全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing

DOI: 10.3390/rs5010254

Keywords: agriculture, bioenergy, biomethane potential, hyperspectral remote sensing

Full-Text   Cite this paper   Add to My Lib

Abstract:

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii)?airborne imaging spectra (HyMap) of maize canopies under field ( in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMP FM, Nm 3 biogas/t fresh matter (Nm 3/t FM)) and BMP per area (BMP area, Nm 3 biogas/ha (Nm 3/ha)). We found that BMP FM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (R cv 2 = 0.82, n = 35), especially at levels >190 Nm 3/t. In the in situ approach PLSR prediction quality declined (R cv 2 = 0.50, n = 20). BMP area, on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.

References

[1]  EU Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Available online: http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=Oj:L:2009:140:0016:0062:en:PDF (accessed on 14 January 2013).
[2]  Roubanis, N.; Dahlstr?m, C.; Noizette, P. Eurostat—Statistics in Focus 56/2010, Environment and Energy, Renewable Energy Statistics, 2010, p. 2. Available online: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-SF-10-056/EN/KS-SF-10-056-EN.PDF (accessed on 24 April 2011).
[3]  Biermayr, P.; Cremer, C.; Faber, T.; Kranzl, L.; Ragwitz, M.; Resch, G.; Toro, F. Bestimmung der Potenziale und Ausarbeitung von Strategien zur verst?rkten Nutzung von erneuerbaren Energien in Luxemburg. Fraunhofer Institut für System- und Innovationsforschung (Fh-ISI); Energy Economics Group TU Wien/BSR-Sustainability: Luxemburg/Karlsruhe, Germany, 26 March 2007. Available online: http://www.eco.public.lu/salle_de_presse/com_presse_et_art_actu/2007/03/26_energies/endbericht.pdf (accessed on 14 January 2013).
[4]  Singh, S. Global food crisis: Magnitude, causes and policy measures. Int. J. Soc. Econ 2009, 36, 23–36, doi:10.1108/03068290910921163.
[5]  Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens., 2013. submitted.
[6]  Chynoweth, D.; Isaacson, R. Anaerobic Digestion of Biomass; Elsevier Applied Sciences: London, UK/New York, NY, USA, 1987; p. 279.
[7]  Malina, J.F.; Pohland, F.G. Design of Anaerobic Processes for the Treatment of Industrial and Municipal Wastes; CRC Press: Boca Raton, FL, USA, 1992; Volume 7, p. 214.
[8]  Ahring, B.K. Biomethanation I. (Advances in Biochemical Engineering Biotechnology), 1st ed. ed.; Springer-Verlag: Berlin/Heidelberg, Germany/New York, NY, USA, 2003; p. 220.
[9]  Ahring, B.K. Biomethanation II. Advances in Biochemical Engineering Biotechnology, 2nd ed. ed.; Springer-Verlag: Berlin/Heidelberg, Germany/New York, NY, USA, 2003; p. 212.
[10]  Gerardi, M.H. The Microbiology of Anaerobic Digesters (Waste Water Microbiology Series); John Wiley & Sons, Inc: Hoboken, NJ, USA, 2003; p. 177.
[11]  Birkmose, T. Digested Manure is a Valuable Fertiliser. The Future of Biogas in Europe III. Proceedings of an EC-Sponsored PROBIOGAS Conference, Esbjerg, Denmark, 27 June 2007; p. 91.
[12]  Palm, O. The Quality of Liquid and Solid Digestate from Biogas Plants and its Application in Agriculture. Proceedings of ECN/ORBIT e.V. Workshop-The Future for Anaerobic Digestion of Organic Waste in Europe, Wageningen, The Netherlands, 13 October 2008. Nr. 20; 2008.
[13]  Plaizier, J.C.; Krause, D.O.; Gozho, G.N.; McBride, B.W. Subacute ruminal acidosis in dairy cows: The physiological causes, incidence and consequences. Vet. J 2008, 176, 21–31, doi:10.1016/j.tvjl.2007.12.016. 18329918
