Skip to main content

Advertisement

SpringerLink
Log in

Modelling forest biomass dynamics in relation to climate change in Romania using complex data and machine learning algorithms

  • ORIGINAL PAPER
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Forest biomass controls climate stability, many ecological processes and various ecosystem services. This study analyzes for the first time the recent changes (1987–2018) of forest above-ground live biomass (AGB) in Romania, based on a complex volume of remote sensing and forest inventory data that were modelled yearly using a series of sophisticated statistical algorithms. Subsequently, after modelling interannual AGB data, yearly raster values (~ 2 billion total pixel values) were explored as trends over the 32 years, using the Sen's slope estimator and Mann–Kendall test. A large volume of climate data was also processed in this study, in order to detect possible statistical relationships between climate and forest biomass, after 1987. Results showed a mean multiannual value of forest biomass of ~ 185 t/ha and a total AGB amount (stock) of about 1.25 billion tons (~ 1249 million tons or megatonnes/Mt) across Romania. Regarding forest biomass changes, findings revealed increasing and decreasing AGB trends that account for ~ 70% and 30%, respectively, of the countrywide forest biomass changes. However, it was found that about half (~ 48%) of all positive AGB trends are statistically significant, while negative AGB trends have a statistical confidence on only one-fifth (~ 21%) of their spatial footprint in Romania. Overall, upon averaging and summing up all statistically significant values of positive and negative trends, an average AGB increase of ~ 3 t/ha/yr and a total forest biomass gain of ~ 205 Mt were found in Romania, over the entire 1987–2018 period. The various regional statistics highlight a more complex picture of AGB changes across the country. The analysis of interannual eco-climate data indicated a low to moderate climate signal in AGB changes, revealing that climate change is not a major driving force of AGB dynamics, at least according to the data and methodology applied in this study. The results can be useful to governmental forestry, climate and sustainable development policies in Romania.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A et al (2016) TensorFlow: Large-scale machine learning on heterogeneous distributed systems, https://doi.org/10.48550/arXiv.1603.04467 (software available at tensorflow.org)

  • Alkama R, Cescatti A (2016) Biophysical climate impacts of recent changes in global forest cover. Science 351:600–604

    Article  CAS  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: Guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper 56, Rome, Italy

  • Arnold TB, Emerson JW (2011) Nonparametric goodness-of-fit tests for discrete null distributions. The R Journal 3:34–39

    Article  Google Scholar 

  • Baccini A, Walker W, Carvalho L, Farina M, Sulla-Menashe D, Houghton RA (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358:230–234

    Article  CAS  Google Scholar 

  • Bar-On Y, Phillips R, Milo R (2018) The biomass distribution on Earth. PNAS 115:6506–6511

    Article  CAS  Google Scholar 

  • Birsan MV, Marin L, Dumitrescu A (2013) Seasonal changes in wind speed in Romania. Rom Rep Phys 65:1479–1484

    Google Scholar 

  • Birsan MV, Dumitrescu A, Micu DM, Cheval S (2014) Changes in annual temperature extremes in the Carpathians since AD 1961. Nat Hazards 74:1899–1910

    Article  Google Scholar 

  • Birsan MV, Micu DM, Nita AI, Mateescu E, Szép R, Keresztesi Á (2019) Spatio-temporal changes in annual temperature extremes over Romania (1961–2013). Rom J Phys 64:1–11

    Google Scholar 

  • Birsan MV, Nita IA, Craciun A, Sfîcă L, Radu C, Szép R, Keresztesi Á, Micheu MM (2020) Observed changes in mean and maximum monthly wind speed over Romania since AD 1961. Rom Rep Phys 72:1–11

    Google Scholar 

  • Blujdea VN, Sikkema R, Dutca I, Nabuurs GJ (2021) Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests. Carbon Balance Manag. https://doi.org/10.1186/s13021-021-00188-1

    Article  Google Scholar 

  • Bouriaud O, Don A, Janssens IA, Marin G, Schulze ED (2019) Effects of forest management on biomass stocks in Romanian beech forests. For Ecosyst. https://doi.org/10.1186/s40663-019-0180-4

