scholarly journals Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

2019 ◽  
Vol 11 (11) ◽  
pp. 1308 ◽  
Author(s):  
Dongliang Wang ◽  
Quanqin Shao ◽  
Huanyin Yue
Keyword(s):  
High Resolution ◽  
Satellite Image ◽  
Wild Animal ◽  
Unmanned Aircraft ◽  
Image Resolution ◽  
Detection Methods ◽  
Unmanned Aerial Systems ◽  
Wild Animals ◽  
Small Areas ◽  
Aerial Surveys

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (<0.6 m) animals at the species level, although high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. This situation will not change unless the satellite image resolution is greatly improved in the future. Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas. However, such aerial surveys are costly to implement in small areas and can cause significant disturbances to wild animals because of their noise. In contrast, UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys, but most UASs can cover only small areas. The proposed use of UAS imagery in combination with VHR satellite imagery would produce critical population data for large wild animal species and colonies over large areas. The development of software systems for automatically producing image mosaics and recognizing wild animals will further improve survey efficiency.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

scholarly journals Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador

2016 ◽  
Vol 4 (1) ◽  
pp. 53-69 ◽  
Author(s):  
Charla Patterson ◽  
William Koski ◽  
Paul Pace ◽  
Brian McLuckie ◽  
David M. Bird
Keyword(s):  
Target Detection ◽  
Rangifer Tarandus ◽  
Survey Methods ◽  
Habitat Type ◽  
Flight Time ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Line Of Sight ◽  
Unmanned Aerial Systems ◽  
Aerial Surveys

Regular, standardized population inventories have been suggested as an important component to the recovery of declining populations of boreal caribou (Rangifer tarandus caribou). Current survey methods typically employ manned aircraft, which can be noisy, expensive to operate, and dangerous for the people conducting the surveys. Small unmanned aerial systems (UAS) have garnered attention as a promising alterative to conducting aerial surveys in manned aircraft. Our research investigates the feasibility of using an UAS to conduct aerial surveys and determine which factors affect the detection of surrogate caribou targets, and hence may affect detection of real caribou. In the fall of 2013, we tested the capabilities of the Brican TD100E, a small, electric-powered fixed-wing UAS, to fly beyond visual line of sight near Goose Bay, Labrador. Seven surveys were done using different flight paths to collect aerial images of 26 surrogate caribou targets placed in six different habitats. Mixed effects logistic regression models were used to evaluate how habitat type, distance of the target from the image centerline, photo analysts’ experience level, flight time, and the target contrast against the landscape influenced the detection of surrogate caribou targets. We found that habitat type, target contrast, and the flight time affected target detection. Overall, 77.5% of the targets were detected; the odds of a photo analyst detecting a target in open habitat were roughly 10.5 times higher than in burned habitat and 42 times higher than in heavy forest. Target detection was influenced by the contrast of the target against the landscape, where a higher corrected integrated density was associated with greater target detection. The detection of targets was 87% during evening flights and 75% for morning flights. This study was the first of its kind to successfully fly a UAS beyond line of sight over land for non-military applications in North America and the findings of our research will provide an evaluation for using UAS to survey caribou in the future.

scholarly journals MSLp: Deep Superresolution for Meteorological Satellite Image

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liling Zhao ◽  
Hao Yu ◽  
Yan Wang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Satellite Image ◽  
Image Resolution ◽  
Complex Data ◽  
Opinion Score ◽  
Phase Loss ◽  
High Resolution Images ◽  
Meteorological Satellite

High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp (Meteorological Satellite Loss phase). Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32 (the full score is 10). These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.

scholarly journals High-Resolution Spaceborne, Airborne and In Situ Landslide Kinematic Measurements of the Slumgullion Landslide in Southwest Colorado

2019 ◽  
Vol 11 (3) ◽  
pp. 265 ◽  
Author(s):  
Austin Madson ◽  
Eric Fielding ◽  
Yongwei Sheng ◽  
Kyle Cavanaugh
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Measurement Techniques ◽  
Remotely Sensed ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Motion Vectors ◽  
Active Portion

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.