[14]  Mayer, F.; Noo, A.; Sinnaeve, G.; Dardenne, P.; Hoffmann, L.; Flammang, J.; Foucart, G.; Gerin, P.; Delfosse, P. Evaluation of the Prediction of Biogas Production from Maize Silages with Near Infrared Spectroscopy (NIRS). Proceedings of the International Congress Progress: Biogas II, Stuttgart, Germany, 30 March–1 April 2011; pp. 235–240.
[15]  Buswell, A.M.; Müller, H.F. Mechanism of methane fermentation. Ind. Eng. Chem 1952, 44, 550–552, doi:10.1021/ie50507a033.
[16]  Boyle, W.C. Energy Recovery from Sanitary Landfills—A Review. In Microbiol Energy Conversion; Schlegel, H.G., Barnea, S., Eds.; Pergamon Press: Oxford, UK, 1976; pp. 119–138.
[17]  Schittenhelm, S. Chemical composition and methane yield of maize hybrids with contrasting maturity. Eur. J. Agron 2008, 29, 72–79, doi:10.1016/j.eja.2008.04.001.
[18]  Delfosse, P.; Lemaigre, S.; Flammang, J.; Neuberg, C.; Hausman, J.F.; Hoffmann, L. Evaluation Variétale du Ma?s, du Tournesol, et du Sorgho Pour la Méthanisation au Grand-Duché de Luxembourg (in German). In Proceedings of a One Day Meeting on Biométhanisation Agricole at Redange; Centre de Recherche Public-Gabriel Lippmann & Administration des Services Techniques de l’Agriculture: Luxembourg, 2007.
[19]  Hoffmann, R.M.; Wilson, J.A.; Kronfeld, D.S.; Cooper, W.L.; Lawrence, L.A.; Sklan, D.; Harris, P. Hydrolizable carbohydrates in pasture, hay, and horse feeds: Direct assay and seasonal variation. J. Anim. Sci 2001, 79, 500–506. 11219461
[20]  Hollung, K.; ?verland, M.; Hrustic, M.; Sekulic, P.; Miladinovic, J.; Martens, H.; Narum, B.; Sahlstr?m, S.; S?rensen, M.; Storebakken, T.; Skrede, A. Evaluation of nonstarch polysaccharides and oligosaccharide content of different soybean varieties (Glycine max) by near-infrared spectroscopy and proteomics. J. Agric. Food Chem 2005, 53, 9112–9121, doi:10.1021/jf051438r. 16277410
[21]  Kumagai, M.; Ohisa, N.; Amano, T.; Ogawa, N. Canonical discriminant analysis of cadmium content levels in unpolished rice using a portable near-infrared spectrometer. Anal. Sci 2003, 19, 1553–1555, doi:10.2116/analsci.19.1553. 14640458
[22]  Martens, H.; N?s, T. Multivariate Calibration by Data Compression. In Near-Infrared Technology in the Agricultural and Food Industries, 2nd eds.; Williams, P., Norris, K., Eds.; American Association of Cereal Chemists: St. Paul, MN, USA, 2001; pp. 59–100.
[23]  Satu, T. New estimation method for fatty acid composition in oil using near infrared spectroscopy. Biosci. Biotechnol. Biochem 2002, 66, 2543–2548, doi:10.1271/bbb.66.2543. 12596846
[24]  Park, R.; Agnew, R.E.; Kilpatrick, D.J. The effect of freezing and thawing on grass silage quality predictions based on near infrared reflectance spectroscopy. Anim. Feed Sci. Technol 2002, 102, 151–167, doi:10.1016/S0377-8401(02)00247-X.
[25]  Tatavarti, A.S.; Fahmy, R.; Wu, H.; Hussain, A.S.; Marnane, W.; Bensley, D.; Hollenbeck, G.; Hoag, S.W. Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms. AAPS Pharm. Sci. Tech 2005, 6, E91–E99, doi:10.1208/pt060115.
[26]  Mentkink, R.L.; Hoffman, P.C.; Bauman, L.M. Utility of near-infrared reflectance spectroscopy to predict nutrient composition and in vitro digestibility of total mixed rations. J. Dairy Sci 2006, 89, 2320–2326, doi:10.3168/jds.S0022-0302(06)72303-7. 16702299
[27]  Galv?o, H.R.K.; Araújo, M.C.U.; Silva, E.C.; José, G.E.; Soares, S.F.C.; Paiva, H.M. Cross-validation for the selection of spectral variables using the successive projections algorithm. J. Braz. Chem. Soc 2007, 18, 8.