    Article  Google Scholar 

  • Brockerhoff E, Barbaro L, Castagneyrol B, Forrester D, Gardiner B, Gonzalez-Olabarria J, Lyver P, Meurisse N et al (2017) Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers Conserv 26:3005–3035

    Article  Google Scholar 

  • Chang CC, Lin CJ (2021) LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  • Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, https://doi.org/10.1145/2939672.2939785

  • Chen T, He T (2022) xgboost: eXtreme Gradient Boosting, https://cran.r-project.org/web/packages/xgboost/vignettes/xgboost.pdf

  • Cheval S, Birsan MV, Dumitrescu A (2014) Climate variability in the Carpathian Mountains Region over 1961–2010. Glob Planet Change 118:85–96

    Article  Google Scholar 

  • Chollet F, Allaire J (2017) R Interface to Keras, https://github.com/rstudio/keras

  • Ciceu A, Popa I, Leca S, Pitar D, Chivulescu S, Badea O (2020) Climate change effects on tree growth from Romanian forest monitoring Level II plots. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134129

    Article  Google Scholar 

  • CLC (1990–2018) CORINE Land Cover databases (1990, 2000, 2006, 2012, 2018), https://land.copernicus.eu/pan-european/corine-land-cover

  • Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V et al (2015) System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci Model Dev 8:1991–2007

    Article  Google Scholar 

  • Croitoru AE, Piticar A, Dragotă CS, Burada DC (2013) Recent changes in reference evapotranspiration in Romania. Glob Planet Change 111:127–137

    Article  Google Scholar 

  • Cutler ME, Boyd DS, Foody GM, Vetrivel A (2012) Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions. ISPRS J Photogramm Remote Sens 70:66–77

    Article  Google Scholar 

  • Djaman K, Irmak S, Kabenge I, Futakuchi K (2016) Evaluation of FAO-56 Penman-Monteith model with limited data and the Valiantzas models for estimating grass-reference evapotranspiration in Sahelian conditions. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001070

    Article  Google Scholar 

  • Djaman K, O’Neill M, Diop L, Bodian A, Allen S, Koudahe K, Lombard K (2019) Evaluation of the Penman-Monteith and other 34 reference evapotranspiration equations under limited data in a semiarid dry climate. Theor Appl Climatol 137:729–743

    Article  Google Scholar 

  • Drake JB, Knox RG, Dubayah RO, Clark DB, Condit R, Blair JB, Hofton M (2003) Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob Ecol Biogeogr 12:147–159

    Article  Google Scholar 

  • Dumitraşcu M, Kucsicsa G, Dumitrică C, Popovici EA, Vrînceanu A, Mitrică B, Mocanu I, Şerban PR (2020) Estimation of future changes in aboveground forest carbon stock in Romania. A prediction based on forest-cover pattern scenario. Forests. https://doi.org/10.3390/f11090914

    Article  Google Scholar 

  • Dumitrescu A, Bojariu R, Bîrsan M, Marin L, Manea A (2015) Recent climatic changes in Romania from observational data (1961–2013). Theor Appl Climatol 122:111–119

    Article  Google Scholar 

  • Dumitrescu A, Birsan MV, Manea A (2016) Spatio-temporal interpolation of sub-daily (6 h) precipitation over Romania for the period 1975–2010. Int J Climatol 36:1331–1343

    Article  Google Scholar 

  • Dumitrescu A, Birsan MV, Nita IA (2017) A Romanian daily high-resolution gridded dataset of snow depth (2005–2015). Geofizika 34:275–295

    Article  Google Scholar 

  • Dumitrescu A, Brabec M, Matreata M (2020) Integrating ground-based observations and radar data into gridding sub-daily precipitation. Water Resour Manag 34:3479–3497

    Article  Google Scholar 

  • Duncanson L, Armston J, Disney M, Avitabile V, Barbier N, Calders K, Carter S, Chave J et al (2019) The importance of consistent global forest aboveground biomass product validation. Surv Geophys 40:979–999

