scholarly journals Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

2020 ◽  
Vol 12 (18) ◽  
pp. 2998
Author(s):  
Robert Jackisch ◽  
Sandra Lorenz ◽  
Moritz Kirsch ◽  
Robert Zimmermann ◽  
Laura Tusa ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Mineral Exploration ◽  
Mine Planning ◽  
Machine Learning Algorithms ◽  
Magnetic Data ◽  
Unmanned Aerial Systems ◽  
Data Set ◽  
Ground Truth Data ◽  
Mapping Approach ◽  
Aerial Systems

Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.

scholarly journals Flight Considerations and Hyperspectral Image Classifications for Dryland Vegetation Management from a Fixed-wing UAS

2016 ◽  
Vol 5 (2) ◽  
pp. 41 ◽  
Author(s):  
Jessica Mitchell ◽  
Nancy Glenn ◽  
Matthew Anderson ◽  
Ryan Hruska
Keyword(s):  
Hyperspectral Image ◽  
Distribution Patterns ◽  
Unsupervised Classification ◽  
Vegetation Management ◽  
Geometric Error ◽  
Hyperspectral Data ◽  
Unmanned Aerial Systems ◽  
Native Grasses ◽  
Management Objectives ◽  
Aerial Systems

<p class="emsd"><span lang="EN-GB">Unmanned Aerial Systems (UAS)-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Boise Center Aerospace Lab were tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The motivation for this study was to better understand the challenges associated with UAS-based hyperspectral data for distinguishing native grasses such as Sandberg bluegrass (<em>Poa secunda</em>) from invasives such as burr buttercup (<em>Ranunculus testiculatus)</em> in a shrubland environment. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. However, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and feathering in areas of flightline overlap. Future UAS flight missions that optimize flight planning; minimize illumination differences between flightlines; and leverage ground reference data and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass from burr buttercup. </span></p>

scholarly journals Observations of the atmosphere and surface state over Terra Nova Bay, Antarctica, using unmanned aerial systems

2016 ◽  
Vol 8 (1) ◽  
pp. 115-126 ◽  
Author(s):  
John J. Cassano ◽  
Mark W. Seefeldt ◽  
Scott Palo ◽  
Shelley L. Knuth ◽  
Alice C. Bradley ◽  
...  
Keyword(s):  
Ross Sea ◽  
The United States ◽  
Unmanned Aircraft ◽  
Unmanned Aerial Systems ◽  
Free Access ◽  
Data Set ◽  
Terra Nova Bay ◽  
Atmospheric State ◽  
Terra Nova ◽  
Aerial Systems

Abstract. In September 2012 five Aerosonde unmanned aircraft were used to make measurements of the atmospheric state over the Terra Nova Bay polynya, Antarctica, to explore the details of air–sea ice–ocean coupling. A total of 14 flights were completed in September 2012. Ten of the flight missions consisted of two unmanned aerial systems (UAS) sampling the atmosphere over Terra Nova Bay on 5 different days, with one UAS focusing on the downwind evolution of the air mass and a second UAS flying transects roughly perpendicular to the low-level winds. The data from these coordinated UAS flights provide a comprehensive three-dimensional data set of the atmospheric state (air temperature, humidity, pressure, and wind) and surface skin temperature over Terra Nova Bay. The remaining UAS flights during the September 2012 field campaign included two local flights near McMurdo Station for flight testing, a single UAS flight to Terra Nova Bay, and a single UAS flight over the Ross Ice Shelf and Ross Sea polynya. A data set containing the atmospheric and surface data as well as operational aircraft data have been submitted to the United States Antarctic Program Data Coordination Center (USAP-DCC, http://www.usap-data.org/) for free access (http://gcmd.nasa.gov/getdif.htm?NSF-ANT10-43657, doi:10.15784/600125).

scholarly journals Minerals and ore deposits exploration using meta-heuristic based optimization on magnetic data

