**4. Methodology**

The MODIS global disturbance index (MGDI) was developed based on the concept that any perceptible disturbance of ecology will result in a significant alteration in vegetation and a concomitant change in the land surface temperature [14, 16].

In the present study, the flood and forest fire were selected as the causative factors that create ecological disturbance to address both the non-instantaneous and instantaneous disturbance, respectively. In case of instantaneous disturbance like forest fire, the disturbance is manifested immediately after the event, resulting in immediate increase in LST with decreased vegetation cover. On the contrary,

#### *Remote Sensing*

non-instantaneous disturbance like flood will not trigger immediate change in either LST or EVI due to availability of abundant moisture for evaporation to offset the loss of transpiration. Whereas, in the following year the effect of flood damage was evident due to vegetation mortality and severe structural damage, which will eventually lead to increase in annual maximum LST due to the reduction in transpiration [14].

The instantaneous (*MGDIinst*) and non-instantaneous (*MGDInon-inst*) MGDI were computed using the following equations:

$$\text{MGDI}\_{\text{out}} = \frac{\left(LST\_{\text{max}} \; / \, EVI\_{\text{max} \cdot \, post} \right)\_{\text{current } year(y)}}{\left(LST\_{\text{max}} \; / \, EVI\_{\text{max} \cdot \, post} \right)\_{\text{mid} \, time \cdot year \, mean(y - 1)}} \tag{1}$$

$$\text{MGDI}\_{\text{non-int}} = \frac{\left(LST\_{\text{max}} \mid EVI\_{\text{max}}\right)\_{\text{current } j \text{acv}(y)}}{\left(LST\_{\text{max}} \mid EVI\_{\text{max}}\right)\_{\text{multi-} y \text{acv } m \text{acn}(y-1)}}\tag{2}$$

Where, *MGDIinst* and *MGDInon-inst* are the instantaneous and non-instantaneous MGDI value, respectively. *LSTmax* and *EVImax* are the annual maximum 16-day composite LST (°C) and EVI, respectively. *EVImax*-post is the maximum 16-day composite EVI following the LST*max*, current year (y) is the year being evaluated for disturbance and multi-year mean (y − 1) is the mean of the ratios excluding the current year [14, 16].

A two-step methodology, as explained by Dutta et al. [16], was adopted for to discriminate the disturbed forest areas caused due to flood and forest fire. In the 1st step, the % change in MGDI values were calculated based on the time-series data for each pixel. Whereas in the second step the MGDI based thresholds were estimated for flood and forest fire, separately. The Percentage change in MGDI for both the instantaneous and non-instantaneous disturbance were calculated using the following equation:

$$\text{96:change in MGDI}\_{\text{current year}(y)} = \frac{\text{MGDI}\_{\text{current year}(y)}}{\text{MdLit} - year \ mean \text{ } MGDI\_{(y=1)}} \times 100 \tag{3}$$

As discussed earlier, the forests prone to flood and fire were selected based on the temporal occurrence of the natural disturbance. Total 16 representative forests frequently affected by flood and forest fire were extensively analyzed to develop the thresholds for flood and forest fire separately based upon the % change of MGDI over multi-year mean. The spatio-temporal variation of the % change in *MGDInon-inst* over the Assam forest area was shown in **Figure 4** for some selected years.

The year-wise % change in MGDI was generated for all the representative forests wherein only the flood affected pixels were considered. Similarly, year-wise % change in MGDI was generated for the pixels undergone forest fire. The temporal profile of the percent change in MGDI of each forest was compared with the area weighted flood and fire intensity, to confirm the effect of natural disturbances on the MGDI. The multi-year mean value plus one standard deviation of the % change in MGDI was considered to be the threshold, and the value was used for

*Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India… DOI: http://dx.doi.org/10.5772/intechopen.94282*

**Figure 4.** *Percent change in MGDInon-inst over the Assam forest area.*

discrimination of the disturbed pixels [16]. Due to the slow and gradual impact of flood, unlike forest fire, a lower disturbance threshold was estimated in case of flood. The % change of MGDI greater than 7% and 11% of the temporal mean was fixed for discriminating the non-instantaneous (flood) and instantaneous (forest fire) disturbed pixels.

The selected thresholds were applied on the % change MGDI images (both non-instantaneous and instantaneous, separately) for identifying the year-wise disturbed forest areas. The year-wise % disturbed area was estimated for each forest and the temporal profiles were used to analyze the disturbance intensity at spatiotemporal scale. Upon integration of the year-wise disturbed area, disturbance prone maps were generated for both instantaneous and non-instantaneous events.