    Article  CAS  Google Scholar 

  • Dymond JR, Shepherd JD (1999) Correction of the topographic effect in remote sensing. IEEE Trans Geosci Remote Sens 37:2618–2619

    Article  Google Scholar 

  • Erb K, Kastner T, Plutzar C, Bais A, Carvalhais N, Fetzel T, Gingrich S, Haberl H et al (2018) Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553:73–76

    Article  CAS  Google Scholar 

  • ESRI (2020) Environmental Systems Research Institute—ArcGIS software capabilities, https://www.esri.com/

  • Evans JS (2020) SpatialEco: Spatial Analysis and Modelling Utilities, R package version 1.3–7, https://github.com/jeffreyevans/spatialEco

  • Faiz MA, Liu D, Fu Q, Naz F, Hristova N, Li T, Niaz MA, Khan YN (2020) Assessment of dryness conditions according to transitional ecosystem patterns in an extremely cold region of China. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120348

    Article  Google Scholar 

  • Forzieri G, Pecchi M, Girardello M, Mauri A, Klaus M, Nikolov C, Rüetschi M, Gardiner B et al (2020) A spatially explicit database of wind disturbances in European forests over the period 2000–2018. Earth Syst Sci Data 12:257–276

    Article  Google Scholar 

  • Friedlingstein P, O’Sullivan M, Jones M, Andrew R, Hauck J, Olsen A, Peters G, Peters W et al (2020) Global carbon budget 2020. Earth Syst Sci Data 12:3269–3340

    Article  Google Scholar 

  • Galidaki G, Zianis D, Gitas I, Radoglou K, Karathanassi V, Tsakiri-Strati M, Woodhouse I, Mallinis G (2017) Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. Int J Remote Sens 38:1940–1966

    Article  Google Scholar 

  • GEE (2021a) Google Earth Engine—LANDSAT (5 TM, 7 ETM+, 8 OLI/TIRS) satellite data in the 1987–2018 period for the Romanian territory, https://earthengine.google.com/

  • GEE (2021b) Google Earth Engine—USGS Landsat 5 Surface Reflectance Tier 1, https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_SR

  • Ghosh SM, Behera MD (2018) Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Appl Geogr 96:29–40

    Article  Google Scholar 

  • Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York

    Google Scholar 

  • Giurgiu V, Decei I, Draghiciu D (2004) Dendrometric methods and tables (in Romanian). Ceres Publishing House, Bucharest

    Google Scholar 

  • Greenwell B, Boehmke B, Cunningham J, GBM Developers (2020) Package ‘gbm’, https://cran.r-project.org/web/packages/gbm/gbm.pdf

  • Günther F, Fritsch S (2010) neuralnet: Training of neural networks. The R Journal 2:30–38

    Article  Google Scholar 

  • Hansen PM, Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ 86:542–553

    Article  Google Scholar 

  • Hansen M, Potapov P, Moore R, Hancher M, Turubanova S, Tyukavina A, Thau D, Stehman S et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853

    Article  CAS  Google Scholar 

  • Haverd V, Smith B, Canadell JG, Cuntz M, Mikaloff-Fletcher S, Farquhar G, Woodgate W, Briggs PR et al (2020) Higher than expected CO2 fertilization inferred from leaf to global observations. Glob Change Biol 26:2390–2402

    Article  Google Scholar 

  • Helsel DR, Hirsch RM, Ryberg KR, Archfield SA, Gilroy EJ (2020) Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, United States

  • Hijmans RJ, van Etten J, Sumner M, Cheng J, Baston D, Bevan A, Bivand R, Busetto L et al. (2022) raster: Geographic data analysis and modeling (R package), https://CRAN.R-project.org/package=raster

  • Huang W, Swatantran A, Duncanson L, Johnson K, Watkinson D, Dolan K, O’Neil-Dunne J, Hurtt G et al (2017) County-scale biomass map comparison: a case study for Sonoma. California Carbon Manag. https://doi.org/10.1080/17583004.2017.1396840