2020 ◽  
Vol 50 (2) ◽  
pp. 161-199
Author(s):  
Mohamed GOBASHY ◽  
Maha ABDELAZEEM ◽  
Mohamed ABDRABOU
Keyword(s):  
Mineral Exploration ◽  
Random Noise ◽  
Synthetic Data ◽  
Ore Deposits ◽  
Mine Planning ◽  
Magnetic Data ◽  
Real Field ◽  
Model Parameters ◽  
Tectonic Structures ◽  
Whale Optimization

The difficulties in unravelling the tectonic structures, in some cases, prevent the understanding of the ore bodies' geometry, leading to mistakes in mineral exploration, mine planning, evaluation of ore deposits, and even mineral exploitation. For that reason, many geophysical techniques are introduced to reveal the type, dimension, and geometry of these structures. Among them, electric methods, self-potential, electromagnetic, magnetic and gravity methods. Global meta-heuristic technique using Whale Optimization Algorithm (WOA) has been utilized for assessing model parameters from magnetic anomalies due to a thin dike, a dipping dike, and a vertical fault like/shear zone geological structure. These structures are commonly associated with mineralization. This modern algorithm was firstly applied on a free-noise synthetic data and to a noisy data with three different levels of random noise to simulate natural and artificial anomaly disturbances. Good results obtained through the inversion of such synthetic examples prove the validity and applicability of our algorithm. Thereafter, the method is applied to real case studies taken from different ore mineralization resembling different geologic conditions. Data are taken from Canada, United States, Sweden, Peru, India, and Australia. The obtained results revealed good correlation with previous interpretations of these real field examples.

UAS magnetics as a non-invasive exploration technology

2020 ◽  
Author(s):  
Yuleika Madriz ◽  
Robert Zimmermann ◽  
Junaidh Shaik Fareedh ◽  
Sandra Lorenz ◽  
Richard Gloaguen
Keyword(s):  
Cost Effective ◽  
Temporal Variations ◽  
Unmanned Aerial Systems ◽  
Aeromagnetic Survey ◽  
Data Set ◽  
Adaptive Workflow ◽  
Non Invasive ◽  
Hazardous Environments ◽  
Geophysical Surveys ◽  
Aerial Systems

&lt;p&gt;The growing demand for innovative and sustainable exploration technologies is boosting opportunities for non-invasive geophysical surveys using unmanned aerial systems (UASs). During the last few years lightweight magnetometers have been increasingly developed for their use on UASs. Aeromagnetic surveys can provide a rapid and cost-effective technology to improve the detection of shallow targets and to delineate magnetite-pyrrhotite-rich mineralizations. With low altitude flights and tight flight lines, magnetometers lifted by rotary wing UAS systems can deliver high resolution maps in small-to-medium scale areas (&lt;100 sq.km). We propose an adaptive workflow for aeromagnetic survey acquisitions by using multi-copters that in combination with a programmed processing tool can efficiently achieve valid observations and reliable maps. Results suggest that minimizing and compensating for the magnetometers attitude changes during flight as well as the removal of temporal variations plays an important role to avoid small anomalies to go undetected. For this study we present a comprehensive data set where UAS aeromagnetic surveys aids to overcome the scale gap between ground and airborne magnetics in potentially hazardous environments where UAS have operational advantage over traditional techniques.&lt;/p&gt;

scholarly journals Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest

2019 ◽  
Vol 11 (1) ◽  
pp. 84 ◽  
Author(s):  
Alexander Graham ◽  
Nicholas Coops ◽  
Michael Wilcox ◽  
Andrew Plowright
Keyword(s):  
Canopy Cover ◽  
Point Clouds ◽  
Unmanned Aerial Systems ◽  
Optimal Parameters ◽  
Conifer Forest ◽  
Terrain Modeling ◽  
Ground Truth Data ◽  
Aerial Photogrammetry ◽  
Terrain Slope ◽  
Aerial Systems

Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.

scholarly journals Merging Unmanned Aerial Systems (UAS) Imagery and Echo Soundings with an Adaptive Sampling Technique for Bathymetric Surveys

