nach oben

Biodiversity and Conservation

Erschienen in:

11.05.2019 | Original Paper

Integration of eddy covariance and process-based model for the intra-annual variability of carbon fluxes in an Indian tropical forest

verfasst von: Nithin D. Pillai, Subrata Nandy, N. R. Patel, Ritika Srinet, Taibanganba Watham, Prakash Chauhan

Erschienen in: Biodiversity and Conservation | Ausgabe 8-9/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Forests play a crucial role by regulating the global and local weather through the exchange of atmospheric gases and water vapor. The present study aims to study the intra-annual variability of net ecosystem exchange (NEE) of carbon dioxide in a sal (Shorea robusta) dominated moist deciduous forest in India by integrating eddy covariance (EC) data and Biome-Biogeochemical Cycle (Biome-BGC) model. The study also attempts to address the spatial variability of NEE with respect to phenology. Monthly average NEE were estimated using a calibrated Biome-BGC model and the spatial NEE was mapped using random forest (RF) regression algorithm. Phenology metrics were generated using the moderate resolution imaging spectrometer (MODIS) enhanced vegetation index product (MOD13A2) and its relationship with the estimated NEE was studied. RF regression model for monthly average spatial NEE estimation was built with an R² of 0.84 and % RMSE of 3.68%. The study revealed that the NEE at the regional scale can be estimated using the basic meteorological variables like mean temperature, vapor pressure deficit, minimum temperature and total precipitation. Biome-BGC model output showed that the sal forest of the study area acted as a net sink of carbon in almost all months of 2015, except April to June. Peak NEE value (− 2.80 to − 2.96 g C m⁻² day⁻¹) was observed during October month. Annual NEE of sal forest in 2015 was found to be − 526.87 g C m⁻² year⁻¹. With the start of season (end of June), sal forest showed an increasing trend in NEE while decreasing trend was observed at the end of season (end of October). The study showed the applicability of Biome-BGC model in Indian forest when integrated with EC data. The study also highlighted the utility of RF in capturing the spatial variability of NEE over large area.

Vorheriger Artikel Modeling net primary productivity of tropical deciduous forests in North India using bio-geochemical model

Nächster Artikel Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Ahongshangbam J, Patel NR, Kushwaha SPS, Watham T, Dadhwal VK (2016) Estimating gross primary production of a forest plantation area using eddy covariance data and satellite imagery. J Indian Soc Remote Sens 44(6):895–904CrossRef

Anonymous (2014) Uttarakhand Action Plan on Climate Change. Government of Uttarakhand

Baishya R, Barik SK (2011) Estimation of tree biomass, carbon pool and net primary production of an old-growth Pinus kesiya Royle ex. Gordon forest in North-eastern India. Ann For Sci 68(4):727–736CrossRef

Boisvenue C, Running SW (2006) Impacts of climate change on natural forest productivity—evidence since the middle of the 20th century. Glob Change Biol 12(5):862–882CrossRef

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

Burman PKD, Sarma D, Williams M, Karipot A, Chakraborty S (2017) Estimating gross primary productivity of a tropical forest ecosystem over North-east India using LAI and meteorological variables. J Earth Syst Sci 126(7):99CrossRef

Champion SH, Seth SK (1968) A revised survey of the forest types of India. The Manager of Publications, Delhi

Chaturvedi OP, Singh JS (1987) The structure and function of pine forest in Central Himalaya. I. Dry matter dynamics. Ann Bot 60(3):237–252CrossRef

Chhabra A, Dadhwal VK (2004) Estimating terrestrial net primary productivity over India using satellite data. Curr Sci 86(2):269–271

Chitale VS, Behera MD (2012) Can the distribution of sal (Shorea robusta Gaertn. f.) shift in the northeastern direction in India due to changing climate? Curr Sci 102:1126–1135

Chu C, Bartlett M, Wang Y, He F, Weiner J, Chave J, Sack L (2016) Does climate directly influence NPP globally? Glob Change Biol 22(1):12–24CrossRef

Dang ATN, Nandy S, Srinet R, Luong NV, Ghosh S, Kumar AS (2019) Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecol Inform 50:24–32CrossRef

Day ME (2000) Influence of temperature and leaf-to-air vapor pressure deficit on net photosynthesis and stomatal conductance in red spruce (Picea rubens). Tree Physiol 20(1):57–63PubMedCrossRef

Dhanda P, Nandy S, Kushwaha SPS, Ghosh S, Murthy YK, Dadhwal VK (2017) Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Prog Phys Geogr 41(3):247–267CrossRef

Eklundh L, Jönsson P (2015) TIMESAT: a software package for time-series processing and assessment of vegetation dynamics. Remote sensing time series. Springer, Cham, pp 141–158

Farquhar GV, von Caemmerer SV, Berry JA (1980) A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species. Planta 149(1):78–90PubMedCrossRef

