Bare-earth Digital Elevation Models Research Articles

Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR

Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications.

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014\u20132015 P\u0101hoa lava flow crisis, Hawai\u2018i

Accurately predicting lava flow path behavior is critical for active crisis management operations. The advance and emplacement of pāhoehoe flows modifies and inverts pre-existing topography, prompting the need for rapid and accurate updates to the topographic models used to forecast flow paths. The evolution and velocity of pāhoehoe flows are dependent on macro and micro topography, the slope of the descent path, effusion rate, and rheology. During the 2014–2015 Pāhoa crisis on the island of Hawai‘i, we used a low-altitude unmanned aerial system (UAS) to quickly and repeatedly image the active front of a slowly advancing pāhoehoe lava flow. This imagery was used to generate a series of 1 m resolution bare-earth digital elevation models (DEMs) and associated paths of steepest descent over the study area. The spatial resolution and timeliness of these UAS-derived models are an improvement over the existing topographic data used by managers during the crisis. Results from a stepwise resampling experiment suggest that the optimum DEM resolution for generating accurate pāhoehoe flow paths through lowland tropical forest environments is between 1 and 3 m. Our updated models show that future flows in this area will likely be deflected by these newly emplaced flows, possibly threatening communities not directly impacted by the original 2014–2015 lava flow. We demonstrate the value of deploying UAS during a dynamic volcanic crisis and suggest that this technology can fill critical monitoring gaps for Kīlauea and other active volcanoes worldwide.

Potential of airborne LiDAR data analysis to detect subtle landforms of slope failure: Portainé, Central Pyrenees

Slope failures have been traditionally detected by field inspection and aerial-photo interpretation. These approaches are generally insufficient to identify subtle landforms, especially those generated during the early stages of failures, and particularly where the site is located in forested and remote terrains. We present the identification and characterization of several large and medium size slope failures previously undetected within the Orri massif, Central Pyrenees. Around 130 scarps were interpreted as being part of Rock Slope Failures (RSFs), while other smaller and more superficial failures were interpreted as complex movements combining colluvium slow flow/slope creep and RSFs. Except for one of them, these slope failures had not been previously detected, albeit they extend across a 15% of the studied region. The failures were identified through the analysis of a high-resolution (1m) LIDAR-derived bare earth Digital Elevation Model (DEM). Most of the scarps are undetectable either by fieldwork, photo interpretation or 5m resolution topography analysis owing to their small heights (0.5 to 2m) and their location within forest areas. In many cases, these landforms are not evident in the field due to the presence of other minor irregularities in the slope and the lack of open views due to the forest. 2D and 3D visualization of hillshade maps with different sun azimuths provided an overall picture of the scarp assemblage and permitted a more complete analysis of the geometry of the scarps with respect to the slope and the structural fabric. The sharpness of some of the landforms suggests ongoing activity, which should be explored in future detailed studies in order to assess potential hazards affecting the Portainé ski resort. Our results reveal that close analysis of the 1m LIDAR-derived DEM can significantly help to detect early-stage slope deformations in high mountain regions, and that expert judgment of the DEM is essential when dealing with subtle landforms. The incorporation of this approach in regional mapping represents a great advance in completing the catalogue of slope failures and will eventually contribute to a better understanding of the spatial factors controlling them.

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

Fusing Digital Elevation Models to Improve Hydrological Interpretations

Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m; SRTM 30 m; ASTER 30 m; CDED 22 m; NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.

Automatic Delineation of Seacliff Limits using Lidar-derived High-resolution DEMs in Southern California

ABSTRACT Palaseanu-Lovejoy, M.; Danielson, J.; Thatcher, C.; Foxgrover, A.; Barnard, P.; Brock, J., and Young, A., 2016. Automatic delineation of seacliff limits using lidar-derived high-resolution DEMs in Southern California. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 162–173. Coconut Creek (Florida), ISSN 0749-0208. Seacliff erosion is a serious hazard with implications for coastal management and is often estimated using successive hand-digitized cliff tops or bases (toe) to assess cliff retreat. Even if efforts are made to standardize manual digitizing and eliminate subjectivity, the delineation of cliffs is time-consuming and depends on the analyst's interpretation. An automatic procedure is proposed to extract cliff edges from high-resolution lidar-derived bare-earth digital elevation models, generalized coastal shoreline vectors, and approxim...