2018 ◽  
Vol 10 (9) ◽  
pp. 1362 ◽  
Author(s):  
Laura Alvarez ◽  
Hernan Moreno ◽  
Antonio Segales ◽  
Tri Pham ◽  
Elizabeth Pillar-Little ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Low Cost ◽  
Sampling Technique ◽  
Ground Truth ◽  
Accurate Method ◽  
Unmanned Aerial Systems ◽  
Southern Plains ◽  
Ground Truth Data ◽  
Light Passing ◽  
Aerial Systems

Bathymetric surveying to gather information about depths and underwater terrain is increasingly important to the sciences of hydrology and geomorphology. Submerged terrain change detection, water level, and reservoir storage monitoring demand extensive bathymetric data. Despite often being scarce or unavailable, this information is fundamental to hydrodynamic modeling for imposing boundary conditions and building computational domains. In this manuscript, a novel, low-cost, rapid, and accurate method is developed to measure submerged topography, as an alternative to conventional approaches that require significant economic investments and human power. The method integrates two types of Unmanned Aerial Systems (UAS) sampling techniques. The first couples a small UAS (sUAS) to an echosounder attached to a miniaturized boat for surveying submerged topography in deeper water within the range of accuracy. The second uses Structure from Motion (SfM) photogrammetry to cover shallower water areas no detected by the echosounder where the bed is visible from the sUAS. The refraction of light passing through air–water interface is considered for improving the bathymetric results. A zonal adaptive sampling algorithm is developed and applied to the echosounder data to densify measurements where the standard deviation of clustered points is high. This method is tested at a small reservoir in the U.S. southern plains. Ground Control Points (GCPs) and checkpoints surveyed with a total station are used for properly georeferencing of the SfM photogrammetry and assessment of the UAS imagery accuracy. An independent validation procedure providing a number of skill and error metrics is conducted using ground-truth data collected with a leveling rod at co-located reservoir points. Assessment of the results shows a strong correlation between the echosounder, SfM measurements and the field observations. The final product is a hybrid bathymetric survey resulting from the merging of SfM photogrammetry and echosoundings within an adaptive sampling framework.

scholarly journals DroneBorne Hyperspectral and Magnetic Data Integration: Otanmäki FeTiV Deposit in Finland

2019 ◽  
Vol 11 (18) ◽  
pp. 2084 ◽  
Author(s):  
Jackisch ◽  
Madriz ◽  
Zimmermann ◽  
Pirttijärvi ◽  
Saartenoja ◽  
...  
Keyword(s):  
Spectral Data ◽  
Mineral Exploration ◽  
Magnetic Data ◽  
Acquisition Time ◽  
Unmanned Aerial Systems ◽  
Electromagnetic Spectrum ◽  
Optical Remote Sensing ◽  
Ore Bodies ◽  
Ore Distribution ◽  
The Individual

The technical evolution of unmanned aerial systems (UAS) for mineral exploration advances rapidly. Recent sensor developments and improved UAS performance open new fields for research and applications in geological and geophysical exploration among others. In this study, we introduce an integrated acquisition and processing strategy for droneborne multisensor surveys combining optical remote sensing and magnetic data. We deploy both fixedwing and multicopter UAS to characterize an outcrop of the Otanmäki FeTiV deposit in central Finland. The lithology consists mainly of gabbro intrusions hosting ore bodies of magnetiteilmenite. Large areas of the outcrop are covered by lichen and low vegetation. We use two droneborne multi and hyperspectral cameras operating in the visible to nearinfrared parts of the electromagnetic spectrum to identify dominant geological features and the extents of ore bodies via ironindicating proxy minerals. We apply band ratios and unsupervised and supervised image classifications on the spectral data, from which we can map surficial ironbearing zones. We use two setups with threeaxis fluxgate magnetometers deployed both by a fixedwing and a multicopter UAS to measure the magnetic field at various flight altitudes (15 m, 40 m, 65 m). The total magnetic intensity (TMI) computed from the individual components is used for further interpretation of ore distribution. We compare to traditional magnetic groundbased survey data to evaluate the UASbased results. The measured anomalies and spectral data are validated and assigned to the outcropping geology and ore mineralization by performing surface spectroscopy, portable Xray fluorescence (pXRF), magnetic susceptibility, and traditional geologic mapping. Locations of mineral zones and magnetic anomalies correlate with the established geologic map. The integrated survey strategy allowed a straightforward mapping of ore occurrences. We highlight the efficiency, spatial resolution, and reliability of UAS surveys. Acquisition time of magnetic UAS surveying surpassed ground surveying by a factor of 20 with a comparable resolution. The proposed workflow possibly facilitates surveying, particularly in areas with complicated terrain and of limited accessibility, but highlights the remaining challenges in UAS mapping.

