NDVI GreenSeeker NDVI SAPR
Nella relazione tra NDVISAPR e l’N del clipping nelle tre specie, il più alto r si trova per Cd×t (0,95) (Figura 19), che risulta essere anche la specie più reattiva alla concimazione azotata con un N % nel clipping che va da 1,2% al 4,1%. Senza la fertilizzazione, il più alto contenuto di N si è registrato per Pv (1,7% N), superiore alle altre due specie macroterme da tappeto erboso (Cd×t e Zm 1,2% N).
All’aumentare delle dosi di N applicato al tappeto erboso, l’assorbimento di Zm è significativamente inferiore a Cd×t e Pv, con un valore di picco del 2,8% N. Inoltre, riguardo i contenuti di N nel clipping e l’NDVI ottenuto dal SAPR, al più alto contenuto di N il valore di NDVI in Cd×t (NDVICd×t = 0,85) è più alto
rispetto alle altre due specie macroterme (NDVIZm = 0,81; NDVIPv = 0,82)
(Figura 22).
Figura 11 – Relazione tra il contenuto di azoto nei residui di taglio e l’NDVI misurato con il SAPR per Cynodon dactylon x transvaalensis, Paspalum vaginatum e Zoysia matrella. In ogni specie i valori indicati sono il risultato della media tra le quattro repliche.
y = 0,1174x + 0,5071 r = 0,89 y = 0,0926x + 0,4969 r = 0,95 y = 0,1056x + 0,4223 r = 0,92 0,45 0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 0,90 0,95 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 N D VI clippings, N% Zm Cdxt Pv
7. Conclusioni
I sistemi aeromobili a pilotaggio remoto (SAPR), o droni, sono in grado di fornire informazioni molto utili al gestore di un tappeto erboso. Come parte di un programma di gestione improntato sui criteri dell’agricoltura di precisione, l'applicazione dei SAPR può portare ad un risparmio di tempo, lavoro e denaro, contribuendo a stabilire, con l'utilizzo di specifici indici di vegetazione, il contenuto di azoto, il colore, la qualità generale, la sostanza secca, lo stress idrico, la clorofilla, i carotenoidi (Agati et al., 2013; Caturegli et al., 2014a; Agati et al., 2015; Bremer e van der Mewer, 2016;).
Osservando i risultati della prova sperimentale risulta evidente come i valori di NDVI ottenuti da un SAPR sono altamente correlati con quelli provenienti dallo strumento manuale (GreenSeeker), con coefficienti di correlazione (r) compresi tra 0,83 (Zm) e 0,97 (Cd×t) (Tabella 3). Un valore di r = 0,97 sta ad indicare la pressoché equivalenza statistica nella rilevazione dei dati da parte di questi due sistemi.
Possiamo dunque affermare come l’utilizzo di un SAPR sia idoneo al telerilevamento dell’NDVI e nell’identificazione delle variazioni del contenuto in azoto delle specie da tappeto erboso prese in esame (Cynodon dactylon x
transvaalensis ‘Patriot’, Zoysia matrella ‘Zeon’, Paspalum vaginatum ‘Salam’).
A seguito di un’attenta analisi tecnica mirata ad escludere altri fattori di stress (per esempio idrico, biotico, ecc.), l’utilizzo di SAPR dotati di GPS e sensori multispettrali consente di creare una mappa tematica basata sui valori di NDVI (c.d. mappe di vigore vegetativo) su cui poter tarare la quantità di concime da distribuire con uno spandiconcime a rateo variabile. Le applicazioni sito specifiche, cardine dell’agricoltura di precisione, permettono una maggiore efficienza degli elementi nutritivi, un risparmio di denaro e un minore impatto ambientale.
tappeti erbosi, perché meno costoso e più pratico. Per aree di dimensioni maggiori, come campi da golf, ippodromi, aziende produttrici di prato in rotoli o di semi di specie da tappeto erboso, in aggiunta o in alternativa all'uso di sensori prossimali può essere utile monitorare l'intera superficie grazie a telecamere e sensori montati sui SAPR.
Abbreviazioni
AdP, Agricoltura di Precisione
Cd×t, Cynodon dactylon x transvaalensis
ESC, Electronic Speed Controller GIS, Geographic Information System GNSS, Global Navigation Satellite System GPS, Global Positioning System
IMU, Inertial Measurement Unit
NDVI, Normalized Difference Vegetation Index NIR, Near InfraRed
PAR, Photosynthetically Active Radiation
Pv, Paspalum vaginatum
RVI, Ratio Vegetation Index
SAPR, Sistemi Aeromobili a Pilotaggio Remoto
Bibliografia
Acevo Herrera R, Aguasca Solé A, Bosch Lluís X, Camps Carmona AJ, Martínez-Fernández J, Sánchez-Martín N, et al. 2010. Design and first results
of an UAV-borne L-band radiometer for multiple monitoring purposes. Remote
Sens.; 2: 1662–1679.
