Abstract
This paper presents the results of studying a promising area related to the remote assessment of canopy spaces in forests by thresholding methods of image segmentation. The study is conducted based on the example of mixed forests in the Losiny Ostrov National Park. The proposed methodological approach to assessing the pattern of forest canopy on super-high (detailed) resolution satellite images is based on an analysis of light and shaded plots of canopy spaces using image-thresholding algorithms.
The pixel count for different brightness thresholds give enough information to estimate a range of biometric indexes, including volume density and average age and height of stands from statistical relationships. The accuracy of estimates is assessed for prescribed deviations and verified against the norms of estimation of corresponding taxation data.
We have found a statistical relationship of forest-canopy morphology indicators with brightness thresholds for shaded plots of canopy spaces and stemwood phytomass in forest ecosystems. Thus, super-high-resolution images may be considered an information basis for estimating the biometric parameters of stands, morphological indicators of forest canopy, and the productivity of forest ecosystems.
Similar content being viewed by others
References
Bartalev, S.A. and Breido, M.D., Automated analysis of taxation characteristics of shrub vegetation of the deserts according to remote data sensing, Izv. Vyssh. Uchebn. Zaved., Geod. Aerofotos’emka, 1989, no. 4, pp. 94–102.
Braslavskaya, T.Yu., Gradient analysis of floristic composition and ecological structure of herbal layer in flood-plain forests, Izv. Samar. Nauchn. Tsentra, Ross. Akad. Nauk, 2012, vol. 14, no. 1 (5), pp. 1202–1205.
Breido, M.D., Zhirin, V.M., Bartalev, S.A., and Bakhtinova, E.V., Characteristics of fodder resources of desert pastures according to aerospace data, Issled. Zemli Kosmosa, 1989, no. 3, pp. 66–76.
Culvenor, D.S., TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery, Comput. Geosci., 2002, vol. 28, no. 1, pp. 33–44.
Daliakopoulos, I., Grillakis, G., Koutroulis, A., and Tsanis, I., Tree crown detection on multispectral VHR satellite imagery, Photogramm. Eng. Remote Sens., 2009, vol. 75, no. 10, pp. 1201–1211.
Evans, B., Lyons, T.J., Barber, P.A., Hardy, G., and Stone, C., Dieback classification modeling using high-resolution digital multispectral imagery and in situ assessments of crown condition, Remote Sens. Lett., 2012, vol. 3, no. 6, pp. 541–550.
Franklin, S.E., Maudie, A.J., and Lavigne, M.B., Using spatial co-occurrence texture to increase forest structure and species composition classification accuracy, Photogramm. Eng. Remote Sens., 2001, no. 67, pp. 849–855.
Frazer, G., Fournier, R., Trofymow, J., and Hall, R., A comparison of digital and film fisheye photography for analysis of forest canopy structure and gap light transmission, Agric. For. Meteorol., 2001, vol. 109, no. 4, pp. 249–263.
Immitzer, M., Vuolo, F., and Atzberger, C., First experience with Sentinel-2 data for crop and tree species classifications in central Europe, Remote Sens., 2016, vol. 8, no. 3, pp. 166.
Karantzalos, K. and Argialas, D., Towards automatic olive trees extraction from aerial and satellite imagery, Int. Arch. Photogramm., Remote Sens. Spatial Inf. Sci., 2004, vol. 35, no. 5, pp. 360–365.
Karantzalos, K. and Argialas, D., Automatic detection and tracking of oil spills in SAR imagery with level set segmentation, Int. J. Remote Sens., 2008, vol. 29, no. 21, pp. 6281–6296.
Katoh, M., Classifying tree species in a northern mixed forest using high-resolution IKONOS data, J. For. Res., 2004, vol. 9, no. 1, pp. 7–14.
Kravtsova, V.I., Spatial structure of tundra-taiga ecotone on Putorana Plateau according to high-resolution satellite images, Vestn. Mosk. Univ., Ser. 5: Geogr., 2012, no. 1, pp. 67–74.
Kravtsova, V.I., Features of interpretation of northern forests in ultrahigh resolution satellite images, Contemp. Probl. Ecol., 2012, vol. 5, no. 7, pp. 612–620.
Kravtsova, V.I. and Loshkareva, A.R., Analysis of northern forest margin according to different-resolution satellite images, Vestn. Mosk. Univ., Ser. 5: Geogr., 2010, no. 6, pp. 49–57.
Kurnaev, S.F., Lesorastitel’noe raionirovanie SSSR (Forest Zonation of USSR), Moscow: Nauka, 1973.
Lertzman, K., Sutherland, G., Inselberg, A., and Saunders S., Canopy gaps and the landscape mosaic in a costal temperate rainforest., Ecology, 1996, vol. 77, no. 4, pp. 1254–1270.
Lesoustroitel’naya instruktsiya, utverzhdennaya Prikazom Rosleskhoza ot 12 dekabrya 2011 g., no. 516 (Forestry Instruction Approved by the Federal Forestry Agency Order No. 516 of December 12, 2011), Moscow, 2012.
Lukina, N.V., Orlova, M.A., Perminova, I.V., Khusainova, V.S., Vorob’eva, D.N., and Artemkina, N.A., Metabolomics of forest ecosystems: problems and prospects, Lesovedenie, 2016, no. 6, pp. 457–465.
Malahlela, O., Cho, M.A., and Mutanga, O., Map** canopy gaps in an indigenous subtropical coastal forest using high-resolution Worldview-2 data, Int. J. Remote Sens., 2014, vol. 35, no. 17, pp. 6397–6417.
