Person:
Montesino San Martín, Manuel

Profile Picture

Email Address

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Montesino San Martín

First Name

Manuel

person.page.departamento

Estadística, Informática y Matemáticas

ORCID

0000-0002-0822-600X

person.page.upna

811682

Name

Filters

2010 - 20192
Reset filters

Settings

End date: 2019 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Ecosystem services in emergy terms: Danish energy crops
    (2010) Montesino San Martín, Manuel; Maté Caballero, Juan; Porter, John R.; Escuela Técnica Superior de Ingenieros Agrónomos; Nekazaritza Ingeniarien Goi Mailako Eskola Teknikoa; University of Copenhagen (Dinamarca); Tecnología de Alimentos; Elikagaien Teknologia
    Currently, benefits generated by natural environments, such as carbon sequestration or water retention and others, are measured in economic terms based on “willingness to pay” of society (using the TEV or Total Economic Valuation). Thanks to these measurements, decisions can be made about items related to natural ecosystems in the field of politics, construction projects and quantification of financial aids for the agricultural sector. However, a group of scientists involved in Environmental Economy, thinks that this method is not suitable, as society do not really know the true value of the functions of the ecosystems. Even more, they add that solution has to do with the energy measurement of those benefits in order to give them their real value (objective value). In contrast, another group of scientists consider that Ecosystem Services (ES) valuation based on society’s opinion is completely necessary, justifying that these values are lated used as a politic tool. Therefore, the Thesis borns on order to answer this controversy, beginning with the energy calculations of the benefits produced by agricultural environments dedicated to energy crops. Besides, it also aims to clear if the current subjective method of valuation, the TEV, is the best choice for the job of political tool or if there is instead, another better way of doing this. To do that, It has been used an innovative method; The Emergy Method (spelled with “m”). This method consist of determining the necessary energy investment for developing services such as carbon sequestration or water retention, among others. In other words, it allows to obtain the environmental effort printed on the implementation of beneficial ecological functions for human being. With results already obtained, it can be seen; first, the energy investment necessary for biomass production; second, the current method, based on Total Economic Valuation does not express the true value of the ecosystem functions as are undervalued in 331 dollars per hectare and year (in energy crops); third, the best way for valuing ES implies a combination of methods, the Emergy and the TEV. Finally, through the obtention of the energy investment required by the environment for the biomass production, first steps for developing an application that allows to define the most proper areas for a biomass burner industry are given. These areas bounds the surface in which the obtention of a joule of electricity using energy crops requires less environmental effort that the same joule based on fossil fuels, taking on account the terrain, the geometry of the roads used for supplying the industry of biomass and the efficiency of the combustion process.
  • Thumbnail Image
    PublicationOpen Access
    Filling missing data and smoothing altered data in satellite imagery with a spatial functional procedure
    (Springer, 2019) Militino, Ana F.; Ugarte Martínez, María Dolores; Montesino San Martín, Manuel; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    Outliers and missing data are commonly found in satellite imagery. These are usually caused by atmospheric or electronic failures, hampering the correct monitoring of remote-sensing data. To avoid distorted data, we propose a procedure called 'spatial functional prediction' (SFP). The SFP procedure consists of the following: (1) aggregating remote-sensing data for reducing the number of missing data and/or outliers; (2) additively decomposing the time series of images into a trend, a seasonal, and an error component; (3) defining the spatial functional data and predicting the trend component using an ordinary kriging; and (4) adding back the seasonal and error components to the predicted trend. The benefits of the SFP procedure are illustrated in the following scenarios: introducing random outliers, random missing data, mixtures of both, and artificial clouds in an extensive simulation study of composite images, and using daily images with real clouds. The following two derived variables are considered: land surface temperature (LST day) and normalized vegetation index (NDVI), which are obtained as remote-sensing data in a region in northern Spain during 2003–2016. The performance of SFP was checked using the root mean squared error (RMSE). A comparison with a procedure based on predicting with thin-plate splines (TpsP) is also made. We conclude that SFP is simpler and faster than TpsP, and provides smaller values of RMSE.