Results 81 to 90 of about 12,562 (250)

Mapping River Bed Topography in Whitewater Rapids Using Bathymetric LiDAR

River Research and Applications, EarlyView.
ABSTRACT Bathymetric LiDAR captures river topography efficiently for clear and shallow water, but for mountain rivers, whitewater rapids still pose challenges. This study proposes a novel method to enable the extraction of bottom returns specifically in turbulent whitewater sections.
Jan Rhomberg‐Kauert, Theresa Himmelsbach, Florian Pöppl, Lucas Dammert, Martin Pfennigbauer, Gottfried Mandlburger   +5 more
wiley   +1 more source

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

Remote Sensing, 2020
Aerial photogrammetry by Unmanned Aerial Vehicles (UAVs) is a widespread method to perform mapping tasks with high-resolution to reconstruct three-dimensional (3D) building and façade models. However, the survey of Ground Control Points (GCPs) represents
Yuri Taddia, Laura González-García, Elena Zambello, Alberto Pellegrinelli   +3 more
doaj   +1 more source

Real Time Airborne Monitoring for Disaster and Traffic Applications [PDF]

, 2011
Remote sensing applications like disaster or mass event monitoring need the acquired data and extracted information within a very short time span. Airborne sensors can acquire the data quickly and on-board processing combined with data downlink is the ...
Kurz, Franz, Leitloff, Jens, Meynberg, Oliver, Reinartz, Peter, Rosenbaum, Dominik   +4 more
core  

Using Fine Sediment Monitoring to Quantify Sand Loading Over Time

River Research and Applications, EarlyView.
ABSTRACT Riverine sand loading can lead to geomorphic change, impacting river processes like lateral migration and vertical aggradation or degradation. Yet many sediment monitoring programs track only fine sediment measurable as total suspended solids (TSS). Here, we assess the ability for TSS data to be used to determine sand loading.
Andrew P. Kasun, Karen B. Gran
wiley   +1 more source

Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park

Remote Sensing in Ecology and Conservation, EarlyView.
A comparison of Convolutional Neural Network (CNN) and Random Forest (RF) model predictions of benthic habitats within Apollo Marine Park. The CNN (left) and RF (right) classification maps show the spatial distribution of three habitat types: high energy circalittoral rock with seabed‐covering sponges, low complexity circalittoral rock with non‐crowded
Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young   +6 more
wiley   +1 more source

Spatial Data Performance Test of Mid-cost UAS with Direct Georeferencing

Periodica Polytechnica Civil Engineering, 2020
Recent development of lightweight and small size multi-frequency GNSS receivers allows determination of the precise position of the moving platform and spatial data acquisition without the need for setting up and measuring of ground control points. The main advantage of this approach is a higher operational capacity with reduced time and cost of field ...
Kordić, Branko, Gašparović, Mateo, Lužar-Oberiter, Borna, Đapo, Almin, Vlastelica, Goran   +4 more
openaire   +3 more sources

Using time‐series remote sensing to identify and track individual bird nests at large scales

Remote Sensing in Ecology and Conservation, EarlyView.
Time‐series airborne imagery can potentially track individuals over time to collect information beyond one‐off counts. Using weekly UAS‐based imagery of wading bird colonies, we develop an automated approach to identifying nests. Our algorithmic approach detected 68–74% of known nests and exhibited similar performance to human review of imagery ...
S. K. Morgan Ernest, Lindsey A. Garner, Ben G. Weinstein, Peter Frederick, Henry Senyondo, Glenda M. Yenni, Ethan P. White   +6 more
wiley   +1 more source

Semi‐automated seal detection on the Western Antarctic Peninsula: an unsupervised machine learning approach for detecting ice seals in aerial survey data

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a semi‐automated, rule‐based image analysis pipeline to detect ice seals in aerial surveys of the Western Antarctic Peninsula during an unusually low sea ice year. By using simple hierarchical clustering instead of deep learning, the method substantially reduced human annotation effort while achieving 82% recall, identifying 758 ...
Claire McGinnity, Connor C.G. Bamford, Nathan Fenney, Andrew Fleming, Jaume Forcada, Michael S. Tift, Luis A. Hückstädt, Daniel P. Costa, Peter T. Fretwell   +8 more
wiley   +1 more source

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Eelgrass Distribution in the Great Bay Estuary and Piscataqua River for 2017 [PDF]

, 2018
Eelgrass distribution in Great Bay, Little Bay, and the Piscataqua River Estuary was mapped from aerial photography acquired on August 24, 2017. The total area of eelgrass beds with 10% or greater cover and a polygon area equal to or greater than 100 ...
Barker, Seth
core   +1 more source