## 3 Results and Discussion
The DFT separated the noisy VI time series data into their individual sinusoids of different frequencies (called harmonics) and filtered the individual sinusoids to rebuild the complex waveform domain of the VI data with the periodic noise patterns of the frequencies removed (Figures 3 and 4). The remote sensing methodology is more accurate for high SNRs [35, 36]. The highest SNR values of the FF VIs associated with the NTEs were Mediterranean and cool temperate forests according to the FF NDVI, and warm temperate forest according to the FF EVI (Table 4). The SLR models for each NTE revealed the percentage of variations in the FF VI data that can be accounted for by the raw VI data, and thus, can be used to reconstruct the noise-filtered VI time series and estimate timing of start, peak, and end of as well as length of growing season (Table 4). Seasonally and annually averaged FF VI values may help to approximate the inherent cyclicity of vegetation dynamics. The seasonal SLR \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \multicolumn{1}{c}{_Biogeoclimate_} & \multicolumn{1}{c}{_Land cover_} & \multicolumn{1}{c}{_Number_} & \multicolumn{1}{c}{_Area_} & \multicolumn{1}{c}{_Mean_} & \multicolumn{1}{c}{_Mean_} & \multicolumn{1}{c}{_Mean_} & \multicolumn{1}{c}{_BT_} & \multicolumn{1}{c}{_PER_} \\ \multicolumn{1}{c}{_zones_} & \multicolumn{1}{c}{_types_} & \multicolumn{1}{c}{_of pixels_} & \multicolumn{1}{c}{_(km\({}^{2}\))_} & \multicolumn{1}{c}{_NDVI_} & \multicolumn{1}{c}{_EVI_} & \multicolumn{1}{c}{_altitude (m)_} & \multicolumn{1}{c}{_(\({}^{\circ}\)C)_} & \\ \hline (Sub)nival & Snow/ice & 5899 & 1475 & 0.11 \(\pm\) 0.06 & 0.10 \(\pm\) 0.04 & 3104 \(\pm\) 258 & 0.6 \(\pm\) 0.9 & 0.27 \(\pm\) 0.14 \\ Alpine & Barren/sparsely vegetated & 20372 & 5093 & 0.17 \(\pm\) 0.08 & 0.17 \(\pm\) 0.07 & 2831 \(\pm\) 220 & 2.3 \(\pm\) 0.4 & 0.33 \(\pm\) 0.10 \\ Boreal & Barren/sparsely vegetated & 9043 & 2261 & 0.15 \(\pm\) 0.08 & 0.16 \(\pm\) 0.07 & 2391 \(\pm\) 264 & 4.8 \(\pm\) 0.8 & 0.52 \(\pm\) 0.14 \\ Forest & 167254 & 41814 & 0.23 \(\pm\) 0.11 & 0.23 \(\pm\) 0.08 & & & & \\ Cool Temperate & Barren/sparsely vegetated & 212169 & 53042 & 0.23 \(\pm\) 0.08 & 0.17 \(\pm\) 0.05 & 1423 \(\pm\) 413 & 9.6 \(\pm\) 1.5 & 1.11 \(\pm\) 0.35 \\ Grassland & 175572 & 43893 & 0.29 \(\pm\) 0.11 & 0.19 \(\pm\) 0.06 & & & & \\ Shrubland/woodland & 278098 & 69525 & 0.30 \(\pm\) 0.13 & 0.20 \(\pm\) 0.07 & & & \\ Forest & 979753 & 244938 & 0.34 \(\pm\) 0.18 & 0.24 \(\pm\) 0.09 & & & \\ Warm Temperate & Barren/sparsely vegetated & 312485 & 78121 & 0.26 \(\pm\) 0.11 & 0.18 \(\pm\) 0.07 & 663 \(\pm\) 371 & 13.5 \(\pm\) 1.1 & 1.40 \(\pm\) 0.41 \\ Grassland & 162401 & 40600 & 0.38 \(\pm\) 0.14 & 0.22 \(\pm\) 0.08 & & & \\ Shrubland/woodland & 281280 & 70320 & 0.43 \(\pm\) 0.15 & 0.25 \(\pm\) 0.08 & & & \\ Forest & 322362 & 80591 & 0.51 \(\pm\) 0.18 & 0.29 \(\pm\) 0.10 & & & \\ Mediterranean & Barren/sparsely vegetated & 101905 & 25476 & 0.33 \(\pm\) 0.13 & 0.21 \(\pm\) 0.09 & 303 \(\pm\) 211 & 17.3 \(\pm\) 1.0 & 1.59 \(\pm\) 0.45 \\ Grassland & 555559 & 13890 & 0.44 \(\pm\) 0.15 & 0.27 \(\pm\) 0.09 & & & & \\ Shrubland/woodland & 54595 & 13649 & 0.45 \(\pm\) 0.13 & 0.28 \(\pm\) 0.08 & & & & \\ Forest & 43459 & 10865 & 0.54 \(\pm\) 0.13 & 0.29 \(\pm\) 0.06 & & & & \\ Grand total/mean & 3182206 & 795552 & 0.32 \(\pm\) 0.13 & 0.21 \(\pm\)0.05 & 1141 \(\pm\) 655 & 11.3 \(\pm\) 3.3 & 1.21 \(\pm\) 0.44 \\ \hline \multicolumn{1}{c}{_BT: Biotemperature; PER: Ratio of potential evapotranspiration to precipitation. Mean NDVI} \\ \multicolumn{1}{c}{_and_ EVI values are for the period of 2000 to 2007.} \\ \end{tabular}
\end{table} 
Table 1: Biogeoclimate zones and natural land cover types of Turkey, and their extent, biophysical properties and MODIS-derived vegetation indices. models of the FF VI had the highest \(r^{2}\) of 99.1% for cool temperate forest and 98.1% for warm temperate forest, based on the raw NDVI and EVI data (\(P<0.001\)) (Table 4). **Figure 2**.