LiDAR Surveys Research Articles

The 2018 Mw7.5 Palu ‘supershear’ earthquake ruptures geological fault's multi-segment separated by large bends: Results from integrating field measurements, LiDAR, swath bathymetry, and seismic-reflection data

Summary On 28 September 2018, 18:02:44 local time, the Magnitude 7.5 earthquake accompanied by a tsunami and massive liquefaction devastated Palu region in Central Sulawesi, Indonesia. Comprehensive post-disaster surveys have been conducted, including field survey of surface ruptures, LiDAR, multibeam-bathymetry mapping, and seismic-reflection survey. We used these data to map fault ruptures and measure offsets accurately. In contrast to previous remote-sensing studies, suggesting that the earthquake broke an immature, hidden-unknown fault inland, our research shows that it occurred on the mappable, mature geological fault line offshore. The quake ruptured 177-km long multi-fault segments, bypassing two large releasing bends (first offshore and second inland). The rupture onset occurred at a large fault discontinuity underwater in a transition zone from regional extensional to compressional tectonic regimes. Then it propagated southward along the ∼110-km submarine fault line before reaching the west side of Palu City. Hence, its long submarine ruptures might trigger massive underwater landslides and significantly contribute to tsunami generation in Palu Bay. The rupture continued inland for another 67 km, showing predominantly left-lateral strike-slip up to 6-m, accompanied by a 5–10% dip-slip on average. The 7km sizeable releasing bend results in a pull-apart Palu basin. Numerous normal faults occur along the eastern margin. They cut the Quaternary sediments, and some of them ruptured during the 2018 event. Our fault-rupture map on mature straight geological fault lines allows the possible occurrence of early and persistent ‘supershear’, but significant asperities and barriers on segment boundaries may prohibit it.

Optimization of Optical Image Geometric Modeling, Application to Topography Extraction and Topographic Change Measurements Using PlanetScope and SkySat Imagery

The volume of data generated by earth observation satellites has increased tremendously over the last few decades and will increase further in the coming decade thanks in particular to the launch of nanosatellites constellations. These data should open new avenues for Earth surface monitoring due to highly improved spectral, spatial and temporal resolution. Many applications depend, however, on the accuracy of the image geometric model. The geometry of optical images, whether acquired from pushbroom or frame systems, is now commonly represented using a Rational Function Model (RFM). While the formalism has become standard, the procedures used to generate these models and their accuracies are diverse. As a result, the RFM models delivered with commercial data are commonly not accurate enough for 3-D extraction, subpixel registration or ground deformation measurements. In this study, we present a methodology for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images provided by SkySat and PlanetScope, respectively. We use SkySat data over the Morenci Mine, Arizona, which is the largest copper mine in the United States. The re-projection error after the RFM refinement is 0.42 pix without using ground control points (GCPs). Comparison of our Digital Elevation Model (DEM with ~3 m GSD) with a reference DEM obtained from an airborne LiDAR survey (with ~1 m GSD) over stable areas yields a standard deviation of the elevation differences of ~3.9 m. The comparison of the two DEMs allows detecting and measuring the topographic changes due to the mine activity (excavation and stockpiles). We assess the potential of PlanetScope data, using multi-date DOVE-C images from the Shisper glacier, located in the Karakoram (Pakistan), which is known for its recent surge. We extracted DEMs in 2017 and 2019 before and after the surge. The re-projection error after the RFM refinement is 0.38 pix without using GCPs. The accuracy of our DEMs (with ~9 m GSD) is evaluated through comparison with the SRTM DEM (GSD ~30 m) and with a DEM (GSD ~2 m) calculated from Geoeye-1 (GE-1) and World-View-2 (WV-2) stereo images. The standard deviation of the elevation differences in stable areas between the PlanetScope DEM and SRTM is ~12 m, and ~7 m with the GE-1&WV-2 DEM. The mass transfer due to the surge is clearly revealed from a comparison of the 2017 and 2019 DEMs. The study demonstrates that, with the proposed scheme for RFM optimization, times series of DEM extracted from SkySat and PlanetScope images can be used to measure topographic changes due to mining activities or ice flow, and could also be used to monitor geomorphic processes such as landslides, or coastal erosion for example.

