Mapeamento de soja e milho com mineração de dados e imagens sintéticas landsat e modis

• Autor(a) principal: Oldoni, Lucas Volochen
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br/handle/tede/3724
• topic: Fusão de imagens espaço-temporal; Métricas fenológicas; Métricas estatísticas; Fusion of spatio-temporal images; Phenological metrics; Statistical metrics; CIENCIAS EXATAS E DA TERRA

Studies related to the monitoring of agricultural production play a decisive and strategic role in the economic planning of the country, due to the importance of agribusiness, as well as food safety. Orbital remote sensing is an effective alternative to perform agricultural crop monitoring due to its low cost, large scale and speed of data collection. However, most of the sensors with high spatial resolution are of low temporal resolution, and the ones with higher temporal resolution have low spatial resolution. Therefore, for the monitoring of agricultural crops with a higher spatial solution, cloud covering can be a limiting factor. Such problems can be circumvented by using a fusion of images of several sensors with different spatial and temporal characteristics, thus creating new images, also called synthetic images. Thus, the objective of the work was the mapping of areas sown with soybean and corn using space-temporal fusion, such as Landsat 8 and MODIS images. In the first part of the research, agricultural crops were separated from other targets. The generated classification served as input to one of the classification algorithms, the Flexta Spatiotemporal Data Fusion (FSDAF), in the second part of the research. In addition to this algorithm, both the Spatial and Temporal Adaptive Reflection Fusion Model (STARFM) and the Advanced and Temporal Spatial Adaptive Reflection Fusion Model (ESTARFM) were employed to generate images for the 2016/2017 summer crops. Then, 5 rating scenarios were created. In the 1st and 2nd scenarios, only the images from the Landsat 8 with no occurrence of clouds were considered. For the 3rd, 4th, and 5th were carried out using images generated by STARFM, ESTARFM and FSDAF. In the third scenario, the metric images of images, Landsat 8 and images of fusion algorithms were used, 4th as a summary of statistical metrics, and in the 5th one as phenological metrics of the temporal profile of the Enhanced Vegetation Index (EVI). The scenario using the EVI phenological metrics from images generated by FSDAF and STARFM yielded better results, with global accuracy of 93.11 and 91.33%, respectively. These results are statistically better than those obtained using only existing Landsat 8 images. Thus, the use of phenological metrics obtained from synthetic images are important alternatives for mapping soybean and corn crops.