Physiognomic vegetation types and their identification by using the decryption of digital images

Keywords: reclamation; vegetation; discriminant analysis; classification; pattern recognition

Abstract

The algorithm of the physiognomic vegetation types and the dead grass cover and the soil surface decryption using digital images is presented for further quantitative assessment of projective cover. The collection of material was held at the remediation site within Nikopol manganese ore Basin in city Pokrov. As objects of study were chosen following tehnosols: pedozems, sod-lithogenic soils on losses-like loam, on red-brown clay and gray-green clay. The visual analysis of the digital images of the surface areas studied revealed several types of images. This open surface soil, dead plants, grasses, plants Seseli campestre, Lactuca tatarica and legumes. The discriminant analysis allowed to accurately classify these objects by color characteristics. In the whole sample classification accuracy was 65.39%. The analysis only color without spatial context (especially form) reduces the accuracy of classification. In addition, structurally homogeneous object can be represented significant range of color values, reflections, shadows, mutual superposition of different objects, which significantly reduces the quality of classification. The following algorithm of the classification was proposed: 1) it is necessary to conduct cluster analysis (classification without training) a plurality of pixels. The number of clusters established must exceed the number of physiognomic types; 2) to analyze the correspondence between physiognomic types and clusters. Stop at that decision, when each physiognomic type corresponds to at least one cluster; 3) the decision to hold the cluster discriminant analysis, on which to perform differentiation pixels in images (classification of training); 4) conduct a segmentation of the image ‒ to unite in clusters corresponding physiognomic types; 5) evaluate physiognomic structure cover experimental plots. The accuracy of the proposed classification algorithm was 91.66%. The physiognomic types of vegetation can act as quantitative characteristics of the vegetation and can be considered as ecogeographic variables to describe the environmental conditions that other components of the ecosystem.

References

Balalaev, A. K., & Skrypnyck, O. A. (2011). Predvaritelnyie rezultatyi primeneniya metoda tsifrovoy obrabotki izobrajeniya dlya opredeleniya proektivnogo pokryitiya rastitelnosti kak osnovnogo indikatora sostoyaniya ekosistem [Preliminary results using the method of digital image processing to determine the plant cover as the primary indicator of ecosystems]. Ekologіya prirodokoristuvannya, 14, 114–123 (in Russian).

Bean, D., & Henry, G. H. R. (2003). CANTTEX Field Manual: Part A – Setting up a basic monitoring site. Burlington, Ontario: Ecological Monitoring and Assessment Network, Environment Canada.

Belgard, A. L. (1950). Lesnaya rastitelnost yugo-vostoka USSR [Forest vegetation of South-Eeast part of the USSR]. Kiev: Kiev State University (in Russian).

Bonham, C. D., & Clark, D. L. (2005). Quantification of plant cover estimates. Grassland Science, 51, 129–137. doi: 10.1111/j.1744-697x.2005.00018.x

Booth, D. T., Cox, S. E., Louhaichi, M., & Johnson, D. E. (2004). Lightweight camera stand for close-to-earth remote sensing. Journal of Range Management, 57(6), 675–678. doi: 10.2307/4004027

Byikov, B. A. (1978). Vvedenie v fitotsenologiyu [Introduction into the phytocoenology]. Nauka, Alma-Ata (in Russian).

Chen, W., Li, J., Zhang, Y., Zhou, F., Koehler, K., Leblanc, S., Fraser, R., Olthof, I., Zhang, Y. S., & Wang, J. (2009). Relating biomass and leaf area index to non-destructive measurements in order to monitor changes in Arctic vegetation. Arctic, 62(3), 281–294. doi: 10.14430/arctic148

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1) 1–22. doi: 10.1111/j.2517-6161.1977.tb01600.x

Jensen, R. K., Rasmussen, J., & Melander, B. (2004). Selectivity of weed harrowing in lupin. Weed Research, 44(4), 245–253. doi: 10.1111/j.1365-3180.2004.00396.x

Krebs, C. J., Danell, K., Angerbjorn, A., Agrell, J., Berteaux, D., Brathen, K. A., & Danell, O. (2003). Terrestrial trophic dynamics in the Canadian Arctic. Canadian Journal of Zoology, 81(5), 827–843. doi: 10.1139/z03-061

Laliberte, A. S., Rango, A., Herrick, J. E., Fredrickson, E. L., & Burkett, L. (2009). An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments, 69(1), 1–14. doi: 10.1016/j.jaridenv.2006.08.016

Luscier, J. D., Thompson, W. L., Wilson, J. M., Gorham, B. E., & Dragut, L. D. (2006). Using digital photographs and object–based image analysis to estimate percent ground cover in vegetation plots. Frontiers in Ecology and the Environment, 4(8), 408–413. doi: 10.1890/1540-9295(2006)4%5B408:udpaoi%5D2.0.co;2

Maslikova, K. P. (2017). Ekologichna struktura roslynnogo pokryvu tehnozemiv Nikopol'skogo margancevorudnogo basejnu [The ecological structure of technosol vegetation of the Nikopol manganese ore basin]. News of Dnipropetrovsk State Agrarian and Economic University, 4(46), 77‒88 (in Ukrainian).

Olmstead, M. A., Wample, R., Greene, S., & Tarara, J. (2004). Nondestructive Measurement of Vegetative Cover Using Digital Image Analysis. HortScience. 39(1), 55–59. doi: 10.21273/hortsci.39.1.55

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/

Rasmussen, I. A. (2004). The effect of sowing date, stale seedbed, row width and mechanical weed control on weeds and yields of organic winter wheat. Weed Research, 44(1), 12–20. doi: 10.1046/j.1365-3180.2003.00367.x

Richardson, M. D., Karcher, D. E., & Purcell, L. C. (2001). Quantifying turfgrass cover using digital image analysis. Crop Science, 41(6), 1884–1888. doi: 10.2135/cropsci2001.1884

Stredansky, J. (1999). Reduction of wind erosion intensity by vegetation cover. Ekologia, 18, 96–99.

Voronov, A. G. (1973). Geobotanika [Geobotany]. Vischaya shola, Moscow (in Russian).

Yeterevska, L., Momot, G. F., & Lehtsiyer, L. V. (2008). Rekultyvovani grunty: pidkhody do klasyfikatsii ta systematyky [Reclaimed soils, approaches to classification and taxonomy]. Soil Science, 9(3–4), 147–150 (in Ukranian).

Published
2019-03-25
How to Cite
Zhukov, O., Kovalenko, D., & Maslykova, K. (2019). Physiognomic vegetation types and their identification by using the decryption of digital images. Agrology, 2(2), 94–99. https://doi.org/10.32819/019013
Section
Оriginal researches