Agati G, Foschi L, Grossi N, Guglielminetti L, Cerovic ZG, Volterrani M. 2013.
Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses. Eur. J. Agron.; 45: 39–
51. Bada black
Agati G, Foschi L, Grossi N, Volterrani M. 2015. In field non-invasive sensing
of the nitrogen status in hybrid bermudagrass (Cynodon dactylon×C. transvaalensis Burtt Davy) by a fluorescence-based method. Eur. J. Agron.; 63:
89–96.
Aguilar C, Zinnert JC, Polo MJ, Young DR. 2012. NDVI as an indicator for
changes in water availability to woody vegetation. Ecol. Indic.; 23: 290–300.
Alsdorf J. 2007. Using remote sensing to determine differences in soybean
seeding rates. Doctoral dissertation, Purdue University. Available:
https://www.agry.purdue.edu/staffbio/Alsdorf_Thesis2008.pdf.
Barton CV. 2012. Advances in remote sensing of plant stress. Plant Soil; 354: 41–44.
Basso B, Sartori L, Castrignanò A. 2012. Agricoltura di precisione: dal gps alla
gestione sostenibile dei sistemi colturali. XLI Convegno Nazionale della Società
Italiana di Agronomia. 96-98.
Bausch WC, Khosla R. 2010. QuickBird satellite versus ground-based multi-
spectral data for estimating nitrogen status of irrigated maize. Precis. Agric.;
Bell GE, Howell BM, Johnson GV, Raun WR, Solie JB, Stone ML. 2004. Optical
sensing of turfgrass chlorophyll content and tissue nitrogen. HortScience; 39:
1130–1132.
Bell GE, Kruse JK, Krum JM. 2013. The evolution of spectral sensing and
advances in precision turfgrass management. In: Horgan B., Stier J., and
Bonos S., editors, Turfgrass: Biology, use and management. Agron. Monogr. 56. ASA, CSSA, and SSSA, Madison, WI; pp. 1151–1188.
Bell GE, Xiong X. 2008. The history, role, and potential of optical sensing for
practical turf management. In Pessaraki P., editor, Handbook of turfgrass
management and physiology. Boca Raton, FL: CRC Press; pp. 641–658.
Berni JA, Zarco-Tejada PJ, Suárez L, Fereres E. 2009. Thermal and
narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens.; 47: 722–738.
Blasi G, Pisante M. 2016. Linee guida per lo sviluppo dell’agricoltura di
precisione in Italia. Ministero delle Politiche Agricole Alimentari e Forestali.
Boschetti M, Bolzan L, Bresciani M, Giardino C, L’Astorina A, Lanari R, Manunta M, Mauri E, Zilioli E. 2005. Telerilevamento. Collana “Diffusione e sperimentazione della cartografia, del telerilevamento e dei sistemi informativi geografici, come tecnologie didattiche applicate allo studio del territorio e dell’ambiente”, Volume 3. Ministero dell’istruzione, dell’università e della ricerca.
Bremer DJ, Lee H, Su K, Keeley SJ. 2011. Relationships between normalized
difference vegetation index and visual quality in cool-season turfgrass: II.
Factors affecting NDVI and its component reflectances. Crop Sci.; 51: 2219– 2227.
Bremer DJ, van der Mewer D. 2016. Small Unmanned Aircraft Systems Detect
Turfgrass Drought. Kansas Agricultural Experiment Station Research
Bremner JM. 1965. Total Nitrogen. In: Black C.A.. editor, Methods of Soil Analysis, Part 2. Agron. Monogr. no. 9. ASA, CSSA and SSSA Madison, WI; pp. 1149–1178.
Caprioli M. 2014. Cartografia Numerica. Appunti delle lezioni. Corso di laurea in Ingegneria Ambientale e del Territorio, anno accademico 2014-2015.
Carrow RN, Krum JM, Flitcroft I, Cline V. 2010. Precision turfgrass
management: challenges and field applications for mapping turfgrass soil and stress. Precis. Agric.; 11: 115–134.
Casa R, et al. 2016. Agricoltura di precisione. Metodi e tecnologie per migliorare
l’efficienza e la sostenibilità dei sistemi colturali. Collana “Edagricole
Università e Formazione”. Edagricole – Edizioni Agricole di New Business Media srl. Milano.
