Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

Abstract

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved OPEN ACCESSRemote Sens. 2015, 7 9706 classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

Windthrow Detection in European Forests with Very High-Resolution Optical Data

“…For the classification and feature selection we used the Random Forest (RF) classifier [47]. This machine learning algorithm consists of an ensemble of decision trees and is currently widely used in remote sensing [25,27,[48][49][50]. RF was chosen for classification as the algorithm can deal with few training data, multi-modal classes, and non-normal data distributions [47,51].…”

“…OBIA is particularly useful when carrying out a CD with VHR imagery, since VHR pixels are in the size of 1 m (measurement unit) and therefore are often smaller than the land surface object (i.e., tree or group of trees) that needs to be detected (mapping unit). Thus, the object size can be scaled to the size of the studied feature [27]. A further advantage of OBIA is that per image-object, statistical, and geometrical measures can be computed in addition to spectral information, which can increase change detection accuracy [22,28].…”

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

“…Compared with the filter method, the wrapper method typically finds better feature subsets to improve classification performance [68]. LiDAR data were used to automatically identify landslides in the Three Gorges reservoir area, and classification accuracy was improved by employing the wrapper method for feature selection [69,70].…”

Object-based integrated landscape change analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos

“…The growing availability of multi-temporal LiDAR-based elevation data facilitates the combined analysis of fine-scale 3D topographical and forest structure changes occurring in landslide areas. OBIA has been successfully applied in the segmentation and classification of VHR imagery for mapping LCC change (Machala & Zejdova, 2014;Zhou et al, 2008) and in many landslide detection studies (Lahousse et al 2011, Li et al 2015. In such cases, OBIA generally outperforms pixel-based approaches in LCC studies (Machala & Zejdova, 2014), although hybrid approaches (Aguirre-Gutierrez et al 2012, Wang, 2004 may sometimes lead to better accuracies.…”