Discussion

From the naked eye, it is difficult to detect a significant change in vegetation by examining the visible image.  As shown above in Figures 6 and 7, no change can be detected.  Thus, the manipulation of the spectral bands of our images is helpful to further understand the change in vegetation in the Chiang Mai region due to slash and burn agriculture.

In ETM+ data, placing bands 3 and 4 in the order of 4, 3, 4 into R, G, B in ENVI shows an infrared image of the vegetation of the regions, based on greenness according to the Normalized Difference Vegetation Index.  By loading images in infrared and comparing them with the NDVI index, it is easier to see changes in greenness (ie. vegetation) between two images, ie. two different times.  In this study, we focused on April 2001 and February 2006, and the changes shown in Figure 8 are apparent.  The differences in greenness between the years 2001 and 2006 are shown in contrasting red and blue colors, red indicating an increase in greenness and blue indicating a decrease in greenness.  From Figure 7, we can deduce that there are notable decreases in greenness from 2001 to 2006 on the eastern portion of the study site.  From 99°15' E to 99°30'E shows a big area of vegetation decrease.  This is a more mountainous region where slash-and-burn agriculture is possibly practiced more intensely in order to clear the terrain for human land use.  Further, slash-and-burn can be practiced heavily in areas that are covered by primary forest (left uncleared) to open it up to agriculture.

Figure 9 and 10 show burned areas that do not necessarily correspond with Figures 11 and 12, but indicate that there were significant detectable fires from the MODIS satellite.  Hence, there is evidence of decrease in greenness and vegetation concentration in the study area, particularly in the eastern portion.  However, there is no significant and certain linkage between slash-and-burn and these decreases in greenness.

Because of difficulties working with MODIS data, future studies using MODIS Burned Area products can significantly improve in accuracy if geographic coordinates are superimposed onto the data points in the MODIS Sinusoidal projection (MODIS data does not come with any).  Knowing exact coordinates is extremely important for pinpointing a specific region and standardizing the geographic extent of all images produced from spatial subsetting in ENVI.