scholarly journals Airborne Electromagnetic and Radiometric Peat Thickness Mapping of a Bog in Northwest Germany (Ahlen-Falkenberger Moor)

2020 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Bernhard Siemon ◽  
Malte Ibs-von Seht ◽  
Stefan Frank
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Borehole Data ◽  
Northwest Germany ◽  
Surface Data ◽  
Airborne Electromagnetic ◽  
Lateral Extent ◽  
Elevated Surface ◽  
Airborne Electromagnetic Data ◽  
Gradient Approach

Knowledge on peat volumes is essential to estimate carbon stocks accurately and to facilitate appropriate peatland management. This study used airborne electromagnetic and radiometric data to estimate the volume of a bog. Airborne methods provide an alternative to ground-based methods, which are labor intensive and unfeasible to capture large-scale (>10 km2) spatial information. An airborne geophysical survey conducted in 2004 covered large parts of the Ahlen-Falkenberger Moor, an Atlantic peat bog (39 km2) close to the German North Sea coast. The lateral extent of the bog was derived from low radiometric and elevated surface data. The vertical extent resulted from smooth resistivity models derived from 1D inversion of airborne electromagnetic data, in combination with a steepest gradient approach, which indicated the base of the less resistive peat. Relative peat thicknesses were also derived from decreasing radiation over peatlands. The scaling factor (µa = 0.28 m−1) required to transform the exposure rates (negative log-values) to thicknesses was calculated using the electromagnetic results as reference. The mean difference of combined airborne results and peat thicknesses of about 100 boreholes is very small (0.0 ± 1.1 m). Although locally some (5%) deviations (>2 m) from the borehole results do occur, the approach presented here enables fast peat volume mapping of large areas without an imperative necessity of borehole data.

scholarly journals 2.5D Inversion Algorithm of Frequency-Domain Airborne Electromagnetics with Topography

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianjun Xi ◽  
Wenben Li
Keyword(s):  
Frequency Domain ◽  
Large Scale ◽  
Jacobian Matrix ◽  
Sparse Matrix ◽  
Inversion Algorithm ◽  
Linear System Of Equations ◽  
Electromagnetic Data ◽  
Electric And Magnetic Fields ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Data

We presented a 2.5D inversion algorithm with topography for frequency-domain airborne electromagnetic data. The forward modeling is based on edge finite element method and uses the irregular hexahedron to adapt the topography. The electric and magnetic fields are split into primary (background) and secondary (scattered) field to eliminate the source singularity. For the multisources of frequency-domain airborne electromagnetic method, we use the large-scale sparse matrix parallel shared memory direct solver PARDISO to solve the linear system of equations efficiently. The inversion algorithm is based on Gauss-Newton method, which has the efficient convergence rate. The Jacobian matrix is calculated by “adjoint forward modelling” efficiently. The synthetic inversion examples indicated that our proposed method is correct and effective. Furthermore, ignoring the topography effect can lead to incorrect results and interpretations.

scholarly journals Large-Scale and Deep-Seated Gravitational Slope Deformations on Mars: A Review

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 174
Author(s):  
Marco Emanuele Discenza ◽  
Carlo Esposito ◽  
Goro Komatsu ◽  
Enrico Miccadei
Keyword(s):  
Large Scale ◽  
High Quality ◽  
Surface Data ◽  
Geomorphological Processes ◽  
Gravitational Processes ◽  
Quality Surface ◽  
High Quality Surface ◽  
Mars Missions

The availability of high-quality surface data acquired by recent Mars missions and the development of increasingly accurate methods for analysis have made it possible to identify, describe, and analyze many geological and geomorphological processes previously unknown or unstudied on Mars. Among these, the slow and large-scale slope deformational phenomena, generally known as Deep-Seated Gravitational Slope Deformations (DSGSDs), are of particular interest. Since the early 2000s, several studies were conducted in order to identify and analyze Martian large-scale gravitational processes. Similar to what happens on Earth, these phenomena apparently occur in diverse morpho-structural conditions on Mars. Nevertheless, the difficulty of directly studying geological, structural, and geomorphological characteristics of the planet makes the analysis of these phenomena particularly complex, leaving numerous questions to be answered. This paper reports a synthesis of all the known studies conducted on large-scale deformational processes on Mars to date, in order to provide a complete and exhaustive picture of the phenomena. After the synthesis of the literature studies, the specific characteristics of the phenomena are analyzed, and the remaining main open issued are described.

Out in the wash: spatial ecology of a temperate marine shallow rocky-reef species derived using acoustic telemetry

2015 ◽  
Vol 66 (6) ◽  
pp. 559 ◽  
Author(s):  
Jerom R. Stocks ◽  
Charles A. Gray ◽  
Matthew D. Taylor
Keyword(s):  
Spatial Ecology ◽  
Large Scale ◽  
Spatial Information ◽  
Activity Space ◽  
Rocky Reef ◽  
Temperate Reef ◽  
Detection Frequency ◽  
Detection Depth ◽  
Eastern Australia ◽  
Temperate Reef Fish

Characterising the movement and habitat affinities of fish is a fundamental component in understanding the functioning of marine ecosystems. A comprehensive array of acoustic receivers was deployed at two near-shore coastal sites in south-eastern Australia, to examine the movements, activity-space size and residency of a temperate rocky-reef, herbivorous species Girella elevata. Twenty-four G. elevata individuals were internally tagged with pressure-sensing acoustic transmitters across these two arrays and monitored for up to 550 days. An existing network of coastal receivers was used to examine large-scale movement patterns. Individuals exhibited varying residency, but all had small activity-space sizes within the arrays. The species utilised shallow rocky-reef habitat, displaying unimodal or bimodal patterns in depth use. A positive correlation was observed between wind speed and the detection depth of fish, with fish being likely to move to deeper water to escape periods of adverse conditions. Detection frequency data, corrected using sentinel tags, generally illustrated diurnal behaviour. Patterns of habitat usage, residency and spatial utilisation highlighted the susceptibility of G. elevata to recreational fishing pressure. The results from the present study will further contribute to the spatial information required in the zoning of effective marine protected areas, and our understanding of temperate reef fish ecology.

scholarly journals Semantic overlay network for large-scale spatial information indexing

2013 ◽  
Vol 57 ◽  
pp. 208-217 ◽  
Author(s):  
Zhiqiang Zou ◽  
Yue Wang ◽  
Kai Cao ◽  
Tianshan Qu ◽  
Zhongmin Wang
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Overlay Network

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

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