Estimation of Flow Velocity During Flash Floods with the synergy of Unmanned Aerial Systems (UAS) data and Ground Observations: The Case of 2017 Mandra Flash Flood, Greece

2020 ◽  
Author(s):  
Nafsika Ioanna Spyrou ◽  
Eirini Spyridoula Stanota ◽  
Michalis Diakakis ◽  
Emmanuel Andreadakis ◽  
Efthymios Lekkas ◽  
...  
Keyword(s):  
High Resolution ◽  
Flow Velocity ◽  
Flash Flood ◽  
Environmental Parameters ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Spatial And Temporal Scales ◽  
Safety Issues ◽  
Aerial Imaging ◽  
Wide Range

&lt;p&gt;Unmanned Aircraft Systems (UAS) can be used to enhance monitoring of a wide range of environmental parameters, including acquiring data on various types of hydro-geomorphic phenomena.&lt;/p&gt;&lt;p&gt;Their capabilities to provide on demand images and videos of high resolution, are particularly useful in the case of flash flood phenomena, which occur in spatial and temporal scales that do not favor traditional monitoring processes.&lt;/p&gt;&lt;p&gt;In this work, flow velocity is estimated using aerial imaging acquired by means of an Unmanned Aircraft Vehicle (UAV) as well as ground observations during the catastrophic flash flood event of November 2017 in Mandra, Greece.&lt;/p&gt;&lt;p&gt;In these imaging detailed tracing of various floating objects and particles such as light trash, debris etc. was carried out using multiple high-resolution video frames with specific time marks. Water velocity estimations were also cross-examined using flood mark-derived velocity hydraulic heads extracted by ground observations after the flood.&lt;/p&gt;&lt;p&gt;The analysis was applied at a variety of locations across the study area, leading to a map of velocities for parts of the floodplain. Velocity values varied significantly depending on location, reaching up to 10m/s.&lt;/p&gt;&lt;p&gt;The UAS proved to be very useful for the collection of important information for an extended area during the flood since a large portion of it was inaccessible due to road closures and safety issues. Nevertheless, the approach comes with certain limitations, including flight regulations, safety precautions and that rainfall is at a level that allows the deployment of a UAV during a flash flood.&lt;/p&gt;&lt;p&gt;The findings show that the integration of aerial with ground observations in post-flood analysis contributes the completeness and accuracy of datasets regarding specific flash flood parameters and in the future could become a useful source of information, especially in data-poor regions.&lt;/p&gt;

scholarly journals Use of High Resolution Unmanned Aerial Systems Imagery and Machine Learning to Evaluate Grain Sorghum Tolerance to Mesotrione

2020 ◽  
Author(s):  
Isaac Barnhart ◽  
Sushila Chauhaudri ◽  
Balaji Aravindhan Pandian ◽  
P.V. Vara Prasad ◽  
Ignacio A. Ciampitti ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Extraction ◽  
Block Design ◽  
Grain Sorghum ◽  
Unmanned Aircraft ◽  
Support Vector ◽  
Unmanned Aerial Systems ◽  
Machine Learning Classification ◽  
Vegetative Indices

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p &lt; 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Towards 1-Ångstrom-resolution STEM

1993 ◽  
Vol 51 ◽  
pp. 996-997
Author(s):  
H.S. von Harrach ◽  
D.E. Jesson ◽  
S.J. Pennycook
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Phase Contrast ◽  
Spherical Aberration ◽  
A Priori ◽  
Dark Field ◽  
Image Resolution ◽  
Objective Lens ◽  
Annular Dark Field ◽  
First Results

Phase contrast TEM has been the leading technique for high resolution imaging of materials for many years, whilst STEM has been the principal method for high-resolution microanalysis. However, it was demonstrated many years ago that low angle dark-field STEM imaging is a priori capable of almost 50% higher point resolution than coherent bright-field imaging (i.e. phase contrast TEM or STEM). This advantage was not exploited until Pennycook developed the high-angle annular dark-field (ADF) technique which can provide an incoherent image showing both high image resolution and atomic number contrast.This paper describes the design and first results of a 300kV field-emission STEM (VG Microscopes HB603U) which has improved ADF STEM image resolution towards the 1 angstrom target. The instrument uses a cold field-emission gun, generating a 300 kV beam of up to 1 μA from an 11-stage accelerator. The beam is focussed on to the specimen by two condensers and a condenser-objective lens with a spherical aberration coefficient of 1.0 mm.

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