High Resolution LiDAR Data Research Articles

Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data

AbstractLiquid layers in clouds affect their microphysical processes, as well as the surface energy budget. Studies focusing on these and other areas of research are often in need of skillful estimation of liquid‐bearing cloud layer boundaries. The bases of these layers are predominantly determined by ground‐based lidar instruments. Most studies requiring liquid cloud base height (LCBH) information use either fixed lidar parameter (depolarization and/or backscatter cross section) thresholds or cloud base height data products that do not distinguish between ice and liquid, all of which might introduce inconsistencies and errors in the resolved LCBH. In this paper, two explicit LCBH detection algorithms are presented. The first algorithm uses the high spectral resolution lidar (HSRL) data. Examination of this algorithm in multiple cases and scenarios during numerous days and first‐order comparison with microwave‐radiometer data show satisfactory results. The second algorithm incorporates widely available micropulse lidar (MPL) data for the LCBH detection. A 1‐year long comparison of data gathered at Barrow, Alaska, and McMurdo Station, Antarctica, which includes other cloud base detection methodologies (ceilometer, MPL value‐added product cloud base height, and LCBHs detected using a fixed MPL depolarization threshold), emphasizes the merits of the presented MPL algorithm. Examination of several unusual LCBH configurations suggests that the current practice of operating lidar at a tilting angle of 4° off zenith may not be sufficient to avoid specular reflection from oriented ice crystals. Data collected at Madison Wisconsin are used to show that specular reflection may impact measurements even at 4°.

Restored saltmarshes lack the topographic diversity found in natural habitat

Saltmarshes can be created to compensate for lost habitat by a process known as managed realignment (MR), where sea defences are deliberately breached to flood low-lying agricultural land. However, the vegetation that develops on MR sites is not equivalent to natural habitat. In natural sites, surface topography and creek networks are drivers of vegetation diversity, but their development on restored sites has not been well studied. We investigate the topographic characteristics of 19 MR areas, and compare these to nearby natural saltmarshes (representing desired conditions) and to coastal agricultural landscapes (representing conditions prior to MR). From high-resolution LiDAR data, we extracted values of elevation, six measures of surface topography (although two were later excluded due to collinearity), and three measures of creek density. MR and natural marshes differed significantly in all surface topographic indices, with MR sites having lower rugosity and more concave features, with greater potential for water accumulation. MR sites also had significantly lower creek density. MRs and coastal agricultural landscapes were more similar, differing in only one topographic measure. Importantly, there was no relationship between age since restoration and any of the topographic variables, indicating that restored sites are not on a trajectory to become topographically similar to natural marshes. MR schemes need to consider actively constructing topographic heterogeneity; better mirroring natural sites in this way is likely to benefit the development of saltmarsh vegetation, and will also have implications for a range of ecosystem functions.

An Integrated Framework for Road Detection in Dense Urban Area from High-Resolution Satellite Imagery and Lidar Data

Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.

GIS estimated potential of rooftop PVs in urban areas - case study Wrocław (Poland)

The authors attempted to determine the amount of a roof's surface required for the installation of PVs panels, in terms of the type of building, roof shape, slope, exposition and potential shading of the structure. The area selected for the study was the city of Wrocław, Poland. Photovoltaics are perceived to be a viable option for reducing the environmental impact of energy production while simultaneously increasing local energy security. Exploiting the potential of cities in generating energy from photovoltaics is increasingly evident; in particular, to provide the individual needs of cities, estates or buildings. Due to the diversity of the construction of buildings' roofs, as well as the impact of neighbouring buildings, determining the technical potential at the stage of preliminary analyses is necessary. For these aims, it seems almost obvious to use high-resolution LiDAR data and a GIS spatial analysis. The LiDAR data in the form of a classified points cloud (12 pt/m2) and the Database of Topographic Object from which information on the building class was taken was used. A digital surface model of roofs with a spatial resolution of 1 m was created from the LiDAR data. A slope and a roof exposition models were created, and the roofs were finally counted in 90 categories. The authors have also analysed the conditions for the shading of their own and neighbouring objects with regard to the vertical and horizontal angle of the sunlight for Wrocław, Poland, on characteristic dates: the March and September equinox, the summer and winter solstice, and analysing the period from 8 am to 4 pm in one hour intervals. Analyses of the available areas were made regarding the building's class.

Geological evolution of the Boset-Bericha Volcanic Complex, Main Ethiopian Rift: 40Ar/39Ar evidence for episodic Pleistocene to Holocene volcanism

The Boset-Bericha Volcanic Complex (BBVC) is one of the largest stratovolcanoes of the northern Main Ethiopian Rift (MER). However, very little is known about its eruptive history, despite the fact that approximately 4 million people live within 100km of the complex. Here, we combine field observations, morphometric analysis using high-resolution LiDAR data, geochemistry and 40Ar/39Ar geochronology to report the first detailed account of the geological evolution of the BBVC, with a focus on extensive young lava flows covering the two edifices, Gudda and Bericha. These lavas exhibit a bimodal composition ranging dominantly from basaltic rift floor lavas and scoria cones, to pantelleritic trachytes and rhyolite flows at Gudda, and comenditic rhyolites at Bericha. Further, several intermediate compositions are associated with fissure vents along the Boset-Kone segment that also appear to link the silicic centres. We divide the BBVC broadly into four main eruptive stages, comprising: (1) early rift floor emplacement, (2) formation of Gudda Volcano within two main cycles, separated by caldera formation, (3) formation of the Bericha Volcano, and (4) sporadic fissure eruptions. Our new 40Ar/39Ar geochronology, targeting a representative array of these flows, provides evidence for episodic activity at the BBVC from ~120ka to the present-day. We find that low-volume mafic episodes are more frequent (~10ka cyclicity) than felsic episodes (~100ka cyclicity), but the latter are more voluminous. Over the last ~30ka, mafic to intermediate fissure activity might have reinvigorated felsic activity (over the last ~16ka), manifested as peralkaline lava flows and pyroclastic deposits at Gudda and Bericha. Felsic episodes have on average a higher eruption rate (2–5/1000years) and productivity at Gudda compared to Bericha (1–2/1000years). The young age of lavas and current fumarolic activity along the fault system, suggest that the BBVC is still potentially active. Coincident episodic activity within the BBVC and at several rift segments in the MER is observed, and facilitates continental rifting.

3D virtual intersection sight distance analysis using lidar data

Sight distance analyses require careful and detailed field measurements to facilitate proper engineering decision making regarding the removal of obstructions, establishment of regulatory and advisory speed limits, and the location of new access points, among numerous other purposes. However, conventional field measurements for these analyses present safety concerns because they require personnel to be in or adjacent to traffic lanes. They can also be time consuming, costly, and labor intensive. Furthermore, the predominantly two-dimensional (2D) methods involve simplifying assumptions such as a “standard” vehicle heights and lengths without considering the wide range of vehicles and drivers present on the road. Recently, many transportation agencies worldwide have begun to acquire mobile lidar data to map their roadway assets. These data provide a rich three-dimensional (3D) environment that enables one to virtually visit a site at any frequency and efficiently evaluate sight distances from the safety of the office. This study investigates advanced safety analysis methodologies for drivers’ sight distance based on high resolution lidar data. The developed simulation method enables users to virtually evaluate available sight distances in a 3D context considering a variety of objects, vehicle types, and multi-modal forms of transportation (e.g., bicycle, pedestrian). The feasibility of this technique was analyzed with a case study at an intersection located in Corvallis, Oregon, USA. The experimental results demonstrated the ability of the proposed methodology to capture significantly more detail on visibility constraints when compared with conventional measurements as well as provide more flexibility in the analysis.

Signatures of Late Pleistocene fluvial incision in an Alpine landscape

Uncertainty regarding the relative efficacy of fluvial and glacial erosion has hindered attempts to quantitatively analyse the Pleistocene evolution of alpine landscapes. Here we show that the morphology of major tributaries of the Rhone River, Switzerland, is consistent with that predicted for a landscape shaped primarily by multiple phases of fluvial incision following a period of intense glacial erosion after the mid-Pleistocene transition (∼0.7 Ma). This is despite major ice sheets reoccupying the region during cold intervals since the mid-Pleistocene. We use high-resolution LiDAR data to identify a series of convex reaches within the long-profiles of 18 tributary channels. We propose these reaches represent knickpoints, which developed as regional uplift raised tributary bedrock channels above the local fluvial baselevel during glacial intervals, and migrated upstream as the fluvial system was re-established during interglacial periods. Using a combination of integral long-profile analysis and stream-power modelling, we find that the locations of ∼80% of knickpoints in our study region are consistent with that predicted for a fluvial origin, while the mean residual error over ∼100 km of modelled channels is just 26.3 m. Breaks in cross-valley profiles project toward the elevation of former end-of-interglacial channel elevations, supporting our model results. Calculated long-term uplift rates are within ∼15% of present-day measurements, while modelled rates of bedrock incision range from ∼1 mm/yr for low gradient reaches between knickpoints to ∼6–10 mm/yr close to retreating knickpoints, typical of observed rates in alpine settings. Together, our results reveal approximately 800 m of regional uplift, river incision, and hillslope erosion in the lower half of each tributary catchment since 0.7 Ma.

Scratching the surface of war. Airborne laser scans of the Great War conflict landscape in Flanders (Belgium)

In light of the growing interest in the Great War – stimulated by the ongoing centennial commemorations – the conflict landscape in Belgium and France is currently the subject of scientific research projects, archaeological excavations, heritage-related initiatives and exhibitions. However, the extent of the archaeological heritage and surface remains of the Great War remain underestimated. Current heritage management and the proposal for a UNESCO nomination focus on the architectural heritage, commemorative monuments and military cemeteries, thereby overlooking the opportunities to acknowledge the conflict landscape in its totality. This paper explores the application of high-resolution Lidar data (DTM-Flanders II 2013–2015) to investigate a layer of war heritage which, until now, has remained invisible, and reveals a wide range of previously unknown archaeological sites related to the Great War. Traces of the war can be found all over the former front zones and hinterland, ranging from remnants of the heavily shelled and devastated war landscapes to more specific archaeo-geomorphological features of trenches, dugouts and other military infrastructure. Both the nature and the scale of the new information support and further expand the concept of the landscape as the last witness of the war.

Comprehensive investigation on non-parametric classification methods in order to separate urban objects using the integration of very high spatial resolution LiDAR and aerial data

Comprehensive investigation on non-parametric classification methods in order to separate urban objects using the integration of very high spatial resolution LiDAR and aerial data

Evolution of the eastern part of the Kuusamo Ice Lobe, based on geomorphological interpretation of high-resolution LiDAR data

Evolution of the eastern part of the Kuusamo Ice Lobe, based on geomorphological interpretation of high-resolution LiDAR data

Monitoring geometric properties of an existing forest road using airborne Lidar data

Accurate information about geometric properties of a forest road is essential for the sustainable forestry and transportation safety. In this study the ability of airborne Lidar in detecting vertical and horizontal profiles and cross section elements of a forest road was investigated in a deciduous forest of Hyrcanian zone. Moreover, Lidar-derived road data was compared with field surveyed data by Leica Total Station device. The results indicated that the average error of Lidar in assessing vertical and horizontal profiles of the existing road was 0.57 m and 4.9°, respectively. The average error of Lidar in detecting the roadbed was 0.78 m. Lidar had an average error of 1.36% in assessing the longitudinal gradient. Based on findings of this study it was concluded that geometric properties of existing forest roads can be monitored rapidly under dense tree canopy using high-resolution Lidar data and without field survey.

Estimation and optimisation of buildings' thermal load using LiDAR data

With the growing urbanization and environmental concerns over buildings' energy consumption and carbon footprint, the demand for energy-efficient building design is greater than ever. This paper addresses these concerns by presenting a novel method for estimating and optimising the thermal load (i.e. total energy load for heating and cooling) of a building within a real environment, provided by high-resolution LiDAR data, while considering long-term climatological parameters, estimated direct and anisotropic diffuse irradiance, shadowing from surroundings, and terrain topography. In the optimisation part of the method, the building's design is optimised regarding the estimated thermal load. The estimation was validated with the well-established EnergyPlus software. In experiments, a rectangular building's design was optimised on a flat and urban dataset. The effect of a building's design parameters on thermal load was inspected as well. On average, the proposed method improved a building's net heat gain by over 103 kWh/m2 and reduced its thermal load by 234.18 kWh/m2 when compared with the initial building design.

FOREST ROADIDENTIFICATION AND EXTRACTIONOF THROUGH ADVANCED LOG MATCHING TECHNIQUES

Abstract. A novel algorithm for forest road identification and extraction was developed. The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments in the forest area. The proposed method used road shape feature to extract the road segments, which have been further processed as objects with orientation preserved. The road network was generated after post processing with tensor voting. The proposed method was tested on Hearst forest, located in central Ontario, Canada. Based on visual examination against manually digitized roads, the majority of roads from the test area have been identified and extracted from the process.

The impact of lidar detection sensitivity on assessing aerosol direct radiative effects

AbstractSpaceborne lidar observations have great potential to provide accurate global estimates of the aerosol direct radiative effect (DRE) in both clear and cloudy conditions. However, comparisons between observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) and multiple years of Atmospheric Radiation Measurement (ARM) program's ground‐based Raman lidars (RL) show that CALIPSO does not detect all radiatively significant aerosol, i.e., aerosol that directly modifies the Earth's radiation budget. We estimated that using CALIPSO observations results in an underestimate of the magnitude of the global mean aerosol DRE by up to 54%. The ARM RL data sets along with NASA Langley airborne high spectral resolution lidar data from multiple field campaigns are used to derive the detection sensitivity required to accurately resolve the aerosol DRE. This shows that a lidar with a backscatter coefficient detection sensitivity of about 1–2 × 10−4 km−1 sr−1 at 532 nm would resolve all the aerosol needed to derive the DRE to within 1%.

