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Original Articles

Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 6033-6051 | Received 24 Feb 2021, Accepted 25 Apr 2021, Published online: 28 May 2021

References

  • Ali I, Cawkwell F, Dwyer E, Barrett B, Green S. 2016. Satellite remote sensing of grasslands: from observation to management. JPECOL. 9(6):649–671.
  • Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G. 2013. Köppen’s climate classification map for Brazil. metz. 22 (6):711–728.
  • Barker JL, Harden MK, Anuta EA, Smid J, Hougt D. 1992. MODIS spectral sensivity study: requirements and characterization. Washington (DC): Nasa, 84 p.
  • Beurs KM, Henebry GM. 2010. A land surface phenology assessment of the northern polar regions using MODIS reflectance time series. Can J Remote Sens. 36:87–110.
  • Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM. 2016. Mlr: Machine learning in R. J. Mach. Learn. Res. 17:1–5.
  • Blüthgen N, Dormann CF, Prati D, Klaus VH, Kleinebecker T, Hölzel N, Alt F, Boch S, Gockel S, Hemp A, et al. 2012. A quantitative index of land-use intensity in grasslands: integrating mowing, grazing and fertilization. Basic Appl Ecol. 13(3):207–220.
  • Bogaerts M, Cirhigiri L, Robinson I, Rodkin M, Hajjar R, Costa JC, Newton P. 2017. Climate change mitigation through intensified pasture management: estimating greenhouse gas emissions on cattle farms in the Brazilian Amazon. J Cleaner Prod. 162:1539–1550.
  • Breiman L. 2001. Random Forests. Mach Learn. 45(1):5–32.
  • Brito JLS, Arantes AE, Ferreira LG, Sano EE. 2018. MODIS estimates of pasture productivity in the Cerrado based on ground and Landsat-8 data extrapolations. J Appl Rem Sens. 12(02):1.
  • Cardoso AS, Berndt A, Leytem A, Alves BJR, Carvalho INO, Soares LHB, Urquiaga S, Boddey RM. 2016. Impact of the intensification of beef production in Brazil on greenhouse gas emissions and land use. Agric Syst. 143 :86–96.
  • Chang J, Ciais P, Viovy N, Soussana JF, Klumpp K, Sultan B. 2017. Future productivity and phenology changes in European grasslands for different warming levels: implications for grassland management and carbon balance. Carbon Balance Manage. 12(1):11.
  • Chen W, Sakai T, Moriya K, Koyama L, Cao CX. 2013. Estimation of vegetation coverage in semi-arid sandy land based on multivariate statistical modeling using remote sensing data. Environ Model Assess. 18(5):547–558.
  • Costa KAP, Oliveira IP, Faquin V. 2006. Adubação nitrogenada para pastagens do gênero Brachiaria em solos do Cerrado. Santo Antônio de Goiás (Brazil): Embrapa Arroz e Feijão, 60 p.: il. – (Documentos/Embrapa Arroz e Feijão) (in portuguese).
  • De Oliveira Silva R, Barioni LG, Hall JAJ, Folegatti Matsuura M, Zanett Albertini T, Fernandes FA, Moran D. 2016. Increasing beef production could lower greenhouse gas emissions in Brazil if decoupled from deforestation. Nat Clim Change. 6(5):493–497.
  • De Oliveira Silva R, Barioni LG, Queiroz Pellegrino G, Moran D. 2018. The role of agricultural intensification in Brazil's nationally determined contribution on emissions mitigation. Agric Syst. 161:102–112.
  • Del Grosso SJ, Parton WJ, Mosier AR, Holland EA, Pendall E, Schimel DS, Ojima DS. 2005. Modeling soil CO2 emissions from ecosystems. Biogeochemistry. 73(1):71–91.
  • Donald G, Ahmad W, Hulm E, Trotter M, Lamb D. 2010. Integrating MODIS satellite imagery and proximal vegetation sensors to enable precision livestock management. First International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Shanghai, China, p. 1–5.
  • Dos Reis AA, Carvalho MC, Mello JM, Gomide LC, Ferraz Filho AC, Acerbi Junior FW. 2018. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods. NZ J For Sci. 48(1):1–17.
  • Edirisinghe A, Clark D, Waugh D. 2012. Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing. Int J Appl Earth Obs Geoinf. 16(16):5–16.
  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: planetary geospatial analysis for all. Remote Sens Ambient. 202:18–27.
  • Graux AI, Bellocchi G, Lardy R, Soussana JF. 2013. Ensemble modelling of climate change risks and opportunities for managed grasslands in France. Agric For Meteorol. 170:114–131.
  • Hartman MD, Parton WJ, Del Grosso SJ, Easter M, Hendryx J, Hilinski T, Kelly R, Keough CA, Killian K, Lutz S, et al. 2016. DayCent ecosystem model: the daily century ecosystem. Soil organic matter, nutrient cycling, nitrogen trace gas, and methane model. Colorado State University.
  • Higginbottom TP, Symeonakis E. 2014. Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sens. 6(10):9552–9575.