    Article  Google Scholar 

  • Huang W, Dolan K, Swatantran A, Johnson K, Tang H, O’Neil-Dunne J, Dubayah R, Hurtt G (2019) High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab2917

    Article  Google Scholar 

  • Hudak AT, Fekety PA, Kane VR, Kennedy RE, Filippelli SK, Falkowski MJ, Tinkham WT, Smith AM et al (2020) A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab93f9

    Article  Google Scholar 

  • Hurni K, Van Den Hoek J, Fox J (2019) Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal. Remote Sens Environ. https://doi.org/10.1016/j.rse.2019.111225

    Article  Google Scholar 

  • Hurni K, Würsch L, Heinimann A (2017) Google earth engine image pre-processing tool. Centre for Development and Environment (CDE), University of Bern, Switzerland

  • Ingram JC, Dawson TP, Whittaker RJ (2005) Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens Environ 94:491–507

    Article  Google Scholar 

  • IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom (in press)

  • Ji L, Wylie BK, Nossov DR, Peterson B, Waldrop MP, McFarland JW, Rover J, Hollingsworth TN (2012) Estimating aboveground biomass in interior Alaska with Landsat data and field measurements. Int J Appl Earth Obs Geoinf 18:451–461

    Google Scholar 

  • Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London

    Google Scholar 

  • Kucsicsa G, Popovici EA, Bălteanu D, Dumitraşcu M, Grigorescu I, Mitrică B (2020) Assessing the potential future forest-cover change in Romania, predicted using a scenario-based modelling. Environ Model Assess 25:471–491

    Article  Google Scholar 

  • Lal R (2018) Digging deeper: A holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems. Glob Change Biol 24:3285–3301

    Article  Google Scholar 

  • Li Y, Li C, Li M, Liu Z (2019) Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms. Forests. https://doi.org/10.3390/f10121073

    Article  Google Scholar 

  • Li Y, Li M, Li C, Liu Z (2020) Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci Rep. https://doi.org/10.1038/s41598-020-67024-3

    Article  Google Scholar 

  • Li C, Jiang TT, Luan XB, Yin YN, Wu PT, Wang YB, Sun SK (2021) Determinants of agricultural water demand in China. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.125508

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22

    Google Scholar 

  • Liu Y, van Dijk A, de Jeu R, Canadell J, McCabe M, Evans J, Wang G (2015) Recent reversal in loss of global terrestrial biomass. Nat Clim Change 5:470–474

    Article  Google Scholar 

  • Lu D, Chen Q, Wang G, Moran E, Batistella M, Zhang M, Vaglio Laurin G, Saah D (2012) Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int J for Res. https://doi.org/10.1155/2012/436537

    Article  Google Scholar 

  • Lu D, Chen Q, Wang G, Liu L, Li G, Moran E (2016) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digit Earth 9:63–105

    Article  Google Scholar 

  • Luo K, Wei Y, Du J, Liu L, Luo X, Shi Y, Pei X, Lei N et al (2021) Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. J for Res. https://doi.org/10.1007/s11676-021-01421-w

    Article  Google Scholar 

  • Main-Knorn M, Moisen GG, Healey SP, Keeton WS, Freeman EA, Hostert P (2011) Evaluating the remote sensing and inventory-based estimation of biomass in the Western Carpathians. Remote Sens 3:1427–1446

    Article  Google Scholar 

  • Mallinis G, Petrila M, Mitsopoulos I, Lorenţ A, Neagu S, Apostol B, Gancz V, Popa I et al (2019) Geospatial patterns and drivers of forest fire occurrence in Romania. Appl Spat Anal Policy 12:773–795

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  • Marin G, Bouriaud O, Dumitru M, Nitu D (2010) Development of Romania's national forest inventory. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts R (eds) National forest inventories. Pathways for common reporting, pp 473–480. https://www.nhbs.com/national-forest-inventories-book-3

  • Marsaglia G, Tsang WW, Wang J (2003) Evaluating Kolmogorov’s distribution. J Stat Softw. https://doi.org/10.18637/jss.v008.i18

