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In order to discover how a storm impacts underwater topographic changes, the underwater terrain morphological and structural changes in Culebrita Island were analysed. Airborne bathymetry LiDAR (Light Detection and Ranging) data from the National Oceanic and Atmospheric Administration (NOAA) with a 1.0 m spatial resolution at different time intervals (2016, 2018, 2019) were used to create three-dimensional (3D) terrain models. Four types of terrain feature attributes, water depth, slope, aspect and rugosity, were analysed to investigate terrestrial structure changes caused by the storms. The variation of underwater topography at each time interval was discussed by five profiles, the volume of deposition and erosion between different water depth intervals was investigated to derive the rate of net volume change per unit area ( m 3 ( m 2 ) − 1 ), spatial patterns of underwater topography dynamics are discussed and dynamic changes in topography structure and changes in topographic erosion deposition are observed and detected. The analysis of the topographic feature attributes for each year discovered an increase in topographic complexity. Analysis of the profiles has discovered that the topographic deposition with an average rate of 0.53 m y e a r − 1 from 2016 to 2018 and an average topographic erosion rate of 0.34 m y e a r − 1 from 2018 to 2019. In addition, it is also found that the deposition was greater than erosion from 2016 to 2018, and the erosion was greater than deposition from 2018 to 2019, with a net volume change of+48794 m 3 and a net volume change rate of+0.042 m 3 ( m 2 ) − 1 . Investigation also found that such large changes in the deposition were mainly due to the sediment carried by the storms. [ABSTRACT FROM AUTHOR]

The spatial resolution of SAR images has been significantly improved in recent years. Conventional geometric correction models are computationally expensive, and require a significant amount of time to process high-resolution SAR data. This study concentrates on the Kriging-based surrogate algorithm to improve a rigorous geometric correction model for high-resolution SAR images. This study exploits a global digital elevation model (DEM) to build a 10-m grid of elevation points. The Kriging-based geometric correction model estimates the elevation at unmeasured locations between the grid points to reconstruct the geometry of high-resolution SAR images. The implemented method is a robust approach to improving the geometric construction of SAR images in mountainous terrains. The obtained results show that the Kriging-based geometric correction model enhances the computation efficiency of rigorous physical geometric models and improves the geometric construction of SAR images. The geometrical error of SAR pixels is found to be between 18 and 169 pixels through 92 selected ground control points. In contrast, the Kriging-based geometric correction model has reduced the geometrical error of SAR pixels by up to 1 pixel (i.e. 6 m) with significantly less computational redundancy. [ABSTRACT FROM AUTHOR]

The hybrid baseline Multi-Channel Synthetic Aperture Radar (MC-SAR) refers to a MC-SAR with both along-track baselines and cross-track baselines. In the hybrid baseline MC-SAR, the phase induced by the target motion couples with the phase induced by terrain, which leads to the difficulty of ground moving target parameters estimation. In this paper, A novel maximum likelihood method based on the migration feature of a moving target in the complex image domain is proposed to estimate the velocity and height of the moving target. Compared with the original maximum likelihood method, the proposed method can decrease the computational load, has a smaller estimated error, and could resolve the ambiguity. The signal model of the terrain clutter and moving target in the hybrid baseline is analysed. A nonlinear antenna configuration is used to decouple the phase induced by moving target and terrain. Finally, some numerical experiments and scene simulation are also provided to demonstrate the estimated performance with different system parameters and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents an approach that uses airborne light detection and ranging (lidar) data and aerial imagery for creating a digital terrain model (DTM) and for extracting building objects. The process of creating the DTM from lidar data requires four steps in this study: pre-processing, segmentation, extraction of ground points, and refinement. In the pre-processing step, raw data are transformed to raster data. For segmentation, we propose a new mean planar filter (MPF) that uses a 3 × 3 kernel to divide lidar data into planar and nonplanar surfaces. For extraction of ground points, a new method to extract additional ground points in forest areas is used, thus improving the accuracy of the DTM. The refinement process further increases the accuracy of the DTM by repeated comparison of a temporary DTM and the digital surface model. After the DTM is generated, building objects are extracted via a proposed three-step process: detection of high objects, removal of forest areas, and removal of small areas. High objects are extracted using the height threshold from the normalized digital surface model. To remove forest areas from among the high objects, an aerial image and normalized digital surface model from the lidar data are used in a supervised classification. Finally, an area-based filter eliminates small areas, such as noise, thus extracting building objects. To evaluate the proposed method, we applied this and three other methods to five sites in different environments. The experiment showed that the proposed method leads to a notable increase in accuracy over three other methods when compared with thein situreference data. [ABSTRACT FROM PUBLISHER]

