Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gigel-Abbay Watershed, Lake Tana Basin, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Recent droughts and the impact of North Atlantic Oscillation in Iberia; a spatiotemporal analysis based on vegetation temperature condition Index (VTCI)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Study of salt stress tolerance in spelt

World population is increasing at an alarming rate while food productivity is decreasing due to the effect of various abiotic stresses. Soil salinity is one of the most important abiotic stress and a limiting factor for worldwide plant production. In addition to its important effects on yield, salt stress affects numerous cellular activities, including cell wall composition, photosynthesis, protein synthesis, ions and organic solutes. Up to 20% of the irrigated arable land in arid and semiarid regions is already salt affected and is still expanding. Improving salt tolerant varieties is of major importance, and efforts should be focused on finding adaptive mechanisms which are involved in salinity tolerance. In this study, several spelt wheat (Triticum aestivum var. Spelta) genotypes and one cultivar of modern bread wheat were used to screen them for salt tolerance. Spelt is an old-European cereal crop currently attracting renewed interest as a food grain because it is said to be harder than wheat and requires less fertilizer. Spelt wheat is also becoming very attractive genetic source by plant breeders due to its wide adaptation ability to various stressful conditions such as soil salinity. In this study morphological parameters (e.g., leaf appearance; shoot elongation), dry matter production, mineral nutrients (especially Na and K), and activity of antioxidative enzymes were measured to select superior genotypes of spelt for salt tolerance. The results showed that Spelt genotype Sp41 is a salt sensitive genotype and genotypes Sp69, Sp96 and Sp912 are good candidates for salt tolerant genotypes.

ENERGÍA EÓLICA Y PAISAJES PROTEGIDOS: CONTROVERSIAS EN EL PARQUE NATURAL DE MONTESINHO

As one of the case studies developed under the international project “Eoliennes et paysage” we could follow the controversial issue of wind power and protected areas in the Montesinho Natural Park, Northeast Portugal, where the local populations demand the setting up of a wind farm in unproductive communal lands, aspiring to benefi t economically from it, while the preservationist claims against wind power within the protected area are sensed by them as an external and illegitimate interference in the communitarian management of a local heritage. Although wind power installation in Montesinho mountains is yet only a virtual possibility (facing hard administrative and technical barriers), this case study contributed to shed light into the kind of negotiations that are being promoted at local and regional levels, and how the present banning of wind power in the region due to conservation restrictions is reactivating ancient antagonisms.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.