## 3 Results and Discussion
### Assessment against Landsat-NDVI  
With higher spatial resolution than NOAA/AVHRR, enable the delineation of NDVI with a higher degree of accuracy. We attempted to assess the agreement of the GIMMS NDVI by comparing with NDVI extracted by Landsat TM/ETM\(+\) sensors. We used Landsat TM and ETM\(+\) data due to the relatively high spatial (30 m) and spectral resolution of the sensors. Moreover, Landsat TM data are available in the 1980s; the response of post-fire vegetation may be estimated using these data. Five 8 km \(\times\) 8 km simples were acquired for the same location with GIMMS NDVI to track the detail vegetation signals in Landsat imagery (Figure 4). Landsat images were selected at close dates (5 June 1986, 15 June 1987, 19 June 2000 and 22 June 2004) in order to avoid the influences of phenological differences (Table 1 and Figure 10). To account for differences in the temporal resolution, GIMMS 15-day images nearest to the Landsat sample acquisition date were selected.  
Figure 11 shows a similar the NDVI trends of GIMMS and Landsat. GIMMS dataset appears positively biased compared to Landsat-NDVI values. Landsat-NDVI values confirm that the GIMMS dataset overestimates NDVI in this region, which is likely due to GIMMS NDVI consisting primarily of  
Figure 10: Landsat TM/ETM\(+\) images (RGB:432) illustrating fire-caused changes on the ground vegetation. The locations of these five tracked simples can be found in Figure 4. Minus and plus signs represent pre-fire and post-fire period, respectively. Fire damage classes: Very High (VH); High (H); Moderate (M); Low (L); Slight (S).  
maximum acquisitions. A stronger regrowth trend of very high fire damage areas was found in both GIMMS and Landsat-NDVI. It indicates that tree planting in very high fire damage areas plays a positive role in the recovery process.  
NDVI is an indicator of vegetation cover density and plant growth condition [38]. Pixel greenness is interpreted as indicating the presence of vegetation chlorophyll. Thus, NDVI can be interpreted originally as a proxy of \"green leaf biomass\", based on experience mainly with crops, grasslands and forest. As an example of the potential usefulness of the NDVI, [PERSON] _et al_. constructed monthly greenness images for Africa in 1981 and 1982 which graphically show the advance and retreat of the tropical summer wet season and year-to-year differences in surface greenness, which they interpreted as primary production [39, 40, 41]. [PERSON] _et al_. presented a simple modification to an existing empirical NDVI-based model for biomass estimation, using the ratio between the mean annual precipitation and a threshold rain level representing the transition from dwarf-shrub to shrub dominance [42]. [PERSON] _et al_. assessed the performance of NDVI as a proxy for plant biomass, which reinforces the idea that NDVI is most often a nonlinear proxy of plant biomass [43]. Although, NDVI has been used to estimate vegetation biomass in many ways, it is also limited due to saturation of value in dense vegetation. Therefore, to better understand the biomass recovery in post-fire area, multiple sources remote sensing data (such as, leaf area index (LAI), GPP, NPP and SAR data) should be considered in the future study. For this study, the whole burned area was treated as a protection zone after the fire. Nobody can enter into this region without an official permission. Only helpful planting actions were taken to restore the timber volume without any other negative human disturbances during recent two decades. Thus, in this case, we believe that the NDVI signal is closely and positive correlated with forest biomass. Although, the GMOIS resolution is not high, the temporal trend of stands regrowth index (SRI) also can reflect the long-term vegetation response after the fire (Figures 8 and 9).