###### Abstract
Ecosystem gross primary productivity (GPP), a metric of photosynthetic activity, has been used to evaluate the recovery level of ecosystem vitality after drought impacts [5; 6; 7]. GPP is useful because it is the largest carbon flux and the largest carbon input for terrestrial ecosystems, its sensitivity to drought is well documented, and its spatiotemporal patterns can be estimated in many ways [6]. In [5] drought recovery was analyzed using the GPP rate with findings that the required time for a region to recover to pre-drought condition is positively correlated with drought severity. Hence, a more severe drought will most likely result in a longer drought recovery time. Similarly, in [6], higher GPP was strongly associated with longer recovery times. Also, increases in drought recovery time were found with higher post drought temperatures suggesting that anticipated future temperature trends affect drought recovery. Whereas in [7], which is a study on drought recovery potential of different land cover types on a global scale, as average GPP increased, the length of recovery days decreased indicating a quick recovery time across various land cover types. In our study drought recovery was not explored, and hence a topic of future study would be to assess the ability of change vector analysis with tasseled cap transformations, NDVI, NDMI to capture drought recovery. Instead, we investigated the unique combination of CVA and TCT. We use three variables brightness, greenness, and wetness from TCT to generate CVA magnitude and direction using vectors in three-dimensions when calculating land use/cover changes during the 2011 sudden drought event in Texas, USA. Our years are limited between 2009 to 2011, specifically to highlight the destructive nature of a sudden severe drought. Three years were sufficient to convert many land use/covers types from vegetation to bare soil. The three years were also sufficient to demonstrate changes on the landscape from a dry year of 2009, to an average year of 2010 to the severe drought of 2011. Note, the 2011 drought continued into 2012 but a wetter latter half of 2012 raised the annual precipitation average. Our methodology includes the computing of the directional component of CVA using the arctangent function ATAN2 (ATAN2(\(y\), \(x\)) returns the arc tangent of two numbers \(x\) and \(y\). It is similar to calculating the arc tangent of \(y\) and \(x\), except that the signs of both arguments are used to determine one of four quadrants of the result), thus giving results in all four possible quadrants (\(+\), \(+\)\(-\), \(-\)\(+\) and \(--\)) and ensuring a complete characterization of all possible change directions. Incidentally, change directions are summarized as one of \(2^{n}\) categories, or'sectors' of change, where \(n\) is the number of variables or bands [8; 9; 10]. Hence, with three tasseled cap variables, the total number of change categories is eight. These eight provide more specific types of land use/cover change information, for example, the direction of 'bare soil expansion' when using vectors in two-dimensions includes drying caused by drought as well as urbanization. Hence to discern any patterns with any particular change direction a 'from-to' land use/cover change assessment is made for each direction class. We also explore the relationship between TCT and NDVI, and its variants by comparing drought and normal (wet) scenes. Our results indicate that remotely sensed data are effective in representing various land use/cover changes caused by improving conditions (i.e., wetter) from a drought, and by deteriorating conditions (i.e., drier) towards a drought. Our work is unique in harnessing the prescriptive abilities of established indices (the normalized difference vegetation index and the normalized moisture difference index) in combination with CVA and TCT applied to measuring land use/cover changes during sudden severe drought conditions.