| Grant number: | 11/05975-4 |
| Support type: | Scholarships in Brazil - Doctorate |
| Effective date (Start): | July 01, 2011 |
| Effective date (End): | November 30, 2012 |
| Field of knowledge: | Engineering - Transportation Engineering |
| Principal Investigator: | José Alberto Quintanilha |
| Grantee: | Luiz Augusto Manfré |
| Home Institution: | Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
| Associated scholarship(s): | 11/19851-5 - Multitemporal analysis for the identification of regions to relocate people in geotechnical hazard areas, BE.EP.DR |
Abstract Geoinformation tools have great applicability in understanding and mapping landslides. Considering the significance of releif components and land cover in this process, it is essential the establishment of methods for the synthesis of the relief information and identification landslides, aiming to facilitate areas risk monitoring. The objective of this Dissertation is to propose digital image processing methodologies for map and identify landslide near to highways. A large landslide with several economic consequences was used as a study area of this work, occurred in 1999, near the Highway Anchieta, in Piloes river basin. Using free data, land cover and relief subdivsion maps were generated and intersected to identify areas of potential landslides in the region of Highways Anchieta and Imigrantes. The relief analysis was performed using based on object classification techniques. The identification of the landslide was performed by evaluating two methodological strategies: one using the supervised classification algorithm SVM (Support Vector Machine) applied to the NDVI vegetation index (Normalized Difference Vegetation Index) and another using combination of different classifiers for the composition of a final classification. The results obtained for relief mapping showed that the proposed method has great potential for the description of the relief features, with greater detail, facilitating the identification of areas with high potential for occurrence of landslides. Both landslides identification methodologies showed good results, and the combination of SVM, Neural Network and Maximum Likelihood algorithms presented the most appropriate result, reaching omission error of less than 10% for the landslide class. The combination of the two products allowed the analysis and identification of several areas of potential landslide scars associated with roads in the study area. The proposed methodology has extensive replication and can be used for risk analysis associated with urban settlements, linear infrastructures and the territorial and environmental planning. | |