LiDAR‐based fluvial remote sensing to assess 50–100‐year human‐driven channel changes at a regional level: The case of the Piedmont Region, Italy

2018 ◽  
Vol 44 (2) ◽  
pp. 471-489 ◽  
Author(s):  
S. Bizzi ◽  
H. Piégay ◽  
L. Demarchi ◽  
W. Van de Bund ◽  
C.J. Weissteiner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regional Level ◽  
Channel Changes ◽  
Fluvial Remote Sensing ◽  
Piedmont Region

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

The Integration of Data Review, Remote Sensing and Ground Survey for a Regional-Level Karst Assessment

2016 ◽  
Vol 22 (1) ◽  
pp. 81-92
Author(s):  
ROBERT KENNETH DENTON ◽  
ASHLEY HOGAN ◽  
RONALD DREW THOMAS
Keyword(s):  
Remote Sensing ◽  
Regional Level ◽  
Ground Survey

Regional Characterisation of soil properties by combining remote sensing, geophysical and pedological methods

2021 ◽  
Author(s):  
Lars Konen ◽  
Richard Mommertz ◽  
Daniel Rückamp ◽  
Malte Ibs-von Seht ◽  
Andreas Möller
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Thermal Analyses ◽  
Arable Land ◽  
Geophysical Methods ◽  
Regional Level ◽  
Soil Survey ◽  
Digital Information ◽  
Sustainable Food ◽  
Research Concept

<p>Knowing our soils well, is the base for a sound land use management, and thus for a worldwide sustainable food production and safe drinking water supply. Especially in countries of the Global South, high quality digital information on soil properties on regional level are rare. While conventional soil inventories are time consuming, digital mapping of soil properties is a promising approach to close the gap more quickly. For this purpose, a reliable method is developed within the BGR project “ReCharBo” (Regional Characterisation of Soil Properties) to minimize field and laboratory work by combining remote sensing techniques like hyperspectral and thermal analyses as well as geophysical methods (e.g. gamma spectrometry) with conventional soil survey from different scales.  At local and field-scale the data acquisition is done by drones, portable equipment and soil sampling, complemented at regional level by helicopter and satellite supported methods. In a corresponding talk in the same session Mommertz et al. (2021) give a detailed technical overview of the selected methods and the research concept of the project. To deploy the method including the concept of ground-truthing on arable land, areas in Germany were selected from Soil Maps of Germany at scale 1:1.000.000 (BÜK1000), 1:200.000 (BÜK200) and 1:50.000 (BK50) depending on representative soil types and region. In a first attempt, the research concept was  carried out with simultaneous field and air borne analyses at two sites  in autumn 2020. The results of this first attempt will be presented at the conference.</p>

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