A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests

Abstract

Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features and digital elevation model (DEM) derivatives for mapping old and new landslides, using object-oriented random forest. Two forest subsets were selected including a protected and non-protected forest in northeast Iran. Landslide samples were obtained from CORONA images and aerial photos (old landslides), and also field mensuration and high-resolution images (new landslides). Segment objects were generated from a set combination of Sentinel-1A, Sentinel-2A, and some topographic-derived indices using multiresolution segmentation algorithm. Various object features were derived from the main channels of Sentinel images and DEM derivatives in the seven main groups, including spectral layers, spectral indices, geometric, contextual, textural, topographic, and hydrologic features. A single database was created, including landslide samples and Sentinel- and DEM-derived object features. Roughly 20% of landslide-affected objects and non-landslide-affected objects were randomly selected as an input for training the random forest classifier. Two-thirds of the selected objects were assigned as learning samples for classification, and the remainder were used for testing the accuracy of landslide and non-landslide classification. Results indicated that: (1) The sensitivity of mapping historical landslides was 86.6% and 80.3% in the protected and non-protected forests, respectively; (2) the object features of Sentinel-2A and DEM obtained the highest importance with the total scores of 55.6% and 32%, respectively in the protected forests, and 65.4% and 21% respectively in the non-protected forests; (3) the features derived from the combination of Sentinel-1 and -2A demonstrated a total importance of 10% for mapping new landslides; and (4) textural features were obtained in approximately two-thirds of the total scores for mapping new landslides, however a combination of topographic, spectral, textural, and contextual features were the effective predictors for mapping old landslides. This research proposes applying a synergetic analysis of Sentinel- and DEM-derived features for mapping historical landslides; however, there are no uniformly pre-defined influential variables for mapping historical landslides in different forest areas.

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

“…The new landslides (~430 samples) were obtained from field observations, the available database [83], and high-resolution images of Google Earth for 2016 ( Figure 1). In addition to these samples, about 210 and 1650 polygons of old and new landslides, which were mapped using Sentinel images by Shirvani et al [72], were used along with the landslide samples for the two study areas (Figure 1). The size of the landslide samples ranged between 0.095 and 239.6 ha in the protected forest and between 0.018 and 60.82 ha in the non-protected forest.…”

“…Location of the study areas in the Hyrcanian ecoregion in NE Iran: the Golestan National Park as a "protected forest" (a); and disturbed forests by mining, logging, and road building as a "non-protected forest" (b). Landslide events I were collected from different resources in the current research and the landslide events II were adopted from Shirvani et al [72].…”

“…After testing different scales by trial and error, the scale of 150 was selected with a higher weight for the near-infrared band (NIR) and the compactness and shape values of 0.8 and 0.2, respectively. The final segmentation was optimized using some digital elevation model (DEM) derivatives [72] such as slope, hillshade, terrain ruggedness index (TRI), and flow direction river (FDR). These image-segmented objects were assigned to calculate anthropogenic-induced deforestation and forest fragmentation from remote sensing data ( Figure 2).…”

“…The Hyrcanian ecoregion has been degraded by different human and natural triggering factors such as forest and rangeland conversion [65,66], forest fires [67,68], flooding [69], landslides [70][71][72], soil erosion [73], and climate hazards [74,75] in northeast (NE) Iran. Several studies have accomplished mapping landslide susceptibility in Hyrcanian forests, but have mostly focused on applying models and using common conditioning factors [51,[76][77][78][79] with less attention given to the temporal dynamics of natural and anthropogenic triggering factors.…”

Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data

“…For the image classification problem, the RF is not the best-performing algorithm. However, due to its simplicity, ease of implementation, strong generalization ability, and good performance on many datasets, it has been widely used in academic research and industrial applications [39,40]. The RF is an algorithm that integrates multiple trees through the idea of ensemble learning.…”