Application of multispectral satellite imagery to monitoring of vegetation activity annual dynamics

Instruments and control methods of environment, substances, materials and products


Kozlov A. V.1*, Kozlova M. V.2**, Skorik N. A.3***, Sharonov A. V.****

1. Lomonosov Moscow State University, 1, Leninskie Gory, Moscow, 119991, Russia
2. Zubov State Oceanographic Institute, 6, Kropotkinskiy lane, 119034, Moscow, Russia
3. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia



The paper presents the results of application of multispectral images obtained from OrbView-2 and Envisat missions to monitoring of vegetation activity annual dynamics at 12 test sites in Volga-Akhtuba floodplain based on conventional FAPAR index over a decade since 1997, including drought summer season of 2006, as a part of a project conducted by N.N. Zubov State Oceanographic Institute for monitoring of Lower Volga. The territory under consideration extends over 9000 square kilometers, thus making the use of satellite imagery to be the only source of assessing the overall ecosystem state. Time series of FAPAR values for regular (non anomalous) years are being approximated by a single harmonic model function. This function then serves as a reference to analyze deviations of particular FAPAR values from it. To introduce stronger physical relation between reference model function and parameters of the environment we consider using public annual temperature data from weather stations of World meteorological organization in addition to satellite images. The two models, one with temperature variations included and one without these, are then calibrated and compared against each other at all test sites. Mathematical and physical rationale for both types of models and some visual examples of their calibration are given. Our study concludes that despite of the fact that using the temperature data improves the accuracy of vegetation dynamics model, this effect does not prove to be significant, while yet increasing demands in the amount of input data for analysis. Quantitative statistical results support the case.


vegetation activity, Earth remote sensing, multispectral imagery, FAPAR


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