    Article  Google Scholar 

  • Matasci G, Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Zald HS (2018) Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots. Remote Sens Environ 209:90–106

    Article  Google Scholar 

  • Mevik BH, Wehrens R (2007) The pls package: Principal component and partial least squares regression in R. J Stat Softw 18:1–23

    Article  Google Scholar 

  • MEWF (2019) Ministry of Environment, Waters and Forests—Report on the state of Romanian forests in 2019 (in Romanian), http://www.mmediu.ro/categorie/starea-padurilor/209

  • Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F, Chang CC, Lin CC, (2021) Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), https://cran.r-project.org/web/packages/e1071/e1071.pdf

  • Micu DM, Dumitrescu A, Cheval S, Nita IA, Birsan MV (2021) Temperature changes and elevation-warming relationships in the Carpathian Mountains. Int J Climatol 41:2154–2172

    Article  Google Scholar 

  • Mihai G, Birsan MV, Dumitrescu A, Alexandru A, Mirancea I, Ivanov P, Stuparu E, Teodosiu M et al (2018) Adaptive genetic potential of European silver fir in Romania in the context of climate change. Ann for Res 61:95–108

    Article  Google Scholar 

  • Mihai G, Teodosiu M, Birsan MV, Alexandru AM, Mirancea I, Apostol EN, Garbacea P, Ionita L (2020) Impact of climate change and adaptive genetic potential of Norway spruce at the south–eastern range of species distribution. Agric Meteorol. https://doi.org/10.1016/j.agrformet.2020.108040

    Article  Google Scholar 

  • Mihai G, Alexandru AM, Stoica E, Birsan MV (2021) Intraspecific growth response to drought of Abies alba in the Southeastern Carpathians. Forests. https://doi.org/10.3390/f12040387

    Article  Google Scholar 

  • MOE (2020) Microsoft Office Excel—Excel software, http://www.microsoft.com

  • Năstase G, Şerban A, Năstase AF, Dragomir G, Brezeanu AI (2018) Air quality, primary air pollutants and ambient concentrations inventory for Romania. Atmos Environ 184:292–303

    Article  Google Scholar 

  • NFI (2012) National Forest Inventory—Cycle I forest data (2008–2012) in Romania, http://roifn.ro/site/rezultate-ifn-1/

  • NFI (2018) National Forest Inventory—Cycle II forest data (2013–2018) in Romania, http://roifn.ro/site/rezultate-ifn-2/

  • NFI (2021) National Forest Inventory—Statistical data on forest resources in Romania, http://roifn.ro/site/rezultate-ifn-2/

  • Nguyen TH, Jones S, Soto-Berelov M, Haywood A, Hislop S (2018) A comparison of imputation approaches for estimating forest biomass using Landsat time-series and inventory data. Remote Sens. https://doi.org/10.3390/rs10111825

    Article  Google Scholar 

  • Nguyen TH, Jones SD, Soto-Berelov M, Haywood A, Hislop S (2020) Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data. Int J Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2019.101952

    Article  Google Scholar 

  • Nichiforel L, Duduman G, Scriban R, Popa B, Barnoaiea I, Drăgoi M (2021) Forest ecosystem services in Romania: Orchestrating regulatory and voluntary planning documents. Ecosyst Serv. https://doi.org/10.1016/j.ecoser.2021.101276

    Article  Google Scholar 

  • NMA (2021) National Meteorological Administration—Interpolated annual (summer season) air temperature, precipitation, and reference evapotranspiration data in the 1987–2018 period for the Romanian territory, https://www.meteoromania.ro/

  • Orsi F, Ciolli M, Primmer E, Varumo L, Geneletti D (2020) Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy. https://doi.org/10.1016/j.landusepol.2020.104840

    Article  Google Scholar 

  • Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A et al (2011) A large and persistent carbon sink in the world’s forests. Science 333:988–993

