High Resolution LiDAR Data Research Articles

Decade-scale coastal bluff retreat from LiDAR data: Lake Erie coast of NW Pennsylvania, USA

Bluff retreat is an ongoing problem on the southern Great Lakes coast of the United States where Pleistocene unconsolidated sediments overlie Devonian bedrock. This retreat results in loss of sediment from the coastal zone and a decrease in coastal water quality, and is of concern because evaluating landslide hazards and developing regulations on coastal construction setbacks must account for spatial variability in coastal retreat. The goal of this pilot study is to explain and quantify short to medium term spatial variability in bluff-retreat patterns, magnitudes, and rates along a 6 km littoral cell on the Pennsylvania coast of Lake Erie. High-resolution LiDAR data covering a one-decade time frame (1998–2007) permit mapping of the bluff-crest position on two comparable, high-quality data sets. The rates of bluff retreat range from unresolvable (~20% of coast) to 2.2 m/year, averaging 0.22 ± 0.1 m/year. Average-annual change rates and 9-year net movement values show an along-coast periodicity, with a spatial wavelength of ~1 km and an amplitude of 0.2 m/year. Observed variability is inferred to occur because variations in surface watershed size and groundwater recharge area, topographic shielding of the bluff from overland flow, and groundwater-table topography, cause groundwater discharge and overland flow to the bluff face to vary along the coast. In addition, short ravines (<300 m in length) fed by groundwater seepage distort groundwater equipotential lines and cause locally enhanced bluff retreat. While other factors likely influence bluff retreat spatially along this coast, their roles are relatively minor at the scale of this study.

Data fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction

The quality of remotely sensed data in regards of accuracy and resolution has considerably improved in recent years. Very small objects are detectable by means of imaging and laser scanning, yet there are only few studies to use such data for large scale mapping of railroad infrastructure. In this paper, an approach is presented that integrates extremely high resolution ortho-imagery and dense airborne laser scanning point clouds. These data sets are used to reconstruct railroad track centre lines. A feature level data fusion is carried out in order to combine the advantages of both data sets and to achieve a maximum of accuracy and completeness. The workflow consists of three successive processing steps. First, object-based image analysis is used to derive a railroad track mask from ortho-imagery. This spatial location information is then combined with the height information to classify the laser points. Lastly, the location of railroad track centre lines from these classified points were approximated using a feature extraction method based on an adapted random sample consensus algorithm. This workflow is tested on two railroad sections and was found to deliver very accurate results in a quickly and highly automated manner.

Understanding Riverine Habitat Inundation Patterns: Remote Sensing Tools and Techniques

Regulated flow releases are key components in the ecological restoration of river ecosystems. Understanding relationships among hydrology, habitat inundation, and ecological processes at appropriate spatial scales is required to optimise ecological outcomes. This study used high-resolution remotely sensed data to develop a three-dimensional (3D) channel and floodplain riverine habitat inundation model. High-resolution LiDAR and digital imagery data were used to develop 3D digital-elevation models. Data segmentation techniques were used to map channel and non-channel areas, while in-channel habitats were identified through topographic characteristics. Object-oriented analysis was used to separate overbank vegetated and non-vegetated areas. Field surveys were undertaken to record the location and diameter of large woody debris (LWD), and canopy foliar density in addition to general landscape description. A qualitative evaluation of the accuracy of the habitat map showed that LiDAR-based discrimination of topographic features and the derivation of a canopy foliar density layer was successful. Mapping of LWD was unsuccessful due to size relative to the LiDAR point cloud density and vertical resolution. The high-resolution remotely sensed data used in this study provide a useful tool for understanding river processes at reach scales by linking inundation patterns to geomorphic features, providing a framework for predicting ecological outcomes from river flow regimes.

