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Prediction of forest NPP in Italy by the combination of ground and remote sensing data

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Abstract

Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.

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References

  • Arrigoni PV, Raffaelli M, Rizzotto M, Selvi F, Viciani D, Lombardi L, Foggi B, Melillo C, Benesperi R, Ferretti G, Benucci S, Turrini S, di Tommaso PL, Signorini M, Bargelli E, Miniati U, Farioli C, de Dominicis V, Casini S, Chiarucci A, Tomei PE, Ansaldi M, Maccioni, S, Guazzi E, Zocco Pisana L, Cenerini A, Dell’Olmo L, Menicagli E (1998) La vegetazione forestale. Serie Boschi e Macchie di Toscana. Regione Toscana, Giunta regionale

  • Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13:95–120

    Article  Google Scholar 

  • Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ 46:213–222

    Google Scholar 

  • Bergeron O, Margolis HA, Coursolle C, Giasson M-A (2008) How does forest harvest influence carbon dioxide fluxes of black spruce ecosystems in eastern North America? Agric For Meteorol 148:537–548

    Article  Google Scholar 

  • Bolle HJ, Eckardt M, Koslowsky D, Maselli F, Melia-Miralles J, Menenti M, Olesen FS, Petkov L, Rasool I, Van de Griend A (2006) Mediterranean land-surface processes assessed from space. Springer, Berlin. Series: Regional Climate Studies XXVIII

  • Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28:281–298

    Article  Google Scholar 

  • Chiesi M, Maselli F, Moriondo M, Fibbi L, Bindi M, Running SW (2007) Application of BIOME-BGC to simulate Mediterranean forest processes. Ecol Model 206:179–190

    Article  Google Scholar 

  • Chiesi M, Moriondo M, Maselli F, Gardin L, Fibbi L, Bindi M, Running SW (2010) Simulation of Mediterranean forest carbon pools under expected environmental scenarios. Can J For Res 40:850–860

    Article  CAS  Google Scholar 

  • Chiesi M, Cherubini P, Maselli F (2012) Adaptation of a modelling strategy to predict the NPP of even-aged forest stands. Eur J For Res 131:1175–1184

    Article  Google Scholar 

  • Chiesi M, Maselli F, Chirici G, Corona P, Lombardi F, Tognetti R, Marchetti M (2014) Assessing the most relevant factors to simulate current annual increments of Italian beech forests. iForest 7:115–122

    Article  Google Scholar 

  • Chirici G, Barbati A, Maselli F (2007) Modelling of Italian forest net primary productivity by the integration of remotely sensed and GIS data. For Ecol Manag 246:285–295

    Article  Google Scholar 

  • Chirici G, Corona P, Marchetti M, Mastronardi A, Maselli F, Bottai L, Travaglini D (2012) K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k-Nearest Neighbors algorithm. Eur J Remote Sens 45:433–442

    Article  Google Scholar 

  • Churkina G, Tenhunen J, Thornton P, Falge EM, Elbers JA, Erhard M, Grunwald T, Kowalski AS, Rannik U, Sprinz D (2003) Analyzing the ecosystem carbon dynamics of four European coniferous forests using a biogeochemistry model. Ecosystems 6:168–184

    Article  CAS  Google Scholar 

  • Ciancio O, Corona P, Lamonaca A, Portoghesi L, Travaglini D (2006) Conversion of clearcut beech coppices into high forests with continuous cover: a case study in central Italy. For Ecol Manag 3:235–240

    Article  Google Scholar 

  • Corona P, Marchetti M (2007) Outlining multi-purpose forest inventories to assess the ecosystem approach in forestry. Plant Biosyst 141:243–251

    Article  Google Scholar 

  • Corona P, Chirici G, McRoberts RE, Winter S, Barbati A (2011) Contribution of large-scale forest inventories to biodiversity assessment and monitoring. For Ecol Manag 262:2061–2069

    Article  Google Scholar 

  • EEA (2002) CORINE land cover update. I&CLC2000 project. European Environmental Agency. Technical Guidelines. European Topic Center-Terrestrial Environment, Final version, Copenhagen, Denmark

