Land cover-land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the Lower Magdalena region, Colombia. Data pre-processing was carried out using the European Space Agency's Sentinel Application Platform and the SEN2COR toolboxes. LCLU classification was performed following an object-based and spectral classification approach, exploiting also vegetation indices. A comparison of classification performance using three commonly used classification algorithms was performed. The radar and visible-near infrared integrated dataset classified with a Support Vector Machine algorithm produce the most accurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappa coefficient of 0.86. The proposed mapping approach has the main advantages of combining the all-weather capability of the radar sensor, spectrally rich information in the visible-near infrared spectrum, with the short revisit period of both satellites. The mapping results represent an important step toward future tasks of aboveground biomass and carbon estimation in the region. ARTICLE HISTORY
Se realizó un muestreo en una red regular de 31 puntos ubicados con GPS y con base en análisis geoestadísticos, se estudió la variabilidad espacial de algunas propiedades del suelo y de la topografía con el fin de establecer su incidencia en el rendimiento de un cultivo de mango. Las propiedades edáficas y el rendimiento presentaron un patrón de distribución espacial que varía de manera considerable dentro del lote. El rango de los modelos de semivarianza ajustados varió entre 10 y 192 metros. Con excepción del pH y del Ca las relaciones efecto pepita/meseta fueron menores del 30%, es decir, que en general hay precisión en las predicciones debido a que los procesos espaciales considerados son explicados en su mayoría por la variación estructural. K, Ca, CIC, pendiente del terreno, relación Ca+Mg/K y altitud fueron las variables que mayor influencia tuvieron en el rendimiento. El método aplicado es útil para definir zonas de manejo dentro de los lotes y algunos criterios como la altitud y la pendiente son aplicables para zonificación de áreas mayores calculándolos a partir de un modelo digital de elevación.
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