Mr. David de la Fuente Profile

David de la Fuente

at GMV Aerospace & Defence

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Proceedings Article | 3 October 2019 Paper

An automatic Sentinel-2 forest types classification over the Roncal Valley, Navarre: Spain

A. Fernandez-Carrillo, D. de la Fuente, F. Rivas-Gonzalez, A. Franco-Nieto

Proceedings Volume 11156, 111561N (2019) https://doi.org/10.1117/12.2533059

KEYWORDS: Remote sensing, Data modeling, Gases, Climate change, Forestry, Web services, Clouds, Vegetation, Image classification, Photointerpretation

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Forests cover 36.5% of Spanish land. Natural and man-induced disturbances are causing important changes in species distribution. As Spanish National Forest Inventory is updated every 10 years, a more recurrent periodic data source providing information on species distribution is needed in order to predict changes in forest area and composition. Remote Sensing meets this demand, as it provides periodic and spatially continuous data on forest status. In this context, MySustainableForest (MSF) H2020 project aims at providing remote sensing-based geo-information services through a web service platform. One of MSF products is a classification of main forest types, whose preliminary development was tested over a 950 km2 area located in Northern Spain. A Random Forest model was trained with data delineated with the help of local forest data. The output was validated using stratified k-fold cross-validation. Validation metrics were computed from the confusion matrix for each class separately and for the total set of classes. Although overall metrics were high (OA = 95%; DC = 85.1%), they varied significantly for different classes (e.g., Fagus sylvatica was classified with higher accuracy than Pinus nigra, which was mainly confused with other Pinus species), showing that species with higher seasonal variations were easier to identify. Random Forest feature importance ranking showed that bands in the near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths were essential to discriminate forest species, since they explained more than 40% of the variations alone and 82% in combination with Red wavelength.

Proceedings Article | 3 October 2019 Presentation + Paper

A Sentinel-2 unsupervised forest mask for European sites

A. Fernandez-Carrillo, D. de la Fuente, F. Rivas-Gonzalez, A. Franco-Nieto

Proceedings Volume 11156, 111560Y (2019) https://doi.org/10.1117/12.2533040

KEYWORDS: Vegetation, Spatial resolution, Climatology, Image segmentation, Forestry, Ecosystems, Biological research, Remote sensing, Image resolution, Near infrared

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Forests cover one third of Europe’s land and significantly contribute to the regional economy. Moreover, they play an essential role in climate regulation. Traditional inventory-based forest data update is often much lower than required. Remote Sensing is a valuable source for forest monitoring, as it provides periodic data on vegetation status. In this context, EU Horizon-2020 MySustainableForest project (MSF, Grant Agreement nº 776045) aims at developing remote sensingderived geo-information services for integrated forest management through a web service platform. An unsupervised method to obtain a forest mask over European forests using optical Sentinel-2 data was implemented. Kmeans algorithm was used for segmenting the images in clusters, which were subsequently assigned to a forest class depending on its overlap with the forest classes of ancillary land cover data. The resulting classification was refined applying a filter and a vegetation mask. The algorithm was tested over 16 sites representing Europe’s main biogeographic regions. A confusion matrix was built using points selected via photointerpretation. Validation metrics were computed from the confusion matrix. The results showed that it is possible to develop an automatic forest mask for Europe, (overall accuracy above 90%). Accuracies varied depending on forest characteristics. Best results were achieved in Boreal and Continental forests. Although the algorithm was tuned to consider the diversity of European forests, there is scope for improving the adaptability of MSF Forest Mask, mainly in the southern Mediterranean region, where the mixed effect of tree-grass formations hindered a better forest discrimination. These results may be of interest to forest and land managers and climate modellers.

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