Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 153-167

Possibilities of tree status inventory in an apple orchard based on remote sensing data

I.Yu. Savin 1, 2 , S.N. Konovalov 3 , E.Yu. Prudnikova 1, 2 , Yu.I. Verniuk 1 , P.G. Grubina 1 , S. Nasser 2 
1 V.V. Dokuchaev Soil Science Institute, Moscow, Russia
2 RUDN University, Moscow, Russia
3 Federal Horticultural Center for Breeding, Agrotechnology and Nursery, Moscow, Russia
Accepted: 14.07.2022
DOI: 10.21046/2070-7401-2022-19-4-153-167
We analyzed the possibilities of using Sentinel-2 satellite data and data obtained from unmanned aerial vehicle (UAV) for rapid assessment of the state of apple trees. The research was carried out on the example of the test site near the settlement of Mikhnevo (Stupino municipality of Moscow region). The data on the apple-tree condition obtained in the field were compared with the parameters calculated from the remote sensing data. It was found that at the moment, remote monitoring of orchard tree condition can be performed on qualitative and semi-quantitative levels. The attempt to construct quantitative assessments did not lead to a positive result. In order to build quantitative dependencies of parameters obtained from satellite data and UAV with tree condition parameters, it is necessary to study in-depth the dynamics of spectral reflectivity of ground objects in an orchard during the whole vegetation season. Besides, it is necessary to introduce additional tree state parameters, which are not currently used in fruit growing practice, but which may give a clue to understanding what properties of vegetation and fruit trees are used to form a remote image during different periods of vegetation season.
Keywords: Sentinel-2, apple orchard, unmanned aerial vehicle, vegetation monitoring, digital elevation model, tree’s status
Full text

References:

  1. Gegechkori B. S., Klad’ A. A., Doroshenko T. N., Praktikum po plodovodstvu (Fruit growing workshop), Krasnodar: KGAU, 2008, 345 p. (in Russian).
  2. Dragavtseva I. A., Savin I. Yu., Zagirov N. G., Kaziev M. R. A., Akhmatova Z. P., Morenets A. S., Battalov S. B., Resursnyi potentsial zemel’ Severnogo Kavkaza dlya plodovodstva (Resource potential of lands in the North Caucasus for fruit farming), Krasnodar, Makhachkala: DagNIISKh, 2016, 138 p. (in Russian).
  3. Egorov E. A., Fisenko A. N., Doroshenko T. N., Khvostova I. V., Teren’ko G. N., Popova V. P., Smol’yakova V. M., Storchevaya E. M., Cherkezova S. R., Dragavtseva I. A., Artyukh S. N., Mozhar N. V., Alekhina E. M., Zaremuk R. Sh., Prichko T. G., Lopatina L. M., Savin I. Yu., Svyatkina O. A., Sistemoobrazuyushchie ekologicheskie faktory i kriterii zon ustoichivogo razvitiya plodovodstva na Severnom Kavkaze (System-forming ecological factors and criteria for sustainable development zones of fruit farming in the North Caucasus), Krasnodar: SKZNIISiV, 2001, 284 p. (in Russian).
  4. Loupian E. A., Bartalev S. A., Savin I. Yu., Satellite monitoring technologies in agriculture in Russia, Aerokosmicheskii kur’er, 2009, No. 6, pp. 47–49 (in Russian).
  5. Rybalko E. A., Baranova N. V., Loupian E. A., Tolpin V. A., Kashnitskiy A. V., Uvarov I. A., Krasheninnikova Yu. S., Ivanchenko V. I., Management of ground data and remote observations data processing aimed at vineyards remote monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 1, pp. 79–92 (in Russian), DOI: 10.21046/2070-7401-2016-13-1-79-92.
  6. Savin I. Yu., Modern satellite monitoring of soils and crops: achievements and problems, In: Primenenie sredstv distantsionnogo zondirovaniya Zemli v sel’skom khozyaistve (Application of remote sensing in agriculture), Moscow: Pochvennyi institut im. V. V. Dokuchaeva, 2015, pp. 29–32.
  7. Tolpin V. A., Rybalko E. A., Baranova N. V., Kashnitskii A. V., Loupian E. A., Uvarov I. A., Formation of an information base of satellite and ground data for testing methods of remote monitoring of viticulture in the Republic of Crimea, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 1, pp. 101–110 (in Russian), DOI: 10.21046/2070-7401-2017-14-1-101-110.
  8. Alvino A., Marino S., Remote Sensing for Irrigation of Horticultural Crops, Horticulturae, 2017, Vol. 3, Art. No. 40, 36 p., https://doi.org/10.3390/horticulturae3020040.