Morpho-chronology of monogenetic scoria cones from their level contour curves. Applications to the Chichinautzin monogenetic field, Central Mexico

The genesis, distribution and hazards associated with monogenetic volcanic fields pose particular difficulties derived from their dispersed character. Establishing a relationship between the spatial and the age distributions of volcanic cones is necessary both to understand the nature of monogenetic field formation and evolution and to set supported hazard-assessment criteria. We present here a new method –specifically developed to quantify the degree of age-dependent erosion of individual scoria cones– aimed to estimate the relative ages of undated scoria cones in a monogenetic volcanic field from a contour level morphometric analysis of the field digital elevation model (DEM). This method is based on the hypothesis that age and the degree and type of erosion causing significant morphological changes on scoria cones are markedly correlated. It is assumed that, at the time of their formation, monogenetic volcanoes have diverse, yet smooth shapes, which mechanical processes of wearing gradually change in different spatial scales. This evolution is largely evidenced both as changes of the volcano slopes and as height-dependent undulations of the cone surface generated by the hydrometeorological ablation in the otherwise uniform elevation contour lines. Here, we characterize the time-dependence of the shape and size of the altitude isoline undulations by means of the Elliptical Fourier Descriptor spectra of the contour curves of volcanic cones. To test the effects of the DEM data type and resolution on this morpho-chronological dating, we analyze the correlations of published radiometric ages of cones with two datasets of contour curves from the Sierra Chichinautzin monogenetic volcanic field in Central Mexico (SCVF): one obtained from airborne LIDAR surveys (INEGI, Mexico) and the other from a satellite radar mission (DLR, Germany).

Mapping of liquefaction risk on road network based on relationship between liquefaction potential and liquefaction-induced road subsidence

On March 11, 2011, a large earthquake of Mw 9.0 shook north-eastern Japan and caused severe liquefaction-induced damage over a wide area of reclaimed lands along the coast of Tokyo Bay. Although regional mapping of the liquefaction hazard had been performed in many automounts bodies in Japan, it seems that the maps were not effectively used on all fronts of disaster-prevention management, because the maps only provide liquefaction susceptibilities and little quantitative information on how seriously the ground might deform in a scenario earthquake, which is absolutely necessary information for discussing what-if scenarios. Konagai et al. (2013) conducted air-borne LiDAR surveys to obtain liquefaction-induced ground deformations over the north-eastern stretch of the Tokyo Bay shore area, including Urayasu City, where approximately 85% of the city area was heavily liquefied. Meanwhile, the authors have developed a geotechnical database for liquefaction risk assessments, compiling all the available borehole logs and soil testing data provided by Urayasu City (2012). Given the potential risk of re-liquefaction in a future scenario earthquake, it is an overriding priority to develop a knowledge-sharing liquefaction hazard map reflecting precise records from the past, such as liquefaction-induced ground subsidence and liquefaction-related damage. This paper attempts to assess the liquefaction-induced damage risk on road network, examining the relationship between the liquefaction potential index and the actual ground subsidence. For this purpose, firstly, the spatial distribution of the liquefaction potential (PL) was estimated over Urayasu City based on the above-mentioned geotechnical database developed by the authors. Secondly, the spatial distribution of the PL values and the actual liquefaction-induced road subsidence confirmed through air-born LiDAR surveys were compared to develop an empirical rule for estimating the potential road subsidence in a scenario earthquake. This empirical rule was found to describe the actual damage to roads and manholes in a satisfactory manner. Therefore, it is expected that a risk map, developed on the basis of this empirical rule, will not only help to assess liquefaction-induced damage, but also to design countermeasures against the what-if scenarios of liquefaction.

