Landslide Detection in Central America Using the Differential Bare Soil Index

Abstract

Abstract. The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

“…In this study, all these aspects were carefully investigated and addressed to achieve the best possible landslide detection accuracy in GEE while using ready-made datasets in GEE. Comparisons with existing approaches [19,29,[31][32][33][34][38][39][40][41][42][44][45][46][47][48] demonstrated superior performance of ML-LaDeCORsat of at least 10% higher detection accuracy.…”

“…The latter is either captured in the field or created via digitization from very high-resolution (VHR) aerial or satellite RGB imagery on which the boundaries of landslides are clearly visible. Ground truthing-based accuracies assessment utilizes confusion matrix-derived metrics that can be categorized into three groups: (a) metrics on the positive class: recall, precision (Prec), F1 score (F1S), false positive rate (FPR), commission error (CE); (b) metrics on the negative class: specificity (Spec), negative predictive values (NPV), omission error (OE); and (c) metrics on both classes for imbalanced data: overall accuracy (OA), balanced error (BE), balanced accuracy (BA), quality percentage (QP), and kappa coefficient [38][39][40].…”

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

“…In this study, all these aspects were carefully investigated and addressed to achieve the best possible landslide detection accuracy in GEE while using ready-made datasets in GEE. Comparisons with existing approaches [14,23,33,[35][36][37][38][42][43][44][45][47][48][49][50][51][52] demonstrated superior performance of ML-LaDeCORsat of at least 10% higher detection accuracy.…”

“…The latter is either captured in the field or created via digitization from Very High-Resolution (VHR) aerial or satellite RGB imagery on which the boundaries of landslides are clearly visible. Ground truthing-based accuracies assessment utilizes confusion matrix derived metrics including Overall Accuracy (OA), Balanced Accuracy (BA), Balanced Error (BE), Specificity (Spec), Recall, Precision (Prec), F1-Score (F1S), Negative Predictive Values (NPV), Positive Predictive Values (PPV), Omission Error (OE), Commission Error (CE), True negative rate (TNR), False positive rate (FPR), False negative rate (FNR), Quality percentage (QP), or Kappa Coefficient [14,42,43].…”