Caturegli L, Casucci M, Lulli F, Grossi N, Gaetani M, Magni S, et al. 2015a.
GeoEye-1 satellite versus groundbased multispectral data for estimating nitrogen status of turfgrasses. Int. J. Remote Sens.; 36: 2238–2251.
Caturegli L, Grossi N, Saltari M, Gaetani M, Magni S, Nikolopoulou AE, et al. 2015b. Spectral reflectance of tall fescue (Festuca Arundinacea Schreb.) under
different irrigation and nitrogen conditions. Agric. Agric. Sci. Procedia; 4: 59–
67.
Caturegli L, Lulli F, Foschi L, Guglielminetti L, Bonari E, Volterrani M. 2014a.
Monitoring turfgrass species and cultivars by spectral reflectance. European J.
Hortic. Sci.; 79: 97–107.
Caturegli L, Lulli F, Foschi L, Guglielminetti L, Bonari E, Volterrani M. 2014b.
Turfgrass spectral reflectance: simulating satellite monitoring of spectral signatures of main C3 and C4 species. Precis. Agric.; 16: 297–310.
Chao H, Baumann M, Jensen A, Chen Y, Cao Y, Ren W, et al. 2008. Band-
management and distributed irrigation control. In: IFAC World Congress,
Seoul, Korea.;17: 11744–11749.
Coifman B, McCord M, Mishalani RG, Iswalt M, Ji Y. 2006. Roadway traffic
monitoring from an unmanned aerial vehicle. Proceedings of the 2006 IEEE Intelligent Transport Systems. IET Digital Library; March 2006. 153: 11–20.
Corwin DL, S M Lesch. 2005. Apparent soil electrical conductivity
measurements in agriculture. Comput. Electron. Agric.; 46: 11–43.
Darvishsefat AA, Abbasi M, Schaepman M. 2011. Evaluation of spectral
reflectance of seven Iranian rice varieties canopies. J. Agr. Sci. Tech.; 13: 1091–
1104.
Doherty P, Rudol P. 2007. A UAV search and rescue scenario with human body
detection and geolocalization. In: AI 2007: Advances in Artificial Intelligence.
Springer Berlin Heidelberg; pp 1–13.
Dordas C. 2008. Role of nutrients in controlling plant diseases in sustainable
agriculture: A review. Agron. Sustainable Dev.; 28: 33–46.
Dorigo WA, Zurita-Milla R, de Wit AJ, Brazile J, Singh R, Schaepman ME. 2007. A review on reflective remote sensing and data assimilation techniques
for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf.; 9: 165–
193.
ENAC. 2015. Regolamento. Mezzi aerei a pilotaggio remoto. Edizione 2, emendamento 1 del 21 dicembre 2015. Disponibile: https://www.enac.gov.it/repository/ContentManagement/information/N12267 1512/Reg_APR_Ed2_Em1.pdf
Fensholt R, Proud SR. 2012. Evaluation of earth observation based global long
term vegetation trends-Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ.; 119: 131–147.
Foschi L, Volterrani M, Grossi N, Miele S. 2009. Monitoring relative water
content in turf with canopy spectral reflectance. Int. Turfgrass Soc. Res. J.; 11:
765–778.
Gates DM, Keegan JH, Schleter JC, Weidner VR. 1965. Spectral properties of
plants. Applied Optics, v.4, p.11-20
Gitelson A, Merzlyak MN. 1994. Spectral reflectance changes associated with
autumn senescence of Aesculus-Hippocastanum L. and Acer-Platanoides L. leaves – spectral features and relation to chlorophyll estimation. J. Plant
Physiol. 143:286-292.
Gupta SG, Ghonge MM, Jawandhiya PM. 2013. Review of unmanned aircraft
system (UAS). IJARCET.; 2: 1646–1658.
Guyot G. 1990. Optical properties of vegetation canopies, in Steven MD e Clark JA, eds. Applications of remote sensing in agriculture, Butterworths, London, England.
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.
Herwitz S, Allmendinger K, Slye R, Dunagan S, Lobitz B, Johnson L, et al. 2004a. Nighttime UAV vineyard mission: Challenges of see-and-avoid in the
NAS. Proceedings of the AIAA, 3rd Unmanned Unlimited Technical
Conference, Workshop and Exhibit. Chicago, Illinois. 20–23 September 2004. Herwitz SR, Dunagan S, Sullivan D, Higgins R, Johnson L, Zheng J, et al. 2003. Solar-powered UAV mission for agricultural decision support. Int. Geosci. Remote Sens.; 3: 1692–1694.