Han F, Zhang Q, Buyantuev A, Niu J, Liu P, Li X, Li Y (2015) Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia. J Arid Land 7(2):251–263CrossRef

Hilker T, Coops NC, Hall FG, Black TA, Chen B, Krishnan P, Wulder MA, Sellers PJ, Middleton EM, Huemmrich KF (2008) A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data. J Geophys Res Biogeosci. https://doi.org/10.1029/2007JG000666 CrossRef

Jangra R, Gupta SR, Kumar R, Singh G (2010) Carbon sequestration in the Grevillea robusta plantation on a reclaimed sodic soil at Karnal in Northern India. Int J Ecol Environ Sci 36(1):75–86

Jarvis PG (1976) The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos Trans R Soc 273(927):593–610CrossRef

Kaul M, Mohren GMJ, Dadhwal VK (2010) Carbon storage and sequestration potential of selected tree species in India. Mitig Adapt Strateg Glob Chang 15(5):489–510CrossRef

Keeling HC, Phillips OL (2007) The global relationship between forest productivity and biomass. Glob Ecol Biogeogr 16(5):618–631CrossRef

Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Munger JW, O’Keefe J, Schmid HP, Wing IS, Yang B (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Change 4(7):598CrossRef

Kumar M, Raghubanshi AS (2011) Sensitivity analysis of Biome-BGC model for dry tropical forests of Vindhyan Highlands, India. Remote Sens Spat Inf Sci 3820:129–133

Kushwaha SPS, Nandy S (2012) Species diversity and community structure in sal (Shorea robusta) forests of two different rainfall regimes in West Bengal, India. Biodivers Conserv 21(5):1215–1228CrossRef

Lai G, Zhang L, Liu Y, Yi F, Zeng X, Pan R (2012) Retrieving leaf area index and extinction coefficient of dominant vegetation canopy in Meijiang Watershed of China using ETM+ data. In: 2012 2nd international conference remote sensing, environment and transportation engineering (RSETE). IEEE, pp 1–5

Landsberg JJ, Waring RH (1997) A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag 95(3):209–228CrossRef

Law B, Turner D, Campbell J, Lefsky M, Guzy M, Sun O, Cohen W (2006) Carbon fluxes across regions: observational constraints at multiple scales. In: Scaling and uncertainty analysis in ecology. Springer, Dordrecht, pp 167–190

Li J, Cui Y, Liu J, Shi W, Qin Y (2013) Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model. Ecol Model 252:3–10CrossRef

Mamkin V, Kurbatova J, Avilov V, Mukhartova Y, Krupenko A, Ivanov D, Levashova N, Olchev A (2016) Changes in net ecosystem exchange of CO₂, latent and sensible heat fluxes in a recently clear-cut spruce forest in western Russia: results from an experimental and modeling analysis. Environ Res Lett 11(12):125012CrossRef

Meier GA, Brown JF (2014) Remote sensing of land surface phenology. Fact Sheet 2014–3052. US Geological Survey, p 2

Nandy S, Ghosh S, Kushwaha SPS, Kumar AS (2019) Remote sensing-based forest biomass assessment in northwest Himalayan landscape. In: Navalgund RR, Senthil Kumar A, Nandy S (eds) Remote sensing of northwest Himalayan ecosystems. Springer, Singapore, pp 285–311CrossRef

Nayak RK, Patel NR, Dadhwal VK (2010) Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ Monit Assess 170(1):195–213PubMedCrossRef

Nayak RK, Mishra N, Dadhwal VK, Patel NR, Salim M, Rao KH, Dutt CBS (2016) Assessing the consistency between AVHRR and MODIS NDVI datasets for estimating terrestrial net primary productivity over India. J Earth Syst Sci 125(6):1189–1204CrossRef

Nortes PA, Baille A, González-Real MM, Ruiz-Salleres I, Verhoef A, Martin-Gorriz B, Egea G (2010) Effects of high temperature and vapour pressure deficit on net ecosystem exchange and energy balance of an irrigated orange orchard in a semi-arid climate (southern Spain). In: XXVIII international horticultural congress on science and horticulture for people (IHC2010): international symposium on 922, August, pp 149–156

Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Dubash NK (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, p 151

Pan S, Tian H, Dangal SR, Ouyang Z, Tao B, Ren W, Running S (2014) Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Adv Meteorol. https://doi.org/10.1155/2014/965936 CrossRef

Powell TL, Gholz HL, Clark KL, Starr G, Cropper WP, Martin TA (2008) Carbon exchange of a mature, naturally regenerated pine forest in north Florida. Glob Change Biol 14(11):2523–2538

Raj R, Hamm NA, van der Tol C, Stein A (2014) Variance-based sensitivity analysis of Biome-BGC for gross and net primary production. Ecol Model 292:26–36CrossRef

Rana BS, Singh SP, Singh RP (1989) Biomass and net primary productivity in Central Himalayan forests along an altitudinal gradient. For Ecol Manag 27(3–4):199–218CrossRef

