Landslide Geoanalytics Using LiDAR-derived Digital Elevation Models

Landslides are natural hazards that contribute to tremendous economic loss and result in fatalities if there is no well-prepared mitigation and planning. Assessing landslide hazard and optimizing quality to improve susceptibility maps with various contributing factors remain a challenge when working with various geospatial datasets. Also, the system of updating landslide inventories which identify geometry, deformation, and type of landslide with semi-automated computing processes in the Geographic Information System (GIS) can be flawed. This study explores landslide geoanalytics approaches combined with empirical approach and powerful analytics in the Zagros and Alborz Mountains of Iran. Light Detection And Ranging (LiDAR)-derived Digital Elevation Models (DEMs), Unmanned Aerial Vehicle (UAV) images, and Google Earth images are combined with the existing inventory dataset. GIS thematic data in conjunction with field observations are utilized along with geoanalytics approaches to accomplish the results. The purpose of this study is to explore the challenges and techniques of landslide investigations. The study is carried out by studying stream length-gradient (SL) index analysis in order to identify tectonic signatures. A correlation between the stream length-gradient index and the graded Dez River profile with slopes and landslides is investigated. By building on the previous study a quantitative approach for evaluating both spatial and temporal factors contributing to landslides for susceptibility mapping utilizing LiDAR-derived DEMs and the Probability Frequency Ratio (PFR) model is expanded. Furthermore, the purpose of this study is to create an algorithm and a software package in MATLAB for semi-automated geometric analysis to measure and determine the length, width, area, and volume of material displacement and flow direction, as well as the type of landslide. A classification method and taxonomy of landslides are explored in this study. LiDAR-derived DEMs and UAV images help to characterize landslide hazards, revise and update the inventory dataset, and validate the susceptibility model, geometric analysis, and landslide deformation. This study makes the following accomplishments and contributions: 1) Operational use of LiDAR-derived DEMs for landslide hazard assessment is estimated, which is a realistic ambition if we can continue to build on recent achievements; 2) While a steeper gradient could potentially be a signature for landslide identification, this study identifies the geospatial locations of high-gradient indices with potential to landslides; 3) An updated inventory dataset is achieved, this study indicates an improved landslide susceptibility map by implementing the PFR model compared to the existing data and previous studies in the same region. This study shows that the most effective factor is the lithology with 13.7% positive influence; and 4) This study builds a software package in MATLAB that can a) determine the type of landslide, b) calculate the area of a landslide polygon, c) determine and measure the length and width of a landslide, d) calculate the volume of material displacement and determine mass movement (i.e. deformation), and e) identify the flow direction of a landslide material movement. In addition to the contributions listed above, a class taxonomy of landslides is introduced in this study. The relative operating characteristic (ROC) curve method in conjunction with field observations and the inventory dataset are used to validate the accuracy of the PFR model. The validation of the result for susceptibility mapping accuracy is 92.59%. Further, the relative error method is applied to validate the performance of relative percentage of error of the selected landslides computing in the proposed software package. The relative percentage of error of the area, length, width, and volume is 0.16%, 1.67%, 0.30%, and 5.50% respectively, compared to ArcGIS. Marzan Abad and Chalus from Mazandaran Province of Iran and Madaling from Guizhou Province of China are used for validating the proposed algorithm

Challenges of solid waste management in Malaysia

Malaysia is faced with challenges with respect to the solid waste management sector because of the increase of population and tourism, economic growth for sustainable development and inadequate waste legislation enforcement, infrastructure and public attitude among residents. This paper gives an approach of the solid waste management in Malaysia with the aim of presenting the state of waste management practices and problems with regards to environmental, economic and other ramifications

PM10 monitoring using MODIS AOT and GIS, Kuala Lumpur, Malaysia.

Remote sensing has been increasingly used in retrieval Aerosol optical thickness (AOT) to particulate matter pollution monitoring. In this study, Moderate resolution image Spectroradiometer (MODIS) data were utilized in particulate matter pollution monitoring. Daily aerosol optical thickness (AOT) data retrieved from MODIS using Non-Linear Correlation Coefficient (NLCC) with polynomial equation Were compared with the amount of particulate matter PMIO measured at Three ground Air Quality Monitoring Stations (AQMS)-Victoria Kl, Cheras Kl and Gombak- in Kuala lumpur and surrounding area. The PMIO data were imported in geographical information system (GIS) environment to derive the PMIO maps in Kuala Lumpur stations. Results showed that the amounts of PMIO in dry season are higher than those in rainy season in stations. The NLCC between MODIS AOT and PMIO concentration was obtained higher in Victoria Kl compared to Gombak and Cheras Kl. GIS maps were found to show better distribution of PMIO compared to the ground station data. This study reveals AOT data from MODIS and GIS map can be utilized to study the air quality, especially distribution of PMIO in the places where there are ground measurements

Real time assessment of haze and PM10 aided by MODIS aerosol optical thickness over Klang Valley, Malaysia

