Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

High-resolution DEM generated from LiDAR data for water resource management

Terrain patterns play an important role in determining the nature of water resources and related hydrological modelling. Digital Elevation Models (DEMs), offering an efficient way to represent ground surface, allow automated direct extraction of hydrological features (Garbrecht and Martz, 1999), thus bringing advantages in terms of processing efficiency, cost effectiveness, and accuracy assessment, compared with traditional methods based on topographic maps, field surveys, or photographic interpretations. However, researchers have found that DEM quality and resolution affect the accuracy of any extracted hydrological features (Kenward et al., 2000). Therefore, DEM quality and resolution must be specified according to the nature and application of the hydrological features. The most commonly used DEM in Victoria, Australia is Vicmap Elevation delivered by the Land Victoria, Department of Sustainability and Environment. It was produced by using elevation data mainly derived from existing contour map at a scale of 1:25,000 and digital stereo capture, providing a state-wide terrain surface representation with a horizontal resolution of 20 metres. The claimed standard deviations, vertical and horizontal, are 5 metres and 10 metres respectively (Land- Victoria, 2002). In worst case, horizontal errors could be up to ±30m. Although high resolution stereo aerial photos provide a potential way to generate high resolution DEMs, under the limitations of currently used technologies by prevalent commercial photogrammetry software, only DSMs (Digital Surface Models) other than DEMs can be directly generated. Manual removal of the nonground data so that the DSM is transformed into a DEM is time consuming. Therefore, using stereo aerial photos to produce DEM with currently available techniques is not an accurate and costeffective method. Light Detection and Ranging (LiDAR) data covering 6900 km² of the Corangamite Catchment area of Victoria were collected over the period 19 July 2003 to 10 August 2003. It will be used to support a series of salinity and water management projects for the Corangamite Catchment Management Authority (CCMA). The DEM derived from the LiDAR data has a vertical accuracy of 0.5m and a horizontal accuracy of 1.5m. The high quality DEM leads to derive much detailed terrain and hydrological attributes with high accuracy. Available data sources of DEMs in a catchment management area were evaluated in this study, including the Vicmap DEM, a DEM generated from stereo aerial photos, and LiDAR-derived DEM. LiDAR technology and LiDAR derived DEM were described. In order to assess the capability of LiDAR-derived DEM for improving the quality of extracted hydrological features, sub-catchment boundaries and drainage networks were generated from the Vicmap DEM and the LiDAR-derived DEM. Results were compared and analysed in terms of accuracy and resolution of DEMs. Elevation differences between Vicmap and LiDAR-derived DEMs are significant, up to 65m in some areas. Subcatchment boundaries derived from these two DEMs are also quite different. In spite of using same resolution for the Vicmap DEM and the LiDARderived DEM, high accuracy LiDAR-derived DEM gave a detailed delineation of sub-catchment. Compared with results derived from LiDAR DEM, the drainage networks derived from Vicmap DEM do not give a detailed description, and even lead to discrepancies in some areas. It is demonstrated that a LiDAR-derived DEM with high accuracy and high resolution offers the capability of improving the quality of hydrological features extracted from DEMs

Spatial prioritisation of revegetation sites for dryland salinity management: an analytical framework using GIS

[Abstract]: To address the lack of analytical and modelling techniques in prioritising revegetation sites for dryland salinity management, a case study of the Hodgson Creek catchment in Queensland, Australia, was conducted. An analytical framework was developed, incorporating the use of spatial datasets (Landsat 7 image, DEM, soil map, and salinity map) which were processed using image processing techniques and a geographic information system (GIS). Revegetation sites were mapped and their priority determined based on recharge area, land use/cover and sub-catchment salinity. The analytical framework presented here enhances the systematic use of land information, widens the scope for scenario testing, and improves the testing of alternative revegetation options. The spatial patterns of revegetation sites could provide an additional set of information relevant in the design of revegetation strategies

Comparative analysis of the differences between using LiDAR and contour-based DEMs for hydrological modeling of runoff generating debris flows in the Dolomites

Present work aims to explore the differences in hydrological modeling when using digital elevation models (DEMs) generated by points from LiDAR surveys and those digitized on the contour lines of the regional technical map (RTM) and their relevance for the simulation of debris flow triggering. Hydrological models for mountainous areas are usually based on digital elevation models (DEMs). DEMs are used to determine the flow path from each pixel, by which the basin is discretized, to the outlet. Hydrological simulations of runoff that triggered debris flows occurred in two rocky headwater basins of Dolomites, Fiames Dimai (area approximately 0.03 km2) and Cancia (area approximately 0.7 km2) are carried out using a DEM-based model designed for simulating runoff that descends from headwater areas. For each basin, the runoff is simulated using DEMs that are generated using points from LiDAR, and those digitized on the contour lines of the regional technical map, respectively. The results show that the peak discharge values corresponding to the simulations carried out using the LiDAR-based DEMs are higher than those corresponding to the simulations carried out using the RTM-based DEMs. Larger differences are observed for the Dimai basin, where the area corresponding to the RTM-based DEM is markedly smaller than the area corresponding to LiDAR-based DEM, whereas for the Cancia basin, the two areas are similar. Both the differences in the peak discharge and the basin area are due to the poor accuracy of the contour-based DEM (i.e., elevation accuracy), that is, a poor representation of the morphological features that leads to errors on the watershed divide and simplifications of the flow paths from each cell to the outlet. This result is highly relevant for estimating the triggering conditions of runoff generated debris flows. An incorrect simulated value of peak discharge can lead to errors both in planning countermeasures against debris flows and in predicting their occurrence

Knowledge discovery from mining the association between H5N1 outbreaks and environmental factors

The global spread of highly pathogenic avian influenza H5N1 in poultry, wild birds and humans, poses a significant panzootic threat and a serious public health risk. An efficient surveillance and disease control system requires a deep understanding of their spread mechanisms, including environmental factors responsible for the outbreak of the disease. Previous studies suggested that H5N1 viruses occurred under specific environmental circumstances in Asia and Africa. These studies were mainly derived from poultry outbreaks. In Europe, a large number of wild bird outbreaks were reported in west Europe with few or no poultry infections nearby. This distinct outbreak pattern in relation to environmental characteristics, however, has not yet been explored. This research demonstrated the use of logistic regression analyses to examine quantitative associations between anthropogenic and physical environmental factors, and the wild bird H5N1outbreaks in Europe. A geographic information system is used to visualize and analyze the data. Our results indicate that the H5N1 outbreaks occur in wild birds in Europe under predictable environmental conditions, which are highly correlated with increased NDVI in December, decreased aspect and slope, increased minimum temperature in October and decreased precipitation in January. It suggests that H5N1 outbreaks in wild birds are strongly influenced by food resource availability and facilitated by the increased temperature and the decreased precipitation. We therefore deduce that the H5N1 outbreaks in wild birds in Europe may be mainly caused by contact with wild birds. These findings are of great importance for global surveillance of H5N1 outbreaks in wild birds

A GIS Model for Predicting Disaster Prone Areas Affected by Global Sea-Level Rise: a Case Study of Semarang City

The issue of global warming that increases sea level will certainly have an effect on sustainability of coastal areas, including coastal cities. Some studies predict that in 2050 the potentially flooded areas around the world will increase approximately one to three meters (WOC, 2007). This tendency will in turn influence the sustainability of many Indonesian cities, particularly those located on coastal areas. Regarding the above issue, this research aims at developing a spatial model using Geographic Information System (GIS) for predicting/delineating disaster prone areas in coastal cities. The model involves some GIS analysis’ capabilities, such as spatial overlay, weighting method and spatial query to delineate flooded areas based on the increase of global sea level and its topography. To test the developed model, Semarang City is selected as the case study with consideration that some previous researches have been done in this area so that most of required data have been collected. The model is then validated using empirical data and field visits to compare between the result and the current situation. The result of application shows that the developed model is satisfied. As for the case of Semarang City, the result shows that 71.6 km2 of Semarang coastal areas are potentially flooded, which 5.04 km2 are highly risk. By superimposing the potentially flooded areas and some vulnerability aspects, the model delineates disaster prone zones as high, moderate and low level. In spite of the fact that the model development purpose is accomplished, further studies are still needed, particularly to specify variables of vulnerability due to the Characteristics of the city. Besides, a 3D model can also be combined with the developed model to improve its visual vie

Estimates of Foreign Exchange Risk Premia: A Pricing Kernel Approach

The goal of this study is to measure market prices of risk and the associated foreign exchange risk premia extending the approach proposed by Balduzzi and Robotti (2001) to an international framework. Estimations of minimum variance stochastic discount factors permits the determination of market prices of risk, which, in turn, in an international framework, allow to compute foreign exchange risk premia. Market prices of risk are time-varying and surge during financial turmoil. This may be interpreted as an increase of the investors' coefficient of risk aversion during turbulent financial markets. Foreign exchange risk premia are also time-varying and they exhibit most variation from the early '70s onwards, when the Bretton Wood exchange rate system collapsed.Foreign exchange, Risk premia, Pricing kernel