    Article  CAS  Google Scholar 

  • Pan Y, Birdsey R, Phillips O, Jackson R (2013) The structure, distribution, and biomass of the world’s forests. Annu Rev Ecol Evol Syst 44:593–622

    Article  Google Scholar 

  • Piao P, Wang X, Park T, Chen C, Lian X, He Y, Bjerke JW, Chen A et al (2020) Characteristics, drivers and feedbacks of global greening. Nat Rev Earth Environ 1:14–27

    Article  Google Scholar 

  • Potapov PV, Turubanova SA, Tyukavina A, Krylov AM, McCarty JL, Radeloff VC, Hansen MC (2015) Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sens Environ 159:28–43

    Article  Google Scholar 

  • Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114:1053–1068

    Article  Google Scholar 

  • Prăvălie R (2018) Major perturbations in the Earth’s forest ecosystems. Possible implications for global warming. Earth Sci Rev 185:544–571

    Article  Google Scholar 

  • Prăvălie R, Bandoc G (2015) Aridity variability in the last five decades in the Dobrogea region, Romania. Arid Land Res Manag 29:265–287

    Article  Google Scholar 

  • Prăvălie R, Bandoc G, Patriche C, Tomescu M (2017) Spatio-temporal trends of mean air temperature during 1961–2009 and impacts on crop (maize) yields in the most important agricultural region of Romania. Stoch Env Res Risk A 31:1923–1939

    Article  Google Scholar 

  • Prăvălie R, Piticar A, Roşca B, Sfîcă L, Bandoc G, Tiscovschi A, Patriche C (2019) Spatio-temporal changes of the climatic water balance in Romania as a response to precipitation and reference evapotranspiration trends during 1961–2013. CATENA 172:295–312

    Article  Google Scholar 

  • Prăvălie R, Patriche C, Tişcovschi A, Dumitraşcu M, Săvulescu I, Sîrodoev I, Bandoc G (2020a) Recent spatio-temporal changes of land sensitivity to degradation in Romania due to climate change and human activities: an approach based on multiple environmental quality indicators. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.106755

    Article  Google Scholar 

  • Prăvălie R, Sîrodoev I, Patriche C, Roşca B, Piticar A, Bandoc G, Sfîcă L, Tişcovschi A et al (2020b) The impact of climate change on agricultural productivity in Romania. A country-scale assessment based on the relationship between climatic water balance and maize yields in recent decades. Agric Syst. https://doi.org/10.1016/j.agsy.2019.102767

    Article  Google Scholar 

  • Prăvălie R, Sîrodoev I, Nita IA, Patriche C, Dumitraşcu M, Roşca B, Tişcovschi A, Bandoc G et al (2022) NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecol Indic. https://doi.org/10.1016/j.ecolind.2022.108629

    Article  Google Scholar 

  • Puliti S, Hauglin M, Breidenbach J, Montesano P, Neigh CS, Rahlf J, Solberg S, Klingenberg TF et al (2020) Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data. Remote Sens Environ. https://doi.org/10.1016/j.rse.2019.111501

    Article  Google Scholar 

  • QGIS Development Team (2022) QGIS Geographic Information System. Open Source Geospatial Foundation Project, http://qgis.osgeo.org

  • R Core Team (2021) R: A language and environment for statistical computing. The R Project for Statistical Computing, https://www.R-project.org/

  • Santoro M, Cartus O, Carvalhais N, Rozendaal D, Avitabile V, Araza A, de Bruin S, Herold M et al (2021) The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst Sci Data 13:3927–3950

    Article  Google Scholar 

  • Sarker LR, Nichol JE (2011) Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens Environ 115:968–977

    Article  Google Scholar 

  • Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D et al (2017) Forest disturbances under climate change. Nat Clim Change 7:395–402

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389

    Article  Google Scholar 

  • Shen W, Li M, Huang C, Tao X, Wei A (2018) Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China. Agric Meteorol 259:23–38

    Article  Google Scholar 

  • Sidor CG, Camarero JJ, Popa I, Badea O, Apostol EN, Vlad R (2019) Forest vulnerability to extreme climatic events in Romanian Scots pine forests. Sci Total Environ 678:721–727

