###### Abstract  
Sudden short-term severe droughts have major impacts on ecosystem balance. Synoptic and replicable measurements from remotely sensed data are essential for calculating changes to land use/cover caused by severe drought conditions. In the US, Texas experienced a particularly severe drought in 2011, which adversely affected forest and grassland ecosystems in addition to $7.62 billion of agricultural loss. To assess the extent and severity of the drought we use satellite sensor data and image processing techniques to measure changes in land use/cover. Our methodology uses change vector analysis (CVA), the normalized difference vegetation index, the normalized difference moisture index, and three variables-brightness, greenness, and wetness-extracted from tasseled cap transforms (TCT). All are established techniques in remote sensing but have as yet been applied in combination to measure land use/cover changes affected by intense short-term drought conditions. Our objective is to calculate not only vegetation and bare soil indices, but also the intensity of change (magnitude) and the type of change (direction). For CVA direction, we include an improved methodology using the arctangent function based on two arguments, ATAN2 which produces results in all four possible quadrants, and complete characterization of all possible change directions. The three variables of TCT are applied to CVA magnitude and direction using vectors in three dimensions, resulting in eight change categories. Our results are based on Landsat TM sensor data for the years 2009, 2010 and 2011, which represent a short period of severe drought, above average precipitation, and severe drought respectively, for two study sites in Texas. Results indicate that land use/cover changes were affected by both an increase in precipitation in 2010 as well as a considerable decrease of precipitation in 2011 resulting in the devastating sudden drought. change vector analysis; NDVI; tasseled cap; drought event 10.3390/rs11192217 10.3390/rs11192217 10.390/rs11192217 10.3390/rs11192217 10.  
of future droughts [2; 3]. Drought events are often categorized as four major types depending on impacts and duration: Meteorological drought occurring due to reduction in precipitation; agricultural drought described as reduced root-zone soil moisture for plant growth; hydrological drought reflecting low stream flow, depleted groundwater, and reduced reservoir levels; and socioeconomic drought identified as the inability to meet societal water demands. The relationships between different drought types are complex and a single unifying technique is usually not suitable to quantify drought severity. Even within an individual drought type, the most effective index is not immediately clear. Drought is often spatially variant and context dependent, hence its accurate monitoring may require specifically tailored methods based on climate region and drought type. A period of drought is monitored using in situ meteorological data from weather stations. However, due to cost constraints and logistical difficulties of locating monitoring stations at multiple sites, in situ variables are insufficient for measuring spatial and temporal extensiveness of droughts. Instead, remotely sensed data are a more expansive means with which to measure severely dry land use/cover. In addition, remotely sensed data are available in variable spatial resolutions and conducive to digital processing [4]. Previous studies on drought recovery have focused on the required precipitation to recover from a drought event as well as the restorative ability of vegetation [5]. From a hydrological and ecological perspective, a region is assumed to recover from drought when water systems (e.g., lakes) and surrounding vegetation return to pre-drought health. It is important to understand drought recovery duration since permanent ecological impacts can occur if a region experiences protracted drought events.