaf photosynthesis increased by 19 % in response to a 38 % increase in Ca. We used the process-based forest canopy model, MAESPA, to upscale these leaf-level measurements of photosynthesis with canopy structure to estimate Gross Primary Production (GPP) and its response to eCa. We assessed the direct impact of eCa, as well as the indirect effect of photosynthetic acclimation to eCa and variability among treatment plots via different model scenarios. At the canopy scale, MAESPA estimated a GPP of 1574 g C m−2 yr−1 under ambient conditions across four years and a direct increase in GPP of +11 % in response to eCa. The smaller canopy-scale response simulated by the model, as compared to the leaf-level response, could be attributed to the prevalence of RuBP-regeneration limitation of leaf photosynthesis within the canopy. Photosynthetic acclimation reduced this estimated response to 10 %. Considering variability in leaf area index across plots, we estimated a mean GPP response to eCa of 6 % with a 95 % CI of (−2 %, 14 %). These findings highlight that the GPP response of mature forests to eCa is likely to be considerably lower than the response of light-saturated leaf photosynthesis. Our results provide an important context for interpreting eCa responses of other components of the ecosystem carbon cycle.

scholarly journals Vegetation modulates the impact of climate extremes on gross primary production

2020 ◽  
Author(s):  
Milan Flach ◽  
Alexander Brenning ◽  
Fabian Gans ◽  
Markus Reichstein ◽  
Sebastian Sippel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Climate Extremes ◽  
Co2 Flux ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
The One ◽  
The Impact ◽  
Heat Events

Abstract. Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e. the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in-situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear not in general to be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, this is in many cases plausible, e.g. when no negative preconditioning has occurred. On the other hand, however, this may also reflect a lack of sensitivity in current remote sensing derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.

scholarly journals Vegetation modulates the impact of climate extremes on gross primary production

2021 ◽  
Vol 18 (1) ◽  
pp. 39-53
Author(s):  
Milan Flach ◽  
Alexander Brenning ◽  
Fabian Gans ◽  
Markus Reichstein ◽  
Sebastian Sippel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Climate Extremes ◽  
Co2 Flux ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
The One ◽  
The Impact ◽  
Heat Events

Abstract. Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing, and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e., the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear in general to not be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, normal-to-increased GPP values are in many cases plausible, e.g., when conditions prior to the event have been particularly positive. On the other hand, however, normal-to-increased GPP values in forests may also reflect a lack of sensitivity in current remote-sensing-derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.

scholarly journals Remote Sensing Evaluation of CLM4 GPP for the Period 2000–09*

2012 ◽  
Vol 25 (15) ◽  
pp. 5327-5342 ◽  
Author(s):  
Jiafu Mao ◽  
Peter E. Thornton ◽  
Xiaoying Shi ◽  
Maosheng Zhao ◽  
Wilfred M. Post
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Large Scale ◽  
Function Analysis ◽  
Gross Primary Production ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystems ◽  
Northeast Russia ◽  
Community Land Model

Abstract Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments.

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.

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

1997 ◽  
Vol 18 (16) ◽  
pp. 3459-3471 ◽  
Author(s):  
S. E. Franklin ◽  
M. B. Lavigne ◽  
M. J. Deuling ◽  
M. A. Wulder ◽  
E. R. Hunt
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Remote Sensing And Gis ◽  
Area Index ◽  
Gis Data
Sign in / Sign up

Export Citation Format

Share Document