scholarly journals Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

2017 ◽  
Vol 21 (1) ◽  
pp. 43-63 ◽  
Author(s):  
Mikkel Skovgaard Andersen ◽  
Áron Gergely ◽  
Zyad Al-Hamdani ◽  
Frank Steinbacher ◽  
Laurids Rolighed Larsen ◽  
...  
Keyword(s):  
Transition Zone ◽  
Water Surface ◽  
High Energy ◽  
Green Laser ◽  
Morphological Features ◽  
Tidal Range ◽  
Surface Model ◽  
Lidar Data ◽  
Surface Detection

Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land–water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m2. The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.

scholarly journals Shrub Fractional Cover Estimation and Mapping of San Clemente Island Shrubland Based on Airborne Multispectral Imagery and Lidar Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3608
Author(s):  
Kelsey Warkentin ◽  
Douglas Stow ◽  
Kellie Uyeda ◽  
John O’Leary ◽  
Julie Lambert ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Coastal Sage Scrub ◽  
Surface Model ◽  
Lidar Data ◽  
Shrub Cover ◽  
Fractional Cover ◽  
Major Factors ◽  
San Clemente Island

The purpose of this study is to map shrub distributions and estimate shrub cover fractions based on the classification of high-spatial-resolution aerial orthoimagery and light detection and ranging (LiDAR) data for portions of the highly disturbed coastal sage scrub landscapes of San Clemente Island, California. We utilized nine multi-temporal aerial orthoimage sets for the 2010 to 2018 period to map shrub cover. Pixel-based and object-based image analysis (OBIA) approaches to image classification of growth forms were tested. Shrub fractional cover was estimated for 10, 20 and 40 m grid sizes and assessed for accuracy. The most accurate estimates of shrub cover were generated with the OBIA method with both multispectral brightness values and canopy height estimates from a normalized digital surface model (nDSM). Fractional cover products derived from 2015 and 2017 orthoimagery with nDSM data incorporated yielded the highest accuracies. Major factors that influenced the accuracy of shrub maps and fractional cover estimates include the time of year and spatial resolution of the imagery, the type of classifier, feature inputs to the classifier, and the grid size used for fractional cover estimation. While tracking actual changes in shrub cover over time was not the purpose, this study illustrates the importance of consistent mapping approaches and high-quality inputs, including very-high-spatial-resolution imagery and an nDSM.

scholarly journals DECISION LEVEL FUSION OF ORTHOPHOTO AND LIDAR DATA USING CONFUSION MATRIX INFORMATION FOR LNAD COVER CLASSIFICATION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 59-64 ◽  
Author(s):  
S. Daneshtalab ◽  
H. Rastiveis
Keyword(s):  
Confusion Matrix ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Surface Model ◽  
Lidar Data ◽  
Single Shot ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion

Automatic urban objects extraction from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Lidar data available today. The aim of this study is to propose a new approach for the integration of high-resolution aerial imagery and Lidar data to improve the accuracy of classification in the city complications. In the proposed method, first, the classification of each data is separately performed using Support Vector Machine algorithm. In this case, extracted Normalized Digital Surface Model (nDSM) and pulse intensity are used in classification of LiDAR data, and three spectral visible bands (Red, Green, Blue) are considered as feature vector for the orthoimage classification. Moreover, combining the extracted features of the image and Lidar data another classification is also performed using all the features. The outputs of these classifications are integrated in a decision level fusion system according to the their confusion matrices to find the final classification result. The proposed method was evaluated using an urban area of Zeebruges, Belgium. The obtained results represented several advantages of image fusion with respect to a single shot dataset. With the capabilities of the proposed decision level fusion method, most of the object extraction difficulties and uncertainty were decreased and, the overall accuracy and the kappa values were improved 7% and 10%, respectively.

scholarly journals EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 747-751
Author(s):  
N. Demir ◽  
M. Kaynarca ◽  
S. Oy
Keyword(s):  
Standard Deviation ◽  
Optical Sensors ◽  
Land Surface ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Sar Image ◽  
Fuzzy Approach ◽  
The Mean

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

scholarly journals EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 747-751 ◽  
Author(s):  
N. Demir ◽  
M. Kaynarca ◽  
S. Oy
Keyword(s):  
Standard Deviation ◽  
Optical Sensors ◽  
Land Surface ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Sar Image ◽  
Fuzzy Approach ◽  
The Mean

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

scholarly journals Reliability of the classification of cartilage and labral injuries during hip arthroscopy

2020 ◽  
Vol 7 (3) ◽  
pp. 448-457
Author(s):  
Stephanie W Mayer ◽  
Tobias R Fauser ◽  
Robert G Marx ◽  
Anil S Ranawat ◽  
Bryan T Kelly ◽  
...  
Keyword(s):  
Femoral Head ◽  
Transition Zone ◽  
Central Compartment ◽  
Classification Systems ◽  
Cartilage Injury ◽  
Intraobserver Agreement ◽  
Acetabular Cartilage ◽  
Femoral Head Cartilage ◽  
Labral Tears