Roughness length estimation along road transects using airborne LIDAR data

AbstractRoughness length (Z0) is traditionally used as the primary measure of the aerodynamic roughness of a surface, but is notoriously difficult to estimate. This study takes a new approach to the estimation of Z0, using high resolution LIDAR data coupled with spatial processing techniques to provide estimates of effective roughness length (Z0eff) based upon the prevailing wind direction and the height of the surface elements (e.g. buildings, trees) within a defined area of upwind fetch. The range of roughness values obtained using this new technique is consistent with published values obtained from detailed boundary layer experiments, and is shown to distinguish between a variety of landuse categories, ranging from high density urban areas to rural farmland. Indeed, comparisons in Z0eff values between different landuse classes using a detailed land‐cover dataset have revealed significant differences in surface roughness between landuse classes, giving confidence not only in the technique itself but also to the validity of the land‐cover dataset used in the study. Copyright © 2011 Royal Meteorological Society

Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data

Impervious surface quantification is important for many planning and management applications because of the impacts that impervious surfaces have on a range of environmental resources such as groundwater. This research proposes an integrated method to quantify impervious surfaces at multiple spatial scales via a synthesis of several machine learning approaches. In this study, we 1) proposed a hierarchical classification method to detect impervious surfaces through a fusion of optimized artificial immune networks (OPTINC) and decision trees at high spatial resolution, 2) evaluated the method using multi-sensor data (i.e., high spatial resolution WorldView-2 and LiDAR data) to map impervious surfaces, 3) tested the applicability of the binary impervious surface maps to quantify sub-pixel imperviousness from Landsat TM data of a larger region using regression trees at moderate spatial resolution, and 4) examined the model sensitivity of regression trees to training sample size for impervious surface quantification. OPTINC and decision trees successfully identified impervious surfaces at high resolution (overall accuracy > 90%). The regression tree predicted imperviousness from the TM data with a moderate success ( R 2 = 0.64 and MAE = 14.2%). Although the regression tree appeared robust when training sample size was sufficiently large, it was not stable with small sample size (e.g., < 100).

Combined retrievals of boreal forest fire aerosol properties with a polarimeter and lidar

Abstract. Absorbing aerosols play an important, but uncertain, role in the global climate. Much of this uncertainty is due to a lack of adequate aerosol measurements. While great strides have been made in observational capability in the previous years and decades, it has become increasingly apparent that this development must continue. Scanning polarimeters have been designed to help resolve this issue by making accurate, multi-spectral, multi-angle polarized observations. This work involves the use of the Research Scanning Polarimeter (RSP). The RSP was designed as the airborne prototype for the Aerosol Polarimetery Sensor (APS), which was due to be launched as part of the (ultimately failed) NASA Glory mission. Field observations with the RSP, however, have established that simultaneous retrievals of aerosol absorption and vertical distribution over bright land surfaces are quite uncertain. We test a merger of RSP and High Spectral Resolution Lidar (HSRL) data with observations of boreal forest fire smoke, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). During ARCTAS, the RSP and HSRL instruments were mounted on the same aircraft, and validation data were provided by instruments on an aircraft flying a coordinated flight pattern. We found that the lidar data did indeed improve aerosol retrievals using an optimal estimation method, although not primarily because of the contraints imposed on the aerosol vertical distribution. The more useful piece of information from the HSRL was the total column aerosol optical depth, which was used to select the initial value (optimization starting point) of the aerosol number concentration. When ground based sun photometer network climatologies of number concentration were used as an initial value, we found that roughly half of the retrievals had unrealistic sizes and imaginary indices, even though the retrieved spectral optical depths agreed within uncertainties to independent observations. The convergence to an unrealistic local minimum by the optimal estimator is related to the relatively low sensitivity to particles smaller than 0.1 (μm) at large optical thicknesses. Thus, optimization algorithms used for operational aerosol retrievals of the fine mode size distribution, when the total optical depth is large, will require initial values generated from table look-ups that exclude unrealistic size/complex index mixtures. External constraints from lidar on initial values used in the optimal estimation methods will also be valuable in reducing the likelihood of obtaining spurious retrievals.

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

The LiDAR-side of Headwater Streams: Mapping Channel Networks with High-resolution Topographic Data

Los métodos automáticos para cartografiar canales principales son revisados y tres métodos se utilizan para cartografiar una pequeña cuenca en la altura de la Piedmont de Carolina del Sur basados en datos de alta resolución de LiDAR. En primer lugar, los canales fueron cartografiados manualmente utilizando crenulaciones a 0.6-m en contornos generados a través de los datos de LiDAR. Investigaciones en el campo indican que los mapas de contorno generados con LiDAR son superiores a los cuadrángulos topográficos a escala 1:24,000 y que el mapa de redes resultante es utilizado como referencia para evaluar las simulaciones de red. En segundo lugar, las redes de canales fueron generadas utilizando métodos de acumulación de flujo estándar con tres umbrales de áreas críticas representando áreas de drenaje de 480,800 y 1600 m 2 . En tercer lugar, las redes de canales fueron generadas utilizando un producto de área con pendiente proporcional a la fuerza de la corriente (Ω i,j ∝ Ai,j S i,j ). Este método de múltiples variable que incluye gradientes de colina-pendiente mejora la identificación de pequeños canales en tierras altas en terreno escarpado, pero puede también reflejar falsos barrancos laterales en los canales. Aunque todos los métodos son capaces de simular de forma certera las redes de canales con una densidad de drenaje apropiada, los errores por omisiones son mayores con el método de acumulación convencional y tiene una asociación con las pendientes.

Automated derivation of urban building density information using airborne LiDAR data and object-based method

Building density information is fundamentally important for urban design, planning and management and for urban environmental studies. This paper demonstrates that Building Coverage Ratio (BCR), Floor Area Ratio (FAR), and other building density indicators can be numerically and automatically derived from high-resolution airborne LiDAR data. An object-based method is proposed to process the LiDAR data for the building density information. The method consists of a sequence of numerical operations: generating the normalized Digital Surface Model (nDSM), extracting building objects, deriving object attributes, associating objects with the corresponding land lots, and computing building density indicators at land lot and urban district scales. The algorithms for these operations have been implemented as an ArcGIS extension module. The object-based method is applied to the processing of airborne LiDAR data over downtown Houston. Various attributes have been derived to quantify the building density, urban physical structure, and landscape morphological characteristics of the downtown area at three different spatial scales.

Sandstone Extrusions and Slope Channel Architecture and Evolution: Mio-Pliocene Monterey and Capistrano Formations, Dana Point Harbor, Orange County, California, U.S.A.

Abstract The Miocene Monterey Formation and deepwater channels of the Mio-Pliocene Capistrano Formation crop out at Dana Point Harbor (California) and are locally separated by a series of extruded sandstone bodies. Stratigraphic columns, photo panels, and high-resolution lidar data are used to describe the stratigraphy of the 840-m-wide, 30-m-thick exposure. The studied exposure contains: (1) a lower unit composed of Monterey slope mudstone, (2) an up to 13-m-thick, discontinuous middle unit of structureless sandstone bodies, and (3) an upper sandstone unit composed of the laterally and vertically stacked breccia-, conglomerate-, and sand-filled channels of the Capistrano Formation. Several observations collectively suggest that the structureless sandstone bodies in the middle unit are remobilized: (1) The lower mudstone unit contains centimeter- to meter-scale injections of structureless, poorly sorted, coarse-grained sandstones. (2) The facies in the middle sandstone unit are identical to the injected sandstones facies observed in the lower mudstone unit. (3) The structureless sandstone bodies of the middle unit form disconnected bodies below the upper sandstone unit and have an average aspect ratio of 4:1 whereas the Capistrano channels have an aspect ratio greater than 10:1. (4) Quartz and feldspar grains in the injected sandstone of the lower unit and the structureless sandstone of the middle unit are fractured. (5) The structureless sandstone bodies of the middle unit contain meter-scale angular mudstone clasts of composition similar to that of the Monterey mudstones. (6) These clasts contain internal sandstone injections and are deformed. (7) The Monterey strata around the structureless sandstone bodies display distinct variations of strike and dip relative to the regional trend. The remobilized sandstone bodies forming the middle unit are interpreted as extruded sandstone bodies that incorporated large mudstone clasts (rafts) during the migration from a deep sand source interpreted as being a lower sandy Monterey interval. These sand extrusions were emplaced at the seafloor in multiple phases, following slope channel incision and preceding channel-fill deposits. These extruded fluidized sands triggered local syndepositional deformation of the channel fills in the form of large load structures. The processes that caused the source sands to fluidize are interpreted to be related to deep fluid migration toward a structural high located below the study area. The mechanism triggering the upward migration of sand is likely related to slope channel incision.