  • Huete AR, Liu HQ, Van Leeuwen WJD. 1997. The use of vegetation indices in forested regions: issues of linearity and saturation. IGARSS'97. IEEE International Geoscience and Remote Sensing Symposium Proc. Remote Sens. A Sci. Vis. Sustain, Singapore. 1966–1968.
  • IBGE, Instituto Brasileiro de Geografia e Estatística. 2017. Censo Agropecuário, IBGE, Rio de Janeiro, Rio de Janeiro. (in portuguese)
  • Institute of Technological Research of the State of São Paulo (ITR). 1981. Geomorphological map of the State of São Paulo, São Paulo.
  • Kuchler PC, Bégué A, Simões M, Gaetano R, Arvor D, Ferraz RPD. 2020. Assessing the optimal pre-processing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil. Int J Appl Earth Obs Geoinf. 92(102150):1–6.
  • Kuemmerle T, Erb K, Meyfroidt P, Müller D, Verburg PH, Estel S, Haberl H, Hostert P, Jepsen MR, Kastner T, Levers C, et al. 2013. Challenges and opportunities in mapping land use intensity globally. Curr Opin Environ Sustain. 5(5):484–493.
  • Kunrath TR, Cadenazzi M, Brambilla DM, Anghinoni I, Moraes A, Barro RS, Carvalho PCF. 2014. Management targets for continuously stocked mixed oat × annual ryegrass pasture in a no-till integrated crop–livestock system. Eur J Agron. 57:71–76.
  • Latham J, Cumani R, Rosati I, Bloise M. 2014. Global Land Cover SHARE (GLC-SHARE) database. Beta-Release Version 1.0. http://www.glcn.org/downs/prj/glcshare/GLC_SHARE_beta_v1.0_2014.pdf.
  • Latifi H, Nothdurft A, Koch B. 2010. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry. 83(4):395–407.
  • Levene H. 1960. Contributions to probability and statistics: essays in honor of harold hotelling. California (USA): Stanford University Press. p. 278–292. Robust tests for equality of variances.
  • Li F, Zeng Y, Luo J, Ma R, Wu B. 2016. Modeling grassland aboveground biomass using a pure vegetation index. Ecol Indic. 62:279–288.
  • Liang T, Yang S, Feng Q, Liu B, Zhang R, Huang X, Xie H. 2016. Multi-factor modeling of aboveground biomass in alpine grassland: a case study in the Three-River Headwaters Region, China. Remote Sens Environ. 186:164–172.
  • Liao Z, Van Dijk AIJM, He B, Larraondo PR, Scarth PF. 2020. Woody vegetation cover, height and biomass at 25-m resolution across Australia derived from multiple site, airborne and satellite observations. Int J Appl Earth Obs Geoinf. 93:102209.
  • Lugassi R, Zaady E, Goldshleger N, Shoshany M, Chudnovsky A. 2019. Spatial and temporal monitoring of pasture ecological quality: Sentinel-2-based estimation of crude protein and neutral detergent fiber contents. Remote Sens. 11(7):799.
  • Manabe V, Melo MRS, Rocha JV. 2018. Framework for mapping integrated crop-livestock systems in Mato Grosso, Brazil. Remote Sens. 10(9):1322.
  • Meyer T, Okin GS. 2015. Evaluation of spectral unmixing techniques using MODIS in a structurally complex savanna environment for retrieval of green vegetation, nonphotosynthetic vegetation, and soil fractional cover. Remote Sens Environ. 161:122–130.
  • Morais TG, Teixeira RFM, Domingos T. 2019. Detailed global modelling of soil organic carbon in cropland, grassland and forest soils. PLoS One. 14(9):e0222604.
  • Oliveira J, Campbell EE, Lamparelli RAC, Figueiredo GKDA, Soares JR, Jaiswal D, Monteiro LA, Vianna MS, Lynd LR, Sheehan JJ. 2020. Choosing pasture maps: an assessment of pasture land classification definitions and a case study of Brazil. Int J Appl Earth Obs Geoinf. 93(102205):1–15.
  • Oertel C, Matschullat J, Zurba K, Zimmermann F, Erasmi S. 2016. Greenhouse gas emissions from soils – a review. Geochemistry. 76(3):327–352.
  • Parton WJ, Schimel DS, Cole CV, Ojima DS. 1987. Analysis of factors controlling soil organic-matter levels in great-plains grasslands. Soil Sci Soc Am J. 51(5):1173–1179.
  • Pimentel-Gomes F. 2009. Curso de Estatística Experimental. Vol. 15. Piracicaba (Brazil): FEALQ. (in Portuguese).
  • Rolim GS, Aparecido LEO. 2016. Camargo-Köppen and Thornthwaite climate classification systems in defining climatical regions of the state of São Paulo, Brazil. Int J Climatol. 36(2):636–643.
