LiDAR-derived Digital Terrain Models Research Articles

Freely available LiDAR-derived digital terrain model (DTM) uncovers the heartland of the Dacian Kingdom

Throughout history, the Dacian landscape has aroused the imagination of many people. For decades, researchers have been fascinated by the magnificent structures the Dacians built and how they altered the mountains to their advantage. Dacian sites, despite their grandeur, remain mostly unknown due to their position deep within Romania's vast forests, generally in remote regions and hidden from the naked eye. Ground exploration in densely forested mountain regions is extremely difficult, and even if campaigns of conventional exploration had been undertaken, they would have been insufficient to provide a comprehensive picture of the Dacian world. The lack of high-resolution remote-sensing data for wide areas has made large-scale assessments of the landscape impractical. This is changing, as new large datasets of LiDAR-derived digital terrain models, covering the entire heartland of the Dacian Kingdom, are now freely available. This paper reports on one of the most recent freely available LiDAR-based high-resolution digital terrain models in Romania, its impact on Romanian mountain archaeology, and how this can shape future research directions in understanding the Dacian landscape.

The Kotroni Archaeological Research Project (KASP): evaluating ancient Aphidna using multimodal landscape analysis

The Kotroni Archaeological Research Project (KASP) seeks to understand the relationship between the natural and human landscape of Aphidna from prehistory through to the present, and how such rural communities contributed to the economic, social and cultural life of larger, ‘core’ centres in the region. As one of the constitutional demes of Classical Athens, Aphidna was steeped in the foundational history of the Athenian polis, which saw in it a primordial, ancestral place tied to myths and legends. It accommodated a Middle Bronze Age cemetery, a Mycenaean/Late Bronze Age citadel, a Classical-Hellenistic fort on the citadel, and a Geometric, Archaic, Hellenistic, and Roman settlement. Furthermore, it was later settled in the form of Byzantine and Frankish monastery estates, Turkish chifliks, Arvanite villages, and the contemporary community of nearby Kapandriti. Being that the area of interest (AOI) is both remarkably well preserved due to construction prohibitions necessitated by the nearby Marathon water reservoir and features a diverse history spanning four millennia, it is an excellent case-study for human landscape inquiry, i.e. understanding diachronic inhabitation and the changing meanings of landscape in the longue durée. Important tasks in this direction include clarifying the spatial extent, chronological framework, and nature of settlement as well as the environmental affordances of the landscape, the combination of which permits a thick description of its cultural history. The main method for this investigation is intensive pedestrian survey. Additional conventional methods include the study of ground historical photographs, conventional maps, older field reports, published scholarship, ancient literature and inscription corpora, traveller accounts (17th-19th century AD), in situ buildings or architectural membra dispersed in the landscape, and artefacts (both those collected during the survey and legacy finds in museum storage, e.g. the National Archaeological Museum). Science-based methods consist of geophysical prospection, geological/geomorphological study, optically stimulated luminescence (OSL) analysis, as well as geospatial informatics focusing on satellite and aerial imagery and Lidar-derived digital terrain models. The results of the intensive survey, the geophysical prospection, and the geological/geomorphological and OSL study are presented elsewhere. In this paper we present the outcomes of remote sensing, arguing that the relevant tools play an important role in the study of human landscapes at all stages of archaeological inquiry (prior, during and after ground truthing), especially when revisiting underexplored areas and employing a multimodal ‘third-wave’ survey methodology. The latter weaves in a multitude of analyses, both conventional and digital, and integrates old and new data.

Power laws for accurate determination of landslide volume based on high-resolution LiDAR data

Landslide volume is critical in landslide risk management due to its close association with the landslide mobility and damage to elements at risk. Limited by accessibility, site conditions and availability of equipment, directly measuring the landslide volume on site is challenging. Instead, many empirical area-volume power-law models have been developed to estimate the landslide volume. Yet the validity of these power-law models for small-scale shallow landslides is in question considering the substantial uncertainty of field-estimated landslide volume data for developing these empirical power laws. In this study, two high-resolution LiDAR-derived digital terrain models taken in 2010 and 2020 are leveraged to interpret the volumes of 1326 shallow landslides that occurred in Hong Kong during the same period. The high precision of the digital terrain models ensures the accurate interpretation of landslides and the identification of landslide source and transport areas. On the basis of the interpreted landslide characteristics, new power-law models and a multivariate distribution model are proposed to accurately estimate the volumes of small-scale shallow landslides based on easy-to-obtain landslide source features such as source area, length, width or maximum depth. The multivariate distribution model rigorously expresses the conditional probability distribution of the landslide volume and quantifies the uncertainty of volume estimation. The models therefore enhance the capability of landslide risk assessment and management.

Assessing the role of slope, lithology and land use in the formation and conservation of war-related geomorphic features (Case study of WW1 railway artillery in Montagne de Reims, Marne, France)

This contribution proposes to analyze a single archaeological object crossed by different conservation processes, to isolate and understand the role of geomorphological parameters in the formation and the conservation of anthropogenic geomorphic features. For these purposes, this study characterizes the remnant morphologies of the WW1 railroad in the Montagne de Reims on the western front (France, Rheims sector) through LiDAR-derived digital terrain models combined with archival documents (maps and historical photographs), spatial analysis and field surveys. This study shows that the Heavy Railway Artillery (Artillerie Lourde sur Voie Ferrée - ALVF) is a significant morphogenic feature, preserved at more than 70%. However, this morphology is subject to a strong variability. Ruggedness analysis shows a much more pronounced morphology on the slopes than on the plateaus, demonstrating a difference in formation and preservation processes. For formation processes, the study's findings reveal that geotechnical constraints related to lithology and geomorphology can be overridden for military purposes. For conservation processes, they are largely dependent on land use practices. This research demonstrates that (i) the role of landscape and topographical parameters in the interpretability of features is predominant; (ii) military engineering has been able to adapt to a much diversified topography.

A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China

ABSTRACT Lidar has been regarded as the most accurate data source for forest-growing stock volume (FGSV) estimation, but inconsistent acquisition dates of lidar data with field survey often result in poor FGSV estimation accuracy. Spaceborne stereo imagery is captured at regular intervals, providing new opportunities for mapping and updating FGSV spatial distributions. Digital Surface Model derived from spaceborne stereo imagery and Digital Terrain Model (DTM) derived from airborne lidar can be used together to produce a canopy height model (CHM) (LS-CHM), which can then be used to predict FGSV spatial distributions, but this methodology has yet to be explored. Our research attempts to compare the performance of LS-CHM and lidar-CHM (L-CHM) for FGSV modeling and to explore the advantages of using the hierarchical Bayesian approach (HBA) over traditional linear regression and random forest modeling approaches when sample size is small. Considering different forest types and topographical conditions, as well as the number of sample plots for each forest type, HBA is used to develop the FGSV estimation model, and the results are compared with those from linear regression and random forest approaches. The research results in a northern subtropical forest ecosystem indicate that overall, L-CHM provides better predictions than LS-CHM using the same modeling approaches, and L-CHM is especially valuable when FGSV is small or large, but when FGSV falls within 100–200 m3/ha, LS-CHM–based variables produce better modeling accuracy than L-CHM–based variables using linear regression or HBA. The HBA based on stratification of both forest type and slope aspect provides the best FGSV estimation, using either L-CHM or LS-CHM, and solves the modeling problem due to limited sample sizes for forest types. Our research provides new insights to using the combination of satellite stereo images and lidar-derived DTM for mapping and updating FGSV in a large area.

