Estimating Change Percentage in Texture Developed by the Water Turndown of Bolivia's Lake Poopo

Change estimation from the satellite image is an important area of investigation these days. Today weather monitoring and forecasting, soil moisture estimation, covert data transmissions all are estimated with the assistance of the satellite data. In this research paper, we have investigated the case of Lake Poopo disappearance which is considered as the lifeline of Bolivia. The post-classification change detection is performed through the Grey Level Co-occurrence Matrix (GLCM) based technique. The satellite data is acquired from Landsat 8 operational land imager (OLI) sensor. Texture classification through grey level co-occurrence matrix is performed for the pre and post image of Lake Poopo, and on the basis of this change in the texture visual parameters contrast, correlation, energy and homogeneity are estimated. Finally, a pattern is also identified for the change in the texture feature from ‘water’ to ‘no water’ condition. This pattern can be considered as a novel pattern for detecting a change in the texture of the landcover for drought or lake dryness conditions.