[28]  Dardenne, P.; Andrieu, J.; Barriere, Y.; Biston, R.; Demarquilly, C.; Femenais, N.; Lila, M.; Maupetit, P.; Riviere, F.; Ronsin, T. Composition and nutritive value of whole maize plants fed fresh to sheep-II Prediction of the in vivo organic matter digestibility. Ann. Zootech 1993, 42, 251–270, doi:10.1051/animres:19930302.
[29]  De Boever, J.L.; Cottyn, B.G.; De Brabander, D.L.; Vanacker, J.M.; Boucque, C.V. Prediction of the feedingvalue of maize silages by chemical parameters, in vitro digestibility and NIRS. Anim. Feed Sci. Technol 1997, 66, 211–222, doi:10.1016/S0377-8401(96)01101-7.
[30]  Lovett, D.K.; Deaville, E.R.; Moulda, F.; Givens, D.I.; Owen, E. Using near infrared reflectance spectroscopy (NIRS) to predict the biological parameters of maize silage. Anim. Feed Sci. Tech 2004, 115, 179–187, doi:10.1016/j.anifeedsci.2004.02.007.
[31]  Sorensen, L.K. Prediction of fermentation parameters in grass and corn silage by near infrared spectroscopy. J. Dairy Sci 2004, 87, 3826–3835, doi:10.3168/jds.S0022-0302(04)73522-5. 15483167
[32]  Todorov, N.; Atanassova, S.; Pavlov, D.; Grigorova, R. Prediction of dry matter and protein degradability of forages by near infrared spectroscopy. Livest. Prod. Sci 1994, 39, 89–91, doi:10.1016/0301-6226(94)90158-9.
[33]  Walters, C.J.; Givens, D.I. Nitrogen degradability of fresh herbage: effect of maturity and growth type, and prediction from chemical composition and by near infrared reflectance spectroscopy. Anim. Feed Sci. Technol 1992, 38, 335–349, doi:10.1016/0377-8401(92)90023-Y.
[34]  Cocks, T.; Jenssen, R.; Steward, A.; Wilson, I.; Shields, T. The Hymap Airborne Hyperspectral Sensor: The System, Calibration and Performance. Proceedings of the 1st EARSEL Workshop on Imaging Spectroscopy, Zurich, German, 6–8 October 1998.
[35]  Schaepman, M. E.; de Vos, L.; Itten, K. I. APEX-airborne PRISM experiment: Hyperspectral radiometric performance analysis for the simulation of the future ESA land surface processes earth explorer mission. Proc. SPIE 1998, 3438, 253–262.
[36]  Berk, A.; Anderson, G.P.; Acharya, P.K.; Chetwind, J.H.; Bernstein, L.S.; Shettle, E.P.; Matthew, M.W.; Alder-Golden, S.M. Modtran4 User’s Manual; Air Force Research Laboratory: Hanscom, MA, USA, 1999; p. 93.
[37]  Richter, R.; Schlapfer, D.; Muller, A. An automatic atmospheric correction algorithm for visible/NIR imagery. Int. J. Remote Sens 2006, 27, 2077–2085, doi:10.1080/01431160500486690.
[38]  Rodger, A.; Lynch, M.J. Determining Atmospheric Column Water Vapour in the 0.4–2.5 μm Spectral Region. Proceedings of the JPL-NASA AVIRIS Workshop 2001, Pasadena, CA, USA, 27 February–2 March 2001.
[39]  Verein Deutscher Ingenieure. VDI 4630-Fermentation of Organic Materials, Characterisation of the Substrates, Sampling, Collection of Material Data, Fermentation Tests; VDI-Handbuch Energietechnik, Beuth Verlag GmbH: Berlin, Germany, 2006; p. 92.
[40]  Fachgrupe Wasserchemie in der Gesellschaft Deutscher Chemiker und Normausschuss Wasserwesen (NAW) im DIN Deutscher Institut für Normung e.V. DIN 38414-Bestimmung des Faulverhaltens (S8). In Deutsche Einheitsverfahren zur Wasser-, Abwasser-, und Schlammuntersuchung. Physikalische, Chemische, Biologische und Bakteriologische Verfahren; VCH Verlagsgesellschaft mbH: Weinheim, Germany, 1987.