This study assesses the performance of three classification trees (CT) models (entropy, gain ratio and gini) for building detection by the fusion of airborne laser scanner data and multispectral aerial images. Data from four study areas with different sensors and scene characteristics were used to assess the performance of the models. The process of performance evaluation is based on four criteria: model validation and testing, classification accuracies, relative importance of input variables, as well as transferability of CT derived from one data set to another. The LiDAR point clouds were filtered to generate a digital terrain model (DTM) based on the orthogonal polynomials, and then a digital surface model (DSM) and the normalized digital surface model (nDSM) were generated. A set of 26 uncorrelated feature attributes were derived from the original aerial images, LiDAR intensity image, DSM and nDSM. Finally, the three CT models were used to classify buildings, trees, roads and ground from aerial images, LiDAR data and the generated attributes, with the most accurate average classifications of 95% being achieved. The entropy splitting algorithm proved to be a preferable algorithm for building detection from aerial images and LiDAR data. [ABSTRACT FROM AUTHOR]

A digital relief model (DRM) of the Swan Coastal Plain and Rottnest Shelf (7400 km2) was built with a range of topographic and high-resolution bathymetric datasets, gridded to a 50 m cell size. The DRM enabled the delineation of relict coastal landforms, benthic habitats and development of a regional morphostratigraphic framework. Well-defined features include: (1) limestone ridges on the coastal plain that sit subparallel to the modern shoreline and were largely formed as coastal dune barriers during or shortly after Quaternary interglacial periods of high sea level; (2) rocky reefs on the inner shelf that rise up to 10 m above the adjacent seafloor, which are remnants of coastal dune barriers that formed when the sea level was 20-30 m lower than present and (3) a discontinuous ridge 3-10 m high along much of the outer shelf, which likely represents a coastal barrier that formed when the sea level was around 60 m lower than present. The DRM provides a useful regional perspective of the distribution and form of these extensive reefs. [ABSTRACT FROM AUTHOR]

Environmental analysis, management and modelling require detailed and precise land-use/land-cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image-based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A-factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non-corrected data. [ABSTRACT FROM AUTHOR]

Artificial targets are generally used in terrestrial laser scanner (TLS) practice for data georeferencing. This is because they are well recognized and modelled from the point cloud and their positions can be contemporarily measured by topographical techniques. The accuracy of target identification directly influences the georeferencing quality. In particular, retro-reflective materials can cause anomalies in range measurement due to the too high amplitude of the returned pulse. If the received pulse intensity exceeds the limits of the sensor dynamic range, the receiver saturates, producing a truncated pulse preventing the correct time-of-flight computation. A series of experiments was performed in order to test the performances of a specific instrument (Optech ILRIS 3D) for the acquisition of artificial targets made of retro-reflective material, resulting in very high reflectance. Dealing with ranges lower than about 300 m, two cases were clearly observed: the wrong distance measurement of points over high reflecting surfaces and the presence of haloes around these surfaces. Neglecting these phenomena has serious implications and can lead to wrong georeferencing. Experiments were executed and data was analysed, providing a qualitative and semi-quantitative phenomenon description. Finally, the design of a target that can be easily recognized and correctly modelled was proposed. [ABSTRACT FROM AUTHOR]

