Autocorrelação espacial direcional para análise da anisotropia com dados agrícolas
| Ano de defesa: | 2017 |
|---|---|
| Autor(a) principal: |
Ribeiro, Dyogo Lesniewski
 |
| Orientador(a): |
Guedes , Luciana Pagliosa Carvalho
|
| Banca de defesa: |
Dalposso, Gustavo Henrique
,
Opazo, Miguel Angel Uribe
|
| Tipo de documento: | Dissertação |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Estadual do Oeste do Paraná
Cascavel |
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Agrícola
|
| Departamento: |
Centro de Ciências Exatas e Tecnológicas
|
| País: |
Brasil
|
| Palavras-chave em Português: | |
| Palavras-chave em Inglês: | |
| Área do conhecimento CNPq: | |
| Link de acesso: | http://tede.unioeste.br/handle/tede/3293 |
Resumo: | Geostatistical techniques have contributed on acquainting the studied area characteristics. They have made the decisions easier to be taken regarding the management of the agricultural yield system and contributed to sustainable development in precision agriculture. Anisotropy is a characteristic that has influenced the precision of thematic maps that represent spatial variability of the studied phenomenon. Thus, this trial aimed at using Moran directional index in anisotropy analysis in georeferenced variables. Moran directional index was calculated considering isotropic and anisotropic geostatistical models to highlight the directional difference in thematic maps when anisotropy is incorporated or not in the geostatistical model. Thus, simulated data were used considering an irregular sample configuration, with 100 points. Data were simulated with an anisotropic (geometric) spatial dependence structure following an exponential model, with an angle of greater spatial continuity equal to 90 ° (azimuth) and varying the anisotropy factor. Moran directional index was calculated for sampled values of simulated data, as a tool to assist in decision making regarding the existence of anisotropy. Then, this process was also used for soil chemical attributes, observed in an agricultural area with soybean cropping, referring to the agricultural year of 2014/2015. The directional spatial autocorrelation was effective in identifying geometric anisotropy for simulated data and soil chemical attributes. It also highlighted the directional difference among the thematic maps, when the existence of anisotropy is considered or not in the geostatistical model. |