ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 1, pp. 49-60

Assessment of space and time changes of NDVI (biomass) in Armenia’s mountain ecosystems using remote sensing data

V.S. Muradyan 1 , Sh.G. Asmaryan 1 , A.K. Saghatelyan 1 
1 Center for Ecological-Noosphere Studies NAS RA, Yerevan, Armenia

Accepted: 23.10.2015
DOI: 10.21046/2070-7401-2016-13-1-49-60

This study of vegetation biomass for different areas in the mountain ecosystems of Armenia was implemented for the vegetation period of 2013. The research was done based on time series of Normalized Difference Vegetation Index (NDVI) as NDVI images have been proven to be a powerful tool to monitor biomass growth during the last decade. NDVI, which can be directly calculated from Landsat satellite data, is related to vegetation canopy characteristics. This NDVI-based research of different-aspect slopes at 1850–3150 m have indicated that during vegetation period the highest biomass is detected on north facing slopes at altitudes of 2300–2400 m, and the lowest – on south facing slopes at altitudes of 2000–2100 m. High correlation coefficients between NDVI values of southwest slopes and absolute altitudes of localities are detected in April and August, whereas variation in NDVI values depending on the altitude is nonlinear.
Keywords: biomass, mountain ecosystem, NDVI, remote sensing data
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  1. Aghababian Sh. M., Gornye senokosy i pastbishchy (Mountain hayfields and pastures), Moscow: GISL, 1959, 341 p.
  2. Ziroyan A. N., Ekologo-bioenergeticheskaya ocenka rastitelnosti Armenni (An ecological and bioenergy assessment of Armenia’s vegetation), Yerevan: Lusabac, 2008, 352 p.
  3. Kronberg P., Disstancionnoe izuchenie zemli (Remote investigation of the Earth), Moscow: Mir, 1988, 352 p.
  4. Maghakian A.K., Rastitelnost Armyanskoi SSR (Vegetation of the Armenian SSR), Moscow: AN SSSR, 1941, 276 p.
  5. Mezhunts B.Kh., Bioenergeticheskie pokazateli fitocenozov v razlichnix ekologicheskix uslovijax (Bioenergy indices of phytocoenoses under different ecological conditions), Tez. Dokl. Mezd. Konf. Fundamentalnye i prikladnye problemy oxrany okruzayushchei sredy (Proc. Int. Conf. Fundamental and applied problems of environmental protection), Tomsk, 2005, p. 73.
  6. Narinian S.G., Sootnoshenie nadzemnoi I podzemnoi massy rastitelnosti alpijskix kovrov gory Aragac /Armenia/ v svjazi s evolyuciei relefa I genezisom pochv (Correlation of aboveground and underground phytomass of Mt. Aragats alpine carpets. (Armenia) in connection with relief evolution and soil genesis), Probl. Bot., 1966, Vol. 8, pp. 231–245.
  7. Trifonova T.A., Mishchenko N.V., Krasnoshchekov A.N., Geoinformatsionnye sistemy I distancionnoe zondirowanie w ekologicheskix issledovanijax (GIS systems and remote sensing in ecological studies), Moscow: Akademicheski Proekt., 2005, 348 p.
  8. Belsius L., Weirich F., The use of the Minnaert correction for land-cover classification in mountainous terrain, International Journal of Remote Sensing, 2005, Vol. 26, pp. 3831–3851.
  9. Bernstein L.S, Adler-Golden S.M, Sundberg R.L., Validation of the QUick Atmospheric Correction (QUAC) Algorithm for VNIR-SWIR Multi- and Hyperspectral Imager, Proceedings of the SPIE Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XI, Orlando, 2005, p. 668.
  10. Biard F., Lepoutre D., On Line Vegetation Condition Monitoring in Europe: Agri-Quest®. A Tool That Helps Users Build Information and Value from Remote Sensing, Proceedings of the conference EFITA 2001, Montpellier, 2001, pp. 507–512.
