andslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaoca, southeastern Brazi

Autor(es)

Dias, Helen Cristina ^{[1, 2]} ; Sandre, Lucas Henrique ^[3] ; Satizabal Alarcon, Diego Alejandro ^[4] ; Grohmann, Carlos Henrique ^{[1, 2]} ; Quintanilha, Jose Alberto ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Energy & Environm, Sao Paulo, SP - Brazil

^[2] Univ Sao Paulo, Inst Energy & Environm, Spatial Anal & Modelling Lab, Sao Paulo, SP - Brazil

^[3] Univ Estadual Campinas, Inst Geosci, Campinas, SP - Brazil

^[4] Univ Sao Paulo, Inst Geosci, Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: BRAZILIAN JOURNAL OF GEOLOGY; v. 51, n. 4 2021.
• Citações Web of Science: 0

Resumo

Landslide identification is important for understanding their conditioning factors, and for constructing susceptibility, risk, and vulnerability maps. In remote sensing this can be accomplished manually or through classifiers. This study compares three image classifiers (Maximum Likelihood, Random Forest, and Support Vector Machines (SVM)) used in identifying landslides in Itaoca (Sao Paulo, Brazil). Two datasets were used: a RapidEye-5 (5 m) image and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) (12.5 m). Seven pixel-based classifications were produced, two for each classifier and a binary class that identified only landslides and non-landslides. One classification contained five spectral bands (5B), while the other contained six bands (6B) and included the slope derived from the DEM. The results were validated using Kappa index and F1 score. The SVM 6B classification achieved the best results among the validation indices used herein. It identified a landslide area of 399,325 m(2). The results contribute to landslide mapping in tropical environments using pixel-based classifiers. However, although the SVM classification was successful, only landslides with larger areas were captured by the algorithms, confirming the importance of conducting further analyses using images with finer spatial resolution. (AU)

• Processo FAPESP: 19/26568-0 - Sensoriamento remoto de alta resolução, deep learning e geomorfometria em análise de deslizamentos naturais e risco geológico
• Beneficiário: Carlos Henrique Grohmann de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 19/17261-8 - Análise de Inventário manuais e semi-automáticos de escorregamentos rasos e sua adequabilidade para utilização em modelos preditivos
• Beneficiário: Helen Cristina Dias
• Modalidade de apoio: Bolsas no Brasil - Doutorado