Accuracy assessment of LIDAR-derived elevation value over vegetated terrain in Tropical region

Airborne LiDAR has been widely used to generate good quality of Digital Terrain Model (DTM). Normally, good quality of DTM would require high density and quality of airborne LiDAR data acquisition which increase the cost and processing time. This study focuses on investigating the capability of low density airborne LiDAR data captured by the Riegl system mounted on an aircraft. The LiDAR data sampling densities is about 2.2 points per m2. The study area is covered by rubber trees with moderately dense understorey vegetation and mixed forest. The ground filtering procedure employs the adaptive triangulation irregular network (ATIN) technique. A reference DTM is generated using 76 ground reference points collected using total station. Based on this DTM the study area is divided into different classes of terrain slopes. The point clouds belong to non-terrain features are then used to calculate the relative percentage of crown cover. The overall root mean square error (RMSE) of elevation values obtained from airborne LiDAR data is 0.611 m. The slope of the study area is divided into class-1 (0-5 degrees), class-2 (5-10 degrees), class-3 (10-15 degrees) and class-4 (15-20 degrees). The results show that the slope class has high correlation (0.916) with the RMSE of the LiDAR ground points. The percentage of crown cover is divided into class-1 (60-70%), class-2 (70-80%), class-3 (80-90%) and class-4 (90-100%). The correlation between percentage of crown cover and RMSE of the LiDAR ground points is slightly lower than the slope class with the correlation coefficient of 0.663

Revised progressive morphological method for ground point classification of airborne LiDAR data

Airborne Light Detection and Ranging (LiDAR) has been very effectively used in collecting terrain information over different scales of area. Inevitably, filtering the non-ground returns is the major step of digital terrain model (DTM) generation and this step poses the greatest challenge especially for tropical forest environment which consists of steep undulating terrain and mostly covered by a relatively thick canopy density. The aim of this research is to assess the performance of the Progressive Morphological (PM) algorithm after the implementation of local slope value in the ground filtering process. The improvement on the PM filtering method was done by employing local slope values obtained either using initial filtering of airborne LiDAR data or ground survey data. The filtering process has been performed with recursive mode and it stops after the results of the filtering does not show any improvement and the DTM error larger than the previous iteration. The revised PM filtering method has decreasing pattern of DTM error with increasing filtering iterations with minimum ±0.520 m of RMSE value. The results also suggest that spatially distributed slope value applied in PM filtering algorithm either from LiDAR ground points or ground survey data is capable in preserving discontinuities of terrain and correctly remove non-terrain points especially in steep area

Geospatial approach for Landslides Vulnerability Assessment of Physical Infrastructures in Sireh Park, Johor Bahru

According to the landslide records between 1993 and 2019 in Malaysia, 171 individuals lost their lives, many others sustained injuries and numerous families evacuated. Additionally, infrastructures and vehicles incurred damage. Even though landslides present a risk to human life, environment, and infrastructures, there are few studies on landslides vulnerability in Malaysia with most of them focusing on social vulnerability neglecting the physical vulnerability. Consequently, a dearth of information on the vulnerability of an area to landslides may amplify the risks associated with landslides, and the relevant authorities in both the landslide-prone and affected regions may not implement the appropriate mitigation measures. The aim of this study is to estimate vulnerability of elements at risk to landslide in the hazard’s affected area of Sireh Park. An indicator based method was used to calculate the relative vulnerability index for each of the elements at risk. In applying the method, experts assigned weights to the indicators and their respective sub indicators based on their significance to landslides vulnerability. The indicators were grouped into clusters, the total values of weights for all the clusters equals to 1. The results of the study showed that about 252 elements at risk are exposed to landslides in Sireh Park, out of which 226 (89.7%) are buildings, while 26 (10.3%) are roads. The general level of buildings' vulnerability to landslides in the study area is relatively low, however about 20.4% of the buildings display a high degree of vulnerability. Meanwhile, the general vulnerability of roads sections to landslides is averagely high, with about 54.8% of the roads sections exhibiting a high degree of vulnerability while 16.1% exhibits low degree of vulnerability. The study can help stakeholders to identify locations with high landslide vulnerability and guide the development of mitigation measures and emergency preparedness plans to reduce the potential impact of landslides in the study area

Individual tree measurement in tropical environment using terrestrial laser scanning

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models

Classification of translational landslide activity using vegetation anomalies indicator (VAI) in Kundasang, Sabah

