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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. 3, pp. 88-102

Detection of drying coniferous forests from aerospace data

L.V. Katkovsky 1 , V.A. Siliuk 1 , B.I. Belyaev 1 , M.Yu. Belyaev 2 , E.E. Sarmin 2 , I.I. Bruchkouski 1 , S.I. Guliaeva 1 , G.S. Litvinovich 1 , Yu.S. Davidovich 1 
1 A.N. Sevchenko Institute of Applied Physical Problems of Belarusian State University, Minsk, Belarus
2 S.P. Korolev Rocket and Space Сorporation “Energia”, Korolev, Russia
Accepted: 20.06.2022
DOI: 10.21046/2070-7401-2022-19-3-88-102
The paper presents the methods and the results of remote sensing of conifers in different dryness stages. Laboratory measurements, airborne measurements and satellite data were used. Spectral devices that were used in ground, airborne and satellite measurements are described. One of the approaches to the detection and classification of drying conifers is using vegetation indices. Most informative vegetation indices were determined that show high correlation with dryness stages. The indices can be used for both high spectral resolution data and multispectral satellite data. A comparative analysis of classification accuracy is carried out when using as initial signs reflectance or vegetation indices. Several classifiers are used: linear discriminant analysis, random forest and maximum likelihood. A higher classification accuracy is shown when using vectors of high-dimensional vegetation indices instead of the reflectance of coniferous trees. Another approach is transforming multispectral images in the original spectral space in order to enhance the existing spectral differences and classify the transformed reflectance images. The developed methods demonstrate the possibility of identifying drying coniferous forests areas based on the processing of simultaneous space images of medium spatial resolution (10–30 m) in the visible and near-IR spectral ranges.
Keywords: reflectance, vegetation indices, vegetation dryness, spectra, remote sensing, methods of classification
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References:

  1. Belyaev Yu. V., Chumakov A. V., Popkov A. P., Determination of drying up spruce forests from the images of the equipment “Multiscan”, Priborostroenie-2020 (Instrumentation-2020, Proc. 13th Intern. Scientific and Technical Conf.), Minsk: BNTU, 2020, pp. 171–173 (in Russian), https://rep.bntu.by/handle/data/86984.
  2. Bruchkousky I. I., Litvinovich G. S., Siliuk O. O., Domoratsky A. V., Multichannel spectral sensor MKSS-1, Priborostroenie-2020 (Instrumentation-2020, Proc. 13th Intern. Scientific and Technical Conf.), Minsk: BNTU, 2020, pp. 355–357 (in Russian), https://rep.bntu.by/handle/data/86863.
  3. Bruchkouski I. I., Siliuk O. O., Litvinovich G. S., Lamaka A. A., Stanchik V. V., Gulyaeva S. I., Goniophotometer for Measurements of Spectral Reflectance Coefficients and Transmission Spectra, Zhurnal prikladnoi spektroskopii, 2021, Vol. 88, No. 2, pp. 303–310 (in Russian).
  4. Katkovskii L. V., Martinov A. O., Krot Yu. A., Bruchkovskaya S. I., Lomako A. A., Siliuk O. O., Stanchik V. V., Khomitsevich A. D., Solar spectropolarimeter SSP 600, Priborostroenie-2016 (Instrumentation-2016, Proc. 9th Intern. Scientific and Technical Conf.), Minsk, 2016, pp. 182–183 (in Russian).
  5. Katkovskii L. V., Belyaev B. I., Sosenko V. A., Ablameiko S. V., Hardware-software complex “Calibration” for ground-based spectrometry of the underlying surface and atmosphere, 7-i Belorusskii kosmicheskii congress (7th Belarusian Space Congress, Proc.), Minsk, 2017, Vol. 2, pp. 36–40 (in Russian).
  6. Bayat B., Tol C., Verhoef W., Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion, Remote Sensing, 2016, Vol. 8, Art. No. 557, 24 p., https://doi.org/10.3390/rs8070557.