  • Evrendilek F (2004) An inventory-based carbon budget for forest and woodland ecosystems of Turkey. J Environ Monit 6:26–30

    Article  CAS  PubMed  Google Scholar 

  • FAO (2010) Global forest resources assessment, 2010-Main report. FAO Forestry Paper 163. Rome, Italy

  • Fattorini L, Marcheselli M, Pisani C (2006) A three-phase sampling strategy for large-scale multiresource forest inventories. J Agric Biol Environ Stat 11(3):296–316

    Article  Google Scholar 

  • Federici S, Vitullo M, Tulipano S, De Lauretis R, Seufert G (2008) An approach to estimate carbon stocks change in forest carbon pools under the UNFCCC: the Italian case. iForest 1:86–95

    Article  Google Scholar 

  • FRA (2010) Terms and definitions. FRA working paper 1. Rome. www.fao.org/forestry/fo/fra/index.jsp

  • Gallaun H, Zanchi G, Nabuurs G-J, Hengeveld G, Schardt M, Verkerk PJ (2010) EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. For Ecol Manag 260:252–261

    Article  Google Scholar 

  • Gasparini P, De Natale F, Di Cosmo L, Gagliano C, Salvadori I, Tabacchi G, Tosi V (2009a) INFC 2007—I caratteri quantitativi 2005—parte 1. MiPAAF—Ispettorato Generale Corpo Forestale dello Stato, CRA-MPF, Trento

  • Gasparini P, Bertani R, De Natale F, Di Cosmo L, Pompei E (2009b) Quality control procedures in the Italian national forest inventory. J Environ Monit 11:761–768

    Article  CAS  PubMed  Google Scholar 

  • Gower ST, McMurtri RE, Murty D (1996) Aboveground net primary production decline with stand age: potential causes. Tree 11(9):378–382

    CAS  PubMed  Google Scholar 

  • Hagedorn F, Maurer S, Egli P, Blaser P, Bucher JB, Siegwolf R (2001) Carbon sequestration in forest soils: effects of soil type, atmospheric CO2 enrichment, and N deposition. Eur J Soil Sci 52:619–628

    Article  CAS  Google Scholar 

  • Hasenauer H, Petritsch R, Zhao MS, Boisvenue C, Running SW (2012) Reconciling satellite with ground data to estimate forest productivity at national scales. For Ecol Manag 276:196–208

    Article  Google Scholar 

  • Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res (Atmospheres) 113:D20119. doi:10.1029/2008JD10201

    Article  Google Scholar 

  • Heinsch FA, Reeves M, Votava P, Kang S, Milesi C, Zhao M, Glassy J, Jolly WM, Loehman R, Bowker CF, Kimball JS, Nemani RR, Running SW (2003) User’s Guide GPP and NPP (MOD17A2/A3) Products NASA MODIS Land Algorithm. Version 2.0, December 2, 2003. www.ntsg.umt.edu/modis/

  • Iqbal M (1983) An introduction to solar radiation. Academic Press, New York

    Google Scholar 

  • ISPRA (2010) La realizzazione in Italia del Progetto Corine Land Cover 2006. Istituto Superiore per la Protezione e la Ricerca Ambientale, vol 131

  • Kolström M, Lindner M, Vilén T, Maroschek M, Seidl R, Lexer MJ, Netherer S, Kremer A, Delzon S, Barbati A, Marchetti M, Corona P (2011) Reviewing the science and implementation of climate change adaptation measures in European forestry. Forests 2:961–982

    Article  Google Scholar 

  • Maisongrande P, Duchemin B, Dedieu G (2004) Vegetation/spot: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens 25:9–14

    Article  Google Scholar 

  • Marziliano P, Menguzzato G, Scuderi A, Corona P (2012) Simplified methods to inventory the current annual increment of forest standing volume. iForest 5:276–282

    Article  Google Scholar 

  • Maselli F (2001) Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses. Remote Sens Environ 75:29–38