Post-earthquake monitoring of landslides along the Southern Kaikōura Transport Corridor, New Zealand

The magnitude (Mw) 7.8 earthquake that struck North Canterbury on New Zealand’s South Island on the evening of 14 November 2016 caused widespread damage to infrastructure, in particular to road and rail within the South Kaikōura Transport Corridor (SKTC). This is the main transport corridor along the east coast of the South Island, and the coseismic landslides covered and destroyed parts of the State Highway 1 (SH1) road, as well as the railway lines. Post-earthquake aftershocks and significant rainfall events (e.g. Cyclones Debbie, Cook and Gita), further mobilised the de-stabilised greywacke rock mass and paleo-landslide debris that mantled the slopes. Indeed, the SKTC was affected by > 80 separate landslides that have impacted either the rail corridor, the road corridor, or both. A number of different landslide mechanisms occurred, including shallow translation slides of existing paleo-landslide debris and rotational slumping, as well as rock mass failure via wedge, planar or toppling. Here, we report on monitoring of three case study landslides, using multi-temporal analyses of digital elevation data (from LiDAR, TLS and UAV surveys), real-time extensometer and land deformation monitoring using GPS receivers, as well as survey prisms. These datasets provided important monitoring of near-surface movement, important for risk management along the transport corridor during the reinstatement works.

Assessing Reef Island Sensitivity Based on LiDAR-Derived Morphometric Indicators

Reef islands are some of the most highly sensitive landforms to the impacts of future environmental change. Previous assessments of island morphodynamics primarily relied on historical aerial and satellite imagery. These approaches limit analysis to two-dimensional parameters, with no ability to assess long-term changes to island volume or elevation. Here, we use high-resolution airborne LiDAR data to assess three-dimensional reef island features for 22 islands along the north-western coast of Australia. Our primary objective was to utilize two regional LiDAR datasets to identify characteristics indicative of island sensitivity and future vulnerability. Results show reef platform area to be an accurate predictor of island area and volume suggesting larger island volumes may reflect (1) increased carbonate production and supply from the reef platform and/or (2) enhanced shoreline protection by larger reef platforms. Locations of foredune scarping (an erosional signature) and island orientations were aligned to the regional wind and wave climate. Reef island characteristics (island area, volume, elevation, scarping, and platform area) were used to rank islands according to sensitivity, using a new Island Sensitivity Characteristics Index (ISCi) where low ISCi indicates stable islands (large areas and volumes, high elevations, and fewer scarped areas) and high ISCi indicates unstable islands (small areas and volumes, low elevations, and more scarped areas). Comparison of two LiDAR surveys from 2016 and 2018 validates the use of 3D morphometrics as important (direct) measurements of island landform change, and can complement the use of 2D parameters (e.g., area) moving forward. Results demonstrate that ongoing use of airborne LiDAR and other 3D technology for monitoring coral reef islands at regional scales will enable more accurate quantification of their sensitivity to future impacts of global environmental change.

A 6-year lidar survey reveals enhanced rockwall retreat and modified rockfall magnitudes/frequencies in deglaciating cirques

Abstract. Cirque erosion contributes significantly to mountain denudation and is a key element of glaciated mountain topography. Despite long-standing efforts, rates of rockwall retreat and the proportional contributions of low-, mid- and high-magnitude rockfalls have remained poorly constrained. Here, a unique, terrestrial-lidar-derived rockfall inventory (2011–2017) of two glaciated cirques in the Hohe Tauern range, Central Alps, Austria, is analysed. The mean cirque wall retreat rate of 1.9 mm a−1 ranks in the top range of reported values and is mainly driven by enhanced rockfall from the lowermost, freshly deglaciated rockwall sections. Retreat rates are significantly elevated over decades subsequent to glacier downwasting. Elongated cirque morphology and recorded cirque wall retreat rates indicate headward erosion is clearly outpacing lateral erosion, most likely due to the cataclinal backwalls, which are prone to large dip-slope failures. The rockfall magnitude–frequency distribution – the first such distribution derived for deglaciating cirques – follows a distinct negative power law over 4 orders of magnitude. Magnitude–frequency distributions in glacier-proximal and glacier-distal rockwall sections differ significantly due to an increased occurrence of large rockfalls in recently deglaciated areas. In this paper, the second of two companion pieces, we show how recent climate warming shapes glacial landforms, controls spatiotemporal rockfall variation in glacial environments and indicates a transient signal with decadal-scale exhaustion of rockfall activity immediately following deglaciation crucial for future hazard assessments.