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

Terrestrial laser scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), has been frequently applied to build bare-earth digital elevation models (DEMs) for high-accuracy geomorphology studies. The point clouds acquired from TLS often achieve a spatial resolution at fingerprint (e.g., 3cm×3cm) to handprint (e.g., 10cm×10cm) level. A downsampling process has to be applied to decimate the massive point clouds and obtain manageable DEMs. It is well known that downsampling can result in aliasing that causes different signal components to become indistinguishable when the signal is reconstructed from the datasets with a lower sampling rate. Conventional DEMs are mainly the results of upsampling of sparse elevation measurements from land surveying, satellite remote sensing, and aerial photography. As a consequence, the effects of aliasing caused by downsampling have not been fully investigated in the open literature of DEMs. This study aims to investigate the spatial aliasing problem of regridding dense TLS data. The TLS data collected from the beach and dune area near Freeport, Texas in the summer of 2015 are used for this study. The core idea of the anti-aliasing procedure is to apply a low-pass spatial filter prior to conducting downsampling. This article describes the successful use of a fourth-order Butterworth low-pass spatial filter employed in the Generic Mapping Tools (GMT) software package as an anti-aliasing filter. The filter can be applied as an isotropic filter with a single cutoff wavelength or as an anisotropic filter with two different cutoff wavelengths in the X and Y directions. The cutoff wavelength for the isotropic filter is recommended to be three times the grid size of the target DEM.

DEM sourcing guidelines for computing 1 Eö accurate terrain corrections for airborne gravity gradiometry

In this paper we investigate digital elevation model (DEM) sourcing requirements to compute gravity gradiometry terrain corrections accurate to 1Eötvös (Eö) at observation heights of 80m or more above ground. Such survey heights are typical in fixed-wing airborne surveying for resource exploration where the maximum signal-to-noise ratio is sought. We consider the accuracy of terrain corrections relevant for recent commercial airborne gravity gradiometry systems operating at the 10Eö noise level and for future systems with a target noise level of 1Eö. We focus on the requirements for the vertical gradient of the vertical component of gravity (Gdd) because this element of the gradient tensor is most commonly interpreted qualitatively and quantitatively.Terrain correction accuracy depends on the bare-earth DEM accuracy and spatial resolution. The bare-earth DEM accuracy and spatial resolution depends on its source. Two possible sources are considered: airborne LiDAR and Shuttle Radar Topography Mission (SRTM). The accuracy of an SRTM DEM is affected by vegetation height. The SRTM footprint is also larger and the DEM resolution is thus lower. However, resolution requirements relax as relief decreases. Publicly available LiDAR data and 1arc-second and 3arc-second SRTM data were selected over four study areas representing end member cases of vegetation cover and relief.The four study areas are presented as reference material for processing airborne gravity gradiometry data at the 1Eö noise level with 50m spatial resolution. From this investigation we find that to achieve 1Eö accuracy in the terrain correction at 80m height airborne LiDAR data are required even when terrain relief is a few tens of meters and the vegetation is sparse. However, as satellite ranging technologies progress bare-earth DEMs of sufficient accuracy and resolution may be sourced at lesser cost. We found that a bare-earth DEM of 10m resolution and 2m accuracy are sufficient for achieving 1Eö accuracy in the terrain correction independent of relief or vegetation cover. For AGG systems operating at greater noise levels the 3arc-second SRTM is adequate for areas having tens of meters of relief and sparse vegetation and even for areas of greater relief and sparse vegetation if a constant elevation survey is flown at 80m above terrain peak.

Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes

Abstract Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.

Detection Thresholds of Archaeological Features in Airborne Lidar Data from Central Yucatán

AbstractIn this article we evaluate ∼48km2of airborne lidar data collected at a target density of 15 laser shots/m in central Yucatán, Mexico. This area covers parts of the sites of Chichén Itzá and Yaxuná, a kilometer-wide transect between these two sites, and a transect along the first few kilometers of Sacbé 1 from Yaxuná to Cobá. The results of our ground validation and mapping demonstrate that not all sizable archaeological features can be detected in the lidar images due to: (1) the slightly rolling topography interspersed with 1-6 m-high bedrock hummocks, which morphologically mimic house mounds, further complicated by the presence of low foundations; (2) the complex forest structure in central Yucatán, which has particularly dense near-ground understory resulting in a high number of mixed-signal ground and low vegetation returns which reduces the fidelity and accuracy of the bare-earth digital elevation models; and (3) the predominance of low archaeological features difficult to discern from the textural noise of the near-ground vegetation. In this article we explore different visualization techniques to increase the identification of cultural features, but we conclude that, in this portion of the Maya region, lidar should be used as a complement to traditional on-the-ground survey techniques.

