Paper
21 October 2014 Remote sensing of climate changes effects on forest biophysical variables
Maria A. Zoran, Adrian I. Dida, Liviu Florin V. Zoran
Author Affiliations +
Abstract
Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modeling vegetation-climate interactions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of forest biophysical parameters like as Normalized Difference Vegetation Index (NDVIs) and Leaf Aria Index (LAI), which requires time-series data with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). This paper will quantify this impact over a forest ecosystem Cernica- Branesti placed in the North-Eastern part of Bucharest town, Romania, with NDVI and LAI parameters extracted from MODIS Terra and NOAA AVHRR satellite images in synergy with meteorological data over 2000-2013 periods. For investigated test area, considerable NDVI and LAI decline have been observed during heat wave and drought events of 2003, 2007 and 2012 years. Under water stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.
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Maria A. Zoran, Adrian I. Dida, and Liviu Florin V. Zoran "Remote sensing of climate changes effects on forest biophysical variables", Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92391V (21 October 2014); https://doi.org/10.1117/12.2067034
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KEYWORDS
Vegetation

Climatology

Climate change

Satellites

Remote sensing

MODIS

Environmental sensing

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