The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

Mapping methods and observations of surficial snow/ice cover at Redoubt and Pavlof volcanoes, Alaska using optical satellite imagery

Thesis (M.S.) University of Alaska Fairbanks, 2014Alaska is a natural laboratory for the study of how active volcanism interacts with underlying seasonal snow, perennial snow, and glacial ice cover. While over half of the historically active volcanoes in Alaska have some degree of perennial snow or glacial ice, all Alaskan volcanoes have a covering of seasonal snow for a period of time throughout the year. Previous research has centered on how volcanic deposits erode away the underlying snow/ice cover during an eruption, producing volcanic mudflows called lahars. Less emphasis has been placed on how variations in the snow/ice cover substrate effect the efficiency of meltwater generation during a volcanic eruption. Glacial ice, perennial snow, and seasonal snow can all contribute significantly to meltwater, and therefore the variations in the types of snow/ice cover present at Alaskan volcanoes must be analyzed. By examining the changing spatial extent of seasonal snow present at a volcano during multiple Alaskan summers, the approximate boundaries of perennial snow and ice can be mapped as the snow/ice cover consistently present at the end of each ablation season. In this study, two methods of snow/ice cover mapping for Redoubt and Pavlof volcanoes are analyzed for efficiency and accuracy. Identification of the best method allows for mapping of the snow/ice cover consistently present during each Alaskan summer month over at least two different years. These maps can serve as approximations for the snow/ice cover likely to be present at both volcanoes during each summer month. Volcanic deposits produced during the 2009 Redoubt and 2013 Pavlof eruptions are spatially linked to these snow/ice cover maps so that future research can focus on the interaction between deposits and type of snow/ice substrate. Additional observations and conclusions are made regarding how the visible snow/ice cover varies during and after each eruption.Chapter 1. Introduction -- 1.1. Background -- 1.2. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 1.3. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 1.4. Summary of final outcomes -- 1.5. References -- Chapter 2. Methods for snow/ice cover mapping of Redoubt and Pavlof volcanoes using optical satellite imagery -- 2.1. Introduction -- 2.1.1. Satellite remote sensing of glaciers and snow cover in Alaska -- 2.1.2. Previous work and methods for studying snow/ice on volcanoes -- 2.1.3. Challenges of mapping snow/ice cover at Alaskan volcanoes -- 2.2. Setting of Redoubt volcano -- 2.2.1. Basic setting of Redoubt volcano -- 2.3. Setting of Pavlof volcano -- 2.3.1. Basic setting of Pavlof volcano -- 2.4. Methods -- 2.4.1. Previous work in snow/ice cover mapping using satellite imagery -- 2.4.2. Sensors used for snow/ice cover mapping -- 2.4.3. Pre-processing of satellite imagery -- 2.4.4. Methods used to map snow/ice cover at Redoubt and Pavlof -- 2.4.5. Technique 1: band ratios -- 2.4.6. Technique 2: principal component analysis -- 2.4.7. Technique 3: linear spectral unmixing -- 2.5. Results and discussion -- 2.5.1. Snow/ice cover mapping using threshold method -- 2.5.2. Snow/ice cover mapping using linear spectral unmixing method -- 2.5.3. Improvements to linear spectral unmixing method for snow/ice cover mapping -- 2.5.4. Validation of results -- 2.6. Conclusion -- 2.7. Figures -- 2.8. Tables -- 2.9. References -- Chapter 3. Observations of surficial snow/ice cover changes due to seasonal and eruptive influences on Redoubt and Pavlof volcanoes, Alaska using optical remote sensing -- 3.1. Introduction -- 3.1.1. Alaskan volcanoes -- 3.2. Volcano-snow/ice interactions -- 3.2.1. Short term interactions -- 3.2.2. Long term interactions -- 3.2.3. Lahar formation and hazards -- 3.2.4. Influence of snow/ice substrate type on lahar generation -- 3.3. Background on Redoubt volcano -- 3.3.1. Setting of Redoubt volcano -- 3.3.2. Recent eruptions at Redoubt volcano -- 3.3.3. Eruption effects on Drift Glacier -- 3.3.4. Lahar hazards at Redoubt volcano -- 3.4. Background on Pavlof volcano -- 3.4.1. Setting of Pavlof volcano -- 3.4.2. Recent eruptions at Pavlof volcano -- 3.4.3. Lahar hazards at Pavlof volcano -- 3.5. Methods -- 3.5.1. Sensors used to create Products 1, 2, and 3 -- 3.5.2. Methods used to produce Product 1: individual snow/ice cover maps -- 3.5.3. Methods used to produce Product 2: snow/ice cover summary maps -- 3.5.4. Methods used to produce Product 3: composite maps of eruptive deposits and snow/ice cover -- 3.6. Results and discussion -- 3.6.1. Product 1: individual snow/ice cover maps of Redoubt subset -- 3.6.2. Product 2: snow/ice cover summary maps of Redoubt subset -- 3.6.3. Product 3: composite maps of eruptive deposits and snow/ice cover of Redoubt subset -- 3.6.4. Product 1: individual snow/ice cover maps of Pavlof subset -- 3.6.5. Product 2: snow/ice cover maps of Pavlof subset -- 3.6.6. Product 3: composite maps of eruptive deposits and snow/ice cover of Pavlof subset -- 3.7. Conclusion -- 3.8. Figures -- 3.9. Tables -- 3.10. References -- Chapter 4. Conclusion -- 4.1. Comparison of snow/ice cover mapping methods for Alaskan volcanoes -- 4.2. Mapping snow/ice on Redoubt and Pavlof during quiescence and eruption -- 4.3. Limitations and future work -- 4.4. References