  7. Cheng T., Rivard B., Sanchez-Azofeifa G. A., Feng J., Calvo-Polanco M., Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation, Remote Sensing of Environment, 2010, Vol. 114(4), pp. 899–910, https://doi.org/10.1016/j.rse.2009.12.005.
  8. Dengel S., Grace J., Aakala T., Hari P., Newberry S. L., Mizunuma T., Spectral characteristics of pine needles at the limit of tree growth in subarctic Finland, Plant Ecology and Diversity, 2013, Vol. 6(1), pp. 31–44, https://doi.org/10.1080/17550874.2012.754512.
  9. Fassnacht F., Latifi H., Ghosh A., Joshi P., Koch B., Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality, Remote Sensing of Environment, 2014, Vol. 140, pp. 533–548, https://doi.org/10.1016/j.rse.2013.09.014.
  10. Fernandez-Carrillo A., Patočka Z., Dobrovolný L., Franco-Nieto A., Revilla-Romero B., Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data, Remote Sensing, 2020, Vol. 12(21), Art. No. 3634, https://doi.org/10.3390/rs12213634.
  11. Fletcher R., Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification, American J. Plant Sciences, 2016, Vol. 7(15), pp. 2186–2198, DOI: 0.4236/ajps.2016.715193.
  12. Frolking S., Palace M. W., Clark D. B., Chambers J. Q., Shugart H. H., Hurtt G. C., Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure, J. Geophysical Research, 2009, Vol. 114, G00E02, https://doi.org/10.1029/2008JG000911.
  13. Guliaeva S., Bruchkousky I., Katkovsky L., Determining the Drying Out of Coniferous Trees Using Airborne and Satellite Data, Advances in Remote Sensing, 2021, Vol. 10, pp. 25–46, DOI: 10.4236/ars.2021.102002.
  14. Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, New York: Springer, 2009, 2nd ed., 746 p.
  15. Hlásny T., Krokene P., Liebhold A., Montagné-Huck C., Müller J., Qin H., Raffa K., Schelhaas M. J., Seidl R., Svoboda M., Viiri H., Living with bark beetles: impacts, outlook and management options. From Science to Policy 8, European Forest Inst., 2019, 52 p., DOI: 10.36333/fs08.
  16. Immitzer M., Atzberger C., Early Detection of Bark Beetle Infestation in Norway Spruce (Picea abies, L.) using WorldView-2 Data, Photogrammetrie – Fernerkundung – Geoinformation, 2014, Vol. 5, pp. 351–367, DOI: 10.1127/1432-8364/2014/0229.
  17. Katkovsky L., Beliaev B., Siliuk V., Beliaev M., Sarmin E., Davidovich Y., Remote spectral methods for detecting stress coniferous, E3S Web Conf., 2020, Vol. 223, 02004, https://doi.org/10.1051/e3sconf/202022302004.
  18. Kharuk V. I., Im S. T., Dvinskaya M. L., Golukov A. S., Ranson K. J., Climate-induced mortality of spruce stands in Belarus, Environmental Research Letters, 2015, Vol. 10, No. 12, DOI: 10.1088/1748-9326/10/12/125006.
  19. Kuhn M., Applied Predictive Modeling, New York: Springer, 2013, 600 p.
  20. Lausch A., Heurich M., Gordalla D., Dobner H.-J., Gwillym-Margianto S., Salbach C., Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales, Forest Ecology and Management, 2013, Vol. 308, pp. 76–89, https://doi.org/10.1016/j.foreco.2013.07.043.
  21. Liang L., Chen Y., Hawbaker T., Zhu Z., Gong P., Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data, Remote Sensing, 2014, Vol. 6, pp. 5696–5716, https://doi.org/10.3390/rs6065696.
  22. Lukes P., Stenberg P., Rautiainen M., Mottus M., Vanhatalo K. M., Optical properties of leaves and needles for boreal tree species in Europe, J. Remote Sensing Letters, 2013, Vol. 4, Issue 7, pp. 667–676, https://doi.org/10.1080/2150704X.2013.782112.