Machine learning and generalized linear model techniques to predict aboveground biomass in Amazon rainforest using LiDAR data

LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables. The aim of our work is to investigate the potential and accuracy of different ML techniques and a generalized linear model (GLM) to learn the relationships between LiDAR-derived metrics and forest inventory data for aboveground biomass (AGB) prediction in Amazon forest sites under selective logging regimes. The predictive performance of three ML techniques, namely random forest (RF), support vector machine (SVM), and artificial neural network (ANN), was compared against result from the GLM technique, across 85 sample plots. Interestingly, the GLM retrieved the most accurate estimations of forest AGB (rho Spearman’s coefficient = 0.87), compared with the ML techniques (RF = 0.77, SVM = 0.67, and ANN = 0.50). A number of possible factors affecting such results are listed and discussed in the text, including sample size and number of predictor variables. Continued research is necessary to improve the confidence of AGB estimation, especially over complex forest structures.

Inferring Distributed Snow Depth by Leveraging Snow Pattern Repeatability: Investigation Using 47 Lidar Observations in the Tuolumne Watershed, Sierra Nevada, California

AbstractSnow distribution is controlled by the interaction between local meteorology and static features like topography and vegetation. The resulting spatial pattern of snow in mountainous terrain is often repeatable and can be used to infer snowpack distribution at periods when observations are limited. This study uses a library of airborne lidar surveys (ALS) in California's Tuolumne watershed to analyze snow patterns at extents (1,650 km2), resolutions (25 m), and temporal scales (47 ALS observations over 7 years) unmatched by previous research. Distributed snow depth was inferred from snow depth observations covering a portion of the domain (< 4%) at a period near peak‐snowpack timing and snow distribution patterns from different years. Snow patterns from different years differed as a function of snow extents, variability, and interannual noise (r = 0.30 to 0.90). However, matching criteria like seasonal timing and snow extents were able to identify pairs of snow patterns with increased spatial agreement (median r > 0.84). Distributed snow depth inferred using a strip of observations (<4% coverage), and a well‐correlated snow pattern (r≥ 0.90) had a mean absolute error (MAE) of 0.22 m and snow volume error of −6%. Distributed snow depth inferred using patterns of reduced accuracy (r < 0.80) were often too homogeneous, thereby increasing MAE and decreasing the duration of the simulated snowmelt season. This work has applications in water management, where distributed snow depth observations in watersheds with interannual snow pattern repeatability could decrease the extent of observations necessary in future years.

Extraction of Roof Planes from Different Point Clouds Using RANSAC Algorithm

Solar energy is a renewable energy source directly from sunlight and its production depends on roof characteristics such as roof type and size. In solar potential analysis, the main purpose is to determine the suitable roofs for the placement of solar panels. Hence, roof plane detection plays a crucial role in solar energy assessment. In this study, a detailed comparison was presented between aerial photogrammetry data and LIDAR data for roof plane recognition applying RANSAC (Random Sample Consensus) algorithm. RANSAC algorithm was performed to 3D-point clouds obtained by both LIDAR (Laser Ranging and Detection) and aerial photogrammetric survey. In this regard, solar energy assessment from the results can be applied. It is shown that, the RANSAC algorithm detects building roofs better on the point cloud data acquired from airborne LIDAR regarding completeness within model, since aerial photogrammetric survey provides noisy data in spite of its high-density data. This noise in the source data leads to deformations in roof plane detection. The study area of the project is the campus of Istanbul Technical University. Accuracy information of the roof extraction of three different buildings are presented in tables.

