Vegetation cover estimation from high-resolution satellite images based on chromatic characteristics and image processing

The change in ecosystems and the loss of biodiversity are global problems, one of the ecosystems with the most signifi-cant degradation is the high areas of the Andes, composed mostly of natural pastures. In the Andahuaylas province, Apurímac region, Peru, there is a high-impact Andean area for the collection of water for human consumption and irrigation; this area is called the Chumbao River Micro-basin. The problem is that this area is presenting essential changes in its surface, corresponding to natural pastures, especially of the species fescue (festuca dolycophylla) and paco (aciachne pulvinata). These changes do not have any estimates or studies that allow adequate decision-making in the adoption of preventive and corrective measures for the conservation of ecology, the environment, and water collection. Under this approach, this work proposes a method of estimating vegetation cover for those species, through the chromatic characteristics of each species, using high-resolution satellite images, extracted from the PERUSAT-1 satellite. The method consists of dividing the global satellite image into small images, converting them into the HSV color system (Hue, Saturation, and Value). Evaluating the range of chromatic characteristics of each species and performing range segmentation, subsequently fine-tuning the segmentation with morphological deformations and calculate the final area. The results obtained an accuracy of 91.56%, taking as a reference the result of the estimation of traditional vegetation cover; this result was tested in an area of 3824.45 m2 with the presence of both species. Therefore, our proposal is a reliable method for calculating vegetation cover and can be used for large surface areas, saving human and financial resources and with almost instantaneous results, compared to the traditional way.