Morfostrukturní doklady ponásunového porušení Pavlovských vrchů, Vnější Západní Karpaty

The article presents geomorphological and structural evidences of post-thrust limestone brittle deformation in the area of Pavlov Hills on the westernmost margin of the Outer Western Carpathians, Czech Republic. Airborne LiDAR-derived digital terrain model with 1 m resolution was used for this analysis, as well as aerial photographs and fieldwork. Combining these methods, we document a distinct N-S directed fault zone that intersects or delineates the majority of the Pavlov Hills Jurassic limestone nappe outliers. Data revealed an almost continuous fault zone of the north-south direction, which ranges from Horní Věstonice in the North, to Mikulov in the South, and extends further south to Austria. The thrusted Jurassic limestone bodies are cut by the fault zone, which tectonically crushed the limestone in its core and cores of the secondary fault branches. The map pattern of the fault zone suggests directional anastomotic branching and reattaching with the production of lenticular tectonic slices. We interpret the fault as a prominent sinistral shear zone. This is indicated by block displacement on the Svatý kopeček Hill and also by the orientation of the accompanying subvertical Riedel shears with identified horizontal lineation. The activity of the fault zone onset tightly after the nappe thrusting at the final stages of the accretion wedge formation, which is suggested from the observed sinistral kinematics. The main movement along the fault is thus most probably of a late Miocene age.

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System

Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.

Revealing Archaeological Sites under Mediterranean Forest Canopy Using LiDAR: El Viandar Castle (husum) in El Hoyo (Belmez-Córdoba, Spain)

Light detection and Ranging (LiDAR) technology is a valuable tool for archaeological prospection in areas covered by dense vegetation. Its capacity to penetrate dense forest environments enables it to detect archaeological remains scattered over orographically complex areas. LiDAR-derived digital terrain models (DTMs) have made an exceptional contribution towards identifying topographic landscapes of archaeological interest. In this study, we focus on an area of intense historic settlement from the Chalcolithic to the Middle Ages, which today is completely covered by Mediterranean forest. Due to the dense canopy, and the fact that it is a protected area on private land, it has never been analyzed. To reveal the settlement, we primarily used a series of LiDAR mapping surveys to gather data and analyzed other open access remote sensing resources from the National Geographic Institute of Spain (IGN). The IGN LiDAR data proved to be of particular interest. These resources enabled us to detect an ancient fortress (El Viandar Castle) and its surrounding settlement. LiDAR, in conjunction with other products, was fundamental in identifying the site. Equally, the mapping surveys enabled us to analyze the limits and scope of the IGN airborne LiDAR and other free access remote sensing products. Our background in this research demonstrates that low-cost products applied to LiDAR research in archaeology have major limitations when it is necessary to have a high level of spatial resolution in order to define the layout and the main components of an archaeological site.

The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

A Comparison of Machine Learning Approaches to Improve Free Topography Data for Flood Modelling

Given the high financial and institutional cost of collecting and processing accurate topography data, many large-scale flood hazard assessments continue to rely instead on freely-available global Digital Elevation Models, despite the significant vertical biases known to affect them. To predict (and thereby reduce) these biases, we apply a fully-convolutional neural network (FCN), a form of artificial neural network originally developed for image segmentation which is capable of learning from multi-variate spatial patterns at different scales. We assess its potential by training such a model on a wide variety of remote-sensed input data (primarily multi-spectral imagery), using high-resolution, LiDAR-derived Digital Terrain Models published by the New Zealand government as the reference topography data. In parallel, two more widely used machine learning models are also trained, in order to provide benchmarks against which the novel FCN may be assessed. We find that the FCN outperforms the other models (reducing root mean square error in the testing dataset by 71%), likely due to its ability to learn from spatial patterns at multiple scales, rather than only a pixel-by-pixel basis. Significantly for flood hazard modelling applications, corrections were found to be especially effective along rivers and their floodplains. However, our results also suggest that models are likely to be biased towards the land cover and relief conditions most prevalent in their training data, with further work required to assess the importance of limiting training data inputs to those most representative of the intended application area(s).

Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

In mountainous landscapes, where strongly deformed pelitic sediments outcrop, earthflows can dominate denudation processes and landscape evolution. This paper investigated geological and geomorphological features and space-time evolution over a 65-year time span (1954–2019) of a large earthflow, representative of wide sectors of the Apennine chain of southern Italy. The landslide, with a maximum length of 1.85 × 103 m, affects an area of 4.21 × 105 m2 and exhibits two source zones: a narrow and elongated transport zone and a lobate accumulation zone. Spatial and temporal morphological changes of the earthflow were assessed, comparing multi-source and multi-temporal data (aerial photographs, Google Earth satellite images, Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) system data). Geomorphic changes, quantified using Digital Terrain Models (DTMs) of differences, highlighted an extensive lowering of the topographic surface in the source area and a significant uplift at the landslide toe. Moreover, the multi-temporal analysis showed a high increase of landslide surface (more than 66%) during the last 65 years. The volumetric analyses showed that different sectors of the earthflow were active at different times, with different rates of topographic change. Overall, the used approach highlighted the great potentiality of the integration of multi-source and multi-temporal data for the diachronic reconstruction of morphological landslide evolution.

Aerial Bombing Crater Identification: Exploitation of Precise Digital Terrain Models

Places of past conflicts and persistent objects that reflect such events often attract the attention of archaeological prospection which facilitates the construction of conflict narratives. Field prospection as a precise method for localization of aerial bombing craters (as an example of such persistent features) is a highly time- and resource-consuming task. Therefore, methods for automatic identification of such features are evolving. We present a comparison of three methods for possible automatic crater detection based on (a) extraterrestrial crater detection algorithms, (b) geomorphology-based edge extraction, and (c) image pattern recognition via a state-of-the-art convolutional neural network (CNN). All methods were preliminarily tested on a case study of eight Second World War (WWII) aerial bombing crater sites in NW Czechia via Airborne Laser Scanned LiDAR-derived digital terrain models with different spatial resolutions. We found that extraterrestrial crater detection algorithms and geomorphology-based edge extraction methods yield worse results given the standard indices of precision and recall. By comparison, the CNN method utilized for a particular task achieved satisfying results, predominantly with 0.5 m/px resolution (which is often available at the country level) of the input raster. Nevertheless, overall performance with this resolution varies significantly among the sites. Therefore, the quality and readability of the input data are crucial factors for the successful acquisition of precise ordinance location identification.

Estimating volume of deep-seated landslides and mass transport in Basihlan river basin, Taiwan

Deep-seated landslides are often caused by severe rainfall, but the displaced potential landslide mass often does not slide downslope entirely in one time. Evaluation of the mobilized volume of the deep-seated landslide and the volume of the remaining potentially unstable landslide mass on the slope is vital for hazard mitigation. In this study, an integrated method was developed for evaluation of the potential landslide volume and mass transport of deep-seated landslides caused by Typhoon Morakot in Basihlan River Basin, Taiwan. A combination of various types of remote-sensing data, including aerial photographs, satellite images, and light detection and ranging (LiDAR) data, from before and after the event were used for locating landslides, and the deep-seated landslides were identified in the Basihlan River Basin, Liuoguey District, Kaohsiung City, Taiwan. A stability analysis and a profile analysis were conducted to determine the critical sliding surface of each deep-seated landslide, and the volumes (masses) of the landslides were estimated. The total volume of all the deep-seated landslides was approximately 6,017,000 m3 in the Basihlan River Basin. A topographic analysis was performed to evaluate the mass transport of the deep-seated landslides using LiDAR-derived digital terrain models (DTMs) before and after Typhoon Morakot. The mass transport of deep-seated landslides in the Basihlan River Basin was determined to be approximately 3,527,300 m3, which suggested the volume of the remaining potentially unstable landslide mass on slope of approximately 41.4%. The available field data suggested subsequent reactivations of the deep-seated landslides after Typhoon Morakot, including debris flow generation, which damaged the infrastructure. Thus, a large landslide volume remaining in the Basihlan River Basin can cause more landslides or debris flow hazards in the future. Estimation of the potential landslide volume and mass volume remaining on slopes is essential for both short- and long-term mitigation strategies.

