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
Abstract
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
Abstract
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.
A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery
By: Fatih Ömrüuzun, Yasemin Yardımcı Çetin, Uğur Murat Leloğlu, and Begüm Demir
With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives.
Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence
By: Juan Sandino, Geoff Pegg, Felipe Gonzalez, Grant Smith
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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
Abstract
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.
Assessing the Effect of Water on Submerged and Floating Plastic Detection Using Remote Sensing and K-Means Clustering
By: Lenka Fronkova, Ralph P. Brayne, Joseph W. Ribeiro, Martin Cliffen, Francesco Beccari, and James H. W. Arnott
This is the first study to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of a hyperspectral plastic signal.