scholarly journals Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean

2021 ◽  
Vol 13 (16) ◽  
pp. 3241
Author(s):  
Amirhossein Hassanzadeh ◽  
Fei Zhang ◽  
Jan van van Aardt ◽  
Sean P. Murphy ◽  
Sarah J. Pethybridge
Keyword(s):  
Crop Yield ◽  
Imaging Spectroscopy ◽  
Optimization Models ◽  
Unmanned Aerial Systems ◽  
Seed Length ◽  
Ground Truth Data ◽  
Snap Bean ◽  
Stage Models ◽  
Time Period ◽  
Aerial Systems

Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.

scholarly journals Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

2021 ◽  
Vol 13 (23) ◽  
pp. 4873
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Random Forest ◽  
Forest Health ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Unmanned Aerial Systems ◽  
Multispectral Imagery ◽  
Field Methods ◽  
Individual Tree ◽  
Management Actions ◽  
Aerial Systems

Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout a complex, mixed-species forests. These methods are first compared to assessments of crown vigor in the field, to evaluate the potential in supplementing this resource intensive practice. This investigation uses the random forest and support vector machine (SVM) machine learning algorithms to classify the imagery into the five forest health classes. Using the random forest classifier, the UAS imagery correctly classified five forest Health classes with an overall accuracy of 65.43%. Using similar methods, the high-resolution airborne NAIP imagery achieved an overall accuracy of 50.50% for the five health classes, a reduction of 14.93%. When these classes were generalized to healthy, stressed, and degraded trees, the accuracy improved to 71.19%, using UAS imagery, and 70.62%, using airborne imagery. Further analysis into the precise calibration of UAS multispectral imagery, a refinement of image segmentation methods, and the fusion of these data with more widely distributed remotely sensed imagery would further enhance the potential of these methods to more effectively and efficiently collect forest health information from the UAS instead of using field methods.

scholarly journals ADDRESSING THE CLASS IMBALANCE PROBLEM IN THE AUTOMATIC IMAGE CLASSIFICATION OF COASTAL LITTER FROM ORTHOPHOTOS DERIVED FROM UAS IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 439-445
Author(s):  
D. Duarte ◽  
U. Andriolo ◽  
G. Gonçalves
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Unmanned Aerial Systems ◽  
Class Imbalance Problem ◽  
Marine Litter ◽  
Imbalance Problem ◽  
Small Set ◽  
Aerial Systems

Abstract. Unmanned Aerial Systems (UAS) has been recently used for mapping marine litter on beach-dune environment. Machine learning algorithms have been applied on UAS-derived images and orthophotos for automated marine litter items detection. As sand and vegetation are much predominant on the orthophoto, marine litter items constitute a small set of data, thus a class much less represented on the image scene. This communication aims to analyse the class imbalance issue on orthophotos for automated marine litter items detection. In the used dataset, the percentage of patches containing marine litter is close to 1% of the total amount of patches, hence representing a clear class imbalance issue. This problem has been previously indicated as detrimental for machine learning frameworks. Three different approaches were tested to address this imbalance, namely class weighting, oversampling and classifier thresholding. Oversampling had the best performance with a f1-score of 0.68, while the other methods had f1-score value of 0.56 on average. The results indicate that future works devoted to UAS-based automated marine litter detection should take in consideration the use of the oversampling method, which helped to improve the results of about 7% in the specific case shown in this paper.

Sign in / Sign up

Export Citation Format

Share Document