Seismic interval velocity in the matte of Posidonia oceanica meadows: Towards a non-destructive approach for large-scale assessment of blue carbon stock

High-resolution seismic reflection data have been used over the last decades to estimate the thickness of the long-term Blue Carbon sink associated to the below-ground sediment deposit (matte) of the Posidonia oceanica meadows. Time-to-depth conversion of these geophysical datasets was usually performed assuming a sound velocity in this structure, but appropriate seismic interval velocity measurements is necessary to achieve accurate calibration. This study describes the first methodology to estimate the seismic interval velocity in the matte. This approach performed on the eastern continental shelf of Corsica island (France, NW Mediterranean) is based on measurements of the vertical matte profile from high-resolution seismic reflection profiles (s TWTT) and from seafloor morpho-bathymetric DTM (multibeam echosounders - MBES and Light Detection and Ranging - LiDAR surveys) calibrated with ground-truthing data. A biogeosedimentological analysis of horizontal cores sampled in vertical matte escarpments has been undertaken to identify the potential relationship of sediment and environmental parameters with sound velocity. The cross-comparison and the data intercalibration show significant correlation of MBES (R2 = 0.872) and LiDAR datasets (R2 = 0.883) with direct underwater measurements. Seismic interval velocities (n = 367) have been found to range between 1631.9 and 1696.8 m s−1 (95% confidence interval) and are estimated on average at 1664.4 m s−1, which is similar to the literature for unconsolidated marine sediments. The prediction map provided by the ordinary kriging method emphasized, however, a high variability of sound velocity within the study area. The results showed that changes in sound velocity in the matte are positively and strongly correlated with sand and gravel content and environmental factors such as distance to coastal river mouths and coastline. However, it was found that a negative relationship linked sound velocity with total and coarse organic content of matte deposits.

LIDAR AND PHOTOGRAMMETRIC DATASETS INTEGRATION USING SUB-BLOCK OF IMAGES

Abstract. Imagery and Lidar datasets have been used frequently to extract geoinformation. Datasets in the same mapping or geodetic frame is a fundamental condition for this application. Nowadays, Direct Sensor Orientation (DSO) can be considered as a mandatory technology to be used in the aerial photogrammetric survey. Although the DSO provides a high degree of automation process due to the GNSS/INS technologies, the accuracies of the obtained results from the imagery and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to improve the tridimensional accuracies of the aerial imagery and Lidar datasets integration using the 3D photogrammetric intersection of single models (pairs of images) with Exterior Orientation Parameters (EOP) estimated from DSO. A Bundle Adjustment with additional parameters (BBA) of a small sub-block of images is used to refine the Interior Orientation Parameters (IOP) and EOP in the job condition. In the 3D photogrammetric intersection experiments using the proposed approach, the horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar checkpoints, increased around of 25% and 75% respectively.