This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities, (ii) tree canopy gap, (iii) density of different layer of vegetation, (iv) vegetation type distribution, (v) vegetation indices (VIs), (vi) root strength index (RSI), and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4-86%, and kappa is between 0.335-0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8-71.3%, and kappa is between 0.243-0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia

Albedo and land surface temperature shift in hydrocarbon seepage potential area, case study in Miri Sarawak Malaysia

The presence of hydrocarbon seepage is generally associated with rock or mineral alteration product exposures, and changes of soil properties which manifest with bare development and stress vegetation. This alters the surface thermodynamic properties, changes the energy balance related to the surface reflection, absorption and emission, and leads to shift in albedo and LST. Those phenomena may provide a guide for seepage detection which can be recognized inexpensively by remote sensing method. District of Miri is used for study area. Available topographic maps of Miri and LANDSAT ETM+ were used for boundary construction and determination albedo and LST. Three land use classification methods, namely fixed, supervised and NDVI base classifications were employed for this study. By the intensive land use classification and corresponding statistical comparison was found a clearly shift on albedo and land surface temperature between internal and external seepage potential area. The shift shows a regular pattern related to vegetation density or NDVI value. In the low vegetation density or low NDVI value, albedo of internal area turned to lower value than external area. Conversely in the high vegetation density or high NDVI value, albedo of internal area turned to higher value than external area. Land surface temperature of internal seepage potential was generally shifted to higher value than external area in all of land use classes. In dense vegetation area tend to shift the temperature more than poor vegetation area

Experimental evaluation of automotive air-conditioning using HFC-134A and HC-134A

An experimental study to evaluate the energy consumption of an automotive air conditioning is presented. In this study, these refrigerants will be tested using the experimental rig which simulated the actual cars as a cabin complete with a cooling system component of the actual car that is as the blower, evaporator, condenser, radiators, electric motor, which acts as a vehicle engine, and then the electric motor will operate the compressor using a belt and pulley system, as well as to the alternator will recharge the battery. The compressor working with the fluids HFC-134a and HC-134a and has been tested varying the speed in the range 1000, 1500, 2000 and 2500 rpm. The measurements taken during the one hour experimental periods at 2-minutes interval times for temperature setpoint of 20°C with internal heat loads 0, 500, 700 and 1000 W. The final results of this study show an overall better energy consumption of the HFC-134a compared with the HC-134a

Flood loss assessment in the Kota Tinggi

Malaysia is free from several destructive and widespread natural disasters but frequently affected by floods, which caused massive flood damage. In 2006 and 2007, an extreme rainfall occured in many parts of Peninsular Malaysia, which caused severe flooding in several major cities. Kota Tinggi was chosen as study area as it is one the seriously affected area in Johor state. The aim of this study is to estimate potential flood damage to physical elements in Kota Tinggi. The flood damage map contains both qualitative and quantitative information which corresponds to the consequences of flooding. This study only focuses on physical elements. Three different damage functions were adopted to calculate the potential flood damage and flood depth is considered as the main parameter. The adopted functions are United States, the Netherlands and Malaysia. The estimated flood damage for housing using United States, the Netherlands and Malaysia was RM 350/m2 RM 200/m2 and RM 100/m2 respectively. These results successfully showed the average flood damage of physical element. Such important information needed by local authority and government for urban spatial planning and aiming to reduce flood risk

Detection of tropical landslides using airborne lidar data and multi imagery: a case study in Genting Highland, Pahang

The landslide geomorphological system in a tropical region is complex, and its understanding often depends on the completeness and correctness of landslide inventorization. In mountainous regions, landslides pose a significant impact and are known as an important geomorphic process in shaping major landscape in the tropics. A modern remote sensing based approach has revolutionized the landslide investigation in a forested terrain. Optical satellite imagery, aerial photographs and synthetic aperture radar images are less effective to create reliable tropical DTMs for landslide recognition, and even so in the forested equatorial regions. Airborne laser scanning (ALS) data have been used to construct the digital terrain model (DTM) under dense vegetation, but its reliability for landslide recognition in the tropics remains surprisingly unknown. The present study aims at providing better insight into the use of airborne laser scanning (ALS) data. For the bare-earth extraction, several prominent filtering algorithms and surface interpolation methods, i.e. progressive TIN densitification, morphological, and command prompt from Lastool are evaluated in a qualitative analysis, aiming at removing non-ground points while preserving important landslide features. As a result, a large landslide can be detected using OOA. Small landslides remain unrecognized. Three out of five landslides can be detected, with a 60 percent overall accurac