Quantifying sand storage capacity of large woody debris on beaches using LiDAR

The sedimentological role of large woody debris (LWD) on beaches is understudied and is relevant for the morphodynamics of sandy, high-energy beach–dune systems of the northeast Pacific Ocean. On the west coast of Canada, this debris consists largely of historical escape logs from the coastal logging industry. In areas with competent wind regimes, LWD can trap appreciable amounts of windblown sand in the backshore, which can alter beach–foredune sediment budgets and initiate incipient dune formation. As this additional store of sediment must be reworked first during high water events, it provides an important buffer that reduces erosion of established foredunes. This study examines the morphology and sand storage capacity of three backshore LWD deposits of varying morphologies on northeastern Graham Island, Haida Gwaii (Queen Charlotte Islands) British Columbia, Canada. A new method was developed using coincident high spatial resolution LiDAR data and digital orthophotographs to derive DEMs for distinct ground cover classes (sand, LWD). These DEMs were then used to quantify relative storage capacities of LWD and sand in the backshore. Significant amounts of sand are stored within and around LWD on beaches in the study region. Existing storage quantities (above HHWMT) range from 9.19 × 10 4 to 1.39 × 10 5 kg m − 1 beach width or ∼ 1.14 to 1.60 m storage depth. The same LWD deposits have a further potential storage capacity ranging from 1.04 to 1.70 × 10 4 kg m − 1 beach width or ∼ 0.21 to 0.28 m depth. The relative storage capacity of these features is reflected in the backshore morphology of each site, with sediment transport further into the backshore dependent upon the morphology and relative in-filling of the log debris jam. With this additional sediment storage, log debris could enhance development of large incipient dunes in the backshore thereby buffering against increasing storminess and gradual sea-level rise in the region. As the use of precise LiDAR data is increasing in geomorphic research, the development of a verifiable methodology for estimating beach–dune storage volumes is timely.

Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston

The heavy concentration of population, economic activities and high-rise buildings have formed a unique and complex urban morphology in the city centre areas of many metropolitan regions. This research exploits high-resolution LIDAR data to quantify three-dimensional urban morphology and its impacts on the spatio-temporal variability of solar radiation in downtown Houston, Texas. Various urban landscape components, including buildings, trees, shrubs and lawns, have been extracted by combining LIDAR data and colour infrared aerial photographs. An efficient solar flux model is re-implemented as an ArcGIS module with extended capabilities. Monthly and seasonal solar radiation fields are computed in terms of radiation intensity and illumination duration. Our analysis suggests that the extensive and dense distribution of tall and large buildings has dramatically changed the spatial pattern of solar radiation and hence imposed significant impacts on other urban landscape components, especially urban vegetation canopy. We have determined three types of vegetation habitats: shade, semi-sunny/partial shade, and sunny habitats. This research represents the first effort to model spatio-temporal variation of solar radiation in an urban built-up environment using high-resolution LIDAR data. The temporal solar radiation maps would benefit the design and selection of appropriate species of trees, shrubs, flowers and lawn grasses for urban vegetation planting and management.

Morphometry of scoria cones located on a volcano flank: A case study from Mt. Etna (Italy), based on high-resolution LiDAR data

By using new high-resolution (2 m) digital elevation model derived from the 2005 LiDAR survey of Mt. Etna volcano (Italy), our study measured the classical morphometrical parameters for scoria cones, i.e. W co (cone width), W cr (crater diameter), H (cone height) as well as volume, inclination of cone slope and substrate, and a number of other parameters for 135 scoria cones of Mt. Etna. Volume and age distribution of cones shows that there is no direct structural control on their emplacement in terms of Etna's rift zones. The cones are progressively smaller in size toward summit, which can be explained by the large volcano's feeding system and progressively frequent lava burial toward top. A careful analysis of H/ W co ratio (determined as 0.18 for other volcanic fields worldwide) shows that this ratio strongly depends on (1) the calculation method of H and (2) lava burial of cone. For Etnean cones, applying an improved method for calculating H relative to the dipping substrate results in a significantly lowered standard H/ W co ratio (0.137), which in turn questions the validity of the classical value of 0.18 in the case of large central volcanoes. The reduction of the ratio is not only due to methodology but also to the common lava burial. This can be expressed even better if H mean is used instead of H max ( H mean/ W co = 0.098). Using this measure, at Etna, well formed cones have higher ratios than structurally deformed (e. g. double or rifted) cones. Furthermore, although the sampled scoria cones at Etna have formed in a relatively narrow time interval (< 6500 yrs BP), there is a slight decrease in H/ W co corresponding to erosional changes detected globally ( H/ W co = 0.143, 0.135 and 0.115 for three age classes of Etna's scoria cones, corresponding to average slopes of 26.6, 23.9 and 23.7°). Because the morphometrical effect of position on a dipping substrate as well as lava burial exceeds the effect of erosion, we call attention to use caution in simply using the H/ W co ratio of scoria cones for detecting age, especially on large active volcanoes.

Mapping northern wetlands with high resolution satellite images and LiDAR

Most wetland maps must be updated for much of North America. High-resolution satellite images and LiDAR-based elevation data have been developed in recent years, and these may prove useful in wetland mapping. We compared the accuracy of the Wisconsin Wetland Inventory maps (WWI) to maps derived from IKONOS high resolution satellite data and LiDAR data for a 63.4 km2 study area in north-central Wisconsin. One-meter, resolution merged multispectral and panchromatic IKONOS data were used with a 1-m resolution LiDAR-based digital elevation model in a manual interpretation of wetland types. IKONOS/LiDAR data were significantly more accurate (74.5% classification accuracy) than WWI data (56%) when wetlands were categorized into WWI classes. Improved accuracies were largely due to less confusion between upland and wetland classes, and generally better distinction among the various wetland classes. LiDAR data improved upland/wetland distinction, based on relative terrain heights and derived terrain-shape indices. Confusion in all classifications was particularly common among lowland coniferous species, and among evergreen shrub and moss classes.

Aerosol and cloud interaction observed from high spectral resolution lidar data

Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adjacency (or three‐dimensional) effects, possible cloud contamination, uncertainty in the AOD retrieval. Some of these limitations do not exist in High Spectral Resolution Lidar (HSRL) observations; for instance, HSRL observations are not affected by cloud adjacency effects, are less prone to cloud contamination, and offer accurate aerosol property measurements (backscatter coefficient, extinction coefficient, lidar ratio, backscatter Angstrom exponent, and aerosol optical depth) at a fine spatial resolution (&lt;100 m) in the vicinity of clouds. Hence the HSRL provides an important data set for studying aerosol and cloud interaction. In this study, we statistically analyze aircraft‐based HSRL profiles according to their distance from the nearest cloud, assuring that all profile comparisons are subject to the same large‐scale meteorological conditions. Our results indicate that AODs from HSRL are about 8–17% higher in the proximity of clouds (∼100 m) than far away from clouds (4.5 km), which is much smaller than the reported cloud three‐dimensional effect on AOD retrievals. The backscatter and extinction coefficients also systematically increase in the vicinity of clouds, which can be explained by aerosol swelling in the high relative humidity (RH) environment and/or aerosol growth through in‐cloud processing (albeit not conclusively). On the other hand, we do not observe a systematic trend in lidar ratio; we hypothesize that this is caused by the opposite effects of aerosol swelling and aerosol in‐cloud processing on the lidar ratio. Finally, the observed backscatter Angstrom exponent (BAE) does not show a consistent trend because of the complicated relationship between BAE and RH. We demonstrate that BAE should not be used as a surrogate for Angstrom exponent, especially at high RH.

Integration of High Resolution Satellite Imagery and LiDAR Data for Forest Damage Detection

Integration of High Resolution Satellite Imagery and LiDAR Data for Forest Damage Detection

Modeling tidal circulation and stratification in Skagit River estuary using an unstructured grid ocean model

Tidal circulation and river plume dynamics in shallow-water estuarine systems with large intertidal zones are complex. Strong asymmetries in tidal currents and stratification often occur in the intertidal zones and subtidal channels over a tidal cycle. The Skagit River is the largest estuary with respect to the discharge of a significant amount of freshwater and sediment into Puget Sound, Washington. It consists of a large intertidal zone with multiple tidal channels near the mouth of the estuary. To simulate the tidal circulation and salinity stratification accurately in the intertidal region, an unstructured grid numerical model with wetting–drying capability and the capability to accurately represent the bathymetry of tidal flats and the geometry of shallow distributary channels is necessary. In this paper, a modeling study for the Skagit River estuary using a three-dimensional unstructured grid, finite-volume coastal ocean model (FVCOM) supported by high-resolution LIDAR data is presented. The hydrodynamic model was validated with observed water surface elevation, velocity, and salinity data over spring and neap tidal cycles under low-river-flow and high-river-flow conditions. Wetting and drying processes in the intertidal zone and strong stratification in the estuary were simulated successfully by the model. Model results indicate that the Skagit River estuary is a highly stratified estuary, but destratification can occur during flood tide. Tides and baroclinic motion are the dominant forcing in the Skagit River estuary, but strong wind events can affect the currents in the intertidal zone significantly. Preliminary analysis also indicated that the salinity intrusion length scale is proportional to the river flow to the −¼ power.

Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images

The objectives of this study were to identify useful predictive factors for tree species identification of individual trees and to compare classifications based on a combination of LiDAR data and multi‐spectral images with classification by the use of each individual data source. Crown segments derived from LiDAR data were mapped to multi‐spectral images for extraction of spectral data within individual tree crowns. Several features, related to height distribution of laser returns in the canopy, canopy shape, proportion of different types of laser returns, and intensity of laser returns, were derived from LiDAR data. Data from a test site in southern Sweden were used (lat. 58°30′ N, long. 13°40′ E). The forest consisted of Norway spruce (Picea abies), Scots pine (Pinus sylvestris), and deciduous trees. Classification into these three tree species groups was validated for 1711 trees that had been detected in LiDAR data within 14 field plots (sizes of 20×50 m2 or 80×80 m2). The LiDAR data were acquired by the TopEye MkII system (50 LiDAR measurements per m2) and the multi‐spectral images were taken by the Zeiss/Intergraph Digital Mapping Camera. The overall classification accuracy was 96% when both data sources were combined.

Emplacement mechanism of gravity flows inferred from high resolution Lidar data: The 1944 Somma–Vesuvius lava flow (Italy)

A Digital Terrain Model derived from high resolution Lidar data allows the determination of the morphometric and physical parameters of a lava flow erupted from the Somma–Vesuvius volcano in 1944. The downstream variation of morphometric parameters including slope, aspect, relative relief, thickness, width, and cross sectional area is analyzed, and the changes in viscosity, velocity and flow rate are estimated. The aims of the analyses are to recognize different flow surfaces, to reconstruct the flow kinematics, and to obtain information on the mechanism of emplacement. The results indicate that the 1944 lava flow can be divided in three sectors: a near vent sector (NVS) characterized by a toe-like surface, an intermediate sector (IS) with an ‘a’ātype brittle surface, and a distal sector (DS) with a sheet-like ductile surface. Lateral leveés and channels do not occur in NVS, whereas they are well developed in IS. In DS, leveés increase with an increasing distance from the vent. Fold-like surfaces occur in NVS and DS, reflecting local shortening processes due to a decrease in the slope of the substratum and overflows from the main channel. IS and DS emplaced between March 18 and 21, 1944, whereas NVS emplaced on March 19 and partly covered IS. The morphometric and physical parameters indicate that IS moved in a ‘tube’-like regime, whereas DS emplaced in a 'mobile crust' regime. The IS to DS transition is marked by an increase in velocity and the flow rate, and by a decrease in thickness, width, cross sectional area, and viscosity. This transition is due to an abrupt increase in the slope of the substratum. The estimated velocity values are in good agreement with the measurements during the 1944 eruption. The analysis used here may be extended to other lava flows. Some gravity flows (debris/mud flows, floods, and avalanches) have rheological properties and shapes similar to those of lavas, and the same process-form relationships may apply to these flows. The approach used here may be therefore useful for evaluating hazards from various gravity currents.

Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction

This paper aims to present a new approach for automatic extraction of building footprints in a combination of the IKONOS imagery with pan-sharpened multi-spectral bands and the low-sampled (∼ 0.1 points/m 2) airborne laser scanning data acquired from the Optech's 1020 ALTM (Airborne Laser Terrain Mapper). Initially, a laser point cluster in 3D object space was recognized as an isolated building object if all the member points were similarly attributed as building points by investigating the height property of laser points and the normalized difference vegetation indices (NDVI) driven from IKONOS imagery. As modelling cues, rectilinear lines around building outlines collected by either data-driven or model-driven manner were integrated in order to compensate the weakness of both methods. Finally, a full description of building outlines was accomplished by merging convex polygons, which were obtained as a building region was hierarchically divided by the extracted lines using the Binary Space Partitioning (BSP) tree. The system performance was evaluated by objective evaluation metrics in comparison to the Ordnance Survey's MasterMap®. This evaluation showed the delineation performance of up to 0.11 (the branching factor) and the detection percentage of 90.1% (the correctness) and the overall quality of 80.5%.