Gross Primary Productivity of the True Steppe in Central Asia in Relation to NDVI: Scaling up CO2 Fluxes

Gilmanov, Tagir G.; Johnson, Douglas A.; Saliendra, Nicanor Z.; Akshalov, Kanat; Wylie, Bruce K.

doi:10.1007/s00267-003-9157-7

Cited by 24 publications

(29 citation statements)

References 71 publications

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“…Following Marshall & Biscoe (1980), we estimated daytime ecosystem respiration, R d , using the γ parameter of the light‐response function (9) or (10). Comparing daytime respiration estimates thus obtained with directly measured night‐time fluxes (Gilmanov et al ., 2003a, 2003b, 2004) demonstrated that light‐curve derived estimates of γ are in fair agreement with values measured by tower during night ( R n ). Combining R d estimates with measured R n values results in 24 h ecosystem respiration values R e = R d + R n .…”

Section: Methodsmentioning

confidence: 99%

“…Our experience with analysis and modelling of the net CO 2 flux data sets from a number of CO 2 flux towers in grasslands and shrublands (Wylie et al ., 2003; Gilmanov et al ., 2004; Wylie et al ., 2004) has also demonstrated rather weak relationships between F and spectral vegetation indices (e.g. NDVI), contrary to the earlier opinion by Bartlett et al .…”

Section: Methodsmentioning

confidence: 99%

“…More recently, quantitative biogeochemistry of terrestrial carbon cycling has entered a new phase with higher quality instrumentation and the addition of new sites. These advances and the accumulated data now allow comparative analyses, modelling, synthesis, and scaling‐up of local flux measurements (Falge et al ., 2002; Gilmanov et al ., 2003a,b; Turner et al ., 2003a; Gilmanov et al ., 2004). The concept of ecoregions is providing a natural framework of data integration, modelling, and scaling‐up (Gilmanov et al ., 2003b) that needs to be integrated with the top‐down approaches of atmospheric models.…”

Section: Introductionmentioning

confidence: 99%

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Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Gilmanov

Tieszen

Wylie

et al. 2005

Global Ecology and Biogeography

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144

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Aim Extrapolation of tower CO 2 fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO 2 fluxes and photosynthetically active radiation ( Q ), other on-site factors, and Normalized Difference Vegetation Index ( NDVI ) from the SPOT VEGETATION data set.Location CO 2 flux data from the following stations and years were analysed: Lethbridge, Alberta 1998 Fort Peck, MT 2000 Miles City, MT 2000 -01; Mandan, ND 1999 and Cheyenne, WY 1997-98.Results Analyses based on light-response functions allowed partitioning net CO 2 flux ( F ) into gross primary productivity ( P g ) and ecosystem respiration ( R e ). Weekly averages of daytime respiration, γ day , estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γ night ( R 2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γ day on γ night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO 2 m − 2 year , and net ecosystem exchange from − 537 (source) to +610 g CO 2 m − 2 year − 1 (sink). Maximum daily ecological light-use efficiencies, ε d , max = P g /Q , were in the range 0.014 to 0.032 mol CO 2 (mol incident quanta)Main conclusions Ten-day average P g was significantly more highly correlated with NDVI than 10-day average daytime flux, P d ( R 2 = 0.46 to 0.77 for P g -NDVI and 0.05 to 0.58 for P d -NDVI relationships). Ten-day average R e was also positively correlated with NDVI , with R 2 values from 0.57 to 0.77. Patterns of the relationships of P g and R e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Gilmanov

Tieszen

Wylie

et al. 2005

Global Ecology and Biogeography

Self Cite

144

View full text Add to dashboard Cite

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“…Seasonal mean values of CO 2 observed during different seasons are 393 ± 5.60, 398 ± 7.60, 392 ± 7.0, and 393 ± 7.0 ppm in winter, pre-monsoon, monsoon, and post-monsoon respectively. Minimum CO 2 during winter (dry season) can be due to respiratory loss of carbon (Gilmanov et al, 2004;Aurela et al, 2004) as decreased temperature and solar radiation during this period inhibit increases in local CO 2 assimilation (Thum et al, 2009). A steady increase in CO 2 concentration is observed as season changes from winter to pre-monsoon months.…”

Section: Seasonal Variations Of Co 2 and Chmentioning

confidence: 99%

Influence of Meteorology and interrelationship with greenhouse gases (CO<sub>2</sub> and CH<sub>4</sub>) at a suburban site of India

et al. 2016

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Abstract. Atmospheric greenhouse gases (GHGs), such as carbon dioxide (CO 2 ) and methane (CH 4 ), are important climate forcing agents due to their significant impacts on the climate system. The present study brings out first continuous measurements of atmospheric GHGs using highprecision LGR-GGA over Shadnagar, a suburban site of Central India during the year 2014. The annual mean CO 2 and CH 4 over the study region are found to be 394 ± 2.92 and 1.92 ± 0.07 ppm (µ ± 1σ ) respectively. CO 2 and CH 4 show a significant seasonal variation during the study period with maximum (minimum) CO 2 observed during pre-monsoon (monsoon), while CH 4 recorded the maximum during postmonsoon and minimum during monsoon. Irrespective of the seasons, consistent diurnal variations of these gases are observed. Influences of prevailing meteorology (air temperature, wind speed, wind direction, and relative humidity) on GHGs have also been investigated. CO 2 and CH 4 show a strong positive correlation during winter, pre-monsoon, monsoon, and post-monsoon with correlation coefficients (R s ) equal to 0.80, 0.80, 0.61, and 0.72 respectively, indicating a common anthropogenic source for these gases. Analysis of this study reveals the major sources for CO 2 are soil respiration and anthropogenic emissions while vegetation acts as a main sink, whereas the major source and sink for CH 4 are vegetation and presence of hydroxyl (OH) radicals.

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“…Cross validation analyses demonstrated good performance of MTEs in predicting among site flux variability and seasonal patterns. Correlations between NEE and broadband vegetation indices (e.g., NDVI) were not as robust [17,18]. These results indicate that utilizing remote sensing techniques to estimate NEE is difficult using broadband indices, and that gross primary production (GPP) is more important to inter annual variability in NEE than ecosystem respiration (Re).…”

Section: Introductionmentioning

confidence: 82%

Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China

Wang

Mickler

et al. 2014

Remote Sensing

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Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI [705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination OPEN ACCESSRemote Sens. 2014, 6 8987 with GPP and Re (R 2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China's temperate broad-leaved Korean pine forest ecosystems.

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Gross Primary Productivity of the True Steppe in Central Asia in Relation to NDVI: Scaling up CO2 Fluxes

Cited by 24 publications

References 71 publications

Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Influence of Meteorology and interrelationship with greenhouse gases (CO<sub>2</sub> and CH<sub>4</sub>) at a suburban site of India

Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China

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Gross Primary Productivity of the True Steppe in Central Asia in Relation to NDVI: Scaling up CO2 Fluxes

Cited by 24 publications

References 71 publications

Integration of CO2 flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Integration of CO2 flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Influence of Meteorology and interrelationship with greenhouse gases (CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt;) at a suburban site of India

Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China

Contact Info

Product

Resources

About

Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Integration of CO₂ flux and remotely‐sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Influence of Meteorology and interrelationship with greenhouse gases (CO<sub>2</sub> and CH<sub>4</sub>) at a suburban site of India