High Resolution LiDAR Data Research Articles

Origin and evolution of Sariñena Lake (central Ebro Basin): A piping-based model

The origin and nature of the numerous lakes in the central Ebro Basin have been interpreted according to the prevailing arid or semiarid conditions, the easily-eroded materials and the solubility of the gypsum- and/or carbonate-rich Tertiary/Cenozoic substratum, involving important dissolution (karstic) and/or aeolian deflation. However, the origin of Sariñena Lake, the largest in the central Ebro Basin, remains unknown since the typical lake-generating processes in the region are not applicable. This work provides significant clues to the genesis and evolution of Sariñena Lake in a regional context. The combination of geomorphological mapping and high resolution LiDAR data together with sedimentological observations, the characterisation of soils and sediments around the lake, and the application of high-resolution geophysical techniques suggest that piping is the major genetic process driving the evolution of the Sariñena depression and lake. Field evidence demonstrates that piping is, at present, the most important erosive process in the region, generating significant collapse and surface lowering. Sariñena Lake is located within a deep endorheic depression excavated from Na-rich Tertiary materials. This work hypothesises that once an early, fluvially-originated palustrine area had developed, the progressive lowering of the regional water table linked to regional fluvial incision favoured the establishment of a hydrological gradient high enough to trigger piping processes within the claystones and siltstones underlying the original palustrine area. The Quaternary evolution of the Sariñena lacustrine basin was then controlled by successive water table fluctuations, linked to different phases of incision and alluvial deposition in the surrounding fluvial systems. All the evidence supporting a piping-related origin for this lake, together with examples of lakes generated by similar processes in different contexts, is used to propose a new genetic type of lacustrine depression, generated by piping processes under favourable conditions.

Recognition and mapping of lacustrine relict coastal features using high resolution aerial photographs and LiDAR data

Shallow lakes in semiarid regions experience frequent water level fluctuations. Each long-lasting episode of water-level lowering leaves behind abandoned littoral forms and deposits whose identification and mapping is hampered by their smooth relief. Given the difficulty of recognising these possible relict forms using traditional geomorphological techniques, two sources of information were employed in the present work: high resolution (1:15,000) aerial photographs and a digital terrain model (DTM) generated from LiDAR data. The improved definition of surface elevation enhanced the quality of geomorphological mapping as well as the accurate delineation of subtle geoforms. The method was applied to Gallocanta Lake, a highly fluctuating shallow lake 14 km2 in area and less than 3 m deep located in a mountainous semiarid area of NE Spain. As a result, a sequence of relict coastal features (RCF) with high lateral continuity has been identified around the lakebed. These include well-preserved spits with recurved hooks, counter-spits, bays closed by barrier islands, beach ridges, deltas and cliffs. The highly precise LiDAR-derived topographic maps suggest a much greater extension of the lacustrine environment during the Late Pleistocene, reaching at least 51 km2 of water surface and about 13 m of depth above the present lake bottom. The method presented in this paper generates very detailed palaeogeographical maps that are particularly useful for reconstructing lake changes in semiarid environments as a function of climate change.

Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data

A methodological approach based on detailed land-use map, high-resolution LiDAR data and field surveys was developed to categorize productive and non-productive mixed forests, both in term of stand attributes and structural diversity. In 2011, leaf-off dedicated airborne LiDAR data were collected in a 20,000ha inland patchy area which was representative of soil land use in the Apennines mountains of southern Italy. By combining field and LiDAR data in 5574ha of forests with coexisting evergreen and deciduous species, we modelled common forest stand variables (height, diameter, volume and biomass) with high accuracy (0.60≤Adj.R2≤0.89). Moreover, a moderate correlation (0.425≤τ≤0.462) between field- and LiDAR-derived diversity indices was found. About 3393ha of forests are enclosed in protected areas of the Natura 2000 network, which in turn possesses 77% (~576,286Mg) of total aboveground dry biomass. Overall, eight forest types were identified, one of which, the European beech, is only found in the Natura 2000 sites, while other forest types are also found elsewhere. This is the first study to undertake a LiDAR analysis of Mediterranean forests in the Campania Region and might help better evaluate trade-off, especially in protected areas, in order to enhance multiple benefits and support sustainable management of forests.

Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m2) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC>0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.