Persistent homology on LiDAR data to detect landslides

Landslides can result in loss of lives, cause damage to property, infrastructure, utilities, and residential structures and can block transportation routes. Landslide inventory maps can provide spatial-temporal information about past and recent landslides and are used for analysis to create models that can characterize susceptibility. As such, these maps are considered an essential source for risk management tasks. In this paper, we propose a persistent homology method applied on LiDAR-derived digital terrain model data to detect landslides for landslide inventory maps. In testing the hypothesis that persistent homology, a method for computing topological features of a space at different resolutions, can be used to accurately detect landslides, we applied the method on LiDAR-derived digital terrain models to detect shapes and patterns that are indicative of landslide surface expressions. We validated our test results by comparing them to currently available landslide inventory maps for selected locations in Pennsylvania, Oregon, Colorado and Washington. The results show a different performance for each state; the accuraces were 0.79, 0.71, 0.53, 0.69, and 0.77 for five study areas. Variations in performance are linked to varying surface roughness between different types of landslides, their size, shape, ages, composition, and a possible history of reactivations of landslides that would further pronounce surface expressions. To overcome some of these challenges that may hinder performance of our method, we recommend that other datasets, containing landslides, be considered as additional nth-order dimensions beyond the spatial and elevation dimensions inherent in topographic datasets.

Detecting World War II bombing relics in markedly transformed landscapes (city of Most, Czechia)

Landscapes carrying remnants of military activity gain greater attention in many fields of research due to intrinsic cultural and natural values as well as hazard of unexploded ordnance (UXO). Identification of military remnants is currently facilitated by the use of high precision LiDAR-derived digital terrain models. Contribution of these models to identification of military remnants is unfortunately of limited use in heavily transformed environment such as post-mining landscapes. Therefore, other approach and sources needs to be employed. We present the results of the extensive study of archival sources, plans and maps, which led to determination of geographical dimension of devastation of the prominent target of allied attacks in Czechia during the World War II - Sudetenländische Treibstoffwerke (STW). Besides the framing of allied bombing in the historical context, we found out and delimited possible remnants of the landscape alternation by bombing in the industrial landscape nowadays markedly transformed. Unreferenced archival aerial imagery, dated shortly after the War ended and yet unpublished and unprocessed, and bombing plans (archival field mapping) were photogrammetrically processed and analysed in GIS in order to localize particular craters. The database of impacts was consecutively confronted on unaltered minor part of our study area with present-day high-precision digital terrain model (DTM) based on LiDAR scanning that revealed the extant craters. We have identified more than 15 thousand craters, the spatial distribution of which indicates relative inaccuracy of bombing. Despite the heavy transformation of the terrain, the mixed method approach also revealed the localities with preserved craters that are susceptible to UXO excavation risk. Overall, the study provided an empirical survey that explored and tested the use and accuracy of archival sources for bombing craters detection, being often the only available source in heavily transformed terrains.

Geomorphometric characterisation of natural and anthropogenic land covers

The scientific community has widely discussed the role of abiotic and biotic forces in reshaping the Earth’s surface. Currently, the literature is debating whether humans are leaving a topographic signature on the landscape. Apart from the influence of humans on processes, does the resulting landscape bear an unmistakable signature of anthropogenic activities? This research analyses from a statistical point of view the morphological signature of anthropogenic and natural land covers in different topographic context, as a fundamental challenge in the emerging debate of human-environment relationships and the modelling of global environmental change. It aims to explore how intrinsically small-scale processes, related to land use, can influence the form of entire landscapes and to determine whether these processes create a distinctive topography. The work focusses on four study areas in floodplains, plain to hilly, hills and mountains, for which LiDAR-derived Digital Terrain Models (DTMs) are available. Surface morphology is described with different geomorphometric parameters (slope, mean curvature and surface peak curvature) and their frequency distribution. The results show that the distribution of geomorphometric indices can reveal anthropogenic land covers and landscapes. In most cases, different land covers show statistically significant differences (p < 0.05) in their morphology. Finally, this study demonstrates the possibility to use a geomorphic analysis to quantify anthropogenic impact based on land covers in different landscape contexts. This provides useful insight into understanding the impact of human activities on the present morphology and offers a comprehensive understanding of coupling human-land interaction from a geomorphological point of view.