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery.

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as highresolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

USE OF HI-RESOLUTION LIDAR IN DISCOVERING THE POLARIS FAULT, MARTIS CREEK DAM, TRUCKEE, CALIFORNIA

Martis Creek Dam, located in the Truckee Basin north of Lake Tahoe, was initially rated as one of the U.S. Army Corps of Engineers9 highest risk dams in the United States. While the dam has performed its flood control purpose, a history of excessive seepage during even moderate reservoir levels has prevented it from also fulfilling its potential water storage function. During seepage and seismic studies to assess and mitigate deficiencies, high resolution LiDAR data was obtained. This imagery provides an unprecedented representation of the ground surface that allows evaluation of geomorphology even in areas with a dense vegetation canopy. At Martis Creek Dam, this geomorphic analysis resulted in the recognition of a previously unknown and through-going lineament between the spillway and dam embankment. This feature extends to the southeast where several lineament splays are exposed on the East Martis Creek Fan. These lineaments were subsequently explored by paleoseismic trenching at two locations and confirmed as faults with Late Quaternary to Holocene displacement. Faulting was confirmed in both trenches as unique splays of a fault zone with several feet of apparent normal (vertical) slip and an unknown magnitude, but a potentially significant, strike-slip component. Faulting was observed near the ground surface in both cases and multiple fault events (a minimum of two) which are at least latest Pleistocene, and probably Holocene-active.

APPLICATION OF A PATTERN RECOGNITION ALGORITHM FOR SINGLE TREE DETECTION FROM LiDAR DATA

Abstract. In the present study, we applied the Particle Swarming Optimization (PSO) procedure to parametrize two Local Maxima (LM) algorithms and a pattern recognition model based on raster and point-cloud datasets in order to extract treetops of coniferous forests from high resolution LiDAR-data of different forest structures (monoplane, biplane and multi-layer) in the Alps region. The approach based on the pattern recognition model uses the geomorphon algorithm applied to the DSM to detect the treetops. The geomorphon model gave good results in terms of matching rates (Rmat: 0.8) with intermediate values of commission and omission rates (Rcom: 0.22, Rom: 0.2). Therefore, it could be a valid alternative to the LM-algorithms when only raster products (DSM – CHM) are available. The geomorphon pattern recognition model has been proved to be a powerful method in order to properly detect treetops of coniferous stands with complex forest structures. This model allows to obtain high detection rates and estimation accuracy of forest volume, also in comparison to the most recent available literature data. The models are developed in Java under Free and Open Source license and are integrated in the JGrassTools library, which is now available as SpatialToolbox of the GIS gvSIG.

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass.

Rasgos básicos de la extracción de variscita en Palazuelo de las Cuevas (Zamora)

Ever since first published in 1970, Palazuelo de las Cuevas (Zamora) became known as “the va- riscite source” for nw Iberia. Later geological surveys and casual finds have shown this mineral to be much more widespread, including other localities in Zamora, Northern Portugal (Tras-os-Montes), Western Leon (Bierzo) and Eastern and Southwest Galicia (Ourense and Pontevedra). Still, the evidence of prehistoric mining is little known, perhaps due to the small size of the veins that otherwise are widely scattered, both circumstances possibly having a strong influence on the nature of the quarrying works and their archaeological visibility. With the aim of assessing the basic characteristics, regarding both the nature and intensity of the extractive works at Las Cercas / Teso del Diablo area, we have made use of high-resolution lidar data and gis tools for obtaining a sharper image of the present surface and revealing the sheer importance of the excavations carried on in earlier times. Also, some brief remarks are made on the chronology and distribution of this commodity across Western Iberia.

A Geographic Object-Based Approach for Land Classification Using LiDAR Elevation and Intensity

By providing detailed height and intensity land surface information, high-resolution LiDAR data have proved to be effective in supporting land classification when combined with other major geospatial data sources, such as hyperspectral images. However, rectifying and fusing multisource geospatial data involves what normally is a manual and time-consuming process. In this letter, we propose a geographic object-based image analysis approach to enable semiautomatic land classification and mapping using LiDAR elevation and intensity data. The methodological framework consists of a series of operations, including preprocessing, object-based segmentation, creation of statistical variables from elevation and intensity, and semisupervised classification. We have successfully applied this approach to the classification of multiple land features, including asphalt, grass, barren land, swimming pool, shrubland, pavement, and buildings. Results show that our proposed approach performs better than LiDAR analysis methods in classifying different land parcels.