Scarcely distribution, installation and maintenance costs for ground monitoring stations are issues in air pollution monitoring. Moderate resolution imaging Spectroradiometer (MODIS) on board of Terra and Aqua satellites is able to retrieve aerosol optical thickness (AOT) in troposphere and can be utilized in particulate matter pollution monitoring. In this study, daily AOT data retrieved from MODIS in 2004 to 2006, using Non-linear correlation coefficient (NLCC) were compared with the amount of particulate matter PM10 measured at eight ground air quality monitoring stations in Klang Valley, Malaysia. Effects of haze on air quality that indicated MODIS AOT before, during and after the severe haze were also studied. Results showed that the air quality conditions in dry season are unhealthy and correlation coefficients between MODIS AOT and PM concentration are higher than those in rainy season. The corresponding AOT change during the rainy 10 season was between lower than 0.1 and 2.5. It shows that for the rainy season it is less than 0.1. This study reveals AOT data from MODIS can be utilized to study the air quality, especially PM in the places where there 10 are not any ground measurements

Landslide susceptibility evaluation and factor effect analysis using Probabilistic-Frequency Ratio model

In the North parts of Iran (Alborz Mountain belt), landslides occur frequently due to climatologic and geologic conditions with high tectonic activities. That results, annually, millions of dollars financial defects excluding casualties and unrecoverable resources. In this paper, the landslide susceptibility and the effect of landslide-related factors at Marzan Abad in Iran, using the Probabilistic-Frequency Ratio (PFR) model, geographic information system (GIS) and remote sensing data have been evaluated. Landslide location map has been generated on the basis of image elements interpretation from aerial photos, satellite data and field observations. Display, manipulate and analysis have been carried out to evaluate layers such as geology, geomorphology, slope, soil, land use, distance from roads and drainages. The area under the prediction rate curve, evaluates how well the method predicts landslides. The results showed satisfactory agreement between prepared susceptibility map and existing data on landslide locations (92.59%). To assess the factor effects, each factor was excluded from the analysis and its effect was verified using the landslide location data. It is revealed that all factors have relatively positive effects, on the landslide susceptibility maps in the study. The most effective factor is the lithology and outcrop of the bedrocks (13.7% positive influence) in this area

Geohazards analysis of Pisa tunnel in a fractured incompetent rocks in Zagros Mountains, Iran.

The Pisa 2 tunnel with 740 m in length and 20° N trend is located along the Kazerun fault zone in Simply Folded Belt of Zagros, Iran. This tunnel has been excavated in the fractured incompetent marl layers with high expansive pressure of up to 2 kg/cm2. In this study, the geological hazards along the tunnel have been recognized and categorized. This study revealed that, in the long-term usage of the tunnel, the lining did not endure against the loading and the secondary leakages. It is mainly attributed due to the non-efficiencies of drainage and isolation systems in the tunnel site. Therefore, it caused asphalt damage, drainage damage, and wall distortion. FLAC3D software has been used in this research. We conducted various analyses for pre-excavation stress states, syn-excavation, and post-excavation strain states. The results showed no indication of instability and critical deformations during the excavation time. It also revealed that due to the non-efficiencies of drainage and isolation systems against secondary leakages and consequently marl expansion, the volumetric and shear strains (i.e., expansions and displacements) have exceeded from the critical states of strain along the tunnel. For various remedy purpose, this paper attempted several measures that can be taken in order to modify the drainage and isolation systems along the tunnel area. The reconstruction of drainage systems with suitable reinforced concrete and adequate slope has been proposed. The width of channel and isolation of backside of lining and implementation of multi-order outlets (i.e., backside of lining) for draining of groundwater into where the main drainage systems are located in the tunnel gallery were suggested

Article Upliftment Estimation of the Zagros Transverse Fault in Iran Using Geoinformatics Technology

Abstract: The Izeh fault zone is a transverse fault zone with dextral strike slip (and some reverse component) in the Zagros Mountains (Iran). It causes some structural deformations. This fault zone is acting as eastern boundary of Dezful Embayment and forms subsidence of the embayment. The fault has been recognized using remote sensing techniques in conjunction with surface and subsurface analyses. The stratigraphic columns have been prepared in 3D form using Geographical Information System (GIS) tools on the basis of structural styles and thickness of lithologic units. Height differences for erosion levels have been calculated in stratigraphic columns with respect to the subsidence in the Dezful Embayment, which is related to Izeh zone. These height differences have been estimated to be 5,430 m in the central part (and 5,844 m in the northern part) from the Eocene to recent times. This study shows that comparison of the same erosion levels in Asmari-Pabdeh formation boundaries for interior and eastern block of the Izeh fault zone with the absolute uplifting due to the fault activity which is about 533 m per million years in the Izeh zone. The present study reveals that subtracting the absolute uplifting from total subsidence; the real subsidence of Dezful embayment from Eocene to Recent is 0.13 mm/year

PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia

Remote sensing and GIS have been increasingly used for air pollution monitoring in past decade. In this study the distribution of PM10 were measured at eight air quality monitoring stations in Klang Valley. The attempt was carried out in GIS environment. The data are belonging to the beginning of the week –Monday- and weekend –Saturday-. Aerosol optical thickness (AOT) values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) were interpolated in GIS for comparison with ground station PM10 data. The validation between AOT and amount of PM10 in the atmosphere were analyzed using non-linear correlation coefficient (NLCC) for 2004. Results showed that the amount of PM10 at the beginning of the week is higher than the weekend. Remote sensing data showed better distribution of PM10 than ground station data. The NLCC results had a range from (0.10) at Petaling Jaya to (0.61) at Shah Alam. This study shows that GIS is useful tool to generate distribution map of PM10. This study shows that MODIS AOT data are able to present the amount of PM10 over large spatial scales that there is no ground stations air quality monitoring