Mapping the maximum inundation extent of lowland intermittent riverine wetland depressions using LiDAR

Accurate high-resolution maps of maximum wetland inundation extents are valuable inventorial resources, but mapping such boundaries can be difficult, time consuming and involve a level of subjectivity from the surveyor. A novel, objective and efficient method is presented for delineating the maximum inundation extents of lowland intermittent riverine wetland depressions using a high resolution LiDAR-derived digital terrain model (DTM). The method was specifically developed for wetland depressions associated with the lower reaches of the Murrumbidgee River in the southern Murray Darling Basin, Australia, but is suitable for any similar intermittent wetland depressions where the natural inundation extent is otherwise unclear. The method is based on the premise that immediately after rising water reaches a threshold level, much larger volumes of water are required to inundate not just the discrete wetland depression but also its surrounding local area; that threshold level can be considered the wetland fill level. Using a series of water levels and a corresponding series of the volume between each water level and a DTM of a wetland area, the maximum rate of increasing acceleration in volume with respect to water level, i.e. the wetland fill level, can be identified. The fill levels of eight wetland areas, ranging in size from 44 to 384 ha, were produced by this method and subsequently used to map their maximum inundation extents. Corresponding maps of the inundation frequency gradient were independently determined from a time series of Landsat derived inundation maps. The level of separation in inundation frequency of map cells inside the maximum inundation extents compared to those outside, determined by the Kolmogorov Smirnov statistic, validated the technique, while identifying that a minimum level of variation in the elevation of the wetland area may be necessary for it to be successfully applied. The technique has potential to be applied more widely as an objective and relatively inexpensive procedure to identify the potential maximum inundation extent of intermittent floodplain wetlands when a good quality high resolution DTM, acquired during a dry phase, is available.

Multi-temporal analysis of the role of check dams in a debris-flow channel: Linking structural and functional connectivity

Check dams represent one of the most common hydraulic engineering solution to manage debris-flow risk. However, the design of these structures only rarely considers their impact on sediment connectivity and sediment fluxes, both at the channel reach and catchment scale. In the last two decades, High-Resolution Topography (HRT) provided fundamental data to analyse the morphology evolution of landscapes through the realization of sequential DTMs (Digital Terrain Models) and DTMs of Difference (DoDs). In the context of mountain catchments, HRT offers essential information on the evolution of erosion and deposition patterns and allows the application of multi-temporal geomorphometric indexes that can enrich the DoDs analysis with important data as the assessment of sediment connectivity. In this research, multi-temporal LiDAR-derived DTMs were used to derive DoDs and to perform a multi-temporal analysis of an index of sediment connectivity (IC) in three reaches of a debris-flow channel (Moscardo torrent, eastern Italian Alps) where several check dams have been built over time. The research aims at outlining the effects of torrent control works on sediment dynamics in steep channels affected by frequent debris flows. The integration of the difference of IC with DoD maps allowed quantifying the impact of newly built check dams on changes in sediment pathways and related erosion processes, highlighting a close relationship between structural and functional connectivity: in areas where erosive processes prevailed, there were increases in connectivity, while deposition areas showed a decrease of IC. The geomorphometric analyses show that the new check dams have affected the sediment transfer along the main channel but also the hillslope-to-channel connectivity by altering sediment pathways. In the Moscardo torrent, however, the strong impact of these structures on sediment transfer is not matched by long-term effectiveness in debris-flow control.

Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013

With a burgeoning global population, the pressures of urbanization are increasingly prevalent. The need to quantify urban greenness remains significant due to environmental impact and its relationship with human well-being. Utilizing 1 m discrete-return airborne lidar-derived digital terrain models (DTMs) and digital surface models (DSMs), aerial imagery, and lidar-imagery fusion, this study assesses vegetation, specifically tree canopy, change within Oklahoma City between 2006 and 2013. Specifically, we (1) identify an accurate object-based image analysis (OBIA) method for the detection of urban vegetation outlines, and (2) apply that method to locate and quantify vegetation change and assess spatial patterns in Oklahoma City between 2006 and 2013. The proposed OBIA approach extracts urban vegetation coverage from aerial imagery and lidar-based models with around 89% accuracy. Regarding vegetation change, Oklahoma City lost 9.69 km2 (3.74 mi2) of tree canopy coverage, which accounted for a 2% loss in total greenness.