Multiple health benefits of urban tree canopy: The mounting evidence for a green prescription

The purpose of this study was to enhance the understanding of the health-promoting potential of trees in an urbanized region of the United States. This was done using high-resolution LiDAR and imagery data to quantify tree cover within 250m of the residence of 7910 adult participants in the California Health Interview Survey, then testing for main and mediating associations between tree cover and multiple health measures. The results indicated that more neighborhood tree cover, independent from green space access, was related to better overall health, primarily mediated by lower overweight/obesity and better social cohesion, and to a lesser extent by less type 2 diabetes, high blood pressure, and asthma. These findings suggest an important role for trees and nature in improving holistic population health in urban areas.

Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm

Recent developments in the field of remote sensing have introduced new sensor technologies in usage of LiDAR, SAR, and high-resolution optical data. Classification performance is expected to increase through combining these various data sources. The purpose of this study is to develop a new approach for automatic extraction of buildings in urbanized and suburbanized areas. For this purpose, multi-feature extraction process including the spatial, spectral, and textural features were conducted on the very high spatial resolution multispectral aerial images and the LiDAR data set. SVM algorithm was trained by using this multi-feature data, and the classification was performed. After the classification of building and non-building, objects were extracted with high accuracy for the test areas. As a result, it has been proven that multi-features derived from combination of optical and LiDAR data can be successfully applied to solve the problem of automatic detection of buildings by using the proposed approach.

A Simplified Semi-Automatic Technique for Highway Extraction from High-Resolution Airborne LiDAR Data and Orthophotos

Information on highways is an essential input for various geospatial applications, including car navigation, forensic analysis on highway geometries, and intelligent transportation systems. Semi-automatic and automatic extractions of highways are critical for the regular updating of municipal databases and for highway maintenance. This study presents a semi-automatic data processing approach for extracting highways from high-resolution airborne LiDAR height information and aerial orthophotos. The method was developed based on two data sets. Experimental results for the first testing site showed that the accuracy of the proposed method for highway extraction was 74.50 % for completeness and 73.13 % for correctness. Meanwhile, the completeness and correctness for the second testing site were 71.20 and 70.72 %, respectively. The proposed method was compared with an object-based approach on a different data set. The accuracy for highway extraction of the object-based approach was 64.29 % for completeness and 63.11 % for correctness, whereas that of the proposed method was 67.14 % for completeness and 65.08 % for correctness. This research aims to promote semi-automatic highway extraction from LiDAR data and orthophotos by proposing a new approach and a multistep post-processing technique. The proposed method provides an accurate final output that is valuable for a wide range of geospatial applications.

Late Wisconsinan deglaciation and proglacial lakes development in the Charlevoix region, southeastern Québec, Canada

The Charlevoix region, in southeastern Quebec, is characterized by a dramatic landscape formed by the junction of the Laurentian Highlands, the Charlevoix Astrobleme and the St Lawrence Estuary. At the Last Glacial Maximum (LGM), the region was completely covered by the Laurentide Ice Sheet (LIS). The complex topography of the region was the stage of many of the major deglacial events of southern Quebec (e.g. Goldthwait Sea Invasion, St Lawrence Ice-Stream, Saint-Narcisse Episode). We present a detailed reconstruction of the pattern of retreat of the LIS in the Charlevoix region based on the interpretation of ice-marginal features (e.g. moraines, fans) and glaciolacustrine landforms and deposits, two extensive field campaigns, and the interpretation of high-resolution 3D digital aerial photographs and LiDAR data. Our results indicate five moraine complexes in the region: the Rochette, the Brulee, the Sainte-Anne, the Saint-Narcisse and the Mars-Batiscan complexes. Deltas, fans, fine-grained sediments, littoral deposits, drainage breaches and deposits were used to identify 91 palaeo-proglacial lakes. The identification of these lakes and their relation to moraine complexes enabled the reconstruction of six stages of lake development during the Charlevoix deglaciation. The development of proglacial lakes occurred in all types of terrain (highlands, lowlands, transitory levels above marine limit). We conclude that local topography had a decisive effect on promoting both moraine deposition and lake development. We suggest that similar topographical regions (hilly-mountainous) that were affected by major ice-margin stabilizations during glacial retreat should have experience small lakes dominating valleys and topographical lows.