  11. Flynn E.S., Dougherty C.T., Wendroth O., Assessment of Pasture Biomass with the Normalized Difference Vegetation Index from Active Ground-Based Sensors, Agronomy Journal, 2008, Vol. 100, pp. 114–121.
  12. Freeman K.W., Martin K.L., Teal R.K., Raun W.R., Girma K., Arnall D.B., Mullen R.W. By-Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height, Agronomy Journal, 2007, Vol. 99, P. 530–536.
  13. Hadjimitsis D.G., Papadavid G., Agapiou A., Themistocleous K., Hadjimitsis M.G., Retalis A., Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices, Nat. Hazards Earth Syst. Sci., 2010, Vol. 10, pp. 89–95.
  14. Hatfi J.L., Gitelson A.A., Schepers J.S., Walthall C.L., Application of Spectral Remote Sensing for Agronomic Decisions, Agronomy Journal, 2008, Vol. 100, pp. 117–131.
  15. Hunt E.R., Everitt J.H., Ritchie J.C., Applications and Research Using Remote Sensing for Rangeland Management, Photogrammetric Engineering and Remote Sensing, 2003, Vol. 69, pp. 675–694.
  16. Kaufman Y., Tanre D., Atmospherically resistant vegetation index (ARVI) for EOS-MODIS, IEEE Transactions on Geoscience and Remote Sensing, 1992, Vol. 30, No. 2, pp. 261–270.
  17. Lu D., Mausel P., Batistella M., Moran E., Land-cover binary change detection methods for use in the moist tropical region of the Amazon, International Journal of Remote Sensing, 2005, Vol. 26, No. 1, pp. 101–114.
  18. Lusch D.P., Introduction to Environmental Remote Sensing, Michigan: Center for Remote Sensing and GIS, 1999, 750 p.
  19. Mezhunts B.Kh., Britt C.P., Mc Millan S.D., Givens D.I., The distribution of root biomass and energy yields in mountain grasslands in Armenia, Electronic J. Natural Sci., NAS of Armenia. Ecology, 2005, Vol. 1, No. 4. pp. 1–5.
  20. Milich L., Weiss E.A., GAC NDVI interannual coefficient of variation (CoV) images: ground truth sampling of the Sahel along north–south transects, International Journal of Remote Sensing, 2000, Vol. 21, No. 2, pp. 235–60.
  21. Minamiguchi N., The Application of Geospatial and Disaster Information for Food Insecurity and Agricultural Drought Monitoring and Assessment by the FAO GIEWS and Asia FIVIMS, Proceedings of the Workshop on Reducing Food Insecurity Associated with Natural Disasters in Asia and the Pacific, Bangkok, 2005, pp. 20–21.
  22. Olson K.C., Cochran R.C., Radiometry for Predicting Tallgrass Prairie Biomass Using Regression and Neural Models, Journal of Range Management, 1998, Vol. 51, No. 2, pp. 186–192.
  23. Rause J.W., Haas R.H., Schell J.A., Deering D.W., Monitoring vegetation systems in the Great Plains with ERTS, Proceedings of the 3rd ERTS symposium, 1973, Vol. 1, pp. 48–62.
  24. CARMAC CS-11/103: Mapping and assessing the community grasslands and developing a grazing scheme, Project report, Yerevan: The Center for Ecological-Noosphere Studies of NAS RA, 2014, 400 p.
  25. Serrano L., Filella I., Penuelas J., Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies, Crop Science, 2000, Vol. 40, No. 3, pp. 723–731.
  26. Singh A., Digital change detection techniques using remotely-sensed data, International Journal of Remote Sensing, 1989, Vol. 10, No. 6, pp. 989–1003.
  27. Williams D., Goward S., Arvidson T., Landsat: Yesterday, today and tomorrow, Photogrammetric Engineering and Remote Sensing, 2006, Vol. 72, No. 10, pp. 1171–1178.
  28. Zhou L., Tucker C.J, Kaufmann R.K, Slayback D., Shabanov N.V., Myneni R.B., Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981–1999, Journal of Geophysical Research, 2001, Vol. 106, pp. 69–83.