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The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy).

Reichenbach P, Busca C, Mondini AC, Rossi M - Environ Manage (2014)

Bottom Line: On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion.After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area.Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation.

View Article: PubMed Central - PubMed

Affiliation: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, Perugia, Italy, Paola.Reichenbach@irpi.cnr.it.

ABSTRACT
The spatial distribution of landslides is influenced by different climatic conditions and environmental settings including topography, morphology, hydrology, lithology, and land use. In this work, we have attempted to evaluate the influence of land use change on landslide susceptibility (LS) for a small study area located in the southern part of the Briga catchment, along the Ionian coast of Sicily (Italy). On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion. After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area. Moreover, two different land use maps were developed: the first was obtained through a semi-automatic classification of digitized aerial photographs acquired in 1954, the second through the combination of supervised classifications of two recent QuickBird images. Exploiting the two land use maps and different land use scenarios, LS zonations were prepared through multivariate statistical analyses. Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation. Susceptibility maps show an increase in the areal percentage and number of slope units classified as unstable related to the increase in bare soils to the detriment of forested areas.

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Results of CM landslide susceptibility models prepared using different land use scenario (see text). a–d Map with the predicted LS in five unequally spaced classes (see legend); (a1, b1, c1, d1) pp-plots showing for each slope-unit the difference between the probability value obtained using the 2009 and the new land use scenario (see caption of Fig. 5); (a2, b2, c2, d2) success rate curve
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Fig6: Results of CM landslide susceptibility models prepared using different land use scenario (see text). a–d Map with the predicted LS in five unequally spaced classes (see legend); (a1, b1, c1, d1) pp-plots showing for each slope-unit the difference between the probability value obtained using the 2009 and the new land use scenario (see caption of Fig. 5); (a2, b2, c2, d2) success rate curve

Mentions: For each scenario, Fig. 6 shows: (i) the CM LS zonation, (ii) a pp-plot where the susceptibility calculated for each scenario is compared with the result of the susceptibility model prepared with the 2009 land use, and (iii) a success rate curve measuring the fitting performance of the LS model.Fig. 6


The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy).

Reichenbach P, Busca C, Mondini AC, Rossi M - Environ Manage (2014)

Results of CM landslide susceptibility models prepared using different land use scenario (see text). a–d Map with the predicted LS in five unequally spaced classes (see legend); (a1, b1, c1, d1) pp-plots showing for each slope-unit the difference between the probability value obtained using the 2009 and the new land use scenario (see caption of Fig. 5); (a2, b2, c2, d2) success rate curve
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4232744&req=5

Fig6: Results of CM landslide susceptibility models prepared using different land use scenario (see text). a–d Map with the predicted LS in five unequally spaced classes (see legend); (a1, b1, c1, d1) pp-plots showing for each slope-unit the difference between the probability value obtained using the 2009 and the new land use scenario (see caption of Fig. 5); (a2, b2, c2, d2) success rate curve
Mentions: For each scenario, Fig. 6 shows: (i) the CM LS zonation, (ii) a pp-plot where the susceptibility calculated for each scenario is compared with the result of the susceptibility model prepared with the 2009 land use, and (iii) a success rate curve measuring the fitting performance of the LS model.Fig. 6

Bottom Line: On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion.After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area.Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation.

View Article: PubMed Central - PubMed

Affiliation: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, Perugia, Italy, Paola.Reichenbach@irpi.cnr.it.

ABSTRACT
The spatial distribution of landslides is influenced by different climatic conditions and environmental settings including topography, morphology, hydrology, lithology, and land use. In this work, we have attempted to evaluate the influence of land use change on landslide susceptibility (LS) for a small study area located in the southern part of the Briga catchment, along the Ionian coast of Sicily (Italy). On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion. After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area. Moreover, two different land use maps were developed: the first was obtained through a semi-automatic classification of digitized aerial photographs acquired in 1954, the second through the combination of supervised classifications of two recent QuickBird images. Exploiting the two land use maps and different land use scenarios, LS zonations were prepared through multivariate statistical analyses. Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation. Susceptibility maps show an increase in the areal percentage and number of slope units classified as unstable related to the increase in bare soils to the detriment of forested areas.

Show MeSH
Related in: MedlinePlus