  23. Meddens A. J. H., Hicke J. A., Vierling L. A., Hudak A. T., Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery, Remote Sensing of Environment, 2013, Vol. 132, pp. 49–58, https://doi.org/10.1016/j.rse.2013.01.002.
  24. Mery G., Katila P., Galloway G., Alfaro R. I., Kanninen M., Lobovikov M., Varjo J., Forests and Society — Responding to Global Drivers of Change, Air pollution impacts on forests in changing climate, Vienna, 2010, IUFRO World Ser., pp. 55–74.
  25. Mohammed G. H., Noland T. L., Irving D., Sampson P. H., Zarco-Tejada P. J., Miller J. R., Natural and stress-induced effects on leaf spectral reflectance in Ontario species, Ontario Forest Research Inst., 2000, 42 p.
  26. Ollinger S. V., Sources of variability in canopy reflectance and the convergent properties of plants, New Phytologist, 2011, Vol. 189, No. 2, pp. 375–394, https://doi.org/10.1111/j.1469-8137.2010.03536.x.
  27. Ortiz S. M., Breidenbach J., Kändler G., Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data, Remote Sensing, 2013, Vol. 5(4), pp. 1912–1931, https://doi.org/10.3390/rs5041912.
  28. Pause M., Schweitzer C., Rosenthal M., Keuck V., Bumberger J., Dietrich P., Heurich M., Jung A., Lausch A., In Situ/Remote Sensing Integration to Assess Forest Health — A Review, Remote Sensing, 2016, Vol. 8(6), Art. No. 471, 21 p., https://doi.org/10.3390/rs8060471.
  29. Rodman K. C., Andrus R. A., Butkiewicz C. L., Chapman T. B., Gill N. S., Harvey B. J., Kulakowski D., Tutland N. J., Veblen T. T., Hart S. J., Effects of Bark Beetle Outbreaks on Forest Landscape Pattern in the Southern Rocky Mountains, U. S.A, Remote Sensing, 2021, Vol. 13(6), Art. No. 1089, 18 p., https://doi.org/10.3390/rs13061089.
  30. Stone C., Mohammed C., Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: A review, Current Forestry Reports, 2017, Vol. 3, pp. 75–92, http://dx.doi.org/10.1007/s40725-017-0056-1.
  31. Synek M., Janda P., Mikoláš M., Nagel T. A., Schurman J. S., Pettit J. L., Trotsiuk V., Morrissey R. C., Bače R., Čada V., Brang P., Bugmann H., Begovič K., Chaskovskyy O., Dušátko M., Frankovič M., Kameniar O., Kníř T., Kozák D., Langbehn T., Málek J., Rodrigo R., Saulnier M., Teodosiu M., Vostarek O., Svoboda M., Contrasting patterns of natural mortality in primary Picea forests of the Carpathian Mountains, Forest Ecology and Management, 2020, Vol. 457, https://doi.org/10.1016/j.foreco.2019.117734.
  32. Trumbore S., Brando P., Hartmann H., Forest health and global change, Science, 2015, Vol. 349(6250), pp. 814–818, DOI: 10.1126/science.aac6759.
  33. Waser L. T., Küchler M., Jütte K., Stampfer T., Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality, Remote Sensing, 2014, Vol. 6(5), pp. 4515–4545, https://doi.org/10.3390/rs6054515.
  34. White J. C., Wulder M. A., Brooks D., Reich R., Wheate R. D., Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery, Remote Sensing of Environment, 2005, Vol. 96, pp. 340–351, https://doi.org/10.1016/j.rse.2005.03.007.
  35. Wulder M. A., White J. C., Carroll A. L., Coops N. C., Challenges for the operational detection of mountain pine beetle green attack with remote sensing, The Forestry Chronicle, 2009, Vol. 85(1), pp. 32–38, https://doi.org/10.5558/tfc85032-1.