Our objective was to assess the accuracy of the forest height and biomass estimates derived from an Ikonos stereo pair and a lidar digital terrain model (DTM). After the Ikonos scenes were registered to the DTM with submetric accuracy, tree heights were measured individually by subtracting the photogrammetric elevation of the treetop from the lidar ground-level elevation of the tree base. The low residual error (1.66 m) of the measurements confirmed the joint geometric accuracy of the combined models. Matched images of the stereo pair were then used to create a digital surface model. The latter was transformed to a canopy height model (CHM) by subtracting the lidar DTM. Plotwise height percentiles were extracted from the Ikonos-lidar CHM and used to predict the average dominant height and above-ground biomass. The coefficient of determination reached 0.91 and 0.79 for average height and biomass, respectively. In both cases, the accuracy of the Ikonos-lidar CHM predictions was slightly lower than that of the all-lidar reference CHM. Although the CHM heights did not saturate at moderate biomass levels, as do multispectral or radar images, values above 300 Mg ha-1 could not be predicted accurately by the Ikonos-lidar or by the all-lidar CHM. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanning (ALS) has been established as a valuable tool for the estimation of biophysical vegetation properties such as tree height, crown width, fractional cover and leaf area index (LAI). It is expected that the conditions of data acquisition, such as viewing geometry and sensor configuration influence the value of these parameters. In order to gain knowledge about these different conditions, we test for the sensitivity of vegetation products for viewing geometry, namely flying altitude and scanning (incidence) angle. Based on two methodologies for single tree extraction and derivation of fractional cover and LAI previously developed and published by our group, we evaluate how these variables change with either flying altitude or scanning angle. These are the two parameters which often need to be optimized towards the best compromise between point density and area covered with a single flight line, in order to reduce acquisition costs. Our test-site in the Swiss National Park was sampled with two nominal flying altitudes, 500 and 900 m above ground. Incidence angle and local incidence angle were computed based on the digital terrain model using a simple backward geocoding procedure. We divided the raw laser returns into several different incident angle classes based on the flight path data; the TopoSys Falcon II system used in this study has a maximum scan angle of ±7.15°. We compared the derived biophysical properties from each of these classes with field measurements based on tachymeter measurements and hemispherical photographs, which were geolocated using differential GPS. It was found that with increasing flying height the well-known underestimation of tree height increases. A similar behaviour can be observed for fractional cover; its respective values decrease with higher flying height. The minimum distance between first and last echo increases from 1.2 metres for 500 m AGL to more than 3 metres for 900 m AGL, which does alter return statistics. The behaviour for incidence angles is not so evident, probably due to the small scanning angle of the system used. fCover seems to be most affected by incidence angles, with significantly higher differences for locations further away from nadir. As expected, incidence angle appears to be of higher importance for vegetation density parameters than local incidence angle. [ABSTRACT FROM AUTHOR]

Small-footprint full-waveform airborne laser scanning (ALS) is a remote sensing technique capable of mapping vegetation in three dimensions with a spatial sampling of about 0.5-2 m in all directions. This is achieved by scanning the laser beam across the Earth's surface and by emitting nanosecond-long infrared pulses with a high frequency of typically 50-150 kHz. The echo signals are digitized during data acquisition for subsequent off-line waveform analysis. In addition to delivering the three-dimensional (3D) coordinates of scattering objects such as leaves or branches, full-waveform laser scanners can be calibrated for measuring the scattering properties of vegetation and terrain surfaces in a quantitative way. As a result, a number of physical observables are obtained, such as the width of the echo pulse and the backscatter cross-section, which is a measure of the electromagnetic energy intercepted and re-radiated by objects. The main aim of this study was to build up an understanding of the scattering characteristics of vegetation and the underlying terrain. It was found that vegetation typically causes a broadening of the backscattered pulse, while the backscatter cross-section is usually smaller for canopy echoes than for terrain echoes. These scattering properties allowed classification of the 3D point cloud into vegetation and non-vegetation echoes with an overall accuracy of 89.9% for a dense natural forest and 93.7% for a baroque garden area. In addition, by removing the vegetation echoes before the filtering process, the quality of the digital terrain model could be improved. [ABSTRACT FROM AUTHOR]

In remote sensing, because of the wide diversity of image characteristics (size, spatial and radiometric resolution, terrain relief, observation poses, etc.), image registration methods that work well on certain satellite images may not produce acceptable results for others, requiring more powerful techniques. A variety of registration techniques that account for images with non-rigid geometric deformations have been proposed, including piecewise (linear or cubic) functions, weighted mean functions, radial basis functions, B-spline functions, etc. This paper compares three of them: polynomial, piecewise-linear and thin-plate-spline functions, and analyses their performance under a variety of factors: off-nadir viewing, terrain relief, density of control points, and 3D geometric correction. Our comparison applies on panchromatic QuickBird imagery, both ortho-ready (as provided by DigitalGlobe) and orthorectified, acquired on different dates, from different observation attitudes, and sensing different land covers: urban area, high-relief terrain, and a combination of both. [ABSTRACT FROM AUTHOR]

Generating multi-resolution terrain models dynamically is necessary for rapid visualization because of the huge volume of data and the limited memory of computers. However, it is difficult to generate dynamic multi-resolution terrain models with the roads embedded. This paper proposes a new method for generating these models. In contrast to previous approaches, our method divides the integrated terrains into multiple levels with a ‘▒’ shape, and dynamically generates multi-resolution terrain models with the roads embedded. Moreover, our method efficiently overcomes thin, long-shaped triangles in multi-resolution terrain models. On the other hand, the number of triangles in adjacent frames is efficiently updated during walking/flying through visualization. The experimental results demonstrate that the proposed method acquires better performance in terms of accuracy for the multi-resolution representation of terrains with the roads embedded. [ABSTRACT FROM AUTHOR]

Automatic DEM deformation detection without ground control points (GCPs) with its application in debris flow change analysis is an attractive but very difficult research topic. Based on the existing methods of M‐LZD and LMS‐LZD, this paper presents an improved method that takes both magnitude and relationship into account through allocating an appropriate weight to each observation and evaluating the contribution of each observation to deformation with the allocated weight. Such improvement enhances greatly the ability of DEM deformation detection. Compared with the M‐LZD and LMS‐LZD algorithm, using the multi‐temporal DEMs of the Puwaigou valley, along the Hengduan mountain ranges in the southwest of China, where debris flows occur frequently during each summer due to heavy rainfall and each Autumn due to rapid snowmelt, the experimental results demonstrate that our method has the highest detection accuracy (RMS) and detection percentage (capability) of over 50%. The advantages of our method are (1) it does not need GCPs and prior information and knowledge of the terrain surface, and (2) it can calculate simultaneously the volume and determine the spatial distribution of terrain surface changes of multi‐temporal DEMs. [ABSTRACT FROM AUTHOR]

The article reports on a research which aims to build a high-resolution digital terrain elevation model for part of the North Inlet estuary in Georgetown, South Carolina. The study uses an airborne Light Detection and Ranging data because of its capability to produce digital terrain models with sub-meter vertical and horizontal resolutions. This an interdisciplinary effort to investigate the relationship between stream order, water quality and natural influences in the North Inlet estuary.

A digital terrain model (DTM) extracted from QuickBird in-track stereo images using a three-dimensional (3D) multisensor physical model developed at the Canada Centre for Remote Sensing, Natural Resources Canada was evaluated. Firstly, the stereo photogrammetric bundle adjustment was set-up with about 10 accurate ground control points and 1-2 m errors in the three axes were obtained over 48 independent checkpoints. The DTM was then generated using an area-based multi-scale image matching method and 3D semi-automatic editing tools and then compared to lidar elevation data with 0.2-m accuracy. An elevation error with 68% confidence level (LE68) of 6.4 m was achieved over the full area. Since the DTM is in fact a digital surface model where the height, or a part, of land cover (trees, houses) is included, the accuracy depends on the land cover types. Using 3D visual classification of the stereo QuickBird images, different classes (deciduous, conifer, mixed and sparse forests, residential areas, bare soils and lakes) were generated to take into account the height of the surfaces (natural and human-made) in the accuracy evaluation. LE68 values of 3.4 m to 6.7 m were thus obtained depending on the land cover types with biases representative of the surface heights. On the other hand, LE68 values of 0.5 m and 1.3 m with no bias were obtained for lakes and bare soils respectively. These last results are more representative of the real stereo QuickBird potential for DTM and 5-m contour line generation, compliant with the highest topographic standard. Since the images were acquired in wintertime and the lidar data in summertime, better results could thus be expected when using stereo images acquired in summertime, mainly in deciduous forests to integrate the full canopy height into the DSM. [ABSTRACT FROM AUTHOR]

What information may be extracted over urban area by means of joint analysis of two-dimensional (2D) and three-dimensional (3D) remote sensing data? We exploit aerial, Synthetic Aperture Radar (SAR) and Laser Induced Detection and Ranging (LIDAR) data to characterize precisely the Presidio area in San Francisco. We discriminate between different objects in the scene using their 2D and 3D characteristics. The final product of the analysis is a set of raster or vector information layers providing land covers, 3D building shapes and Digital Terrain Models (DTMs) of the Presidio. This paper investigates the relative merits of the collected data in retrieving each of these information layers, and examines how automatic algorithms to extract land cover, Digital Terrain Model (DTM) and 3D building shape could be integrated in a processing chain. [ABSTRACT FROM AUTHOR]

One of the largest terrain corrected SAR mosaics, a mosaic of the state of Alaska, was created from 800 ERS-1 SAR summer images. Because of rain events, significant temporal changes of soil moisture often cause substantial changes in the signature of adjacent SAR images acquired on different dates. After an extensive search for suitable images from a large database of the available 1992 and 1993 images, a nearly seamless large-scale digital mosaic of Alaska was successfully generated at a pixel resolution of 100m. The applications potential of the mosaic are demonstrated by examples in mapping soil moisture, forest fire scars, glaciers, geological, and volcanic features. [ABSTRACT FROM AUTHOR]