    Article  Google Scholar 

  • Maselli F (2002) Improved estimation of environmental parameters through locally calibrated multivariate regression analyses. Photogr Eng Remote Sens 68:1163–1171

    Google Scholar 

  • Maselli F, Barbati A, Chiesi M, Chirici G, Corona P (2006) Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sens Environ 100(4):563–575

    Article  Google Scholar 

  • Maselli F, Chiesi M, Fibbi L, Moriondo M (2008) Integration of remote sensing and ecosystem modelling techniques to estimate forest net carbon uptake. Int J Remote Sens 29(8):2437–2443

    Article  Google Scholar 

  • Maselli F, Chiesi M, Moriondo M, Fibbi L, Bindi M, Running SW (2009a) Integration of ground and satellite data to simulate the forest carbon budget of a Mediterranean region. Ecol Model 220(3):330–342

    Article  Google Scholar 

  • Maselli F, Papale D, Puletti N, Chirici G, Corona P (2009b) Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems. Remote Sens Environ 113(3):657–667

    Article  Google Scholar 

  • Maselli F, Chiesi M, Barbati A, Corona P (2010) Assessment of forest net primary production through the elaboration of multisource ground and remote sensing data. J Environ Monit 12:1082–1091

    Article  CAS  PubMed  Google Scholar 

  • Maselli F, Pasqui M, Chirici G, Chiesi M, Fibbi L, Salvati R, Corona P (2012) Modeling primary production using a 1 km daily meteorological data set. Clim Res 54:271–285

    Article  Google Scholar 

  • Maselli F, Chiesi M, Mura M, Marchetti M, Corona P, Chirici G (2014) Combination of optical and LiDAR satellite imagery with forest inventory data to improve wall-to-wall assessment of growing stock in Italy. Int J Appl Earth Obs Geoinf 26:377–386

    Article  Google Scholar 

  • McRoberts RE, Tomppo EO, Schadauer K, Vidal C, Stahl G, Chirici G, Lanz A, Cienciala E, Winter S, Smith WB (2009) Harmonizing national forest inventories. J For 107:179–187

    Google Scholar 

  • Myneni RB, Williams DL (1994) On the relationship between fAPAR and NDVI. Remote Sens Environ 49:200–211

    Article  Google Scholar 

  • Odum EP (1971) Fundamentals of ecology, 3rd edn. W. B. Saunders, Philadelphia

    Google Scholar 

  • Osborne CP, Mitchell PL, Sheehy JE, Woodward FI (2000) Modelling the recent historical impacts of atmospheric CO2 and climate change on Mediterranean vegetation. Glob Change Biol 6:445–458

    Article  Google Scholar 

  • Petritsch R, Hasenauer H, Pietsch SA (2007) Incorporating forest growth response to thinning within biome-BGC. For Ecol Manag 242:324–336

    Article  Google Scholar 

  • Pietsch SA, Hasenauer H (2006) Evaluating the self-initialization procedure for large-scale ecosystem models. Glob Change Biol 12:1658–1669

    Article  Google Scholar 

  • Running SW, Hunt ER (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer JR, Field CB (eds) Scaling physiological processes: leaf to globe. Academic Press, San Diego, pp 141–158

    Chapter  Google Scholar 

  • Song C, Woodcock CE (2003) A regional forest ecosystem carbon budget model: impacts of forest age structure and landuse history. Ecol Model 164:33–47

    Article  CAS  Google Scholar 

  • Thornton PE, Hasenauer H, White MA (2000) Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. Agric For Meteorol 104:255–271

    Article  Google Scholar 

  • Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric For Meteorol 113:185–222

    Article  Google Scholar 

  • Tosi V and Monteccone M (2004) Standard per gli inventari forestali di area vasta. Uno studio comparativo per il territorio italiano. Forest@ 1:148–164

  • Turner DP, Ollinger SV, Kimball JS (2004) Integrating remote sensing and ecosystem process models for landscape- to regional-scale analysis of the carbon cycle. Bioscience 54(6):573–584