Ruiz A, Villa N (2008) Storms prediction: logistic regression vs random forest for unbalanced data

Ryan MG, Binkley D, Fownes JH (1997) Age-related decline in forest productivity: pattern and process. In: Advances in ecological research, vol 27. Academic, pp 213–262. https://doi.org/10.1016/s0065-2504(08)60009-4

Schwieder M, Leitão PJ, Pinto JRR, Teixeira AMC, Pedroni F, Sanchez M, Bustamante MM, Hostert P (2018) Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna. Carbon Balance Manag 13(1):7PubMedPubMedCentralCrossRef

Syed KH, Flanagan LB, Carlson PJ, Glenn AJ, Van Gaalen KE (2006) Environmental control of net ecosystem CO₂ exchange in a treed, moderately rich fen in northern Alberta. Agric For Meteorol 140(1–4):97–114CrossRef

Tan ZH, Hughes A, Sato T, Zhang YP, Han SJ, Kosugi Y, Hu YH (2017) Quantifying forest net primary production combining eddy flux inventory and metabolic theory. iForest Biogeosci For 10(2):475CrossRef

Tatarinov FA, Cienciala E (2006) Application of Biome-BGC model to managed forests: 1. Sensitivity analysis. For Ecol Manag 237(1):267–279CrossRef

Tripathi P, Patel NR, Kushwaha SPS (2018) Estimating net primary productivity in tropical forest plantations in India using satellite-driven ecosystem model. Geocarto Int 33(9):988–999.CrossRef

Troup RS (1921) The silviculture of Indian forest trees, vol 1. Natraj Publishers, Dehradun, pp 55–130

Tyralis H, Papacharalampous G (2017) Variable selection in time series forecasting using random forests. Algorithms 10(4):114CrossRef

Verduzco VS, Garatuza-Payán J, Yépez EA, Watts CJ, Rodríguez JC, Robles-Morua A, Vivoni ER (2015) Variations of net ecosystem production due to seasonal precipitation differences in a tropical dry forest of northwest Mexico. J Geophys Res Biogeosci 120(10):2081–2094CrossRef

Watham T, Patel NR, Kushwaha SPS, Dadhwal VK, Kumar AS (2017a) Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. Int J Remote Sens 38(18):5069–5090CrossRef

Watham T, Kushwaha SPS, Patel NR, Dadhwal VK, Kumar AS (2017b) Ecosystem productivity and its response to environmental variable of moist Indian sal forest. Trop Ecol 58(4):761–768

Weiskittel RA, Hann WD, Kershaw AJ Jr, Vanclay KJ (2011) Forest growth and yield modeling. Wiley-Blackwell, Wiley, Oxford, pp 227–252CrossRef

Wu J (1999) Hierarch and scaling: extrapolation information along a scaling ladder. Can J Remote Sens 25:367–380CrossRef

Wutzler T, Lucas-Moffat A, Migliavacca M, Knauer J, Sickel K, Šigut L, Menzer O, Reichstein M (2018) Basic and extensible post-processing of eddy covariance flux data with ReddyProc. Biogeosciences 15:5015–5030CrossRef

Yan M, Tian X, Li Z, Chen E, Li C, Fan W (2016) A long-term simulation of forest carbon fluxes over the Qilian Mountains. Int J Appl Earth Obs Geoinf 52:515–526CrossRef

Yuan J, Jose S, Hu Z, Pang J, Hou L, Zhang S (2018) Biometric and eddy covariance methods for examining the carbon balance of a Larix principis-rupprechtii Forest in the Qinling Mountains, China. Forests 9(2):67CrossRef

Zhang Y, Chen HY, Reich PB (2012) Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J Ecol 100(3):742–749CrossRef

Titel: Integration of eddy covariance and process-based model for the intra-annual variability of carbon fluxes in an Indian tropical forest
verfasst von: Nithin D. Pillai
Subrata Nandy
N. R. Patel
Ritika Srinet
Taibanganba Watham
Prakash Chauhan
Publikationsdatum: 11.05.2019
Verlag: Springer Netherlands
Erschienen in: Biodiversity and Conservation / Ausgabe 8-9/2019
Print ISSN: 0960-3115
Elektronische ISSN: 1572-9710
DOI: https://doi.org/10.1007/s10531-019-01770-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 8-9/2019

Predicting invasion potential and niche dynamics of Parthenium hysterophorus (Congress grass) in India under projected climate change

Spatial patterns of plant functional types and environmental proxies of plant richness in alpine region of Western Himalaya, India

Assessing the vulnerability of Oak (Quercus) forest ecosystems under projected climate and land use land cover changes in Western Himalaya

Himalayan arc and treeline: distribution, climate change responses and ecosystem properties

Impact of climate change on the distribution range and niche dynamics of Himalayan birch, a typical treeline species in Himalayas

Modeling net primary productivity of tropical deciduous forests in North India using bio-geochemical model