[41]  Kowalewska, G.; Szymczak, M. Influence of selected abiotic factors on the decomposition of chlorophylls. Oceanologia 2001, 43, 315–328.
[42]  Woodman, H.E. The nature of the pigment of silage. J. Agr. Sci 1923, 13, 240–242, doi:10.1017/S0021859600003348.
[43]  Wold, S.; Sj?str?m, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemometr. Intell. Lab. Syst 2001, 58, 109–130, doi:10.1016/S0169-7439(01)00155-1.
[44]  Cho, A.K.; Skidmore, M.A. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. Int. J. Remote Sens 2009, 30, 499–515, doi:10.1080/01431160802392596.
[45]  Koppe, W.; Li, F.; Gnyp, M.; Miao, Y.; Jia, L.; Chen, X.; Zhang, F.; Bareth, G. Evaluating multispectral and hyperspectral satellite remote sensing data for estimating winter wheat growth parameters at regional scale in the North China plain. Photogramm. Fernerkun 2010, 3, 167–178.
[46]  Darvishzadeh, R.; Skidmore, A.; Atzberger, C.; van Wieren, S. Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture. Int. J. Appl. Earth Obs. Geoinf 2008, 10, 358–373, doi:10.1016/j.jag.2008.02.005.
[47]  Ranney, J.W.; Cushman, J.H. Regional Evaluation of Woody Biomass Production for Fuels in the Southeast. In Biotechnology and Bioengineering Symposium No. 10; Wiley: New York, NY, USA, 1980; pp. 10–20.
[48]  Gehrung, J.; Scholz, Y. The application of simulated NPP data in improving the assessment of the spatial distribution of biomass in Europe. Biomass Bioenerg 2009, 33, 712–720, doi:10.1016/j.biombioe.2008.11.005.
[49]  Phillips, V.D.; Liu, W.; Merriam, R.A.; Singh, D. Biomass systems model estimates of short-rotation hardwood production in Hawaii. Biomass Bioenerg 1993, 5, 421–429, doi:10.1016/0961-9534(93)90037-5.
[50]  Liu, W.; Merriam, R.A.; Phillips, V.D.; Singh, D. Estimating shortrotation Eucalyptus saligna production in Hawaii: An integrated yields and economic model. Bioresour. Technol 1993, 45, 167–176, doi:10.1016/0960-8524(93)90109-O.
[51]  Voivontas, D.; Assimacopoulos, D.; Koukios, E.G. Assessment of biomass potential for power production: a GIS based method. Biomass Bioenerg 2001, 20, 101–112, doi:10.1016/S0961-9534(00)00070-2.
[52]  Milbrandt, A. A Geographic Perspective on the Current Biomass Resource Availability in the United States. Technical Report No. NREL/TP-560-39181; NREL: Golden, CO, USA, 2005.
[53]  Beccali, M.; Columba, P.; D’Alberti, V.; Franzitta, V. Assessment of bioenergy potential in Sicily: A GIS–based support methodology. Biomass Bioenerg 2009, 33, 79–87, doi:10.1016/j.biombioe.2008.04.019.
[54]  van Diepen, C.A.; van der Wal, T. Crop Growth Monitoring and Yield Forecasting at Regional and National Scale. Proceedings of Workshop for Central and Eastern Europe on Agrometeorological Models: Theory and Applications in the MARS Project, Ispra, Italy, 21–25 November 1994; pp. 143–157.
[55]  Svendsen, H.; Hansen, S.; Jensen, H.E. Simulation of crop production, water and nitrogen balances in two German agro-ecosystems using the DAISY model. Ecol. Model 1995, 81, 197–212, doi:10.1016/0304-3800(94)00171-D.
[56]  Diekrüger, B.; S?nergerath, D.; Kersebaum, K.C.; McVoy, C.W. Validity of agroecosystem models—A comparison of results of different models applied to the same data set. Ecol. Model 1995, 81, 3–29, doi:10.1016/0304-3800(94)00157-D.
[57]  Wallach, D.; Makowski, D.; Jones, J. Working with Dynamic Crop Models—Evaluation, Analysis, and Parameterization, and Applications; Elsevier: Amsterdam, The Netherlands, 2006; p. 446.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133