A remote sensing technique for the assessment of stable interannual dynamical patterns of vegetation

M. Y. Chernetskiy; A. P. Shevyrnogov; N. F. Ovchinnikova

doi:10.1117/12.896748

7 October 2011 A remote sensing technique for the assessment of stable interannual dynamical patterns of vegetation

M. Y. Chernetskiy, A. P. Shevyrnogov, N. F. Ovchinnikova

Proceedings Volume 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII; 817420 (2011) https://doi.org/10.1117/12.896748
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

The time series of various parameters of satellite imagery (NDVI/EVI, temperature) during the growing season were considered in this work. This means that satellite images were considered not like a number of single scenes but like temporal sequences. Using time series enables estimating the integral phenological properties of vegetation. The basis of the developed technique is to use one of the methods of transformation of the multidimensional space in order to get the principal components. The technique is based on considering each dimension of the multidimensional space as satellite imagery for a specific date range. The technique automatically identifies spatial patterns of vegetation that are similar by phenology and growing conditions. Subsequent analysis allowed identification of the belonging of derived classes. Thus, the technique of revealing the spatial distribution of different dynamical vegetation patterns based on the phenological characteristics has been developed. The technique is based on a transformation of the multidimensional space of states of vegetation. Based on the developed technique, areas were obtained with similar interannual trends.

Citation Download Citation

M. Y. Chernetskiy, A. P. Shevyrnogov, and N. F. Ovchinnikova "A remote sensing technique for the assessment of stable interannual dynamical patterns of vegetation", Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 817420 (7 October 2011); https://doi.org/10.1117/12.896748

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KEYWORDS

Vegetation

Satellites

Earth observing sensors

Satellite imaging

Remote sensing

Data modeling

Ecosystems

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