  • Rolim GS, Camargo MPB, Lania DG, Moraes JFL. 2007. Classificação climática de Köppen e de Thornthwaite e sua aplicabilidade na determinação de zonas agroclimáticas para o estado de São Paulo. Bragantia. 66(4):711–720. (in Portuguese)
  • Rossi M. 2017. Mapa pedológico do Estado de São Paulo: revisado e ampliado, Vol. 1. São Paulo (Brazil): Instituto Florestal. p. 118. In Portuguese.
  • Rouse JW, Haas RH, Schell JA, Deering DW. 1973. Monitoring vegetation systems in the Great Plains with ERTS. In: Third Symposium of ERTS, Greenbelt (MD), Vol. 1, p. 309–317.
  • Ruviaro CF, De Le Is CM, Lampert VN, Barcellos JOJ, Dewes H. 2015. Carbon footprint in different beef production systems on a southern Brazilian farm: a case study. J Clean Prod. 96:435–443.
  • Serrano J, Shahidian S, Silva JM. 2018. Monitoring seasonal pasture quality degradation in the Mediterranean Montado Ecosystem: proximal versus remote sensing. Water. 10(10):1422.
  • Serrano J, Shahidian S, Silva JM. 2019. Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a Mediterranean Agro-Silvo-Pastoral system. Water. 11(1):62.
  • Shepherd A, Hartman MD, Fitton N, Horrocks CA, Dunn RM, Hastings A, Cardenas LM. 2019. Metrics of biomass, live-weight gain and nitrogen loss of ryegrass sheep pasture in the 21st century. Sci Total Environ. 685:428–441.
  • Sibanda M, Mutanga O, Rouget M. 2015. Examining the potential of Sentinel-2 MSI spectral resolution in quantifying aboveground biomass across different fertilizer treatments. ISPRS J Photogramm Remote Sens. 110:55–65.
  • Socher SA, Prati D, Boch S, Müller J, Baumbach H, Gockel S, Hemp A, Schöning I, Wells K, Buscot F, et al. 2013. Interacting effects of fertilization, mowing and grazing on plant species diversity of 1500 grasslands in Germany differ between regions. Basic Appl. Ecol. 14(2):126–136.
  • Socher SA, Prati D, Boch S, Müller J, Klaus VH, Hölzel N, Fischer M. 2012. Direct and productivity-mediated indirect effects of fertilization, mowing and grazing on grassland species richness. J Ecol. 100(6):1391–1399.
  • Soussana J, Lemaire G. 2014. Coupling carbon and nitrogen cycles for environmentally sustainable intensification of grasslands and crop-livestock systems. Agric Ecosyst Environ. 190:9–17.
  • Stafford J. 2000. Implementing precision agriculture in the 21st century. J Agric Eng Res. 76(3):267–275. v.
  • Sun P, Wu Y, Xiao J, Hui J, Hu J, Zhao F, Qiu L, Liu S. 2019. Remote sensing and modeling fusion for investigating the ecosystem water-carbon coupling processes. Sci Total Environ. 697:134064.
  • Testa S, Soudani K, Boschetti L, Borgogno Mondino E. 2018. MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. Int J Appl Earth Obs Geoinf. 64:132–144.
  • Veloso GA, Ferreira ME, Ferreira LG, Silva BB. 2020. Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil. Remote Sens Appl Soc Environ. 17: 1–8.
  • Wang L, Zhou X, Zhu X, Dong Z, Guo W. 2016. Estimation of biomass in wheat using Random Forest regression algorithm and remote sensing data. Crop J. 4(3):212–219.
  • Wang Y, Wu G, Deng L, Tang Z, Wang K, Sun W, Shangguan Z. 2017. Prediction of aboveground grassland biomass on the Loess Plateau. Sci Rep. 7(1):6940.
  • Wilm HG, Costello DF, Klipple GE. 1944. Estimating forage yield by the double sampling methods. Agron J. 36(3):194–203.
  • Wójtowicz M, Wojtowics A, Piekarczyk J. 2016. Application of remote sensing methods in agriculture. Commun Biometry Crop Sci. 11:31–50.
  • Wright KS, Rocha AV. 2018. A test of functional convergence in carbon fluxes from coupled C and N cycles in Arctic tundra. Ecol Modell. 383:31–40.
  • Wu C, Shen H, Shen A, Deng J, Gan M, Zhu J, Xu H, Wang K. 2016. Comparison of machine-learning methods for aboveground biomass estimation based on Landsat imagery. J Appl Remote Sens. 10(3):035010.
  • Yin J, Feng Q, Liang T, Meng B, Yang S, Gao J, Ge J, Hou M, Liu J, Wang W, et al. 2020. Estimation of grassland height based on the random forest algorithm and remote sensing in the Tibetan Plateau. IEEE J Selected Topics Appl Earth Obs Remote Sens. 13: 178–186.
  • Yu H, Wang L, Wang Z, Ren C, Zhang B. 2019. Using Landsat OLI and random forest to assess grassland degradation with aboveground net primary production and electrical conductivity data. IJGI. 8(11):511.
  • Zaks DPM, Kucharik CJ. 2011. Data and monitoring needs for a more ecological agriculture. Environ Res Lett. 6(1):014017.

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