    Article  CAS  Google Scholar 

  • Silva JP, da Silva ML, da Silva EF, da Silva GF, de Mendonça AR, Cabacinha CD, Araújo EF, Santos JS et al (2019) Computational techniques applied to volume and biomass estimation of trees in Brazilian savanna. J Environ Manag. https://doi.org/10.1016/j.jenvman.2019.109368

    Article  Google Scholar 

  • Singh C, Karan SK, Sardar P, Samadder SR (2022) Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. J Environ Manag. https://doi.org/10.1016/j.jenvman.2022.114639

    Article  Google Scholar 

  • Su H, Shen W, Wang J, Ali A, Li M (2020) Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. For Ecosyst. https://doi.org/10.1186/s40663-020-00276-7

    Article  Google Scholar 

  • Sun G, Ranson KJ, Guo Z, Zhang Z, Montesano P, Kimes D (2011) Forest biomass mapping from lidar and radar synergies. Remote Sens Environ 115:2906–2916

    Article  Google Scholar 

  • Tanase MA, Borlaf-Mena I, Santoro M, Aponte C, Marin G, Apostol B, Badea O (2021) Growing stock volume retrieval from single and multi-frequency radar backscatter. Forests. https://doi.org/10.3390/f12070944

    Article  Google Scholar 

  • Taye F, Folkersen M, Fleming C, Buckwell A, Mackey B, Diwakar K, Le D, Hasan S et al (2021) The economic values of global forest ecosystem services: a meta-analysis. Ecol Econ. https://doi.org/10.1016/j.ecolecon.2021.107145

    Article  Google Scholar 

  • Terrer C, Jackson RB, Prentice IC, Keenan TF, Kaiser C, Vicca S, Fisher JB, Reich PB et al (2019) Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat Clim Change 9:684–689

    Article  CAS  Google Scholar 

  • Therneau TM, Atkinson EJ, Mayo Foundation (2022) An Introduction to Recursive Partitioning Using the RPART Routines, https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf

  • Tian Y, Huang H, Zhou G, Zhang Q, Tao J, Zhang Y, Lin J (2021) Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.146816

    Article  Google Scholar 

  • Tsui OW, Coops NC, Wulder MA, Marshall PL, McCardle A (2012) Using multi-frequency radar and discrete-return LiDAR measurements to estimate aboveground biomass and biomass components in a coastal temperate forest. ISPRS J Photogramm Remote Sens 69:121–133

    Article  Google Scholar 

  • Uniyal S, Purohit S, Chaurasia K, Rao SS, Amminedu E (2022) Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India. Urban for Urban Green. https://doi.org/10.1016/j.ufug.2021.127445

    Article  Google Scholar 

  • Urbazaev M, Thiel C, Cremer F, Dubayah R, Migliavaca M, Reichstein M, Schmullius C (2018) Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Manag 13:5. https://doi.org/10.1186/s13021-018-0093-5

    Article  Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York

    Book  Google Scholar 

  • Vlăduţ AŞ, Licurici M (2020) Aridity conditions within the region of Oltenia (Romania) from 1961 to 2015. Theor Appl Climatol 140:589–602

    Article  Google Scholar 

  • Wu Y, Strahler AH (1994) Remote estimation of crown size, stand density, and biomass on the Oregon transect. Ecol Appl 4:299–312

    Article  Google Scholar 

  • Wulder M (1998) Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog Phys Geogr 22:449–476

    Article  Google Scholar 

  • Xu L, Saatchi S, Yang Y, Yu Y, Pongratz J, Bloom A, Bowman K, Worden J et al (2021) Changes in global terrestrial live biomass over the 21st century. Sci Adv. https://doi.org/10.1126/sciadv.abe9829

    Article  Google Scholar 

  • Zhang Y, Liu J, Shen W (2022a) A review of ensemble learning algorithms used in remote sensing applications. Appl Sci. https://doi.org/10.3390/app12178654

