Natural hazard events are becoming more frequent, mainly due to human interventions, which take place without careful planning of the territory and without proper risk analysis. In order to accomplish these operations it is necessary to consider mapping tools that allow you to represent the territory: one of the main instruments are the DTM (Digital Terrain Model), which represent morphologically the elevation of the terrain and therefore allow us to make various hydrological analysis. In this work we will be shown how the quality of DTM available and consequently the hydrological information associated with them, are not only related to the step of the mesh and the declared precision of the altitude, but also to the checks carried out during its construction in order to improve the quality and to eliminate systematic or coarse errors localized and hidden in these large amounts of data.
The DTM accuracy for hydrological analysis / Dabove, Paolo; Manzino, Ambrogio; Taglioretti, Cinzia. - In: GEAM. GEOINGEGNERIA AMBIENTALE E MINERARIA. - ISSN 1121-9041. - STAMPA. - 144:1(2015), pp. 15-22.
The DTM accuracy for hydrological analysis
DABOVE, PAOLO;MANZINO, AMBROGIO;TAGLIORETTI, CINZIA
2015
Abstract
Natural hazard events are becoming more frequent, mainly due to human interventions, which take place without careful planning of the territory and without proper risk analysis. In order to accomplish these operations it is necessary to consider mapping tools that allow you to represent the territory: one of the main instruments are the DTM (Digital Terrain Model), which represent morphologically the elevation of the terrain and therefore allow us to make various hydrological analysis. In this work we will be shown how the quality of DTM available and consequently the hydrological information associated with them, are not only related to the step of the mesh and the declared precision of the altitude, but also to the checks carried out during its construction in order to improve the quality and to eliminate systematic or coarse errors localized and hidden in these large amounts of data.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2625171
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