Martin, M.E., Newman, S.D., Aber, J.D., and Congalton, R.G., Determining forest species composition using high spectral resolution remote sensing data, Remote Sens. Environ., 1998, no. 65, pp. 249–254.
Mirin, D.M., Phytocenotic elements of heterogeneity of vegetation cover, Izv. Samar. Nauchn. Tsentra, Ross. Akad. Nauk, 2012, vol. 14, no. 1 (5), pp. 1320–1323.
Nefed’ev, V.V., Zhirin, V.M., Lyameborshai, S.Kh., Shapochkin, M.S., Shatalov, A.V., and Eidlina, S.P., Istoriya i sostoyanie lesov Losinogo ostrova (The History and Conditions of Forests of Losinyi Island), Moscow: Prima-Press-M, 2000.
Nelson, T., Niemann, O., and Wulder, M., Spatial statistical techniques for aggregating point objects extracted from high spatial resolution remotely sensed imagery, J. Geogr. Syst., 2002, vol. 4, no. 4, pp. 423–433.
Nelson, T., Roots, B., Wilder, M., and Feick, R., Predicting Forest age classes from high spatial resolution remotely sensed imagery using Voronoi polygon aggregation, Geoinformatica, 2004, vol. 8, no. 2, pp. 143–155.
Novichikhin, A.E. and Tutubalina, O.V., Integration of algorithms for processing the high-resolution satellite images for automated decryption of forest vegetation, in Zemlya iz kosmosa: naibolee effektivnye resheniya (The Earth form Space: Most Effective Solutions), Moscow: Skaneks, 2009, no. 3, pp. 40–42.
Orlova, M.A., Lukina, N.V., and Smirnov, V.E., Selection of samples of forest litter taking to account mosaic structure of forest biogeocenoses, Lesovedenie, 2015, no. 3, pp. 214–221.
Palace, M., Keller, M., Asner, G., Hagen, S., and Braswell, B., Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties, Biotropica, 2008. V 40, no. 2, pp. 141–150.
Pinz, A., Tree isolation and species classification, Proc. Int. Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, BC: Can. For. Service, Pac. For. Center, 1999, pp. 127–139.
Samoilovich, G.G., Primenenie aerofotos”emki i aviatsii v lesnom khozyaistve (Use of Aerospace Imaging and Aviation in Forestry), Moscow: Lesnaya Prom-st’, 1964.
Smirnova, O.V. and Bobrovskii, M.V., Structural-dynamic organization of forest ecosystems, in Monitoring biologicheskogo raznoobraziya lesov Rossii (Monitoring of Biological Diversity of Russian Forests), Isaev, A.S., Ed., Moscow: Nauka, 2008, pp. 58–70.
Song, C., Estimating tree crown size with spatial information of high-resolution optical remotely sensed imagery, Int. J. Remote Sens., 2007, vol. 28, no. 15, pp. 3305–3322.
Sukhikh, V.I., Aerokosmicheskie metody v lesnom khozyistve i landshaftnom stroitel’stve: uchebnik (Aerospace Methods in Forestry and Landscape Construction: Tutorial), Yoshkar-Ola: Mariisk. Gos. Tekh. Univ., 2005.
Sukhikh, V.I., Zhirin, V.M., Ziemelis, T.A., and Shatalov, A.V., Use the high-resolution satellite images for forest taxation, Issled. Zemli Kosmosa, 1996, no. 2, pp. 45–56.
Warner, T., Lee, J., and McGraw, J., Delineation and identification of individual trees in the eastern deciduous forest, Proc. Int. Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, BC: Can. For. Service, Pac. For. Center, 1999, pp. 81–91.
Wulder, M.A., Nieman, K.O., and Goodenough, D.G., Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery, Remote Sens. Environ., 2000, vol. 73, pp. 103–114.
Zhirin, V.M., Determination of volume density of plain forests by means of super-high resolution satellite images, Materialy VI Vserossiiskoi konferentsii s mezhdunarodnym uchastiem “Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii, lesnom khozyaistve i ekologii,” g. Moskva, 20–22 aprelya 2016 g. (Proc. VI All-Russ. Conf. with Int. Participation “Aerospace Methods and GIS Technologies in Forestry and Ecology,” Moscow, April 20–22, 2016), Moscow: Tsentr Probl. Ekol. Produkt., Ross. Akad. Nauk, 2016, pp. 111–113.
Zhirin, V.M., El’man, R.I., and Ageev, V.N., Automated analysis of resources of larch plantations according to aerospace images, in Lesoustroistvo, taksatsiya, aerometody (Forestry, Taxation, and Aerospace Methods), Leningrad: Leningr. Nauchno-Issled. Inst. Lesn. Khoz., 1978, pp. 128–132.
Zhirin, V.M., Knyazeva, S.V., and Eidlina, S.P., The Ecodynamical Study of Forest-forming Process with Space Imagery, Lesovedenie, 2013, no. 5, pp. 76–85.
Zhirin, V.M., Knyazeva, S.V., and Eydlina, S.P., Influence of Forest-Canopy Morphology and Relief on Spectral Characteristics of Taiga Forests, Izv., Atmos. Ocean. Phys., 2017, vol. 53, no. 9, pp. 1019–1028.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © V.M. Zhirin, S.V. Knyazeva, S.P. Eidlina, 2018, published in Lesovedenie, 2018, No. 3, pp. 163–177.
Rights and permissions
About this article
Cite this article
Zhirin, V.M., Knyazeva, S.V. & Eidlina, S.P. Estimation of Linkages between Biometric Indexes of Forests and Pattern of Canopy Spaces on Super-High-Resolution Satellite Images. Contemp. Probl. Ecol. 11, 743–753 (2018). https://doi.org/10.1134/S1995425518070107
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1995425518070107