Lidar detection of individual tree size in tropical forests

Abstract Characterization of tropical forest trees has been limited to field-based techniques focused on measurement of diameter of the cylindrical part of the bole, with large uncertainty in measuring large trees with irregular shapes, and other size attributes such as total tree height and the crown size. Here, we introduce a methodology to decompose lidar point cloud data into 3D clusters corresponding to individual tree crowns (ITC) that enables the estimation of many biophysical variables of tropical forests such as tree height, crown area, crown volume, and tree number density. The ITC-based approach was tested using airborne high-resolution lidar data collected over the 50-ha Center for Tropical Forest Science (CTFS) plot in the Barro Colorado Island, Panama. The lack of tree height and crown size measurements in the field prohibits the direct validation of the ITC metrics. We assess the reliability of our method by comparing the aboveground biomass (AGB) estimated using ground and lidar individual tree measurements at multiple spatial scales, namely 1 ha, 2.25 ha, 4 ha, and 6.25 ha. We examined four different lidar-derived AGB models, with three based on individual tree height, crown volume, and crown area, and one with mean top canopy height (TCH) calculated at the plot level using the lidar canopy height model. Results show that the predictive power of all models based on ITC size and TCH increases with decreasing spatial resolution from 16.9% at 1 ha for the worst model to 5.0% at 6.25 ha for the best model. The TCH-based model performed slightly better than ITC-based models except at higher spatial scales (~ 4 ha) and when errors due to edge effects associated with tree crowns were reduced. Unlike the TCH models that change regionally depending on forest type and structure allometry, the ITC-based models are derived as a function of individual tree allometry and can be extended globally to all tropical forests. The method for lidar detection of individual crown size overcome some limitations of ground-based inventories such as 1) it is able to access crowns of large trees and 2) it enables the assessment of directional changes in tree density, canopy architecture and forest dynamics over large and inaccessible areas to support robust tropical ecological studies.

Vulnerability assessment for preliminary flood risk mapping and management in coastal areas

Planning and management of coastal environment, both terrestrial and marine, is affected by several actions in environment resource conservation and improvement, paying specific attention to risk forecasting and preventing. In such context, the EU flood Directive 2007/60/EC, which requires Member States the assessment and management of flood risk, and the EU water framework Directive (2000/60/EC) are the key factors in the integrated river basin management to assure an efficient and rational use of resources. Afterwards, coastal risk assessment and mapping are a propaedeutic phase to plan and manage coastal areas. In this work, risk analysis refers to the results obtained by the combined application of coastal flooding and erosion risks in the activities carried out to prepare the Regional Coast Management Plan for the Ionian coast of Basilicata Region located in the south of Italy. In order to define the driving forces acting on the shore, high-resolution lidar data, bathymetric information and wave climate statistics referred to different acquisition times are used. The systemic vulnerability estimation is achieved by composing both hazard factors combined in the Coastal Criticality Index depending on the assessment of Coastal Flood Index and Coastal Erosion Index based on morphologic and socio-economic variables.

Improving the identification of hydrologically sensitive areas using LiDAR DEMs for the delineation and mitigation of critical source areas of diffuse pollution

Identifying critical source areas (CSAs) of diffuse pollution in agricultural catchments requires the accurate identification of hydrologically sensitive areas (HSAs) at highest propensity for generating surface runoff and transporting pollutants. A new GIS-based HSA Index is presented that improves the identification of HSAs at the sub-field scale by accounting for microtopographic controls. The Index is based on high resolution LiDAR data and a soil topographic index (STI) and also considers the hydrological disconnection of overland flow via topographic impediment from flow sinks. The HSA Index was applied to four intensive agricultural catchments (~7.5–12km2) with contrasting topography and soil types, and validated using rainfall-quickflow measurements during saturated winter storm events in 2009–2014. Total flow sink volume capacities ranged from 8298 to 59,584m3 and caused 8.5–24.2% of overland-flow-generating-areas and 16.8–33.4% of catchment areas to become hydrologically disconnected from the open drainage channel network. HSA maps identified ‘breakthrough points’ and ‘delivery points’ along surface runoff pathways as vulnerable points where diffuse pollutants could be transported between fields or delivered to the open drainage network, respectively. Using these as proposed locations for targeting mitigation measures such as riparian buffer strips reduced potential costs compared to blanket implementation within an example agri-environment scheme by 66% and 91% over 1 and 5years respectively, which included LiDAR DEM acquisition costs. The HSA Index can be used as a hydrologically realistic transport component within a fully evolved sub-field scale CSA model, and can also be used to guide the implementation of ‘treatment-train’ mitigation strategies concurrent with sustainable agricultural intensification.