Abstract To determine interobserver and intraobserver reliabilities of the combination of classification systems, including the Beck and acetabular labral articular disruption (ALAD) systems for transition zone cartilage, the Outerbridge system for acetabular and femoral head cartilage, and the Beck system for labral tears. Additionally, we sought to determine interobserver and intraobserver agreements in the location of injury to labrum and cartilage. Three fellowship trained surgeons reviewed 30 standardized videos of the central compartment with one surgeon re-evaluating the videos. Labral pathology, transition zone cartilage and acetabular cartilage were classified using the Beck, Beck and ALAD systems, and Outerbridge system, respectively. The location of labral tears and transition zone cartilage injury was assessed using a clock face system, and acetabular cartilage injury using a five-zone system. Intra- and interobserver reliabilities are reported as Gwet’s agreement coefficients. Interobserver and intraobserver agreement on the location of acetabular cartilage lesions was highest in superior and anterior zones (0.814–0.914). Outerbridge interobserver and intraobserver agreement was >0.90 in most zones of the acetabular cartilage. Interobserver and intraobserver agreement on location of transition zone lesions was 0.844–0.944. The Beck and ALAD classifications showed similar interobserver and intraobserver agreement for transition zone cartilage injury. The Beck classification of labral tears was 0.745 and 0.562 for interobserver and intraobserver agreements, respectively. The Outerbridge classification had almost perfect interobserver and intraobserver agreement in classifying chondral injury of the true acetabular cartilage and femoral head. The Beck and ALAD classifications both showed moderate to substantial interobserver and intraobserver reliabilities for transition zone cartilage injury. The Beck system for classification of labral tears showed substantial agreement among observers and moderate intraobserver agreement. Interobserver agreement on location of labral tears was highest in the region where most tears occur and became lower at the anterior and posterior extents of this region. The available classification systems can be used for documentation regarding intra-articular pathology. However, continued development of a concise and highly reproducible classification system would improve communication.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

Hydrodynamic influences on sedimentology and geomorphology of nearshore parts of carbonate ramps: Holocene, NE Yucatán Shelf, Mexico

2021 ◽  
Vol 91 (10) ◽  
pp. 1040-1066
Author(s):  
Thomas C. Neal ◽  
Christian M. Appendini ◽  
Eugene C. Rankey
Keyword(s):  
Numerical Models ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
High Energy ◽  
Tidal Range ◽  
Carbonate Platforms ◽  
Trade Winds ◽  
Tidal Forces ◽  
Carbonate Ramps ◽  
Geologic Record

ABSTRACT Although carbonate ramps are ubiquitous in the geologic record, the impacts of oceanographic processes on their facies patterns are less well constrained than with other carbonate geomorphic forms such as isolated carbonate platforms. To better understand the role of physical and chemical oceanographic forces on geomorphic and sedimentologic variability of ramps, this study examines in-situ field measurements, remote-sensing data, and hydrodynamic modeling of the nearshore inner ramp of the modern northeastern Yucatán Shelf, Mexico. The results reveal how sediment production and accumulation are influenced by the complex interactions of the physical, chemical, and biological processes on the ramp. Upwelled, cool, nutrient-rich waters are transported westward across the ramp and concentrated along the shoreline by cold fronts (Nortes), westerly regional currents, and longshore currents. This influx supports a mix of both heterozoan and photozoan fauna and flora in the nearshore realm. Geomorphically, the nearshore parts of this ramp system in the study area include lagoon, barrier island, and shoreface environments, influenced by the mixed-energy (wave and tidal) setting. Persistent trade winds, episodic tropical depressions, and winter storms generate waves that propagate onto the shoreface. Extensive shore-parallel sand bodies (beach ridges and subaqueous dune fields) of the high-energy, wave-dominated upper shoreface and foreshore are composed of fine to coarse skeletal sand, lack mud, and include highly abraded, broken and bored grains. The large shallow lagoon is mixed-energy: wave-dominated near the inlet, it transitions to tide-dominated in the more protected central and eastern regions. Lagoon sediment consists of Halimeda-rich muddy gravel and sand. Hydrodynamic forces are especially strong where bathymetry focuses water flow, as occurs along a promontory and at the lagoon inlet, and can form subaqueous dunes. Explicit comparison among numerical models of conceptual shorefaces in which variables are altered and isolated systematically demonstrates the influences of the winds, waves, tides, and currents on hydrodynamics across a broad spectrum of settings (e.g., increased tidal range, differing wind and wave conditions). Results quantify how sediment transport patterns are determined by wave height and direction relative to the shoreface, but tidal forces locally control geomorphic and sedimentologic character. Similarly, the physical oceanographic processes acting throughout the year (e.g., daily tides, episodic winter Nortes, and persistent easterly winds and waves) have more impact on geomorphology and sedimentology of comparable nearshore systems than intense, but infrequent, hurricanes. Overall, this study provides perspectives on how upwelling, nutrient levels, and hydrodynamics influence the varied sedimentologic and geomorphic character of the nearshore areas of this high-energy carbonate ramp system. These results also provide for more accurate and realistic conceptual models of the depositional variability for a spectrum of modern and ancient ramp systems.

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