In this study, we present a comprehensive analysis of landforms derived from a 10-meter resolution digital terrain model (DTM) using unsupervised classification with different combination of morphometric variables and established algorithms, including the Topographic Position Index (TPI; Weiss, 2001) and Geomorphons (Jasiewicz & Stepinski, 2013). These methodologies allowed us to delineate distinct landforms, which were subsequently subjected to detailed spatial and statistical analyses to evaluate their geomorphological characteristics and interrelationships. Specifically, we aim to compare how different landform classifications, derived from these approaches, correlate with geothematic variables such as lithology, engineering geological characteristics, and the distribution of shallow landslides. To statistically assess the congruence between landform classifications and geothematic variables, we applied statistical tests such as chi-square tests for independence (for categorical variables) which is used to determine whether there is a significant relationship between landform classes and categorical geothematic variables. Moreover, the strength and direction of these relationships are further evaluated using Cramér’s V. These tests provided insights into the relative effectiveness of each different landform classification in describing the variability of geothematic variables. The study was conducted in northern Tuscany, a region characterized by a complex interplay of geological, morphological, and climatic factors that make it particularly susceptible to shallow landslides and debris flows. These phenomena are frequently triggered by intense rainfall events, which highlight the importance of understanding the distribution of predisposing factors for slope instability in such areas. In conclusion, this study explores different methods to perform the landform classification and establishes a framework to evaluate how they are related to independent geothematic variables, which may be used to assess landslide susceptibility and hazard.
Pippi, M., D'Addario, E., Masoni, G., Marques E Silva Rocha De Oliveira, E., Disperati, L. (2025). The performance of different landform classification methods as assessed by their relationship with geotemathic variables. In EGU General Assembly 2025. Vienna : EGU [10.5194/egusphere-egu25-19353].
The performance of different landform classification methods as assessed by their relationship with geotemathic variables
Pippi, MoiraMembro del Collaboration Group
;D'Addario, EnricoMembro del Collaboration Group
;Masoni, GiulioMembro del Collaboration Group
;Marques e Silva Rocha de Oliveira, EduardoMembro del Collaboration Group
;Disperati, LeonardoMembro del Collaboration Group
2025-01-01
Abstract
In this study, we present a comprehensive analysis of landforms derived from a 10-meter resolution digital terrain model (DTM) using unsupervised classification with different combination of morphometric variables and established algorithms, including the Topographic Position Index (TPI; Weiss, 2001) and Geomorphons (Jasiewicz & Stepinski, 2013). These methodologies allowed us to delineate distinct landforms, which were subsequently subjected to detailed spatial and statistical analyses to evaluate their geomorphological characteristics and interrelationships. Specifically, we aim to compare how different landform classifications, derived from these approaches, correlate with geothematic variables such as lithology, engineering geological characteristics, and the distribution of shallow landslides. To statistically assess the congruence between landform classifications and geothematic variables, we applied statistical tests such as chi-square tests for independence (for categorical variables) which is used to determine whether there is a significant relationship between landform classes and categorical geothematic variables. Moreover, the strength and direction of these relationships are further evaluated using Cramér’s V. These tests provided insights into the relative effectiveness of each different landform classification in describing the variability of geothematic variables. The study was conducted in northern Tuscany, a region characterized by a complex interplay of geological, morphological, and climatic factors that make it particularly susceptible to shallow landslides and debris flows. These phenomena are frequently triggered by intense rainfall events, which highlight the importance of understanding the distribution of predisposing factors for slope instability in such areas. In conclusion, this study explores different methods to perform the landform classification and establishes a framework to evaluate how they are related to independent geothematic variables, which may be used to assess landslide susceptibility and hazard.| File | Dimensione | Formato | |
|---|---|---|---|
|
EGU25-19353-print.pdf
accesso aperto
Tipologia:
PDF editoriale
Licenza:
Creative commons
Dimensione
284.61 kB
Formato
Adobe PDF
|
284.61 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1293317
