In the management of ecosystem services, it is signi cant to relate land use with the physical characteristics of the terrain, which allows establishing the conditioning factors of human activities and planning their distribution. These analyzes are based on thematic cartography, usually generated with visual classi cations of satellite images. Traditional mapping techniques involve limiting the timely availability of information by taking extended periods for interpretation and integration of multiple data sets. This article presents a methodology to overcome these di culties, implements big data, machine learning, and cloud computing to generate timely thematic cartography and spatial analysis to support land use planning. The study area was delimited according to altitudinal levels that de ne braided and anastomosed river systems. Acquisition, processing, and classi cation of input data for modeling were performed on the Google Earth Engine platform. The spatial correlation between hemeroby and geomorphology was calculated with the odds ratio and its respective con dence interval. Maps of 27 geomorphological units, 11 types of land use, and six hemeroby levels are presented at a scale of 1: 50,000. Confusion matrices of implemented classi cation models were also reported, allowed evaluating global, user's, and producer's accuracy. Correlations between relict of natural areas with the structural environment and urban infrastructure with alluvial fans stand out. The information generated by these procedures is essential for planning land use and prioritizing the maintenance of ecosystem services.
<p>Ocho compuestos conocidos fueron<br />aislados del extracto etanólico de corteza<br />de Nectandra turbacensis (Kunth) Nees<br />(Lauraceae). Estos fueron identificados como<br />ácido meso-dihidroguayarético 1, ácido<br />treo-dihidroguayarético 2, sauriol B 3, y<br />treo-austrobailignano-6 4; vitexina (8-C-β-<br />D-glucopiranosil-5,7,4’-trihidroxiflavona) 5;<br />estigmast-4-en-3-ona 6 y la mezcla sitosterol<br />7 / estigmasterol 8. Las estructuras de los<br />compuestos fueron elucidadas por métodos<br />espectroscópicos, que incluyeron técnicas de<br />RMN en 1D y 2D, CG/EM y por comparación<br />de los datos espectroscópicos, reportados<br />en la literatura de compuestos relacionados.<br />Este es el primer reporte de la presencia<br />de este tipo de compuestos en la especie.<br />Se describen también las implicaciones<br />quimiotaxonómicas; relacionadas con la<br />presencia frecuente de lignanos en especies<br />del género Nectandra.</p>
Ciências exatas e da terra. 2. Engenharia. I. Maraviesk, Sabrina Passoni. CDD 507 Elaborado por Maurício Amormino Júnior -CRB6/2422 O conteúdo do livro e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. 2018 Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. www.atenaeditora.com.br
In the management of ecosystem services, it is significant to relate land use with the physical characteristics of the terrain, which allows establishing the conditioning factors of human activities and planning their distribution. These analyzes are based on thematic cartography, usually generated with visual classifications of satellite images. Traditional mapping techniques involve limiting the timely availability of information by taking extended periods for interpretation and integration of multiple data sets. This article presents a methodology to overcome these difficulties, implements big data, machine learning, and cloud computing to generate timely thematic cartography and spatial analysis to support land use planning. The study area was delimited according to altitudinal levels that define braided and anastomosed river systems. Acquisition, processing, and classification of input data for modeling were performed on the Google Earth Engine platform. The spatial correlation between hemeroby and geomorphology was calculated with the odds ratio and its respective confidence interval. Maps of 27 geomorphological units, 11 types of land use, and six hemeroby levels are presented at a scale of 1: 50,000. Confusion matrices of implemented classification models were also reported, allowed evaluating global, user's, and producer's accuracy. Correlations between relict of natural areas with the structural environment and urban infrastructure with alluvial fans stand out. The information generated by these procedures is essential for planning land use and prioritizing the maintenance of ecosystem services.
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