Spatial modeling of multi-hazard threat to cultural heritage sites

Cultural heritage is the foundation upon which global and historical values are based on. It connects us to the legacy left by our ancestors and identifies who we are as part of the modern society. Globally and specifically in the northeastern Romania, the landscape where cultural heritage sites were built on is constantly evolving due to mass wasting processes. Among these processes, landslide and gullies can disrupt the gravitational equilibrium directly or around these sites, threatening their very existence and our capacity to pass them on to future generations. Because landsliding and gullying are stochastic processes, the use of spatial statistics has often been employed to map locations at risk. In this work, we make use of advanced spatial Bayesian statistics to model landslide and gully erosion susceptibilities, separately. And, we ultimately combine these two outputs into a unified multi-hazard susceptibility model which we cross with the known cultural heritage sites in a study area close to the city of Iaşi, in Romania. Specifically, we implement a Bayesian version of a Generalized Additive Model (GAM) which assumes that the two separate landslide and gully presence/absence distributions to behave according to a Bernoulli probability distribution. Contrary to common practices in the literature, the two susceptibility models both feature fixed and random effects, including covariates acting at a latent level. We do this to also capture the unexplained but spatially coherent distribution of properties not directly included in the model. As for the properties directly expressed as covariates, our GAM features terrain attributes obtained from a LIDAR survey, in addition to land use and soil layers.The two single models outstandingly perform (AUC > 0.9) both during the calibration and validation phases.This modeling procedure ensures that the probability of occurrence of the two mass wasting processes under consideration is well estimated and therefore can be used to reliably plan conservation practices for local cultural heritage sites deemed at risk.

Beyond Never-Never Land: Integrating LiDAR and Geophysical Surveys at the Johnston Site, Pinson Mounds State Archaeological Park, Tennessee, USA

Archaeologists often use near-surface geophysics or LiDAR-derived topographic imagery in their research. However, rarely are the two integrated in a way that offers a robust understanding of the complex historical palimpsests embedded within a social landscape. In this paper we present an integrated aerial and terrestrial remote sensing program at the Johnston Site, part of the larger Pinson Mounds landscape in the American MidSouth. Our work at Johnston was focused on better understanding the history of human landscape use and change so that we can begin to compare the Johnston Site with other large Middle Woodland (200 BC–AD 500) ceremonial centers in the region. Our research allowed us to examine the accuracy of an early map of the Johnston Site made in the early 20th century. However, our integrated remote sensing approach allows us to go well beyond testing the usefulness of the map; it helps identify different uses of the site through time and across space. Our research emphasizes the importance of an integrated remote sensing methodology when examining complex social landscapes of the past and present.

Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Evaluating the performance of topobathymetric LiDAR to support multi‐dimensional flow modelling in a gravel‐bed mountain stream

AbstractStream biophysical processes are commonly studied using multi‐dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high‐resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground‐based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR‐derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR‐derived topobathymetry in support of multi‐dimensional flow modelling and ecohydraulics calculations in two gravel‐bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km‐long stream segment) along a 15 m‐wide river in central Idaho, USA. We compared metre and sub‐metre‐resolution DEMs generated from RTK‐GPS ground and Experimental Advanced Airborne Research LiDAR‐B (EAARL‐B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two‐dimensional (2D) hydraulic models supported by LiDAR and ground‐surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground‐survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s−1 (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m−2) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s−1 and SD = 0.22 m s−1 for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.

Some like it slow: a bioenergetic evaluation of habitat quality for juvenile Chinook salmon in the Lemhi River, Idaho

Management and conservation of freshwater habitat requires fine spatial resolution and watershed-scale and life-stage-specific methods due to complex linkages among land, climate, water uses, and aquatic organism necessities. In this study, we present a valley-scale microhabitat resolution, process-based bioenergetics approach that combines high-resolution topobathymetric LiDAR survey with two-dimensional hydrodynamic and bioenergetics modeling. We applied the model to investigate the role of lateral habitat, stream morphological complexity, water use, and temperature regimes on aquatic habitat quality distribution of juvenile Chinook salmon (Oncorhynchus tshawytscha) within the Lemhi River (eastern Idaho, USA). Modeling results showed two key aspects: (i) a reduction in diverted flows is not sufficient to improve habitat quality potentially because of a legacy of morphological simplification (directly due to straightening and wood removal and indirectly due to low in-channel flows) and (ii) morphological complexity and connectivity with side channels and margin areas, which are key and vital elements to support suitable habitats that meet or exceed energetic needs to sustain or promote growth of individuals and populations.

Monumental architecture at Aguada Fénix and the rise of Maya civilization.

