Mr. Garegin Tepanosyan Profile

Garegin Tepanosyan

at Ctr for Ecological-Noosphere Studies

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Proceedings Article | 6 September 2017 Paper

Studying the dynamics of mountain ecosystems in the context of climate change employing remotely sensed data

V. Muradyan, G. Tepanosyan, Sh. Asmaryan , A. Saghatelyan

Proceedings Volume 10444, 104440A (2017) https://doi.org/10.1117/12.2279423

KEYWORDS: Ecosystems, Climate change, Meteorology, Remote sensing, Vegetation

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This study was aimed to assess the spatio-temporal dynamic of ME using remote sensing methods. SPOT and AVHRR satellite data were used. Average annual, monthly and decade precipitation and temperature data obtained between 1982-2014 from 5 meteorological stations were used. NDVI, Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) were calculated and compared with meteorological data. Analyzing the dynamics of average NDVI, VCI, TCI, VHI for the entire area of Syunik marz (Armenia) has indicated that it has a cyclic character with a growth trend. NDVI and VCI show a steady growth, whereas TCI decreases, so wholly the dynamic trend of VHI is stable. Collation between average decade meteorological data for 1998-2013 and NDVI has indicated that during vegetation growing season the vegetation dynamics is determined by the amount of precipitation and average temperature recorded not in the given, but in previous and particularly 2th and 3th decades. So, collation between RS and meteorological data for more than 30 years supports a conclusion that there is a clear rise in productivity of the studied region’s ecosystems in the context of climate change.

Proceedings Article | 6 September 2017 Paper

Predicting heavy metal concentrations in soils and plants using field spectrophotometry

V. Muradyan, G. Tepanosyan, Sh. Asmaryan , L. Sahakyan , A. Saghatelyan , T. Warner

Proceedings Volume 10444, 1044411 (2017) https://doi.org/10.1117/12.2279184

KEYWORDS: Metals, Spectrophotometry, Vegetation, Soil contamination

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Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R²~0.9, RPD~2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R²~0.7, RPD~1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R², rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

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