Joint International Conference on Long-term Experiments ...

ABSTRACT
ANALYZE OF BIOMASS PRODUCTIVITY BY TIMESERIES
REMOTESENSING DATA IN REGION OF NYÍRLUGOS
János Tamás – Tibor Bíró – Nikolett Szőllősi
University of Debrecen Agricultural Sciences,
Faculty of Agronomy,
Department of Water and Environmental Management
The biomass growth can be described in regional space and time by continuous
equations. Effective calculation of biomass was not available because there was not
large scale resolution punctual data. We have introduced a math treatment first time in
Hungarian biomass research which is made by dissociation of 6 years time series from
earth satellite multispectral remote sensing data in region of Nyírlugos. This model
consists of linear trend, periodic, autoregressive and random component which were
successfully transformed forward. Result of trend analyze filtered out long term effect.
This means that the affects of biomass increase and decrease like wet or drought years
were detached. Long term climate effects forecast was difficult but periodic and
autoregressive component in short-term and in middle term forecast provided more
responsible values. Finished and practical method is more fitting than foregoing to
determinate biomass potential.
Keywords: biomass, time series, multispectral, remote sensing
INTRODUCTION
According to Csete and Láng sustainable competitiveness is accomplished through
sustainable farming methods, whose key motives are sustainable production,
adaptability, quality to any extent and favourable investments levels, consequently this
kind of competitiveness is altogether different from any old practice.
The U.S. Department of Agriculture (USDA) and the National Aeronautics and Space
Administration (NASA) signed a Memorandum of Understanding (MOU) to strengthen
future collaboration. In support of this collaboration, NASA and the USDA Foreign
Agricultural Service (FAS) jointly funded a new project to assimilate NASA's Moderate
Resolution Imaging Spectroradiometer (MODIS) data and products into an existing
decision support system (DSS) operated by the Production Estimates and Crop
Assessment Division. It produces objective, timely and regular assessments of global
agricultural production outlook and the conditions affecting food security. In monitoring
crop conditions for a specific region, remotely sensed vegetation index data are used to
track the evolution of the growing season compared to reference long-term mean
conditions (Tucker, 1985). A global normalized difference vegetation index (NDVI) is
produced from MODIS data, and is referred to as the "continuity index" similar to the
existing archive of NOAA-AVHRR derived NDVI . SPOT Vegetation NDVI data and
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