    Article  Google Scholar 

  • Zhang Y, Ma J, Liang S, Li X, Liu J (2022b) A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets. Gisci Remote Sens 59:234–249

    Article  Google Scholar 

  • Zhao P, Lu D, Wang G, Wu C, Huang Y, Yu S (2016) Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sens. https://doi.org/10.3390/rs8060469

    Article  Google Scholar 

  • Zhu X, Liu D (2015) Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J Photogramm Remote Sens 102:222–231

    Article  Google Scholar 

  • Zhu Z, Piao S, Myneni RB, Huang M, Zeng Z, Canadell JG, Ciais P, Sitch S et al (2016) Greening of the Earth and its drivers. Nat Clim Change 6:791–795

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The article has enjoyed the support of the CLIMFOREST project (PD 20/2020), financed by UEFISCDI program, Romania. Also, the article benefited from the support of the PN-III-P2-2.1-PED-2019-5436 and PN-III-P4-PCE-2021-1350 projects, funded by the UEFISCDI program, Romania. The authors would like to thank Alexandru Dumitrescu from Meteo Romania for the interpolated climate data provided for this study. At the same time, the authors would like to thank the anonymous reviewers for their highly constructive comments and suggestions that helped improve this paper.

Funding

Funding was provided by Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (Grant No. PD 20/2020).

Author information

Authors and Affiliations

  1. Faculty of Geography, University of Bucharest, 1 Nicolae Bălcescu Street, 010041, Bucharest, Romania

    Remus Prăvălie & Georgeta Bandoc

  2. University of Bucharest, Research Institute of the University of Bucharest (ICUB), 90-92 Sos, Panduri Street, 050663, Bucharest, Romania

    Remus Prăvălie

  3. Academy of Romanian Scientists, 54 Splaiul Independentei Street, 050094, Bucharest, Romania

    Remus Prăvălie & Georgeta Bandoc

  4. Faculty of Geography and Geology, Department of Geography, Alexandru Ioan Cuza University, 20A Carol I Street, 700506, Iasi, Romania

    Mihai Niculiţă

  5. Geography Department, Romanian Academy, Iaşi Divison, 8 Carol I Street, 700505, Iasi, Romania

    Bogdan Roşca & Cristian Patriche

  6. Institute of Geography, Romanian Academy, 12 Dimitrie Racoviţă Street, 023993, Bucharest, Romania

    Monica Dumitraşcu

  7. National Institute for Research and Development in Forestry Marin Dracea, 128 Blvd. Eroilor, 077190, Voluntari, Romania

    Gheorghe Marin

  8. Visual Flow, 140 Aurel Vlaicu, 020099, Bucharest, Romania

    Ion-Andrei Nita

  9. Ministry of Environment, Waters and Forests, General Directorate for Impact Assessment, Pollution Control and Climate Change, 12 Libertăii Street, 040129, Bucharest, Romania

    Marius-Victor Birsan

Authors
  1. Remus Prăvălie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mihai Niculiţă
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bogdan Roşca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cristian Patriche
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Monica Dumitraşcu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gheorghe Marin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ion-Andrei Nita
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Georgeta Bandoc
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marius-Victor Birsan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RP: Conceptualization; Methodology; Investigation; Writing—Original Draft; Supervision; Project administration; Funding acquisition. MN: Methodology; Software; Validation. BR: Methodology; Software; Validation. CP: Software; Formal analysis. MD: Writing—Review & Editing; Data Curation. GM: Data Curation; Formal analysis. I-AN: Software; Formal analysis. GB: Investigation; Writing—Review & Editing. M-VB: Software; Data Curation.

Corresponding author

Correspondence to Remus Prăvălie.

Ethics declarations

Competing interests

The authors have not disclosed any competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prăvălie, R., Niculiţă, M., Roşca, B. et al. Modelling forest biomass dynamics in relation to climate change in Romania using complex data and machine learning algorithms. Stoch Environ Res Risk Assess 37, 1669–1695 (2023). https://doi.org/10.1007/s00477-022-02359-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-022-02359-z

Keywords

Search

Navigation