Khamarrul Azahari Razak

Characterizing tree growth anomaly induced by landslides using LiDAR

Disrupted vegetation is often used as an indicator for landslide activity in forested regions. The extraction of irregular trees from airborne laser scanning data remains problematic because of low quality of observed data and paucity of field data validation. We obtained high density airborne LiDAR (HDAL) data with 180 points m−2 for characterizing tree growth anomalies caused by landslides in the Barcelonnette region, the Southern French Alps. HDAL allowed the mapping of a complex landslide and its three kinematic zones. The TreeVaW method detecting trees from the HDAL data and determined their position and height, while the SkelTre-skeletonization method extracted the tree inclination. The tree growth anomalies are parameterized by tree height dissimilarities and tree inclinations. These parameters were successfully extracted from the HDAL and compared with field data. We revealed that the distribution of LiDAR-derived tree growth anomalies was statistically different for landslide areas as compared to stable areas.

High density airborne lidar estimation of disrupted trees induced by landslides

Airborne laser scanning (ALS) data has revolutionized the landslide assessment in a rugged vegetated terrain. It enables the parameterization of morphology and vegetation of the instability slopes. Vegetation characteristics are by far less investigated because of the currently available accuracy and density ALS data and paucity of field data validation. We utilized a high density ALS (HDALS) data with 170 points m-2 for characterizing disrupted vegetation induced by landslides by means of a variable window filter and the SkelTre-skeletonisation. Tree analyses in landslide areas resulted in relatively low height, small crown and more irregularities, whereas these peculiarities are not so obvious in the healthy forests. The statistical tests unveiled the clear differences between the extracted parameters in landslide and non-landslide zones and supported the field evidences. We concluded that HDALS is a promising tool to geometrically retrieve disrupted woody vegetation structures and can be good bioindicator to landslide activity.

Airborne laser scanning for forested landslides investigation in temperate and tropical environments

Landslide hazard and risk have increased over the last decades and pose a significant threat to modern society. Despite remarkable efforts of compiling and updating landslide maps at regional, national or global scales, the number of landslide events is often underestimated, especially in forested areas where the vegetation obscures the geomorphic features indicative of landsliding. The primary objective of this study was to investigate the suitability of an active remote sensing technique, airborne laser scanning (ALS), for mapping and classifying landslides in temperate and tropical forest environments. The methods were developed in two study areas: (1) the Bois Noir area in France (Southern French Alps), (2) the Cameron Highlands in Malaysia (tropical rainforest region). In conclusion, the emergence of ALS for investigating geomorphic processes and activities has improved our ability to map, monitor and model the topographic terrain signature and landslide-induced vegetation anomalies. This study explicitly proved that ALS can be a very important new data source and mapping tool to characterize landslides even in a complex environment. The increased prevalence of modern ALS system and advanced point cloud processing had led the ways to improve future landslide maps and subsequently reduce landslide risk. Given the complexity of the terrain, automating the inventorization will still be challenging in the tropics with extensive anthropogenic activity, and differentiating the vegetative reaction in response to different earth surface processes requires further research. Airborne remote sensing is a critical and supportive tool for better understanding of landslide geomorphology in a changing environment.

Landslide Mapping Using LiDAR in the Kundasang Area: A Review

Kundasang has been identified as one of the major geological hazardous area in Malaysia. This is due to the existence of numerous landslides occurrences at some locations in the Kundasang area. The occurrence of landslide has resulted substantial damaged to the building structure, access road, telecommunication towers and agriculture orchards. Several studies and attempts of landslide investigation have been conducted in the Kundasang area using various methods such as localized drilling method, geodynamic mapping and a regional geological structure mapping. These methods have not gathered sufficient information which is considered important in developing a complete landslide inventory to assess landslide susceptibility due to constraint of time and cost. This paper presents a critical review to determine the potential use of light detection and ranging (LiDAR), specifically airborne laser scanning (ALS) for landslides mapping to assess landslide susceptibility of the Kundasang area. Several researchers found that the usage of light detection and ranging (LiDAR) such as airborne laser scanning (ALS) has been a successful technique in landslides mapping. Availability of this new technology for identification and mapping of landslides will assist in obtaining of landslide inventory in term of providing clear, complete and accurate information to investigator for the process of interpretation. With such comprehensive information, landslide susceptibility assessment can be conducted precisely. Most likely the usage of ALS may be a way forward for investigating the landslide phenomena in Malaysia.

Extraction of Digital Terrain Model (DTM) over vegetated area in tropical rainforest using LiDAR

This paper presents investigations on the combination effect of landcover types, ground filtering approach and interpolation methods on Digital Terrain Model (DTM) generated from airborne LiDAR over vegetated area in tropical environment. The study area is separated into three landcover types i.e. oil palm, mangrove and mixed forest. The LiDAR data is filtered based on: 1) Adaptive TIN (ATIN), 2) Progressive morphology (Morph), and 3) Elevation Threshold with Expand Window (ETEW). The DTMs are generated by interpolating the ground points using Ordinary Kriging and Inverse Distance Weighted (IDW) methods. The quality of DTMs is evaluated based on the combination of quantitative and qualitative approaches. The results show that combination of ATIN and Ordinary Kriging has produced DTMs with higher quality compared to other combination of filtering and interpolation technique. The smallest value of RMSE obtained for terrain covered by oil palm (0.21m) followed by mixed forest (0.25m) and mangrove (0.32m).

Mapping of post-event earthquake induced landslides in Sg. Mesilou using LiDAR

Earthquake is a common natural disaster in active tectonic regions. The disaster can induce cascading disasters such as debris flow, mudflow and reactivated old landslides. M 6.0 Ranau earthquake dated on June 05, 2015 coupling with intense and prolonged rainfall caused several mass movements such as debris flow, deep-seated and shallow landslides in Mesilou, Sabah. This study aims at providing a better insight into the use of advanced LiDAR mapping technology for recognizing landslide induced by earthquakes particularly in a vegetated terrain, assessing post event hazard and analyzing its distribution for hazard zonation. We developed the landslide inventory using LiDAR-derived visual analysis method and validated in the field. A landslide inventory map improved with the support of LiDAR derivative data. Finally, landslide inventory was analysed by emphasizing its distribution and density in such a way that it provides clues of risky zone as a result of debris flow. We recommend that mitigation action and risk reduction should be taken place at a transport zone of the channel compared to other zones. This study indicates that modern airborne LiDAR can be a good complementary tool for improving landslide inventory in a complex environment, and an effective tool for rapid regional hazard and risk assessment in the tropics.

Integration of high density airborne LiDAR and high spatial resolution image for landcover classification

This paper discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is between 50 and 100 points per m2. Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only.

Geodynamic activity of Malaysia: Insight from space-based technology and knowledge driven approach

Malaysia situated within the buffer zone of the Pacific Ring of Fire, is vulnerable to seismic hazards as a result of the prevalence of geodynamic activities in Southeast Asia within the past decade particularly since the 8.9 Mw Megathrust in 2004. Prolonged plates activities have been resulting in the on-land lineament along the mid-ridges of the peninsular since the Paleozoic period. In this study, we explored the space-based technology in particular the positioning data and high resolution satellite images, which are crucial for analyzing the past and present geodynamic activity in the tropics. Coupled with efficient GIS processing routine, the geophysical events are carefully mapped, monitored and modeled. An expert knowledge and field investigation provide substantial steps forward into an effective interpretation and decision making process. This study highlights the geospatial intelligence (GEOINT) as a promising and valuable tool in characterizing and assessing seismo-tectonic related disasters in an equatorial region.