Application of innovative technologies in landslide research in the area of the City of Zagreb (Croatia, Europe)

This paper describes the application of innovative technologies for landslide detection, mapping and monitoring in the City of Zagreb since 2010. Airborne Light Detection and Ranging (LiDAR) data are used to visually identify landslides in the Podsljeme Zone over the area of 180 km2 for the purpose of landslide inventory mapping. The total number of landslides in the analyzed area (approx. 1,600 landslides) is estimated based on the average landslide density (9 landslides per square km) from the most reliable geomorphological historical inventory, which was produced in 1979. Examples of the visual interpretation of very-high-resolution bare-earth DEMs (Digital Elevation Model) are given to show the potential of these innovative techniques to identify the landslide contours of the very small, small and moderately small landslides that are characteristic of the study area, which is composed of engineering soils and/or soft rocks (marls). In the framework of the described landslide research, the biggest landslide in the Podsljeme Zone, the Kostanjek landslide (landslide area of 1 km2), was also equipped with an automated near-real time monitoring system that encompassed approximately 40 sensors to monitor landslide movement and landslide causal factors. Monitoring of this deep and large landslide during multiple extreme hydro-meteorological events from January 2013 to January 2015 enabled collection of data for the analysis of landslide movement on the surface and in the underground and as well as analysis of the relationship between landslide reactivations and its triggers, i.e., changes in groundwater level and precipitation. The final aim of the described scientific research in terms of landslide mapping and monitoring is its practical application in land-use planning and civil protection systems in the City of Zagreb. Namely, the conclusions propose reliable and efficient methods for landslide identification and monitoring, which are necessary to provide data and solutions for hazard and risk reduction to local authorities that are managed by the City.

AUOTOMATIC CLASSIFICATION OF POINT CLOUDS EXTRACTED FROM ULTRACAM STEREO IMAGES

Abstract. Automatic extraction of building roofs, street and vegetation are a prerequisite for many GIS (Geographic Information System) applications, such as urban planning and 3D building reconstruction. Nowadays with advances in image processing and image matching technique by using feature base and template base image matching technique together dense point clouds are available. Point clouds classification is an important step in automatic features extraction. Therefore, in this study, the classification of point clouds based on features color and shape are implemented. We use two images by proper overlap getting by Ultracam-x camera in this study. The images are from Yasouj in IRAN. It is semi-urban area by building with different height. Our goal is classification buildings and vegetation in these points. In this article, an algorithm is developed based on the color characteristics of the point’s cloud, using an appropriate DEM (Digital Elevation Model) and points clustering method. So that, firstly, trees and high vegetation are classified by using the point’s color characteristics and vegetation index. Then, bare earth DEM is used to separate ground and non-ground points. Non-ground points are then divided into clusters based on height and local neighborhood. One or more clusters are initialized based on the maximum height of the points and then each cluster is extended by applying height and neighborhood constraints. Finally, planar roof segments are extracted from each cluster of points following a region-growing technique.

Improving Low-Relief Coastal LiDAR DEMs with Hydro-Conditioning of Fine-Scale and Artificial Drainages

Improvements in Light Detection and Ranging (LiDAR) technology and spatial analysis of high-resolution digital elevation models (DEMs) have advanced the accuracy and diversity of applications for coastal hazards and natural resources management. This article presents a concise synthesis of LiDAR analysis for coastal flooding and management applications in low-relief coastal plains and a case study demonstration of a new, efficient drainage mapping algorithm. The impetus for these LiDAR applications follows historic flooding from Hurricane Floyd in 1999, after which the State of North Carolina and the Federal Emergency Management Agency undertook extensive LiDAR data acquisition and technological developments for high-resolution floodplain mapping. An efficient algorithm is outlined for hydro-conditioning bare earth LiDAR DEMs using available US Geological Survey National Hydrography Dataset canal and ditch vectors. The methodology is illustrated in Moyock, North Carolina, for refinement of hydro-conditioning by combines pre-existing bare earth DEMs with spatial analysis of LiDAR point clouds in segmented and buffered ditch and canal networks. The methodology produces improved maps of fine-scale drainage, reduced omission of areal flood inundation, and subwatershed delineations that typify heavily ditched and canalled drainage areas. These preliminary results illustrate the capability of the technique to improve the representation of ditches in DEMs as well as subsequent flow and inundation modeling that could spur further research on low-relief coastal LiDAR applications.

Lidar Detection of the Ten Tallest Trees in the Tennessee Portion of the Great Smoky Mountains National Park

AbstractThis paper describes a method for predicting the locations and heights of the ten tallest trees in the Tennessee portion of the Great Smoky Mountains National Park. Iterative computation tools were utilized to process the data along with the lidarderived bare earth digital elevation models and digital surface models to create canopy height models for the Tennessee portion of the park. A height threshold of 51.8 meters was chosen as the minimum value for a tree of extraordinary height. Ten potential sites containing tall trees were identified using this methodology, and seven of the top ten ranking trees’ heights were field measured using accepted forestry methodology. The trees detected using these methods are potentially the tallest trees ever measured on the East Coast of the United States. These methods show that unique tall trees can be successfully detected in a large, heterogeneous forest area using lidar data.

Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction

Abstract. This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.

Extracting Ground-Level DEM From SRTM DEM in Forest Environments Based on Mathematical Morphology

The demands for bare-earth or ground-level digital elevation models (DEMs) are significant for various environmental and ecological studies. As one of the most widely used global DEMs, the Shuttle Radar Topography Mission (SRTM) DEM is available to the public. However, the SRTM DEM is not a bare-earth DEM because it includes man-made structures and vegetation. The objective of this paper is to develop a mathematical morphology-based approach to generate ground-level DEM (GLD) from the SRTM DEM in forest environments. The proposed algorithm is implemented as follows. First, an initial GLD is derived from the SRTM DEM by using morphological operations with a single structuring element. Second, homogeneous forest patches are generated by applying watershed segmentation to pseudocanopy height (PCH) that is obtained by subtracting the initial GLD from the SRTM DEM. Based on segmented patches, a refined GLD is derived by using morphological operations with adaptive structuring elements of different sizes. Third, PCH is updated with the refined GLD and then resegmented into forest patches, from which a final GLD is obtained by subtracting the updated mean PCH from the SRTM DEM. Finally, bare-earth DEMs from the National Elevation Data Set (NED) are used to validate the extracted GLD. The results show that the root mean square error of the final GLD compared with the NED is significantly reduced for two study sites. This type of GLD would be applicable to large-scale environmental studies where accurate topographical information is not available.

Application of Lidar to resolving tectonic and glacial fabrics in glaciated terrain: An example from an Archean granite-greenstone belt, northeastern Minnesota

In this study we examine an Archean granite-greenstone terrane in northeastern Minnesota (USA) to illustrate the application of high-resolution Lidar (light detection and ranging) altimetry to mapping regional tectonic fabrics in forested glaciated areas. We describe the recognition of lineaments and distinguish between tectonic and glacial lineament fabrics. We use a 1-m Lidar derived bare earth digital elevation model to construct multiple shaded-relief images for lineament mapping with sun elevation of 45° and varying sun azimuth in 45° intervals. Two suites of lineaments are apparent. Suite A has a unimodal orientation, mean trend of 035, and consists of short (

High-Density LiDAR Mapping of the Ancient City of Mayapán

A 2013 survey of a 40 square kilometer area surrounding Mayapán, Yucatan, Mexico used high-density LiDAR data to map prehispanic architecture and related natural features. Most of the area is covered by low canopy dense forest vegetation over karstic hilly terrain that impedes full coverage archaeological survey. We used LiDAR at 40 laser points per square meter to generate a bare earth digital elevation model (DEM). Results were evaluated with comparisons to previously mapped areas and with traditional archaeological survey methods for 38 settlement clusters outside of the city wall. Ground checking employed full coverage survey of selected 500 m grid squares, as well as documentation of the chronology and detail of new public and domestic settlement features and cenotes. Results identify the full extent of continued, contemporary Postclassic settlement (A.D. 1150–1450) outside of the city wall to at least 500 meters to the east, north, and west. New data also reveal an extensive modified landscape of terraformed residential hills, rejolladas, and dense settlement dating from Preclassic through Classic Periods. The LiDAR data also allow for the identification of rooms, benches, and stone property walls and lanes within the city.