Herwitz SR, Johnson LF, Dunagan SE, Higgins RG, Sullivan DV, Zheng J, et
al. 2004b. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput. Electron. Agric.; 44: 49–61.
Horler, DNH, Dockray M, Barber J. 1983. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4:273-288.
Hruska R, Mitchell J, Anderson M, Glenn NF. 2012. Radiometric and
geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sens.; 4: 2736–2752.
Hunt ER, Hively WD, Fujikawa SJ, Linden DS, Daughtry CS, McCarty GW. 2010. Acquisition of NIR-greenblue digital photographs from unmanned
aircraft for crop monitoring. Remote Sens.; 2: 290–305.
Jackson RD, Huete AR. 1991. Interpreting vegetation indices. Prev. Vet. Med., 11, 185-200.
Jiang Y, Carrow RN. 2007. Broadband spectral reflectance models of turfgrass
species and cultivars to drought stress. Crop Sci.; 47: 1611–1618.
Johnsen AR, Horgan BP, Hulke BS, Cline V. 2009. Evaluation of remote
sensing to measure plant stress in Creeping Bentgrass (L.) fairways. Crop Sci.;
49: 2261–2274.
Johnson LF, Herwitz S, Dunagan S, Lobitz B, Sullivan D, Slye R. 2003.
Collection of ultra high spatial and spectral resolution image data over California vineyards with a small UAV. Proceedings of the 30th International
Symposium on Remote Sensing of Environment, Honolulu, HI, USA, 10–14 November 2003; 20: 845–849.
Jordan CF. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, v.50, p.663-666.
Krum JM, Carrow RN, Karnok K. 2010. Spatial mapping of complex turfgrass
sites: Site-specific management units and protocols. Crop Sci.; 50: 301–315.
Kruse JK. 2004. Remote sensing of moisture and nutrient stress in turfgrass
Laliberte AS, Rango A. 2011. Image processing and classification procedures
for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIsci. Remote Sens.; 48: 4–23.
Lelong CC, Burger P, Jubelin G, Roux B, Labbé S, Baret F. 2008. Assessment
of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors; 8: 3557–3585.
Lillesand TM, Kiefer RW. 1987. Remote sensing and image interpretation. 2nd ed. John Wiley & Sons, New York.
Lin Y, Saripalli S. Road detection from aerial imagery. 2012. In: Robotics and Automation (ICRA), 2012 IEEE International Conference on. IEEE, May 2012. pp. 3588–3593.
Masali L. 2016. Multicotteri & droni. Guida pratica. DronEzine. Associazione Nuova Editoria. Bologna.
Matese A, Primicerio J, Di Gennaro SF, Fiorillo E, Vaccari FP, Genesio L. 2013. Development and application of an autonomous and flexible unmanned
aerial vehicle for precision viticulture. Acta Horticulturae, 978, 63-69.
Matese A, Toscano P, Di Gennaro SF, Genesio L, Vaccari FP, Primicerio J, Belli C, Zaldei, Bianconi R, Gioli B. 2015. Intercomparison of UAV, aircraft
and satellite remote sensing platforms for precision viticulture. Remote
Sensing, 7, 2971-2990.
Michez A, Piégay H, Lisein J, Claessens H, Lejeune P. 2016. Classification of
riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ. Monit. Assess.;
188: 1–19.
Morris KN, Shearman RC. 2008. NTEP turfgrass evaluation guidelines. Nat. Turfgrass Eval. Prog. 1–5. Beltsville, MD.
Nagendra H, Lucas R, Honrado JP, Jongman RH, Tarantino C, Adamo M, et
areas, habitat extent, habitat condition, species diversity, and threats. Ecol.
Indic.; 33: 45–59.
Nex F, Remondino F. 2013. UAV for 3D mapping applications: a review. Applied Geomatics, 6: 1-15.
Padilla FM, Peña-Fleitas MT, Gallardo M, Thompson RB. 2014. Evaluation of
optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon. Eur. J.
Agron.; 58: 39–52.
Pan Z, Huang J, Zhou Q, Wang L, Cheng Y, Zhang H, et al. 2015. Mapping
crop phenology using NDVI timeseries derived from HJ-1 A/B data. Int. J.
Appl. Earth Obs. Geoinf.; 34: 188–197.
Perry EM, Davenport JR. 2007. Spectral and spatial differences in response of
vegetation indices to nitrogen treatments on apple. Comput. Electron. Agric.;
59: 56–65.
Rango A, Havstad K, Estell R. 2011. The utilization of historical data and
geospatial technology advances at the Jornada Experimental Range to support western America ranching culture. Remote Sens.; 3: 2089–2109.
