Mapeamento de área e de unidades de armazenamento de grãos no Alto Paraná do Paraguai

• Autor(a) principal: Díaz, Sergio Manuel Chamorro
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: https://tede.unioeste.br/handle/tede/6699
• topic: Google Earth Engine; Sentinel-2; SRTM; Modelagem estatística; Áreas sem cobertura de armazenamento; Google Earth Engine; Sentinel-2; SRTM; Statistical modeling; Areas without storage coverag; SISTEMAS BIOLÓGICOS E AGROINDUSTRIAIS

An efficient way to locate cultivated areas is through remote sensing tools. Similarly, locating available storage units (SUs) in production areas quickly and without the need for georeferencing in the field is possible. However, this tool has limitations in calculating the static storage capacity (SSC) and dynamic storage capacity (DSC) required to study SUs. The general objective of this work was to map by satellite images the agricultural production area (soybean and corn), geolocate the distribution of storage units (SUs), estimate their static storage capacity (SSC) and dynamic storage capacity (DSC) in the Department of Alto Paraná, in Paraguay, and, finally, identify the regions with areas without storage coverage to install new SUs to meet the production of the Department. The study area for this experiment was the Department of Alto Paraná, located between the parallels 24 ° 30 ‘and 26 ° 15’ south latitude and the meridians 54 ° 20 ‘and 55 ° 20’ west longitude. The Google Earth Engine (GEE) platform was considered, using Sentinel-2 and SRTM (Shuttle Radar Topography Mission) multispectral images for the 2019/20 crop. For the location of the storage units, QGIS software was used, with Google Hybrid, ERSI, and Bing satellite images. For vertical silos, it was necessary to know each SU’s height (h). For this, mathematical modeling was performed using SSC, height and diameter data for some SUs measured in the field. However, since there was little data collected, the catalogs of the brands of silos that are built in Paraguay (brands such as Kepler Weber, Comil, GSI, Cash and Carry, and Consilos) were also used. To calculate the Dynamic Storage Capacity (DSC), the rotation factor of 1.5 of the SSC per SUs was used. Finally, to identify the areas without storage coverage and to define possible regions for installing new SUs, it was necessary to use production maps in raster format, together with the location information of the SUs with their respective information on the SSC. With this information, it was possible to determine new locations in the Department with a deficit of SUs, but agricultural areas exist. The generation of maps of agricultural crops in Alto Paraná, with the Google Earth Engine (GEE) platform, using Sentinel-2 images, allowed the identification of the planted area and the estimation of production for the 22 municipalities within the Department. Notably, this data is not available on government websites dedicated to monitoring the agricultural sector. It was possible to identify 688,683 ha of soybeans and 118,893 ha of corn throughout the Department. The municipalities with the largest planted area were Itakyry for soybeans, Yguazu, for summer corn, and Minga Porá, for winter corn for the 2019/20 crop. It was possible to geolocate the 187 SUs in the Department and the SSC and DSC estimates, totaling over 2.6 million tons and over 3.8 million tons distributed across the Department. These figures were also calculated for the 22 municipalities that make up the Department. This differentiation by the municipality is crucial because government institutions publish no figures on the subject for the different municipalities. Also, areas with agricultural production were found but without SUs (areas not included). Thus, they are potential sites for installing new SUs, especially in Itakyry, which had an estimated production of more than 300 thousand tons in this harvest year but with an SSC of only 49 thousand tons, showing an evident deficiency of SUs.