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization

In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow are not only sparse but also smooth along the horizontal and vertical direction in images while the clean images are smooth along the temporal direction between images. Therefore, the sparsity norm is used to boost the sparsity of the cloud and cloud shadow, and unidirectional total variation (UTV) regularizers are applied to ensure the unidirectional smoothness. This paper utilizes alternation direction method of multipliers to solve the presented model and generate the cloud and cloud shadow element as well as the clean element. The cloud and cloud shadow element is purified to get the cloud area and cloud shadow area. Then, the clean area of the original cloud-contaminated images is replaced to the corresponding area of the clean element. Finally, the reference image is selected to reconstruct details of the cloud area and cloud shadow area using the information cloning method. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints

Detection and Characterization of Low Temperature Peat Fires during the 2015 Fire Catastrophe in Indonesia Using a New High-Sensitivity Fire Monitoring Satellite Sensor (FireBird)

Vast and disastrous fires occurred on Borneo during the 2015 dry season, pushing Indonesia into the top five carbon emitting countries. The region was affected by a very strong El Nino-Southern Oscillation (ENSO) climate phenomenon, on par with the last severe event in 1997/98. Fire dynamics in Central Kalimantan were investigated using an innovative sensor offering higher sensitivity to a wider range of fire intensities at a finer spatial resolution (160 m) than heretofore available. The sensor is onboard the TET-1 satellite, part of the German Aerospace Center (DLR) FireBird mission. TET-1 images (acquired every 2-3 days) from the middle infrared were used to detect fires continuously burning for almost three weeks in the protected peatlands of Sebangau National Park as well as surrounding areas with active logging and oil palm concessions. TET-1 detection capabilities were compared with MODIS active fire detection and Landsat burned area algorithms. Fire dynamics, including fire front propagation speed and area burned, were investigated. We show that TET-1 has improved detection capabilities over MODIS in monitoring low-intensity peatland fire fronts through thick smoke and haze. Analysis of fire dynamics revealed that the largest burned areas resulted from fire front lines started from multiple locations, and the highest propagation speeds were in excess of 500 m/day (all over peat > 2m deep). Fires were found to occur most often in concessions that contained drainage infrastructure but were not cleared prior to the fire season. Benefits of implementing this sensor system to improve current fire management techniques are discussed. Near real-time fire detection together with enhanced fire behavior monitoring capabilities would not only improve firefighting efforts, but also benefit analysis of fire impact on tropical peatlands, greenhouse gas emission estimations as well as mitigation measures to reduce severe fire events in the future

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

The potential of multi-sensor satellite data for applications in environmental monitoring with special emphasis on land cover mapping, desertification monitoring and fire detection

