Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region.

Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI) related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus) daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year.

Overstory-understory land cover mapping at the watershed scale: accuracy enhancement by multitemporal remote sensing analysis and LiDAR.

In forested watersheds, density, land cover, and its vertical structure are crucial factors for flood management, ecosystem monitoring, and biomass inventory. Nowadays, producing land cover maps with high accuracy has become a reality with the application of remote sensing techniques, but in some situations, it is not so easy to distinguish between the overstory and understory vegetation with only spectral information. The main goal of this study was to analyze the accuracy enhancement in overstory and understory land cover mapping at the watershed scale when using the data fusion of seasonal and annual time series of Sentinel-2 images complemented with low-density LiDAR and soil and vegetation indices.

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods.