Predictive models for assessing 'Spartina argentinensis' biomass
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Abstract
Vast areas of Argentina are covered by rangelands, which provide important ecosystem services. Traditionally, livestock farming has been the main productive activity in these environments, with the use of fire as a common management practice. Although burning stimulates the regrowth of grasses with better forage quality, it also poses environmental challenges due to the emission of CO2eq into the atmosphere. In response to the growing demand for renewable energy sources, rangelands under livestock production systems with frequent burning present a sustainable alternative for bioenergy production. In the province of Santa Fe, rangelands dominated by Spartina argentinensis in the Bajos Submeridionales region, which covers more than two million hectares, have high potential for bioenergy production without compromising the existing biodiversity. This work focuses on the development and evaluation of predictive models for S. argentinensis biomass using spectral images obtained by drones. Multiple linear regression and classification models were developed, considering spectral variables, site, and seasons, to predict total biomass and its green and senescent fractions. The models obtained to estimate the total biomass of Spartina explained up to 62% of its variability. The green and senescent biomass fractions were predicted with greater accuracy, showing an R² of 66% for both. These findings highlight the potential of remote sensing technology to optimize the planning and sustainable management of biomass resources in Argentine grasslands.
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References
BARNES, E.M.; CLARKE, T.R.; RICHARDS, S.R.; COLAIZZI, P.D.; HABERLAND, J.; KOSTRZEWSKI, M.; WALLER, P.; CHOI, C.; RILEY, E.; THOMPSON, T.; LASCANO, R.J.; LI, H.; MORAN, M.S. (2000) Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral dataed. In ASA-CSSA-SSSA, ed. the fifth international conference on precision agriculture and other resource management. Bloomington, MN, USA, pp. 1–15.
BREIMAN, L. (2001) Random Forests. Machine Learning 2001 45:1. 45(1), 5–32.
BREMER, D.J.; LEE, H.; SU, K.; KEELEY, S.J. (2011) Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: II. Factors Affecting NDVI and its Component Reflectances. Crop Science. 51(5), 2219–2227.
BYRD, K.B.; BALLANTI, L.; THOMAS, N.; NGUYEN, D.; HOLMQUIST, J.R.; SIMARD, M.; WINDHAM-MYERS, L. (2018) A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States. ISPRS Journal of Photogrammetry and Remote Sensing. 139, 255–271.
DASH, J.; CURRAN, P.J. (2004) The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing. 25(23), 5403–5413.
DAUGHTRY, C. (2000) Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment. 74(2), 229–239.
FERNÁNDEZ, O. A.; BUSSO, C. (2017) Arid and Semiarid Rangelands of Argentina. In M. K. Gaur & V. R. Squires, eds. Climate Variability Impacts on Land Use and Livelihoods in Drylands. pp. 1–348.
GAO, X.; DONG, S.; LI, S.; XU, Y.; LIU, S.; ZHAO, H.; YEOMANS, J.; LI, Y.; SHEN, H.; WU, S.; ZHI, Y. (2020) Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau. Ecological Indicators. 112.
GITELSON, A.A.; VIÑA, A.; ARKEBAUER, T.J.; RUNDQUIST, D.C.; KEYDAN, G.; LEAVITT, B. (2003) Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters. 30(5), n/a-n/a.
GODDE, C.M.; BOONE, R.B.; ASH, A.J.; WAHA, K.; SLOAT, L.L.; THORNTON, P.K.; HERRERO, M. (2020) Global rangeland production systems and livelihoods at threat under climate change and variability. Environmental Research Letters. 15(4).
GONZÁLEZ TRILLA, G.; PRATOLONGO, P.; KANDUS, P.; BEGET, M.E.; DI BELLA, C.; MARCOVECCHIO, J. (2016) Relationship Between Biophysical Parameters and Synthetic Indices Derived from Hyperspectral Field Data in a Salt Marsh from Buenos Aires Province, Argentina. Wetlands. 36(1), 185–194.
GROSS, M.F.; HARDISKY, M.A.; WOLF, P.L.; KLEMAS, V. (1991) Relationship between aboveground and belowground biomass of Spartina alterniflora (Smooth Cordgrass). Estuaries. 14(2), 180–191.
HABOUDANE, D.; MILLER, J.R.; TREMBLAY, N.; ZARCO-TEJADA, P.J.; DEXTRAZE, L. (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment. 81(2–3), 416–426.
HAO, S.; RYU, D.; WESTERN, A.; PERRY, E.; BOGENA, H.; FRANSSEN, H.J.H. (2021) Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis. Agricultural Systems. 194.
HARDISKY, M.A.; DAIBER, F.C.; ROMAN, C.T.; KLEMAS, V. (1984) Remote sensing of biomass and annual net aerial primary productivity of a salt marsh. Remote Sensing of Environment. 16(2), 91–106.
HE, W.; GRANT, B.B.; SMITH, W.N.; VANDERZAAG, A.C.; PIQUETTE, S.; QIAN, B.; JING, Q., RENNIE, J. (2019) Assessing alfalfa production under historical and future climate in eastern Canada: DNDC model development and application. . 122(June 2018).
HUNT, E.R.; DAUGHTRY, C.S.T.; Eitel, J.U.H.; Long, D.S. (2011) Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index. Agronomy Journal. 103(4), 1090–1099.
INDEC (2021) Censo Nacional Agropecuario 2018. Resultados definitivos. Instituto Nacional de Estadística y Censos, ed. Buenos Aires, Argentina.
IPCC, I.P. on C.C. (2019) 2019 Refinement to the 2006 IPCC guidelines for national greenhouse gas inventories. Agriculture, forestry and other land use. 4, 824.
JORDAN, C.F. (1969) Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology. 50(4), 663–666.
JOZAMI, E.; L. SOSA, L.; R. FELDMAN, S. (2013) Spartina argentinensis as feedstock for bioethanol. Applied Technologies and Innovations. 9(2), 37–44.
KNYAZIKHIN, Y.; SCHULL, M.A.; STENBERG, P.; MÕTTUS, M.; RAUTIAINEN, M.; YANG, Y.; MARSHAK, A.; LATORRE CARMONA, P.; KAUFMANN, R.K.; LEWIS, P.; DISNEY, M.I.; VANDERBILT, V.; DAVIS, A.B.; BARET, F.; JACQUEMOUD, S.; LYAPUSTIN, A.; MYNENI, R.B. (2013) Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences. 110(3).
DI LEO, N.; JOZAMI, E.; BULACIO, E.M.; RIGALLI, N.; ROMAGNOLI, M.; BARBONA, I.; PORTAPILA, M.; FELDMAN, S. (2022) Biomass Estimation of Sporobolus spartinus with Multispectral Images from a Fixed-Wing Unmanned Aerial Vehicle and a Spectrometer. In 2022 IEEE Biennial Congress of Argentina (ARGENCON). IEEE, pp. 1–5.
LEVINE, J.S. (1991) Global biomass burning: Atmospheric, climatic and biospheric implications.
LOUHAICHI, M.; BORMAN, M.M.; JOHNSON, D.E. (2001) Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat. Geocarto International. 16(1), 65–70.
MANZO, C.; VALENTINI, E.; TARAMELLI, A.; FILIPPONI, F.; DISPERATI, L. (2015) Spectral characterization of coastal sediments using Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR Data (FHyL). International Journal of Applied Earth Observation and Geoinformation. 36, 54–68.
MASSA, E.; PRADO, D.E.; FELDMAN, S.R. (2017) Efecto del fuego o el corte sobre la producción y la calidad forrajera de un pajonal dominado por Panicum prionitis. Revista Argentina de Producción Animal. 37(1), 9–20.
Mc FEETERS, S.K. (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 17(7), 1425–1432.
MERZLYAK, M.N.; GITELSON, A.A.; CHIVKUNOVA, O.B.; RAKITIN, V.Y. (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum. 106(1), 135–141.
PARAJULI, P.B.; NELSON, N.O.; FREES, L.D.; MANKIN, K.R. (2009) Comparison of AnnAGNPS and SWAT model simulation results in USDA-CEAP agricultural watersheds in south-central Kansas. Hydrological Processes. 23(5), 748–763.
PINILLA VARGAS, DIANA, K. (2012) Influencia del clima, la vegetación y el hombre sobre la frecuencia, intensidad y distribución de los incendios en Argentina. Universidad de Buenos Aires.
POLEY, L.G.; MCDERMID, G.J. (2020) A systematic review of the factors influencing the estimation of vegetation aboveground biomass using unmanned aerial systems. Remote Sensing. 12(7).
PORTER, T.F.; CHEN, C.; LONG, J. A.; LAWRENCE, R.L.; SOWELL, B.F. (2014) Estimating biomass on CRP pastureland: A comparison of remote sensing techniques. Biomass and Bioenergy. 66, 268–274.
RAMOELO, A.; CHO, M.A.; MATHIEU, R.; MADONSELA, S.; VAN DE KERCHOVE, R.; KASZTA, Z.; WOLFF, E. (2015) Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation. 43, 43–54.
RONDEAUX, G.; STEVEN, M.; BARET, F. (1996) Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment. 55(2), 95–107.
ROUSE, J.W.; HASS, R.H.; SCHELL, J.A.; DEERING, D.W.; HARLAN, J.C. (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.
SOSA, L.L.; JOZAMI, E.; OAKLEY, L.J.; MONTERO, G.A.; FERRERAS, L.A.; VENTURI, G.; FELDMAN, S.R. (2019) Using C4 perennial rangeland grasses for bioenergy. Biomass and Bioenergy. 128(July), 105299.
SRIPADA, R.P.; HEINIGER, R.W.; WHITE, J.G.; WEISZ, R. (2005) Aerial Color Infrared Photography for Determining Late‐Season Nitrogen Requirements in Corn. Agronomy Journal. 97(5), 1443–1451.
THORNTHWAITE, C. (1948) An approach toward a rational classification of climate. Geographical review. 38(1), 55–94.
TILMAN, D. (2009) Carbon-Negative Biofuels. . 1598(2006).
TILMAN, D.; SOCOLOW, R.; FOLEY, J.A.; HILL, J.; LARSON, E.; LYND, L.; PACALA, S.; REILLY, J.; SEARCHINGER, T.; SOMERVILLE, C.; WILLIAMS, R. (2009) Benefi cial Biofuels—The Food, Energy, and Environment Trilemma. Science. 325(5938), 4–5.
TODD, S.W.; HOFFER, R.M.; MILCHUNAS, D.G. (1998) Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing. 19(3), 427–438.
TRILLA, G.G.; DE MARCO, S.; MARCOVECCHIO, J.; VICARI, R.; KANDUS, P. (2010) Net primary productivity of Spartina densiflora brong in an SW Atlantic Coastal salt marsh. Estuaries and Coasts. 33(4), 953–962.
VERÓN, S.R.; JOBBÁGY, E.G.; DI BELLA, C.M.; PARUELO, J.M.; JACKSON, R.B. (2012) Assessing the potential of wildfires as a sustainable bioenergy opportunity. GCB Bioenergy. 4(6), 634–641.
VILJANEN, N.; HONKAVAARA, E.; NÄSI, R.; HAKALA, T.; NIEMELÄINEN, O.; KAIVOSOJA, J. (2018) A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture (Switzerland). 8(5).
VILLOSLADA PECIÑA, M.; BERGAMO, T.F.; WARD, R.D.; JOYCE, C.B.; SEPP, K. (2021) A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows. Ecological Indicators. 122, 107227.
WANG, S ming; CUI, G. qing; WANG, H.; MA, F. ying, XIA, S. sai; LI, Y. feng; YANG, Z. lin; LING, Y. hua; ZHANG, C. wei; HE, G. hua; ZHAO, F. ming (2019) Identification and QTL mapping of Z550, a rice backcrossed inbred line with increased grains per panicle. Journal of Integrative Agriculture. 18(3), 526–531.
YANG, X.; GUO, X. (2014) Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands. Remote Sensing. 6(5), 4289–4304.
ZENG, N.; REN, X.; HE, H.; ZHANG, L.; ZHAO, D.; GE, R.; LI, P.; NIU, Z. (2019) Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecological Indicators. 102, 479–487.