Unprecedented pressure on the physical, chemical and biological systems of the Earth results in environment problems locally and globally, therefore the detection and understanding of environmental change based on long-term environmental data is very urgent. In developing countries/regions, because the natural resources are depleted for development while environmental awareness is poor, environment is changing faster. The insufficient environmental investment and sometimes infeasible ground access make the environment information acquisition and update inflexible through standard methods. With the main advantages of global observation, repetitive coverage, multispectral sensing and low-cost implementation, satellite remote sensing technology is a promising tool for monitoring environment, especially in the less developed countries. Multi-sensor satellite images may provide increased interpretation capabilities and more reliable results since data with different characteristics are combined and can achieve improved accuracies, better temporal coverage, and better inference about the environment than could be achieved by the use of a single sensor alone. The objective of this thesis is to demonstrate the capability and technique of the multi-sensor satellite data to monitor the environment in developing countries. Land cover assessment of Salonga national park in the democratic republic of Congo of Africa, desertification monitoring in North China and tropical/boreal wildland fire detection in Indonesia/Siberia were selected as three cases in this study for demonstrating the potential of multi-sensor application to environment monitoring. Chapter 2 demonstrates the combination of Landsat satellite images, Global Position System (GPS) signals, aerial videos and digital photos for assessing the land cover of Salonga national park in Congo. The purpose was to rapidly assess the current status of Salonga national park, especially its vegetation, and investigated the possible human impacts by shifting cultivation, logging and mining. Results show that the forests in the Salonga national park are in very good condition. Most of the area is covered by undisturbed, pristine evergreen lowland and swamp forests. No logging or mining activity could be detected. Chapter 3 demonstrates the one full year time series SPOT VEGETATION with coarse resolution of 1 km and the ASTER images with higher resolution of 15 meters as well as Landsat images for land cover mapping optimised for desertification monitoring in North-China. One point six million km2 were identified as risk areas of desertification. Results show within a satellite based multi-scale monitoring system SPOT VEGETATION imagery can be very useful to detect large scale dynamic environmental changes and desertification processes which then can be analysed in more detail by high resolution imagery and field surveys. Chapter 4 demonstrates the detection of tropical forest fire and boreal forest fire. Firstly, the ENVISAT ASAR backscatter dynamics and ENVISAT full resolution MERIS characteristics of fire scars were investigated in Siberian boreal forest, and results show these two sensors are very useful for fire monitoring and impact assessment. Secondly, the general capability and potential of ENVISAT multi-sensor of MERIS, AATSR, ASAR as well as NOAA-AVHRR and MODIS for tropical forest fire event monitoring and impact assessment in tropical Indonesia were investigated, and results show the capability of ENVISAT to acquire data from different sensors simultaneously or within a short period of time greatly enhances the possibilities to monitor fire occurrence and assess fire impact. Finally, the multi-sensor technology was applied to the disastrous boreal forest fire event of 2003 around East and West Lake Baikal in Siberia, and results show that 202,000 km2 burnt in 2003 within the study area of 1,300,000 km2, which is more than the total burnt area between 1996-2002. 71.4% of the burnt areas were forests, and 11.6% were wetlands or bogs. In total 32.2% of the forest cover has been burnt at least once from 1996 to 2003, 14% of the area has been affected at least twice by fire. These demonstrations show that in spite of the two disadvantages of indirect satellite measurements and the difficulty of detecting the cause of environment change, multi-sensor satellite technology is very useful in environment monitoring. However more studies on multi-sensor data fusion methods are needed for integrating the different satellite data from various sources. The lack of personnel skilled in remote sensing is a severe deficiency in developing countries, so the technology transfer from the developed countries is needed

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions

Agriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. Existing research has demonstrated the potential of grain yield predictions on Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire sufficient plant growth factors influenced by field conditions and farmers' decisions. One factor critical for yield prediction is the crop type planted on a per-field basis. This research effort proposes a novel approach for improving crop yield predictions using a hybrid deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical data. The crop yield prediction system is supported by a field-based crop type classification model, which supplies features related to crop type and field area. Our crop classification system takes advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled datasets, fully benefiting Norwegian data acquisition. Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70\% and is currently state-of-the-art for country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic in-season early predictions that could benefit actors in real-life scenarios

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies