Xiaoye Liu

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.Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (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 high-density 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.

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.

Accuracy Assessment of Lidar Elevation Data Using Survey Marks

Abstract Airborne LiDAR has become the preferred technology for digital elevation data acquisition in a wide range of applications. The vertical accuracy with respect to a specified vertical datum is the principal criterion in specifying the quality of LiDAR elevation data. The quantitative assessment of LiDAR elevation data is usually conducted by comparing high-accuracy checkpoints with elevations estimated from the LiDAR ground data. However, the collection of a sufficient number of checkpoints by field surveying is a time-consuming task. This study used survey marks to assess the vertical accuracy of LiDAR data for different land covers in a rural area and explored the performance of different methods for deriving elevations from LiDAR data corresponding to the locations of checkpoints. Normality tests using both frequency histograms and quantile-quantile plots were performed for vertical differences between the LiDAR data and the checkpoints, so the appropriate methods were used for the vertical accuracy assessment of LiDAR data for different land cover categories. The results demonstrated the suitability of using survey marks as checkpoints for the assessment of the vertical accuracy of LiDAR data.

SDI and crowdsourced spatial information management automation for disaster management

Abstract Modern disaster reporting is becoming increasingly sophisticated with the ready access to social media and user-friendly online mapping tools. Citizen engagement in location enabled disaster reporting is more obvious, and the availability of crowd generated geospatial data is higher than ever before. Crowd generated geospatial content is current and more diverse than conventional geographic information; however, quality and credibility issues exist. Although spatial data infrastructures (SDIs) have proven to be successful in supporting disaster management activities in the past, delays in providing public mapping portals and gaps in data are common. Crowd support and crowd generated spatial data have the potential to speed up disaster management actions and disaster mitigations. Within the study, crowd communications that occurred during the 2011 Queensland floods through the Australian Broadcasting Corporations (ABCs) QLD flood crisis map were critically analysed to investigate the readiness of current information sources to support disaster management. The accuracy of the reported event locations were compared to the authoritative Queensland Government street network, Open Street Maps (OSMs) streets and Google streets to compare the accuracy of the street and address names provided through the crowdsourced data. The study reveals that several issues exist regarding the quality of the data provided and the intent of the data provider. Moreover, the results indicate that the direct usage of reported location is problematic and that the semantic processing of the information location along with available spatial data may be required to improve data quality.

Effects of LiDAR Data Reduction and Breaklines on the Accuracy of Digital Elevation Model

Abstract This paper explores the effects of LiDAR data reduction on the accuracies of produced TINs and gridded DEMs. It examined to what extent a set of LiDAR (light detection and ranging) data can be reduced without sacrificing the accuracy of produced terrain model. A primary focus was on the integration of breaklines to the reduction process to assess the contribution of breaklines to improving the accuracy of terrain models in data reduction. A series of TINs and gridded DEMs were produced and assessed at reduced data density levels with and without breaklines respectively. The results showed that LiDAR data can be reduced to a certain level without significantly decreasing the accuracy of produced terrain models. When incorporating breaklines into terrain modelling, the accuracy of produced TINs and gridded DEMs decreased only slightly as data density decreased, indicating that breaklines made a significant contribution to improving the accuracy of terrain models in data reduction.

Support vector machines for tree species identification using LiDAR-derived structure and intensity variables

Tree species identification and forest type classification are critical for sustainable forest management and native forest conservation. Recent success in forest classification and tree species identification using LiDAR (light detection and ranging)-derived variables has been reported in many studies. However, there is still considerable scope for further improvement in classification accuracy. It has driven research into more efficient classifiers such as support vector machines (SVMs) to take maximum advantage of the information extracted from LiDAR data for potential increases in the accuracy of tree species classification. This study demonstrated the success of the SVMs for the identification of the Myrtle Beech (the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species – notably, the Silver Wattle at individual tree level using LiDAR-derived structure and intensity variables. An overall accuracy of 92.8% was achieved from the SVM approach, showing significant advantages of the SVMs over the traditional classification methods such as linear discriminant analysis in terms of classification accuracy.

VGI and crowdsourced data credibility analysis using spam email detection techniques

ABSTRACT Volunteered geographic information (VGI) can be considered a subset of crowdsourced data (CSD) and its popularity has recently increased in a number of application areas. Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high. However, quality issues such as credibility, reliability and relevance are limiting many of the advantages of utilising CSD. Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens. VGI and CSD are also highly unstructured and the quality and metadata are often undocumented. In the 2011 Australian floods, the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards, evacuations, emergency services, road closures and property damage. This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems. The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90% using a forced classification methodology.

WorldView-2 Satellite Imagery and Airborne LiDAR Data for Object-Based Forest Species Classification in a Cool Temperate Rainforest Environment

High resolution spatial data including airborne LiDAR data and newly available WorldView-2 satellite imagery provide opportunities to develop new efficient ways of solving conventional problems in forestry. Those responsible for monitoring forest changes over time relevant to timber harvesting and native forest conservation realize the potential for improved documentation from using such data. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees offers an alternative to aerial photograph interpretation in support of forest classification and mapping. This study explored ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The effectiveness of variables derived from different data sets, in particular, the four new bands of WorldView-2 imagery was examined. The results showed that using LiDAR data alone or four conventional bands only, the overall accuracies achieved were 61.39 and 61.42 % respectively, but the overall accuracy increased to 82.35 % when all eight bands and the LiDAR data were used. This study demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.

Thermal and flame retardancy properties of thermoplastics/natural fiber biocomposites

Environmental issues, such as global warming, have been increasing, due to the rapid consumption of petrol-based resources. Therefore green composites based on natural fibers and biodegradable polymers have rapidly developed and have found wide applications as building and transport materials because of their high specific strength and eco-friendliness. However, compared with traditional materials like metal and ceramics, these plant fiber-based biocomposites are less thermally stable and much more flammable, due to their chemical structure features. Thus it is extremely necessary to enhance the thermal stability and flame retardancy to ensure their safe applications. This chapter focuses on discussing types of natural fibers, biopolymers, and currently available flame retardants, the thermal properties and flammability of natural fiber-based biocomposites and possible flame retardancy mechanisms. Although great advances have been made with regards to enhancing thermal stability and flame retardancy of these biocomposites, there are still some challenges to be addressed.

Ground truth extraction from LiDAR data for image orthorectification

The availability of high accuracy GCPs (ground control points) and DEMs (digital elevation models) becomes the key issue for successful implementation of an image orthorectification project. It is a very difficult task for collecting a large number of high quality GCPs by using traditional methods to meet all the requirements for digital photogrammetric and orthorectification process. Airborne light detection and ranging (LiDAR) - also referred to as airborne laser scanning (ALS), provides an alternative for high-density and high-accuracy three-dimensional terrain point data acquisition. One of the appealing features in the LiDAR output is the direct availability of three dimensional coordinates of points and intensity data in object space. With LiDAR data, high- accuracy and high-resolution intensity image, hillshade DSM (digital surface model) image, and DEM can be generated. Due to high planimetric accuracy characteristics of LiDAR data, ground truth can be extracted from these LiDAR-derived products (e.g., hillshade image and intensity image). This study investigated the feasibility of using LiDAR-derived hillshade DSM image and intensity image to extract ground truth for aerial image orthorectification. Two sets of GCPs were extracted from hillshade image and intensity image separately, and then were used as the inputs for aerial triangulation processing. LiDAR- derived DEM was then employed for differential rectification to produce the final orthoimage. The assessment of the planimetric accuracy of orthorectified images by using different set of GCPs was conducted by comparing the coordinates of some checking points from orthoimages and correspondent GPS surveyed coordinates.