  9. Ampatzidis Y., Partel V., Meyering B., Albrecht U., Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence, Computers and Electronics in Agriculture, 2019, Vol. 164, 9 p., https://doi.org/10.1016/j.compag.2019.104900.
  10. Atzberger C., Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs, Remote Sensing, 2013, Vol. 5, pp. 949–981, DOI: 10.3390/rs5020949.
  11. Barnes E., Clarke T. R., Richards S. E., Colaizzi P., Haberland J., Kostrzewski M., Waller P., Choi C., Riley E., Thompson T. L., Coincident detection of crop water stress, nitrogen status, and canopy density using ground based multispectral data, 5 th Intern. Conf. Precision Agriculture, Bloomington, 2000, pp. 1–15.
  12. Broge N. H., Leblanc E., Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density, Remote Sensing of Environment, 2000, No. 76, pp. 156–172.
  13. Calera A., Campos I., Osann A., D’Urso G., Menenti M., Remote Sensing for Crop Management: from ET Modelling to Services for the End Users, Sensors, 2017, No. 17. Art. No. 1104, 2 p., https://doi.org/10.3390/s17051104.
  14. Capolupo A., Monterisi C., Tarantino E., Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment, Remote Sensing, 2020, No. 12, Art. No. 1201, 28 p., https://doi.org/10.3390/rs12071201.
  15. Casa R., Rossi M., Sappa G., Trotta A., Assessing Crop Water Demand by Remote Sensing and GIS for the Pontina Plain, Central Italy, Water Resources Management, 2009, Vol. 23, pp. 1685–1712, DOI: 10.1007/s11269-008-9347-4.
  16. Ceccato P., Gobron N., Flasse S., Pinty B., Tarantola S., Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach, Remote Sensing of Environment, 2002, No. 82, pp. 188–197, DOI: 10.1016/S0034-4257(02)00037-8.
  17. Corgne S., Hubert-Moy L., Betbeder J., Monitoring of Agricultural Landscapes Using Remote Sensing Data, Land Surface Remote Sensing in Agriculture and Forest, 2016, pp. 221–247.
  18. Dorigo W. A., Zurita-Milla R., Wit A., Brazile J., Singh R., Schaepman M. E., A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling, Intern. J. Applied Earth Observation and Geoinformation, 2007, No. 9, pp. 165–193, DOI: 10.1016/j.jag.2006.05.003.
  19. Escadafal R., Huete A., Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil “noise”, Comptes Rendus de l’Académie des Sciences, Série 2, 1991, No. 312, pp. 1385–1391.
  20. Escadafal R., Belghit A., Ben-Moussa A., Indices spectraux pour la télédétection de la dégradation des milieux naturels en Tunisie aride, Actes du 6eme Symp. Intern. sur les mesures physiques et signatures en télédétection, de G. Guyot (réd), Val d’Isère, France, 1994, No. 17, pp. 253–259.
  21. Gamon J. A., Surfus J. S., Assessing leaf pigment content and activity with a reflectometer, New Phytologist, 1999, No. 143, pp. 105–117, DOI:10.1046/j.1469-8137.1999.00424.x.
  22. Gitelson A. A., Kaufman Y. J., Merzlyak M. N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sensing of Environment, 1996, No. 58, pp. 289–298, DOI: 10.1016/S0034-4257(96)00072-7.
  23. Gitelson A., Kaufman Y., Stark R., Rundquist D., Novel Algorithms for Remote Estimation of Vegetation Fraction, Remote Sensing of Environment, 2002, No. 80, pp. 76–87.
  24. Gitelson A. A., Viña A., Arkebauer T. J., Rundquist D. C., Keydan G., Leavitt B., Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophysical Research, 2003, Vol. 30, Issue 5, Art. No. 1248, 4 p., https://doi.org/10.1029/2002GL016450.
  25. Glenn E. P., Nagler P. L., Huete A. R., Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing, Surveys Geophysics, 2010, No. 31, pp. 531–555, DOI: 10.1007/s10712-010-9102-2.
  26. Gobron N., Pinty B., Verstraete M. M., Widlowski J., Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications, Geoscience and Remote Sensing, 2000, No. 38, pp. 2489–2505.
  27. Hancock D. W., Dougherty C. T., Relationships between blue- and red-based vegetation indices and leaf area and yield of alfalfa, Crop Science, 2007, No. 47, pp. 2547–2556, DOI: 10.2135/cropsci2007.01.0031.
