Land Cover Change Using Change Vector Analysis of Landsat 5 Remote Sensor Data

Accurate and replicable measurements of changes to land cover from drought conditions are essential for monitoring ecosystem disturbances. Techniques designed to measure land cover changes have been developed using data from remote sensing but with variable success. In my three study areas of southeastern parts of the American State of Texas, the change vector analysis (CVA) technique was tested on remote sensing data captured by the Landsat TM sensor taken in the years 2009, 2010, and 2011. This study monitors land use/land cover (LULC) changes due to the extreme Texas drought of 2011; the worst single year drought ever recorded in the state. The Landsat data are converted to vegetation indices; the normalized difference vegetation index (NDVI), bare soil index (BI), normalized difference moisture index (NDMI), as well as Tasseled Cap Transformations (TCT) brightness, greenness and wetness. CVA was used to determine the intensity of change (magnitude) and the type of changes that occurred (direction) between the multi-temporal data. This represents a new and improved method for calculating the direction component. Additionally, the relationship between NDVI and NDMI and between TCT variables and their application in CVA are further explored. The results show that land cover changes occurred due to an increase in precipitation in 2010 as well as considerable decrease of precipitation in 2011 resulting in the devastating drought. Validation procedures show that the CVA method was effective in capturing both magnitude of change and type of change that occurred. The remote sensing approach to monitoring drought-induced land cover changes is systematic, replicable and globally available at any time. Such a reliable methodology is essential for measuring ecosystem threats and human population vulnerability.