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

An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces

Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatic Methods and Machine Learning

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management

LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on structural models

This study was carried out as part of a University of Aberdeen provided PhD supported by The NERC Centre for Doctoral Training in Oil & Gas, (grant reference: NE/M00578X/1). Thanks to Magda Chmielewska for her training and help with LiDAR processing, without which this study could not have been undertaken. Midland Valley Exploration is thanked for academic use of Move 2016 software. We gratefully acknowledge the detailed and constructive reviews by Mike James and an anonymous reviewer, and thanks to Bill Dunne for careful and thorough editorial comments, all of which greatly improved the manuscript.Peer reviewedPublisher PD

Recent remote sensing applications for hydro and morphodynamic monitoring and modelling

It is not new to recognise that data from remote sensing platforms is transforming the way we characterise and analyse our environment. The ability to collect continuous data spanning spatial scales now allows geomorphological research in a data rich environment and this special issue (coming just 7 years after the 2010 special issue of ESPL associated with the remote sensing of rivers) highlights the considerable research effort being made to exploit this information, into new understanding of geomorphic form and process. The 2010 special issue on the remote sensing of rivers noted that fluvial remote sensing papers made up some 14% of the total river related papers in ESPL. A similar review of the papers up to 2017 reveals that this figure has increased to around 25% with a recent proliferation of articles utilising satellite based data and structure from motion derived data. It is interesting to note, however that many studies published to date are proof of concept, concentrating on confirming the accuracy of the remotely sensed data at the expense of generating new insights and ideas on fluvial form and function. Data is becoming ever more accurate and researchers should now be concentrating on analysing these early data sets to develop increased geomorphic insight challenging paradigms and moving the science forward. The prospect of this occurring is increased by the fact that many of the new remote sensed platforms allow accurate spatial data to be collected cheaply and efficiently. This is providing the individual researcher or small research grouping with tremendous opportunity to move the science of fluvial geomorphology forward unconstrained to a large degree of the need to secure substantial research funding. Fluvial geomorphologists have never before been in such a liberated position! As techniques and analytical skills continue to improve it is inevitable that Marcus and Fondstad's (2010) prediction that remotely sensed data will revolutionising our understanding of geomorphological form and process will prove true, altering our ideas on the very nature of system functioning in the process

Geospatial technologies for physical planning: Bridging the gap between earth science and planning

The application of geospatial information technologies has increased recently due to increase in data sources from the earth sciences. The systematic data collection, storage and processing together with data transformation require geospatial information technologies. Rapidly developing computer technology has become an effective tool in design and physical planning in international platforms. Especially, the availability of geospatial information technologies (remote sensing, GIS, spatial models and GPS) for diverse disciplines and the capability of these technologies in data conversion from two dimensions to the three dimensions provide great efficiency. Thus, this study explores how digital technologies are reshaping physical planning and design. While the potential of digital technologies is well documented within physical planning and visualization, its application within practice is far less understood. This paper highlights the role of the geospatial information technologies in encouraging a new planning and design logic that moves from the privileging of the visual to a focus on processes of formation, bridging the interface of the earth science and physical planning

A remote sensing based integrated approach to quantify the impact of fluvial and pluvial flooding in an urban catchment

Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing based integrated approach to enhance current practice in the estimation of flood extent and damage and characterise the spatial distribution of pluvial and fluvial flooding. Cockermouth, a town which is highly prone to flooding, was selected as a study site. The flood event caused by named storm Desmond in 2015 (5-6/12/2015) was selected for this study. A high resolution digital elevation model (DEM) was produced from a composite digital surface model (DSM) and a digital terrain model (DTM) obtained from the Environment Agency. Using this DEM, a 2D flood model was developed in HEC-RAS (v5) 2D for the study site. Simulations were carried out with and without pluvial flooding. Calibrated models were then used to compare the fluvial and combined (pluvial and fluvial) flood damage areas for different land use types. The number of residential properties affected by both fluvial and combined flooding was compared using a combination of modelled results and data collected from Unmanned Aircraft Systems (UAS). As far as the authors are aware, this is the first time that remote sensing data, hydrological modelling and flood damage data at a property level have been combined to differentiate between the extent of flooding and damage caused by fluvial and pluvial flooding in the same event. Results show that the contribution of pluvial flooding should not be ignored, even in a catchment where fluvial flooding is the major cause of the flood damages. Although the additional flood depths caused by the pluvial contribution were lower than the fluvial flood depths, the affected area is still significant. Pluvial flooding increased the overall number of affected properties by 25%. In addition, it increased the flood depths in a number of properties that were identified as being affected by fluvial flooding, in some cases by more than 50%. These findings show the importance of taking pluvial flooding into consideration in flood management practices. Further, most of the data used in this study was obtained via remote sensing methods, including UAS. This demonstrates the merit of developing a remote sensing based framework to enhance current practices in the estimation of both flood extent and damage