Resop JP, Cundiff JS, Heatwole CD. 2011. Spatial analysis to site satellite
storage locations for herbaceous biomass in the piedmont of the Southeast.
Appl. Eng. Agric.; 27: 25–32.
Rossi S, Rampini A, Bocchi S, Boschetti M. 2010. Operational monitoring of
daily crop water requirements at the regional scale with time series of satellite data. J. Irrig. Drain. Eng.; 136: 225–231.
Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC. 1974. Monitoring the
vernal advancements and retrogradation of natural vegetation. NASA/GSFC
Final Report (371 pp.). MD, USA Greenbelt.
Salamì E, Barrado C, Pastor E. 2014. UAV flight experiments applied to the
remote sensing of vegetated areas. Remote Sensing, 6: 11051-11081.
Samborski SM, Tremblay N, Fallon E. 2009. Strategies to make use of plant
sensors-based diagnostic information for nitrogen recommendations. Agron. J.;
101: 800–816.
Sankaran S, Khot LR, Espinoza CZ, Jarolmasje S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Pavek M. 2015. Low-
altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70: 112-123.
Sankaran S, Mishra A, Ehsani R, Davis C. 2010. A review of advanced
techniques for detecting plant diseases. Computer and Electronics in
Agriculture, 72: 1-13.
Stowell L, Gelernter W. 2006. Sensing the future. Golf Course Manage.; 74: 107–110.
Stroppiana D, Migliazzi M, Chiarabini V, Crema A, Musanti M, Franchino C,
et al. 2015. Rice yield estimation using multispectral data from UAV: A preliminary experiment in northern Italy. Proceedings of the 2015 IEEE
International Geoscience and Remote Sensing Symposium (IGARSS 2015), Milan, Italy. 26–31 July 2015. pp. 4664–4667.
Sugiura R, Noguchi N, Ishii K. 2005. Remote-sensing technology for vegetation
monitoring using an unmanned helicopter. Biosyst. Eng.; 90: 369–379.
Swain KC, Thomson SJ, Jayasuriya HP. 2010. Adoption of an unmanned
helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Trans. ASAE; 53: 21–27.
Trenholm LE, Carrow RN, Duncan RR. 1999. Relationship of multispectral
radiometry data to qualitative data in turfgrass research. Crop Sci.; 39: 763–
Turner D, Lucieer A, Watson C. 2012. An automated technique for generating
georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sens.; 4:
1392–1410.
Valente J, Sanz D, Barrientos A, Cerro JD, Ribeiro Á, Rossi C. 2011. An air-
ground wireless sensor network for crop monitoring. Sensors; 11: 6088–6108.
Viña A, Gitelson AA, Nguy-Robertson AL, Peng Y. 2011. Comparison of
different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ.; 115: 3468–3478.
Viscarra Rossel RA, McBratney AB, Minasny B. 2010. Proximal Soil Sensing.
Springer, Berlin.
Volterrani M, Grossi N, Foschi L, Miele S. 2005. Effects of nitrogen nutrition
on bermudagrass spectral reflectance. Int. Turfgrass Soc. Res. J.; 10: 1005–
1014.
Walters DR, Bingham IJ. 2007. Influence of nutrition on disease development
caused by fungal pathogens: implications for plant disease control. Ann. Appl.
Biol.; 151: 307–324.
Watts AC, Ambrosia VG, Hinkley EA. 2012. Unmanned aircraft systems in
remote sensing and scientific research: Classification and considerations of use.
Remote Sens.; 4: 1671–1692.
Whitehead K, Hugenholtz CH. 2014. Remote sensing of the environment with
small unmanned aircraft systems (UASs), part 1: a review of progress and challenges. Journal of Unmanned Vehicle System 2, 69-85.
Xiang H, Tian L. 2011. Method for automatic georeferencing aerial remote
sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosyst.
Xiong X, Bell GE, Solie JB, Smith MW, Martin B. 2007. Bermudagrass
seasonal responses to nitrogen fertilization and irrigation detected using optical sensing. Crop Sci.; 47: 1603–1610.
Zarco-Tejada PJ, Guillén-Climent ML, Hernández-Clemente R, Catalina A, González MR, Martín P. 2013. Estimating leaf carotenoid content in vineyards
using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol.; 171: 281–294.
Zhang C, Kovacs JM. 2012. The application of small unmanned aerial systems
for precision agriculture: a review. Precision Agriculture; 13: 693–712.
Zvara O. 2015. UAV - based imagery processing using Structure from Motion