영구지반변형이 매설된 하수도관로 성능에 미치는 영향

In recent years, the earthquake sequence in Christchurch, New Zealand (NZ) was unprecedented in terms of repeated earthquake shocks with substantial levels of ground motion affecting modern infrastructure, and in particular, broad and precise reports for liquefaction-induced permanent ground deformation (PGD) and repairs of wastewater (WW) pipelines were collected. In this study, a geographical information system (GIS) and linear regression analysis were performed using data for the length and repair points of earthenware (EW) and concrete (CONC) wastewater pipelines acquired after the MW 6.2 February 22, 2011 earthquake. The repair rates (repairs/km) for the EW and CONC wastewater pipelines were evaluated inside the areas of PGD, and both angular distortion of ground and lateral ground strain were calculated from the high resolution LiDAR data acquired before and after the seismic event. The research results showed that both pipelines have similar trends of damage but the CONC wastewater pipeline with higher stiffness showed less damage. The results of linear regression analyses can be used to predict the repair rates for EW and CONC wastewater pipelines inside the areas of PGD induced by future earthquakes.

Drizzle Measurements Using High Spectral Resolution Lidar and Radar Data

The ratio of millimeter radar and High Spectral Resolution Lidar (HSRL) backscatter are used to determine drizzle rates which are compared to conventional ground based measurements. The robustly calibrated HSRL backscatter cross section provides advantages over measurements made with traditional lidars.

Ocean Lidar Measurements of Beam Attenuation and a Roadmap to Accurate Phytoplankton Biomass Estimates

Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

Accuracy and Computational Efficiency of 2D Urban Surface Flood Modelling Based on Cellular Automata

There is an emerging abundance of freely available high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and wastewater systems in the UK contain over 700,000km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required, calling for the most computational efficient models possible.Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES [1], a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.

Quantitative identification of erosional Lake Bonneville shorelines, Utah

Although the major shorelines of Pleistocene Lake Bonneville have been recognized for more than 100years, less prominent shorelines have proven difficult to quantitatively identify and correlate since the time of G. K. Gilbert. By applying the Savitzky–Golay filter and methodology of Hare et al. to high resolution LiDAR data of selected erosional shorelines, five distinct, correlatable shorelines between the well-described Bonneville and Provo shorelines on the transgressive limb of the Lake Bonneville hydrograph are documented. The results provide a framework for determining whether these transgressive features represent lake oscillations or stillstands during the height of the Last Glacial Maximum in the eastern Great Basin of the western United States with possible attendant paleoclimate implications.

A Flood Risk Assessment of the LaHave River Watershed, Canada Using GIS Techniques and an Unstructured Grid Combined River-Coastal Hydrodynamic Model

A flexible mesh hydrodynamic model was developed to simulate flooding of the LaHave River watershed in Nova Scotia, Canada, from the combined effects of fluvial discharge and ocean tide and surge conditions. The analysis incorporated high-resolution lidar elevation data, bathymetric river and coastal chart data, and river cross-section information. These data were merged to generate a seamless digital elevation model which was used, along with river discharge and tidal elevation data, to run a two-dimensional hydrodynamic model to produce flood risk predictions for the watershed. Fine resolution topography data were integrated seamlessly with coarse resolution bathymetry using a series of GIS tools. Model simulations were carried out using DHI Mike 21 Flexible Mesh under a variety of combinations of discharge events and storm surge levels. Discharge events were simulated for events that represent a typical annual maximum runoff and extreme events, while tide and storm surge events were simulated by using the predicted tidal time series and adding 2 and 3 m storm surge events to the ocean level seaward of the mouth of the river. Model output was examined and the maximum water level for the duration of each simulation was extracted and merged into one file that was used in a GIS to map the maximum flood extent and water depth. Upstream areas were most vulnerable to fluvial discharge events, the lower estuary was most vulnerable to the effect of storm surge and sea-level rise, and the Town of Bridgewater was influenced by the combined effects of discharge and storm surge. To facilitate the use of the results for planning officials, GIS flood risk layers were intersected with critical infrastructure, identifying the roads, buildings, and municipal sewage infrastructure at risk under each flood scenario. Roads were converted to points at 10 m spacing for inundated areas and appended with the flood depth calculated from the maximum water level subtracted from the lidar digital elevation model.

Report on the 2015 IEEE GRSS Data Fusion Contest-Extremely High Resolution LiDAR and Optical Data: A Data Fusion Challenge [Technical Committees

Presents information on the 2015 IEEE GRSS Data Fusion Contest-Extremely High Resolution LiDAR and Optical Data Challenge.