Sistemas de información geográfica y cartografía geomorfológica aplicados al inventario de deslizamientos y cartografía de susceptibilidad en la cuenca del río El Estado, Pico de Orizaba, México

With the aim of raising awareness on the prevention of landslide disasters, this work develops a methodology that incorporates geomorphological mapping into the mapping of landslide susceptibility using Geographic Information Systems (gis) and Multiple Logistic Regression (mlr). In Mexico, some studies have evaluated the stability of hillsides using gis. However, these studies set a general framework and guidance (that includes basic concepts and explanations of landslide classification, triggering mechanisms, criteria, considerations, and analysis for landslide hazard reconnaissance, etc.) for preparing a landslide atlas at state and city levels. So far, these have not developed a practical and standardized approach incorporating geomorphological maps into the landslide inventory using gis. This paper describes the analysis conducted to develop an analytical technique and morphometric analysis for a multi-temporal landslide inventory. Three data management levels are used to create gis thematic layers. For the first level, analogue topographic, geological, land-use, and climate paper are converted to raster format, georeferenced, and incorporated as gis thematic layers. For the second level, five layers are derived from topographic elevation data: slope angles, slope curvature, contributing area, flow direction, and saturation. For the third level, thematic maps are derived from the previous two levels of data: a hypsometric map (heuristically classified to highlight altimetric levels), a reclassified slope map (allowing to highlight differences in relief), and a morphographic map (derived from a heuristic reclassification of the slope map to highlight volcanic landforms). The theoretical aspects of geomorphological mapping contribute to set the conceptual basis to support landslide mapping. The gis thematic layers provide context and establish an overall characterization of landslide processes within the watershed. Through the retrieval and on-off switching of layers in the gis system, a base map is created to assist in the digitizing of landslides and the modeling of susceptibility. A landslide inventory is created from aerial photographs, field investigations, and all the above gis thematic layers. El Estado river watershed on the southwestern flank of Pico de Orizaba volcano has been selected as study area. The watershed is located in the southwestern slope of Citlaltepetl or Pico de Orizaba volcano. Geological (the stream channel of El Estado river erodes Tertiary and Quaternary lavas, disjointed volcanoclastic materials such as pyroclastic flows, fall deposits, lahars deposits, and alluvium) and geomorphological factors (steep slopes, energy relief, and vertical erosion) in combination with high seasonal rainfall (annual rainfall averages 1000-1100 mm/yr at > 4000 m a.s.l. and 927 mm/yr at <1500 m a.s.l.), and the high degree of weathering, make the study area susceptible to landslides. To assess landslide susceptibility, a landslide inventory map and geomorphometric cartography (altimetry, slope and geomorphography) were reviewed, and field work was conducted. In the study area, more than one hundred landslides were mapped. Shallow landslides (including debris slides and debris flows) are the predominant type. Shallow landslides predominate on hills capped with ash and pyroclastic deposits. The second major landslide process includes rock falls (which occur where the stream erodes lava flows and lahars) and deep-seated landslides (which occur in ash and pyroclastic deposits where lava flows act as a slip plane). In parallel, the spatial geodatabase of landslides was constructed from standardized gis datasets. Pertinent attributes are recorded on a geo-dataset. These include 1) mass wasting process, 2) level of certainty of the observation, 3) photo identification date, 4) landslide size, 5) landslide activity, 6) landslide parts (head, evacuation zone, deposit), 7) slope shape, 8) field slope gradient, 9) map gradient measured from the 10 m digital elevation model (dem), 10) landslide delivery, 11) land use, 12) elevation at which the landslide started, 13) aerial photograph identification number, 14) landslide area, and 15) researcher comments. Each attribute is standardized by the geo-dataset domains in the gis system. With this information the landslide susceptibility is modeled using mlr within a gis platform. mlr is used to examine the relation between land sliding and several independent variables (elevation, slope, contributing area, land use, geology, and terrain curvature) to create the susceptibility map. With six independent variables, the multiple logistic model susceptibility map tends to overpredict landslides at a 10 m pixel resolution. However, the model is statistically valid and able to predict 79.81% of the existing landslides. The implementation of a landslide inventory and susceptibility mapping techniques demonstrate the feasibility of the method for use in other volcanic areas of Mexico.