Archaeologists have traditionally thought that the development of Maya civilization was gradual, assuming that small villages began to emerge during the Middle Preclassic period (1000-350BC; dates are calibrated throughout) along with the use of ceramics and the adoption of sedentism1. Recent finds of early ceremonial complexes are beginning to challenge this model. Here we describe an airborne lidar survey and excavations of the previously unknown site of Aguada Fénix (Tabasco, Mexico) with an artificial plateau, which measures 1,400m in length and 10 to 15m in height and has 9causeways radiating out from it. We dated this construction to between 1000 and 800BC using a Bayesian analysis of radiocarbon dates. To our knowledge, this is the oldest monumental construction ever found in the Maya area and the largest in the entire pre-Hispanic history of the region. Although the site exhibits some similarities to the earlier Olmec centre of San Lorenzo, the community of Aguada Fénix probably did not have marked social inequality comparable to that of San Lorenzo. Aguada Fénix and other ceremonial complexes of the same period suggest the importance of communal work in the initial development of Maya civilization.

Evaluation of SfM for surface characterization of a snow-covered glacier through comparison with aerial lidar

The combined use of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) is rapidly growing as a cost-effective alternative to airborne laser scanning (lidar) for reconstructing glacier surfaces. Here we present a thorough analysis of the precision and accuracy of a photogrammetric point cloud (PPC) constructed through SfM from UAV-acquired imagery over the spring snow surface at Haig Glacier, Alberta, Canada, the first of its kind in a glaciological setting. An aerial lidar survey conducted concurrently with UAV surveys was used to examine spatial patterns in the PPC accuracy. We found a median error in the PPC of −0.046 ± 0.067 m, with a 95% quantile of 0.218 m. Mean precision of the PPC was 0.199 m, with large spatially clustered outliers. We found an association between high-error, low-precision, and high-surface roughness in the PPC, likely due to illumination characteristics of the snow surface. Glacier surface reconstructions are important for geodetic mass balance measurements, giving key insights into changing climate where in situ measurements are difficult to obtain. The PPC errors are small enough that they would have minimal effects on total mass balance, should the technique be applied across the glacier.

Beach-dune Recovery from the Extreme 2013-2014 Storms Erosion at Truc Vert Beach, Southwest France: New Insights from Ground-penetrating Radar

Robin, N.; Billy, J.; Castelle, B.; Hesp, P.; Laporte-Fauret, Q.; Nicolae-Lerma, A.; Marieu, V.; Rosebery, D.; Bujan, S.; Destribats, B., and Michalet., 2020. Beach-dune recovery from the extreme 2013-2014 storms erosion at Truc Vert Beach, Southwest France: New insights from ground-penetrating radar. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 588–592. Coconut Creek (Florida), ISSN 0749-0208.Foredunes are efficient natural coastal defenses acting as protective barriers during storm events. A striking example is the response of coastal dunes along the Aquitaine Coast (SW France) to the 2013-2014 winter, which was the most energetic winter since at least the mid-20th century in most of the NE Atlantic. The aim of this study is to analyze the (partial) foredune recovery from the 2013-2014 winter at Truc Vert beach using ground-penetrating radar (GPR) and through the coastal dune internal structure. Geophysical data was compared with semi-monthly DGPS topographic data of the beach, UAV-photogrammetry and Airborne LiDAR surveys of the coastal dune. Three main recovery phases were identified from the 2013-2014 winter: (i) aggradation of the upper-beach and dune front-crest during the post-storm summer; (ii) distinct phases of incipient dune-foot development with dune-crest stability during the following year, and (iii) since 2016, aggradation of both dune-foot and dune front-crest tending towards an equilibrium profile. This study shows that GPR surveys, combined with chronological topographic data, is an efficient tool to provide comprehensive understanding of the beach-dune system evolution at a decadal scale. This approach opens new perspectives to document foredune recovery timing and magnitude along sandy coasts.