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

Multiscale Landforms Classification Based on UAV Datasets

The advance uses of Unmanned Aerial Vehicles (UAV) in geosciences by producing very high spatial resolution Digital Surface Models (DSMs), the various UAV flight altitudes led to different scales DSM. In this paper, we analyzed terrain forms using Topographic Position Index (TPI), landforms extracted by Iwahashi and Pike method and morphometric features of three different spatial resolutions DSM processed from different UAV flights height datasets of the same study area.Topographic Position Index (TPI) is an algorithm for measuring topographic slope positions and to automate landform classi?cations, Iwahashi and Pike had developed an unsupervised method for classification of Landforms and we have used the techniques developed by Peuker and Douglas, a method classifying terrain surfaces into 7 classes.Landforms extracted from the three indices listed above at the three flight heights of 120, 240 and 360 meters and compared with each other to understand the generalization of different scale and to highlight which landforms are more affected by the scale changes

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

The effects of data reduction on LiDAR-based digital elevation models

LiDAR data enables highly accurate terrain representations, however, various applications are hampered by data handling efficiency; specifically lengthy processing times. To address this, both point density reductions and the use of various resolution grids are compared as data reduction methods to test their effects on the accuracy and handling efficiency of the derived Digital Elevation Model (DEM). A series of point densities of 1%, 10%, 25%, 50% and 75% were interpolated along a range of horizontal resolutions (1-, 2-, 3-, 4-, 5-, 10-, and 30- m). Results indicate that resolution reduction provides the most efficient DEMs in terms of their data handling. DEMs generated at a 3 m resolution using all of the data points deviated less than 6% from the 1mDEM100%, while significantly only taking 10% of the processing time. Resolution reduction provided sufficient accuracies for varying terrain complexities

Modelling the Evolution of Ice-rich Permafrost Landscapes in Response to a Warming Climate

Permafrost is a component of Earth's cryosphere which is of importance for ecosystems and infrastructure in the Arctic, and plays a key role in the global carbon cycle. Global climate warming which is particularly pronounced in polar regions constitutes a major disturbance to permafrost environments which rely on a vulnerable thermal equilibrium between the atmosphere and the land surface. Large-scale climate models reveal high uncertainties in projections of how much permafrost would thaw in response to climate warming scenarios, since they do not represent key complexities of permafrost environments such as small-scale landscape heterogeneities and feedbacks through lateral transport processes. In particular, large-scale models do not take into account thaw processes in ice-rich permafrost which cause widespread landscape change referred to as thermokarst. For this thesis, I have developed a numerical model to investigate thaw processes in ice-rich permafrost landscapes, and I have used it to obtain improved projections of how much permafrost would thaw in response to climate warming. The focus of my research was on cold, ice- and carbon-rich permafrost deposits in the northeast Siberian Arctic, and on landscapes characterized by ice-wedge polygons. In three closely interrelated research articles I have demonstrated that the novel modelling approach of laterally coupled ``tiles'' can be used to realistically simulate the evolution of ice-rich permafrost landscapes. The numerical simulations have revealed that small-scale lateral transport of heat, water, snow, and sediment crucially affect the dynamics of permafrost landscapes and how much permafrost would thaw under climate warming scenarios. My research revealed that substantially more permafrost carbon is affected by thaw in numerical simulations which take into account thermokarst processes, than in simulations which lack a representation of excess ice. These results suggest that conventional large-scale models used for future climate projections might considerably underestimate permafrost thaw and associated carbon-cycle feedbacks. Overall, the research presented in this thesis constitutes a major progress towards the realistic assessment of ice-rich permafrost landscape dynamics using numerical models, and demonstrates the high potential of tile-based modelling paradigms for the computationally efficient representation of subgrid-scale heterogeneity and lateral processes in large-scale climate models

The evaluation of Corona and Ikonos satellite imagery for archaeological applications in a semi-arid environment

Archaeologists have been aware of the potential of satellite imagery as a tool almost since the first Earth remote sensing satellite. Initially sensors such as Landsat had a ground resolution which was too coarse for thorough archaeological prospection although the imagery was used for geo-archaeological and enviro-archaeological analyses. In the intervening years the spatial and spectral resolution of these sensing devices has improved. In recent years two important occurrences enhanced the archaeological applicability of imagery from satellite platforms: The declassification of high resolution photography by the American and Russian governments and the deregulation of commercial remote sensing systems allowing the collection of sub metre resolution imagery. This thesis aims to evaluate the archaeological application of three potentially important resources; Corona space photography and Ikonos panchromatic and multispectral imager). These resources are evaluated in conjunction with Landsat Thematic Mapper (TM) imagery over a 600 square km study area in the semi-arid environment around Homs, Syria. The archaeological resource in this area is poorly understood, mapped and documented. The images are evaluated for their ability to create thematic layers and to locate archaeological residues in different environmental zones. Further consideration is given to the physical factors that allow archaeological residues to be identified and how satellite imagery and modern technology may impact on Cultural Resource Management. This research demonstrates that modern high resolution and historic satellite imagery can be important tools for archaeologists studying in semi-arid environments. The imagery has allowed a representative range of archaeological features and landscape themes to be identified. The research shows that the use of satellite imagery can have significant impact on the design of the archaeological survey in the middle-east and perhaps in other environments

The application of GIS-based binary logistic regression for slope failure susceptibility mapping in the Western Grampian Mountains, Scotland

Slope failure has resulted in significant disruption to the Scottish road network in recent years and failure processes are widely considered to pose a very real risk to both infrastructure and road users. The manifestation of proposed regional climate variations could increase the hazard posed by landslide and debris flow activity within upland environments. It is therefore in the interests of decision makers and land managers to delineate the susceptibility of these areas to failure activity. The availability of accurate and high resolution geophysical data presents an opportunity to conduct a susceptibility analysis of proposed risk areas based on existing sites of failure. It is considered that failure sites are identifiable prior to activity and that events are triggered by external forcing in the form of excessive antecedent precipitation conditions. Binary logistic regression analysis is utilized to identify independent geophysical parameters that have been most associated with instances of past failure events. This technique facilitates the delineation of locations characterized by key parameter conditions most inductive to failure given the occurrence of an external trigger. It is proposed that when exposed to external forcing these locations are most susceptible to failure. To identify these locations is paramount to the successful application of any monitoring and/or preventative strategy. A Geographical Information System (GIS) is the ideal platform from which to undertake such a susceptibility analysis as it facilitates the precise identification of key independent parameter data associated with recorded instances of existing failure locations. The preparation, storage, extraction and analysis of intrinsic geophysical parameters promotes the development of a consistent modelling approach which can be applied to additional regions in the future

Multi-Scale Analysis of the Spatial Distribution of Soil Organic Carbon Stocks in Permafrost-Affected Soils in West Greenland

Soils of the northern circumpolar region are a key organic carbon storage strained by global warming. Thawing of permafrost-affected soils from global warming increases greenhouse-gas emissions whose quantification is limited by sparse, uncertain and spatially diverse data of soil organic carbon stocks (SOCS) across the Arctic region, especially in Greenland. The accurate assessment of the effects of global warming requires better understanding of environmental interactions and feedbacks on SOCS which, however, vary spatially and across scales in Arctic environments. Therefore, different scales were selected to identify scale-dependent effects of environmental factors and processes on the SOCS distribution in permafrost-affected soils in Arctic environments, exemplified by two study areas in West Greenland. Three controlling factors (vegetation, landscape, aspect) were used as representation of spatial varying environmental conditions to investigate the spatial SOCS distribution over short distances separately in both areas on the local scale and over a long distance between both areas on the regional scale. Further, the spatial SOCS distribution was analyzed using a set of multi-scale terrain and spatial features representing environmental processes acting parallel but differing in their intensity on the moraine, valley and catchment scale. The soil data set comprises of SOCS from 140 locations distributed over a study area at the coast and at the ice margin of West Greenland being characterized by oceanic and continental climate. On the local scale, the SOCS distribution was best explained by vegetation and aspect as both reflect the importance of wind and solar radiation in both areas. Furthermore, aspect and curvature best mapped the SOCS distribution shaped by water-driven relocation processes on the moraine and valley scale in SISI and wind-induced processes acting parallel on the moraine, valley and catchment scale in RUSS. On the regional scale, differences in the SOCS distribution result from contrasting climate conditions between the coast and the ice margin which both are reflected by differences in the importance of relevant terrain features and scales and vegetation units between both study areas. Consequently, it is recommended to apply multi-scale terrain features in combination with vegetation to address scale-dependent soil-landscape interrelations being essential for spatial analysis of SOCS in West Greenland