Development of a Digital Surface Model (DSM) to evaluate wind potential in a region in southern Brazil
ISBN 978-85-88783-11-9
Authors
1Lucas, E.; 2Branco, V.; 3Moreira, D.; 4Schafer, A.
1FEDERAL UNIVERSITY OF PAMPA Email: evertonlucas1990@hotmail.com
2FEDERAL UNIVERSITY OF PAMPA Email: viviantabranco@gmail.com
3SENAI CIMATEC Email: davidson.moreira@gmail.com
4FEDERAL UNIVERSITY OF PAMPA Email: alexandro.schafer@unipampa.edu.br
Abstract
Brazil has a large potential for renewable energies like wind power. To introduce this type of renewable energy to the Brazilian energy matrix, it is necessary to conduct a thorough study of the area of interest. Currently, the methodology of surveys of wind potential is being increasingly developed, either by methods of numerical simulations or by acquiring data needed for such analysis. The technical and economical evaluation of a wind generation project begins with the thematic mapping of the area of interest and the production of digital terrain models (DTM) and digital surface models (DSM). In this context, this paper presents the results of the development of a digital surface model (DSM), that will be used to assess the wind potential in an area partially covered by the municipalities of Aceguá, Bagé, Candiota, Hulha Negra and Dom Pedrito, in the state of Rio Grande do Sul, Brazil. For the development of the DSM, we used data from Landsat 8 and Topodata. The study area encompasses two distinct sites of Landsat 8 (orbit / point 222/82 and 223/82). To create the composite of the study area, we searched for recent images, within a short time period. The images were imaged on September 25, 2014 (222/82) and October 2, 2014(223/82). Initially, techniques of digital image processing were applied. For the supervised classification, we used the method of maximum likelihood. The resulting image underwent a process of post-classification, in which the interference generated in the previous step was corrected, particularly with respect to urban areas and areas that had exposed soil. This image resulted in a kappa coefficient of 0.9949. The next step was to assign roughness values for each type of land use and land cover, generating the roughness map of the study area, and the DMS. Finally, the DMS file was inserted into a micrositing software used to evaluate wind potential.
Keywords
digital surface model; roughness length; wind energy