Vegetation anomaly index from remote sensing for landslide activities mapping

Remote sensing has long been used for landslide mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. A high-density airborne Light Detection and Ranging (LiDAR), aerial photo and satellite imagery were captured over the landslide prone area along Sungai Mesilau, Kundasang, Sabah. This study aims to evaluate landslide inventory, generate vegetation properties and vegetation anomalies using high density airborne LiDAR and other remotely sensed data. There are four research objectives. The first objective is to delineate and characterize landslide inventory based on different landslide type, depth and activity. Second objective is to generate vegetation properties and vegetation anomalies using high density airborne LiDAR and other remotely sensed data. The third objective is to generate landslide activity probability map and the fourth objective is to analyze the capability of vegetation anomalies in characterizing landslide activity for different landslide type and depth. Landslide identification has been conducted using orthophoto and three terrain-derived raster layers. Series of landslide validations were conducted to ensure the certainty level of the delineated landslide. These validation processes were conducted by visiting the landslide areas and based on expert-knowledge. Remote sensing data have been used in characterizing vegetation into several classes of height, density, types and structures in the study area. There were 13 vegetation anomalies derived from remotely sensed data. To produce a probability map for landslide activity, different combinations of landslide type, activity and depth have been used as the input data together with the vegetation anomalies raster layer. The use of statistical model was based-on data-driven approach which focusing on the bivariate model (hazard index). The capabilities of landslide probability maps are later evaluated using Receiver Operating Characteristic (ROC) curve together with success and prediction rate values. There were 14 scenarios have been modeled in this study by focusing on two landslide depths, three main landslide types, and three landslide activities. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model which indicates high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. Indices of vegetation for 13 vegetation anomalies layers were tabulated by conducting statistical analysis on the weightage for each of the model. This study introduces new method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. In conclusion, this integrated disaster study provides a better understanding into the utilization of advanced remote sensing data for extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes

The geoenvironmental factors influencing slope failures in the Majerda basin, Algerian-Tunisian border

In mountainous regions globally, landslides pose severe threats to both human lives and infrastructure, with the Mediterranean region, in particular, being highly susceptible to these destructive events that result in substantial damage to settlements and infrastructure. In this study, we employ a GIS-based approach to comprehensively characterize terrain instabilities along the Algerian-Tunisian border, recognizing the critical need for effective land planning and disaster mitigation strategies in this context. Our methodology integrates geological, geophysical, and geotechnical reconnaissance techniques and multi-criteria analysis, with a particular focus on geotechnical parameters. Our findings reveal significant slope instability within the study area; it is particularly concentrated in the mid-altitude slopes of the eastern basin, with high and very high susceptibility zones covering 20.89% of the study area. Validation of our model through ROC analysis demonstrates its high accuracy, with an area under the curve (AUC) value of 0.92. Crucially, slope gradients and precipitation emerge as key contributors to landslide occurrence, alongside Triassic lithofacies, which is a significant geological factor influencing susceptibility. These results emphasize the necessity of identifying high-landslide-susceptibility regions for sustainable land management and risk reduction, which will ultimately enhance the resilience of the studied region and mitigate the associated natural hazard risks

GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES

Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia

Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran’I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management

Characterizing slope instability kinematics by integrating multi-sensor satellite remote sensing observations

Over the past few decades, the occurrence and intensity of geological hazards, such as landslides, have substantially risen due to various factors, including global climate change, seismic events, rapid urbanization and other anthropogenic activities. Landslide disasters pose a significant risk in both urban and rural areas, resulting in fatalities, infrastructure damages, and economic losses. Nevertheless, conventional ground-based monitoring techniques are often costly, time-consuming, and require considerable resources. Moreover, some landslide incidents occur in remote or hazardous locations, making ground-based observation and field investigation challenging or even impossible. Fortunately, the advancements in spaceborne remote sensing technology have led to the availability of large-scale and high-quality imagery, which can be utilized for various landslide-related applications, including identification, monitoring, analysis, and prediction. This efficient and cost-effective technology allows for remote monitoring and assessment of landslide risks and can significantly contribute to disaster management and mitigation efforts. Consequently, spaceborne remote sensing techniques have become vital for geohazard management in many countries, benefiting society by providing reliable downstream services. However, substantial effort is required to ensure that such benefits are provided. For establishing long-term data archives and reliable analyses, it is essential to maintain consistent and continued use of multi-sensor spaceborne remote sensing techniques. This will enable a more thorough understanding of the physical mechanisms responsible for slope instabilities, leading to better decision-making and development of effective mitigation strategies. Ultimately, this can reduce the impact of landslide hazards on the general public. The present dissertation contributes to this effort from the following perspectives: 1. To obtain a comprehensive understanding of spaceborne remote sensing techniques for landslide monitoring, we integrated multi-sensor methods to monitor the entire life cycle of landslide dynamics. We aimed to comprehend the landslide evolution under complex cascading events by utilizing various spaceborne remote sensing techniques, e.g., the precursory deformation before catastrophic failure, co-failure procedures, and post-failure evolution of slope instability. 2. To address the discrepancies between spaceborne optical and radar imagery, we present a methodology that models four-dimensional (4D) post-failure landslide kinematics using a decaying mathematical model. This approach enables us to represent the stress relaxation for the landslide body dynamics after failure. By employing this methodology, we can overcome the weaknesses of the individual sensor in spaceborne optical and radar imaging. 3. We assessed the effectiveness of a newly designed small dihedral corner reflector for landslide monitoring. The reflector is compatible with both ascending and descending satellite orbits, while it is also suitable for applications with both high-resolution and medium-resolution satellite imagery. Furthermore, although its echoes are not as strong as those of conventional reflectors, the cost of the newly designed reflectors is reduced, with more manageable installation and maintenance. To overcome this limitation, we propose a specific selection strategy based on a probability model to identify the reflectors in satellite images

Spatiotemporal Landslide Activity Derived from Tree-rings: The Tieliku Mingsui Landslide, Northern Taiwan

Spatiotemporal landslide activity records are reconstructed for the Tieliku Mingsui landslide. Periods and the extent of scar activity at the foot of the landslide body are estimated from satellite and aerial photo records. The location of landslide features at the densely forested head of the landslide body are surveyed in the field using a VBS-RTK survey and periods of activity are inferred from growth disturbances recorded in 14 conifer and broadleaf trees growing adjacent to the features. Together, image and growth disturbance records produce a detailed spatiotemporal landslide activity record that spans 34 years and includes 8 years of activity. A comparison of landslide activity records with rainfall data collected near the landslide reveals that years of landslide activity coincide with years of high summer season and event accumulated rainfall

Thermal Detection of Water Saturation Spots For Landslide Prediction

Nowadays, we heard many serious issues about landslide phenomenon in Malaysia. It became serious when landslide phenomenon affects human’s life. It causes human injury, loss of life and economical problem. There are a few factors that caused landslide but the main factor is heavy rain. Hence, to solve this issue, this study investigates a new method to detect spots of high water saturation which is integrated with a thermal camera system to provide early detection of landslide. The thermal camera is selected because it provides accurate predict where landslide going to occur. Thermal camera can be used to detect spots of high water saturation which is a key component that contributes to landslide activity. The technique of neural network is used to classify the image of water saturation. The analysis is done using 40 samples. It was tested to classify the data into two groups which are low water saturation and high water saturatio