Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests

Neumann, Mathias; Moreno, Adam; Thurnher, Christopher; Mues, Volker; Härkönen, Sauli; Mura, Matteo; Bouriaud, Olivier; Lang, Mait; Cardellini, Giuseppe; Thivolle-Cazat, Alain; Bronisz, Karol; Merganič, Ján; Alberdi, Iciar; Astrup, Rasmus; Mohren, Frits; Zhao, Maosheng; Hasenauer, Hubert

doi:10.3390/rs8070554

Cited by 49 publications

(58 citation statements)

References 35 publications

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“… Note . Temperature and precipitation from Moreno and Hasenauer (), leaf area index (LAI) after Yang et al (), and net primary production (NPP) from Neumann, Moreno, Thurnher, et al ().…”

Section: Methodsmentioning

confidence: 99%

Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Neumann

Ukonmaanaho

Johnson

et al. 2018

Global Biogeochemical Cycles

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Litterfall is a major, yet poorly studied, process within forest ecosystems globally. It is important for carbon dynamics, edaphic communities, and maintaining site fertility. Reliable information on the carbon and nutrient input from litterfall, provided by litter traps, is relevant to a wide audience including policy makers and soil scientists. We used litterfall observations of 320 plots from the pan‐European forest monitoring network of the “International Co‐operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests” to quantify litterfall fluxes. Eight litterfall models were evaluated (four using climate information and four using biomass abundance). We scaled up our results to the total European forest area and quantified the contribution of litterfall to the forest carbon cycle using net primary production aggregated by bioregions (north, central, and south) and by forest types (conifers and broadleaves). The 1,604 analyzed annual litterfall observations indicated an average carbon input of 224 g C · m−2 · year−1 (annual nutrient inputs 4.49 g N, 0.32 g P, and 1.05 g K · m−2), representing a substantial percentage of net primary production from 36% in north Europe to 32% in central Europe. The annual turnover of carbon and nutrient in broadleaf canopies was larger than for conifers. The evaluated models provide large‐scale litterfall predictions with a bias less than 10%. Each year litterfall in European forests transfers 351 Tg C, 8.2 Tg N, 0.6 Tg P, and 1.9 Tg K to the forest floor. The performance of litterfall models may be improved by including foliage biomass and proxies for forest management.

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“… Note . Temperature and precipitation from Moreno and Hasenauer (), leaf area index (LAI) after Yang et al (), and net primary production (NPP) from Neumann, Moreno, Thurnher, et al ().…”

Section: Methodsmentioning

confidence: 99%

Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Neumann

Ukonmaanaho

Johnson

et al. 2018

Global Biogeochemical Cycles

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“…Regional products exist, such as the Global Ecosystems Monitoring (GEM) network (http://gem.tropicalforests. ox.ac.uk/, last access: 4 July 2018) and the European National Forest Inventory (Neumann et al, 2016), which could be combined into a global dataset.…”

Section: Discussionmentioning

confidence: 99%

Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

et al. 2018

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Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES was expanded from five to nine to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, which allows for any number of PFTs to compete based solely on their height; therefore, the previous hardwired dominance hierarchy is removed.With the new set of nine PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former five PFT version. Improvements include the coverage of trees within tropical and boreal forests and a reduction in shrubs, the latter of which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon (C) cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982 to 2011 is 133 Pg C yr −1 , compared to observation-based estimates (over the same time period) between 123 ± 8 and 150-175 Pg C yr −1 . NPP from 2000 to 2013 is 72 Pg C yr −1 , compared to satellite-derived NPP of 55 Pg C yr −1 over the same period and independent estimates of 56.2 ± 14.3 Pg C yr −1 .

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“…By excluding the variable Z DAO from the model, only 16% of variation of NPP NTSG was described compared to the initial 43% (Table 2). The MODIS data and downscaled meteorological data-based NPP EURO had a positive correlation with the pixel location longitude similarly to NPP FIDB which indicates successful use of European downscaled climate data by Neumann et al (2016). Although the relationship with NPP SPP was negative and signifi cant in the model, NPP SPP described only 2% of the variation in NPP EURO .…”

Section: Evaluation Of the Npp Sat Estimatesmentioning

confidence: 91%

“…Globaalsetele ilmaandmetele tuginevate NPP NTSG ja NPP NTSG_GPP seos NPP SPP väärtustega oli äraspidine ja tähtsaimaks teguriks osutus hoopis Z DAO . Ruumiliselt parema lahutusega ilmaandmete kasutamise tulemusena Neumann et al (2016) saadud NPP EURO kasvas läänest ida suunas nagu ka NPP SPP . NPP EURO puhul oli NPP aga saadud lehepinnaindeksiga seotud kasvu-ning säili-tushingamise arvestamise kaudu fotosün-teesi üldisest produktist (GPP).…”

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“…Locally at specifi c sites, however, the estimates may have large uncertainties due to errors in model parameters and assumptions (Zhao et al, 2005), coarse scale input data (Eenmäe et al, 2011) and errors in land cover maps (Quaife et al, 2008;Lang et al, 2013). Higher accuracy could be achieved by using local or downscaled meteorological data (Olofsson et al, 2007;Härkönen et al, 2010;Nilson et al, 2012;Neumann et al, 2016) and combining remote sensing data and sample plot-based forest inventory data (Härkönen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

et al. 2017

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Lang, M., Kõlli, R., Nikopensius, M., Nilson, T., Neumann, M., Moreno, A. 2017. Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests. -Forestry Studies | Metsanduslikud Uurimused 66, 49-64. ISSN 1406-9954. Journal homepage: http://mi.emu.ee/forestry. studies Abstract. Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use effi ciency-type models. Although spatial resolution of the NPP data sets is still too coarse (500-1000 m) for single forest stands, regional monitoring of forest management and growth with 25-100 ha sampling units is feasible if the NPP SAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPP SAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPP SPP ), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPP FIDB ). A linear multiple regression model was then used to explore the relationships of NPP SAT with the proportion of coniferous forests, the NPP SPP and distance of the pixels from the Baltic Sea coast -the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPP SPP and NPP FIDB . The local or downscaled meteorological data-based NPP SAT estimates were more consistent with the NPP SPP and NPP FIDB , but the correlation with NPP SAT was weak and sometimes even negative. The range of NPP estimates in NPP SAT data sets was much narrower than the range of NPP SPP or NPP FIDB . Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identifi ed as the main reasons for NPP SAT inconsistencies.

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Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests

Cited by 49 publications

References 35 publications

Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

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