Assessing the potential for leaf-off LiDAR data to model canopy closure in temperate deciduous forests

Estimates of canopy closure have many important uses in forest management and ecological research. Field measurements, however, are typically not practical to acquire over expansive areas or for large numbers of locations. This problem has been addressed, in recent years, through the use of airborne light detection and ranging (LiDAR) technology which has proven effective in modeling canopy closure remotely. The techniques developed to use LiDAR for this purpose have been designed and evaluated for datasets acquired during leaf-on conditions. However, a large number of LiDAR datasets are acquired during leaf-off conditions since their primary purpose is to generate bare-earth Digital Elevation Models. In this paper, we develop and evaluate techniques for leveraging small-footprint leaf-off LiDAR data to model leaf-on canopy closure in temperate deciduous forests.We evaluate three techniques for modeling canopy closure: (1) the canopy-to-total-return-ratio (CTRR), (2) the canopy-to-total-pixel-ratio (CTPR), and (3) the hemispherical-viewshed (HV). The first technique has been used widely, in various forms, and has been shown to be effective with leaf-on LiDAR datasets. The CTRR technique that we tested uses the first-return LiDAR data only. The latter two techniques are new contributions that we develop and present in this paper. These techniques use Canopy Height Models (CHM) to detect significant gaps in the forest canopy which are of primary importance in estimating closure.The techniques we tested each showed good promise for predicting canopy closure using leaf-off LiDAR data with the CTPR and HV models having particularly high correlations with closure estimates from hemispherical photographs. The CTRR model had performance on par with results from previous studies that used leaf-on LiDAR, although, with leaf-off data the model tended to be negatively biased with respect to species having simple and compound leaf types and positively biased for coniferous species. The CTPR and HV models also showed some slight negative biases for compound-leaf species. The biases for the CTPR and HV models were mitigated when the CHM data were smoothed to fill in small gaps. The CHM-based models were robust to changes in the CHM model resolution which suggests that these methods may be applicable to a variety of small-footprint LiDAR datasets. In this research, the new CTPR and HV methods showed a strong ability to predict canopy closure using leaf-off data, however, future work will be needed to test the applicability of the models to variations in LiDAR datasets, forest types, and topography.

Developing sub 5-m LiDAR DEMs for forested sections of the Alpine and Hope faults, South Island, New Zealand: Implications for structural interpretations

Kilometre-wide airborne light detection and ranging (LiDAR) surveys were collected along portions of the Alpine and Hope faults in New Zealand to assess the potential for generating sub 5-m bare earth digital elevation models (DEMs) from ground return data in areas of dense rainforest (bush) cover as an aid to mapping these faults. The 34-km long Franz-Whataroa LiDAR survey was flown along the densely-vegetated central-most portion of the transpressive Alpine Fault. Six closely spaced flight lines (200 m apart) yielded survey coverage with double overlap of swath collection, which was considered necessary due to the low density of ground returns (0.16 m−2 or a point every 6 m2) under mature West Coast podocarp-broadleaf rainforest. This average point spacing (∼2.5 m) allowed for the generation of a robust, high quality 3-m bare earth DEM. The DEM confirmed the zigzagged form of the surface trace of the Alpine Fault in this area, originally recognised by Norris and Cooper (1995, 1997) and highlights that the surface strike variations are more variant than previously mapped. The 29-km long Hurunui-Hope LiDAR survey was flown east of the Main Divide of the Southern Alps along the dextral-slip Hope Fault, where the terrain is characterised by lower rainfall and more open beech forest. Flight line spacings of ∼275 m were used to generate a DEM from the ground return data. The average ground return values under beech forest were 0.27 m−2 and yielded an estimated cell size suitable for a 2-m DEM. In both cases the LiDAR revealed unprecedented views of the surface geomorphology of these active faults. Lessons learned from our survey methodologies can be employed to plan cost-effective, high-gain airborne surveys to yield bare earth DEMs underneath vegetated terrain and multi-storeyed canopies from densely forested environments across New Zealand and worldwide.