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ResumoO presente trabalho tem como objetivo analisar o potencial de imagens SAR polarimétricas do sensor TerraSAR-X, no modo StripMap, para mapear o uso e cobertura da terra na região sudoeste da Amazônia brasileira. No procedimento metodológico imagens de amplitude nas polarizações A HH e A VV , A derivada da matriz de covariância, bem como da entropia A Entropia derivada da decomposição de alvos por auto-valores fizeram parte, de forma individual ou combinada, do conjunto de dados investigados. Na classificação das imagens foram empregados dois classificadores: um baseado nas funções estatísticas de máxima verossimilhança (MAXVER); e outro, o método contextual (Context). Os resultados temáticos dessas classificações foram avaliados através da matriz de confusão e pelo índice Kappa. De forma sintetizada pode-se afirmar que as componentes A e A Entropia , têm significativa contribuição no procedimento classificatório, sobretudo pelo método Context, cujo desempenho alcançou com 78% de exatidão global e índice Kappa de 0,70.PalavRas-chave: mapeamento florestal, radar, classificação polarimétrica, Amazônia. Analysis of the potential use from TerraSAR-X images for thematic mapping in SW Brazilian Amazon region abstRactThe objective of this work was to analyze the potential use of SAR polarimetric images from the TerraSAR-X sensor system, at StripMap mode, to map land use and land cover in SW Brazilian Amazon. Amplitude images at polarizations A HH , A VV , A , derived from the co-variance matrix, as well as the entropy A Entropia , derived from the decomposition of targets by eigenvalues, are parts of the datasets investigated individually or in combined form. Two classifiers were used: the first is based on statistical functions of maximum likelihood (MAXVER), and the second is the contextual method (Context). The thematic results from these classifications were evaluated by a confusion matrix and by the Kappa index. Summarizing we can state that the components A and A Entropia , gave a significant contribution to the image classification procedure, considering specially the Context method, whose performance reached 78% of Global Accuracy and a Kappa index of 0.70.

Resumen: Los principales factores que inciden en la producción y el rendimiento de la caña de azúcar son la variedad, el manejo agronómico, el tipo de suelo y el clima. Mientras que en las tres primeras se tiene cierto control, el clima es un factor del cual no se puede tener control alguno, por lo tanto, se debe realizar un seguimiento de este. Colombia, al estar ubicado en el pacífico ecuatorial, es afectada por dos fenómenos atmosféricos oceánicos conocidos como "El Niño" y "La Niña", que componen el fenómeno climático del ENOS (El Niño-Oscilación del Sur) y que afectan la cantidad y el número de días con lluvias influyendo en la producción de caña de azúcar. El objetivo de este trabajo es identificar espacial y temporalmente las zonas con mayor y menor impacto del fenómeno del ENOS en el cultivo de la caña de azúcar en Colombia mediante el uso del Índice de Vegetación Normalizado (SVI) y el Índice de Anomalía de la Precipitación (RAI) utilizando imágenes EVI/MODIS y datos de precipitación de estaciones meteorológicas de manera trimestral para el periodo 2000-2015. Se encontró una tendencia similar entre ambos índices en las temporadas de "El Niño" y las temporadas "Neutral", mientras que en la temporada de "La Niña" el RAI tendió a elevarse mientras que el SVI disminuyó cuando el RAI fue muy alto, siendo esta tendencia mucho más marcada en zonas con inundaciones causadas por el desbordamiento de los principales ríos. Adicionalmente se realizó una comparación entre el índice SVI y un índice de anomalía de productividad (IAP), encontrando una correlación directamente proporcional entre ambos (R 2 =0,4, p<0,001). Este trabajo demostró que por medio del uso de índices de vegetación se puede realizar un análisis temporal del impacto del clima en un cultivo agrícola, especialmente con condiciones ENOS.Palabras clave: caña de azúcar, índice de vegetación, El Niño-Oscilación del Sur, anomalías de precipitación, MODIS. Vegetation anomalies associated with the ENSO phenomenon in the Cauca river valley, ColombiaAbstract: The main factors affecting the production and yield of sugarcane are variety, agronomic management, soil type and climate, of which the first three there is some control, while the climate is one factor of which you cannot have any control, therefore, it should be monitored. Colombia, being located in the equatorial pacific, is affected by two atmospheric oceanic phenomena known as "El Niño" and "La Niña", which make up the climatic phenomenon of ENSO (El Niño-Southern Oscillation) and affect the quantity and the number of days with rainfall and influences the production of sugarcane. The objective of this work is to identify spatially and temporally the zones with greater and lower impact of the ENSO phenomenon in the cultivation of sugarcane in Colombia through the use of the Standard Vegetation Index (SVI) and the Rainfall Anomally Index (RAI) using EVI/MODIS images and precipitation data from meteorological stations on a quarterly basis for the period 2000-2015. A similar trend was found between bo...

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

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