    Article  Google Scholar 

  • Valentini R et al (2014) The greenhouse gas balance of Italy. An insight on managed and natural terrestrial ecosystems. Springer, Berlin

    Google Scholar 

  • van den Besselaar EJM, Haylock MR, van der Schrier G, Klein Tank AMG (2011) A European daily high-resolution observational gridded data set of sea level pressure. J Geophys Res 116:D11110. doi:10.1029/2010JD015468

    Article  Google Scholar 

  • Van Tuyl S, Law BE, Turner DP, Gitelman AI (2005) Variability in net primary production and carbon storage in biomass across Oregon forests—an assessment integrating data from forest inventories, intensive sites, and remote sensing. For Ecol Manag 209:273–291

    Article  Google Scholar 

  • Veroustraete F, Sabbe H, Eerens H (2002) Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sens Environ 83:376–399

    Article  Google Scholar 

  • Veroustraete F, Sabbe H, Rasse DP, Bertels L (2004) Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors. Int J Remote Sens 25:769–792

    Article  Google Scholar 

  • Vidal C, Lanz A, Tomppo E, Schadauer K, Gschwantner T, Di Cosmo L, Robert N (2009) Establishing forest inventory reference definitions for forest and growing stock: a study towards common reporting. Silva Fennica 42(2):247–266

    Google Scholar 

  • Waring HR, Running SW (2007) Forest ecosystems. Analysis at multiples scales, 3rd edn. Academic Press, San Diego

    Google Scholar 

  • Waring RH, Landsberg JJ, Williams M (1998) Net primary production of forests: a constant fraction of gross production? Tree Physiol 18:129–134

    Article  PubMed  Google Scholar 

  • White MA, Thornton PE, Running SW, Nemani RR (2000) Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interact 4:1–85

    Article  Google Scholar 

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Acknowledgments

The work was partially carried out under the FIRB2008 program, Project “Modelling the carbon sink in Italian forest ecosystems using ancillary data, remote sensing data and productivity models” C_FORSAT (Grant RBFR08LM04, national coordinator: G. Chirici), and the PRIN2012 program, Project “Development of innovative methods for forest ecosystems monitoring based on remote sensing” IDEM (Grant 2012EWEY2S national coordinator: G. Chirici) both funded by the Italian Ministry of Education, University and Research. The authors acknowledge the E-OBS dataset from the EU-FP6 Project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D Project (http://eca.knmi.nl). The authors wish to thank Prof. F. Veroustraete and Prof. S.W. Running for their precious suggestions on the application of C-Fix and BIOME-BGC, respectively. Dr. M. Pasqui and Dr. L. Fibbi are thanked for assisting in the processing of the E-OBS dataset. Thanks are finally due to the EJFR editor and reviewer who provided useful comments and suggestions on the original manuscript.

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Authors and Affiliations

  1. geoLAB – Laboratory of Geomatics, Department of Agricultural, Food and Forestry Systems, Università degli Studi di Firenze, Via San Bonaventura, 13, 50145, Florence, Italy

    Gherardo Chirici

  2. IBIMET-CNR, Via Madonna del Piano 10, 50019, Sesto Fiorentino, FI, Italy

    Marta Chiesi & Fabio Maselli

  3. Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Forestry Research Centre (CRA-SEL), Arezzo, Italy

    Piermaria Corona & Nicola Puletti

  4. Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Contrada Fonte Lappone snc, 86090, Pesche, IS, Italy

    Matteo Mura

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  1. Gherardo Chirici
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  2. Marta Chiesi
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  4. Nicola Puletti
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Correspondence to Gherardo Chirici.

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Communicated by Arne Nothdurft.

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Chirici, G., Chiesi, M., Corona, P. et al. Prediction of forest NPP in Italy by the combination of ground and remote sensing data. Eur J Forest Res 134, 453–467 (2015). https://doi.org/10.1007/s10342-015-0864-4

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  • DOI: https://doi.org/10.1007/s10342-015-0864-4

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