A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations
By: Richard Chalkley, Rich Andrew Crane, Matthew Eyre, Kathy Hicks, Kim-Marie Jackson, Karen A. Hudson-Edwards
Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments.
A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data
By: Fernando Vanegas, Dmitry Bratanov, Kevin Powell, John Weiss, Felipe Gonzalez
Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors.
Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence
By: Juan Sandino, Geoff Pegg, Felipe Gonzalez, Grant Smith
This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning.
Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches
By: J. L. Pancorbo, M. Alonso-Ayuso, C. Camino, M. D. Raya-Sereno, P. J. Zarco-Tejada, I. Molina, J. L. Gabriel, M. Quemada
This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering.
An automatic hyperspectral scanning system for the technical investigations of Chinese scroll paintings
By: G.H. Li, Y. Chen, X.J. Sun, P.Q. Duan, Y. Lei, L.F. Zhang
In order to nondestructively investigate the techniques of large-scale Chinese scroll paintings, an automatic hyperspectral scanning system, composed of a hyperspectral camera, a halogen light source, an automatic scanning platform, data processing software and a reference spectral library, was set up. This system was applied to the study of Portrait of Bazalibudala Arhat, an important Chinese scroll painting, along with macroscopic X-ray fluorescence technique.
An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors
By: Juan M. Jurado, Luís Pádua, Jonas Hruška, Francisco R. Feito, Joaquim J. Sousa
In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic.
An evaluation of hyperspectral imaging for characterising milk powders
By: M.T. Munir, David I. Wilson, W. YuB.R. Young
Hyperspectral imaging can quantify and qualify subtle differences between milk powders when used with multivariate analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression.
Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review
By: Mercedes Vélez-Nicolás, Santiago García-López, Luis Barbero, Verónica Ruiz-Ortiz, and Ángel Sánchez-Bellón
In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. This is a review of Applications of Unmanned Aerial Systems (UASs) in Hydrology for all researchers and water managers who are interested in embracing this novel technology.
Assessment of bacterial biofilm on stainless steel by hyperspectral fluorescence imaging
By: Won Jun, Moon S. Kim, Kangjin Lee, Patricia Millner, Kuanglin Chao
Hyperspectral fluorescence imaging techniques were investigated for detection of two genera of microbial biofilms on stainless steel material which is commonly used to manufacture food processing equipment.
Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors
By: Yoseline Angel, Darren Turner, Stephen Parkes, Yoann Malbeteau, Arko Lucieer, Matthew F. McCabe
Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan.