## 3 Results and Discussion  
Results from CVA were assessed for accuracy using 200 random sample points. Post-classification comparison (PCC) maps were created and used as reference to produce a 'change/no change' error matrix based on the random points. Supported by visual interpretation of the remotely sensed imagery  
Figure 3: Possible change sector codes in three bands (after [31]). Figure 2: Representation of possible change vectors in three dimensions (after [30]). from Google Earth, overall accuracy and Kappa coefficients for both the Houston and South Texas sites were found to be higher for the NDVI-NDMI and TCT calculations when compared with NDVI-BI. The supervised classification highlights the severity of the drought year in 2011 (Table 1). For both sites, forest land cover decreases markedly between 2010 and 2011; a moderate fall from 58.8% to 47.9% in Houston, and an almost halving from 15.0% to 8.6% in South Texas. Agriculture too declines between 2010, but only for the more rural site of South Texas, not for the more urbanized Houston site. It's worth noting that both forest and agriculture increase between the relatively moderate drought year of 2009 to the wet-to-average year of 2010, but only slightly; a matter of a few percentage points. In contrast, urban and rock/soil (which are highly co-dependent spectrally) increase in the 2011 severe drought year. Water bodies remain the same across all three years. To spatially visualize these changes in land use/cover we can use image difference maps based on NDVI, BI, and NDMI. (Figure 4a,b). Across the majority of the area of the Houston site, image differences between the two years (i.e., 2009 and 2010) when conditions were getting wetter are substantiated by increasing NDVI values, decreasing BI values, and increasing NDMI values. All three indices reverse for the 2010 to 2011 image differences, which confirms drying conditions. For the South Texas site, the 2009 to 2010 difference illustrates that a majority of the area is shifting towards less green and brighter conditions respectively (according to NDVI and BI), although NDMI values indicate that approximately half of the area is moving towards improved wet conditions. This is an indication, to some degree, of a persisting drought. It maybe be explained by the use of an image for 2009 that was captured in October--a relatively wet month for the San Antonio region, while the 2010 image was taken during the month of August--which is typically one of the driest months. However, the severe drought of 2011 is again highlighted by expected decreasing NDVI values, increasing BI values, and decreasing NDMI values; just like the Houston site. We can use CVA to further illustrate changing wetness and drying. Using NDVI and NDMI as the basis for measuring magnitude and change direction, the period 2009 to 2010 experienced chlorophyll expansion, and an increase in wetness over most of the Houston site (Figure 5a). By contrast, between 2010 and 2011 most areas experienced bare soil expansion (Figure 5b). Similar results were evident for South Texas (Figure 6a,b). Applying a threshold identifies which pixels represent change and which represent no change. We used 0.5 standard deviation threshold as the low change cutoff and 1.0 standard deviation for the high change cutoff (in line with work by [8; 10; 16; 18; 28; 30]). Using high change pixels (dark purple in Figures 7a and 8a) the spatial contrast in wetter and drier year is clearly demonstrated. For example, for Houston, those dark purple pixels mostly correspond to chlorophyll increase (green), indicating wetter conditions for years 2009 to 2010, while for the years 2010 to 2011 they mostly correspond to bare soil expansion (yellow), representing the severe drought (Figure 7b). However, the same situation is clearly not evident for South Texas (Figure 8b). The high level change pixels for the years 2009 to 2010 to not correspond to widespread chlorophyll increase (only a few, especially in the south of the site) but instead equate to bare soil expansion.