Chestnuts have been part of the landscape and popular culture of the Canary Islands (Spain) since the sixteenth century.
Many crops of this species are in state of abandonment and an updated mapping for its study and evaluation is needed.
This work proposes the elaboration of this cartography using two satellite images of very high spatial resolution captured
on two different dates and representing well-differentiated phenological states of the chestnut: a WorldView-2 image of
March 10th, 2015 and a WorldView-3 image of May 12th, 2015 (without and with leaves respectively). Two study areas
were selected within the municipality of La Orotava (Tenerife Island). One of the areas contains chestnut trees dispersed
in an agricultural and semi-urban environment and in the other one, the specimens are grouped forming a forest merged
with Canarian pines and other species of Monteverde. The Maximum Likelihood (ML), the Artificial Neural Networks
(ANN) and the Spectral Angle Mapper (SAM) classification algorithms were applied to the multi-temporal image
resulting from the combination of both dates. The results show the benefits of using the multi-temporal image for
Pinolere with the ANN algorithm and for Chasna area with ML algorithm, in both cases providing an overall accuracy
close to 95%.
Regular updating of fuels maps is important for forest fire management. Nevertheless complex and time consuming field work is usually necessary for this purpose, which prevents a more frequent update. That is why the assessment of the usefulness of satellite data and the development of remote sensing techniques that enable the automatic updating of these maps, is of vital interest. In this work, we have tested the use of the spectral bands of OLI (Operational Land Imager) sensor on board Landsat 8 satellite, for updating the fuels map of El Hierro Island (Spain). From previously digitized map, a set of 200 reference plots for different fuel types was created. A 50% of the plots were randomly used as a training set and the rest were considered for validation. Six supervised and 2 unsupervised classification methods were applied, considering two levels of detail. A first level with only 5 classes (Meadow, Brushwood, Undergrowth canopy cover >50%, Undergrowth canopy cover <15%, and Xeric formations), and the second one containing 19 fuel types. The level 1 classification methods yielded an overall accuracy ranging from 44% for Parellelepided to an 84% for Maximun Likelihood. Meanwhile, level 2 results showed at best, an unacceptable overall accuracy of 34%, which prevents the use of this data for such a detailed characterization. Anyway it has been demonstrated that in some conditions, images of medium spatial resolution, like Landsat 8-OLI, could be a valid tool for an automatic upgrade of fuels maps, minimizing costs and complementing traditional methodologies.
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