Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Abstract

Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km 2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p<0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

“…Using remote sensing images to extract UBD information is also a fast and effective method to make up for the shortcomings of traditional manual mapping methods. Existing studies on UBD tend to use high-resolution remote sensing satellites for research because they are more accurate [11,28,29], but their shortcomings are also obvious, such as complex building analysis, the cumbersome automated extraction process, and a large number of computations.NTL remote sensing is widely used in macroeconomic and social parameter estimation [30][31][32], urban monitoring [33][34][35][36], great event change [37,38], energy consumption [39,40], ecological environment assessment [41,42], and other fields. Due to the low spatial resolution of NTL images, previous studies focused on macroscopic dimensions, such as national or urban scales.…”

“…NTL remote sensing is widely used in macroeconomic and social parameter estimation [30][31][32], urban monitoring [33][34][35][36], great event change [37,38], energy consumption [39,40], ecological environment assessment [41,42], and other fields. Due to the low spatial resolution of NTL images, previous studies focused on macroscopic dimensions, such as national or urban scales.…”

Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

“…The RF model was introduced by Breiman [54], and it belongs to a non-parametric ML classifier that has proven to accurately differentiate spectrally complex classes [55]. An RF model is an ensemble classifier that grows multiple decision trees, and is trained using bagging, thereby letting the trees determine the probability of the class membership [25].…”

“…However, the spatial resolution of these images is usually less than 1 m (Quickbird and GeoEye images), and different sequences of applying the multiple rules may lead to different segmentation results, which are hard to define [24]. For a large-scale area, the number of images to be processed is large and time-consuming [25], and that makes the object-based method impractical to apply [26]. In contrast, repetitive observations with dense satellite time-series, such as Landsat (30 m) imagery, are favored by researchers in pixel-based landslide detection [27,28].…”

“…For instance, the Normalized Difference Vegetation Index (NDVI) time-series allows separation between permanently non-vegetated and post-event landslide areas in different geographic settings [29][30][31], and the Normalized Difference Water Index (NDWI), derived from multi-spectral imagery, is commonly used to differentiate landslides from water bodies of mountainous areas [27,32]. In addition, Chen et al [25] also confirmed that the multi-seasonal change characteristics of the nighttime light index can also separate landslides from anthropogenic activities interlinked with light use.…”