  28. Jimenez-Brenes F. M., Lopez-Granados F., de Castro A. I., Torres-Sanchez J., Serrano N., Pena J. M., Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling, Plant Methods, 2017, Vol. 13, Art. No. 55, 15 p., DOI: 10.1186/s13007-017-0205-3.
  29. Lymburner L., Beggs P. J., Jacobson C. R., Estimation of canopy-average surface-specific leaf area using Landsat TM data, Photogrammetric Engineering and Remote Sensing, 2000, No. 66, pp. 183–191.
  30. Maimaitijiang M., Sagan V., Sidike P., Daloye A. M., Erkbol H., Fritschi F. B., Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning, Remote Sensing, 2020, Vol. 12(9), Art. No. 1357, 23 p., https://doi.org/10.3390/rs12091357.
  31. Mushtaq G., Asima N., Estimation of apple orchard using remote sensing and agro-meteorology land-based observation in Pulwama district of Kashmir valley, Intern. J. Remote Sensing and Geoscience, 2014, Vol. 3(6), pp. 2319–3484.
  32. Ok A. O., Ozdarici-Ok A., 2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models, Intern. J. Digital Earth, 2018, Vol. 11(6), pp. 583–608, DOI: 10.1080/17538947.2017.1337820.
  33. Panda S. S., Hoogenboom G., Paz J. O., Remote sensing and geospatial technological ap-plications for site-specific management of fruit and nut crops: a review, Remote Sensing, 2010, Vol. 2(8), pp. 1973–1997, DOI: 10.3390/rs2081973.
  34. Pinder J. E., McLeod K. W., Indications of relative drought stress in longleaf pine from Thematic Mapper data, Photogrammetric Engineering and Remote Sensing, 1999, No. 65, pp. 495–501.
  35. Prudnikova E., Savin I., Vindeker G., Grubina P., Shishkonakova E., Sharychev D., Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy, Remote Sensing, 2019, No. 11(16), Art. No. 1932, 25 p., https://doi.org/10.3390/rs11161932.
  36. Pujar D. U., Pujar U. U., Shruthi C. R., Wadagave A., Chulaki M., Remote sensing in fruit crops, J. Pharmacognosy and Phytochemistry, 2017, Vol. 6(5), pp. 2479–2484.
  37. Rembold F., Atzberger C., Savin I., Rojas O., Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection, Remote Sensing, 2013, Vol. 5(4), pp. 1704–1733, DOI: 10.3390/rs5041704.
  38. Savin I., Klyukina A., Dragavtseva I., About possibilities of apple trees flowering date detection based on MODIS data, Proc. 20 th Intern. Multidisciplinary Scientific GeoConf. SGEM, 2020, Book No. 2.2, pp. 157–164, DOI: 10.5593/sgem2020/2.2/s10.019.
  39. Torres-Sanchez J., Lopez-Granados F., Serrano N., Arquero O., Pena J. M., High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology, PLoS ONE, 2015, Vol. 10(6), Art. No. e0130479, 20 p., https://doi.org/10.1371/journal.pone.0130479.
  40. Tucker C. J., Elgin J. H., McMurtrey J. E., Fan C. J., Monitoring corn and soybean crop development with hand-held radiometer spectral data, Remote Sensing of Environment, 1979, No. 8, pp. 237–248, DOI: 10.1016/0034-4257(79)90004-X.
  41. Usha K., Singh B., Potential applications of remote sensing in horticulture — A review, Scientia Horticulturae, 2013, Vol. 153, pp. 71–83, DOI:10.1016/j.scienta.2013.01.008.
  42. Vincini M., Frazzi E., D’Alessio P., A broad-band leaf chlorophyll vegetation index at the canopy scale, Precision Agriculture, 2008, No. 9, pp. 303–319, DOI: 10.1007/s11119-008-9075-z.
  43. Weiss M., Jacob F., Duveiller G., Remote sensing for agricultural applications: A metareview, Remote Sensing of Environment, Elsevier, 2020, Vol. 236, 19 p., DOI: 10.1016/j.rse.2019.111402.
  44. Zhang C., Valente J., Kooistra L., Guo L., Wang W., Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches, Precision Agriculture, 2021, No. 22, pp. 2007–2052, DOI:10.1007/s11119-021-09813-y.
  45. Zhang P., Deng L., Lyu Q., He S. L., Yi S. L., Liu Y., Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution, Intern. J. Agricultural and Biological Engineering, 2016, Vol. 9, pp. 45–52, DOI: 10.3965/j.ijabe.20160904.2178.