Mapping shoreline indicators on a sandy beach with supervised edge detection of soil moisture differences

This study describes a method to map shoreline indicators on a sandy beach. The hypothesis is that, on this beach, spectral albedo is predominantly determined by moisture content and water lines can, therefore, be detected as albedo contrasts. A laboratory experiment is performed to relate moisture content to image albedo, and supervised edge detection is subsequently used to map the shoreline indicators with remote sensing imagery. The algorithm is tested with data from visible, near-infrared and shortwave-infrared wavelength regions. These results are compared to shoreline indicators obtained by a field survey and a shoreline indicator derived from a digital elevation model. Both the water line present when the imagery was acquired, as well as the maximum extent of the last flood, can be detected as a single edge. Older high water lines are confused with the last high water line and appear dispersed, as there are multiple debris lines present on the beach. The low water line, usually in saturated sand, also appears dispersed due to the presence of channels and troughs. Shorelines are constant moving boundaries, which is why shoreline indicators are used as a proxy. Unlike a mathematical indicator that is based on an elevation model, our method is more sensitive to the dynamic nature of shorelines. Supervised edge-detection is a technique for generating reproducible measurements of shoreline indicator positions over time, and aids in the monitoring of coastline migration

The 2014-2015 lava flow field at Holuhraun: Deriving physical properties of the lava using multi remote sensing techniques and datasets

The purpose of this thesis is to employ remote sensing to study lava flow products during the 2014-2015 eruption at Holuhraun, Iceland. Multimodal remote sensing techniques and datasets were applied and developed for three study themes (1) deriving thermal properties from satellite infrared remote sensing, (2) differentiating lava surface using airborne hyperspectral remote sensing, and (3) quantifying lava surface roughness from elevation model acquired by airborne LiDAR. In the first study, we present a new approach based on infrared satellite images to derive thermal properties within the lava field during eruption and then compare the results with field measurement during the 2014-2015 eruption at Holuhraun. We develop a new spectral index for Landsat 8, named the thermal eruption index (TEI), based on the SWIR and TIR bands (bands 6 and 10). The purpose of the TEI consists mainly of two parts: (i) as a threshold for differentiating between different thermal domains; and (ii) to apply dualband technique to determine the maximum subpixel temperature (Th) of the lava. Lava surface roughness effects are accounted for by using the Hurst exponent (H), which is estimated from radar backscattering profiles. A higher H (smooth surface) generates thinner crust and high thermal flux meanwhile a lower H (rough surface) generates thicker crust and lower thermal flux. The total thermal flux peak is underestimated compared to other studies, although the trend shows good agreement with both field observation and other studies. In the second study, we focus on retrieving the lava surface types contributing to the signal recorded by airborne hyperspectral at the very top surface of the 2014-2015 lava flow field at Holuhraun. For this purpose, an airborne hyperspectral image acquired at Holuhraun with an AisaFENIX sensor onboard a NERC (Natural Environment Research Council Airborne Research Facility) campaign. For sub-pixel analysis, we used the sequential maximum angle convex cone (SMACC) algorithm to identify the spectral image endmembers and the linear spectral mixture analysis (LSMA) method was employed to retrieve the abundances. SMACC and LSMA methods offer a fast selection for volcanic product segregation. However, ground-truthing of spectra is recommended for future work. In the third study, we perform both the topographic position index (TPI) and onedimensional Hurst Exponent to derived lava flow unit roughness on the 2014-2015 lava flow field at Holuhraun using both airborne LiDAR and photogrammetry topography datasets. The roughness assessment was acquired from four lava flow features: (1) spiny pāhoehoe, (2) lava pond, (3) rubbly pāhoehoe lava, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values characterize lava with rough blocky surfaces, i.e. rubbly pāhoehoe to 'ā'a flows and lobes and their margins. These lobes and margins may give the impression of having similar roughness as the ”rough” surface on meters scale since this is an “apparent” roughness. On centimeters scale these multitudes of lobes feature coherent and smooth surfaces because they are pāhoehoe. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The rubbly pāhoehoe / 'ā'a has the roughest surface and the inflated lava channel along with pāhoehoe feature the smoothest surfaces among these four surface types. In general, the Hurst exponent values in the 2014-2015 lava field at Holuhraun has a strong tendency in 0.5, which is compatible with results from other study of geological surface roughness. Overall, this project provides an important insights into the application of remote sensing for monitoring and studying active lava flow fields and the techniques developed here will benefit such work in future events.Tilgangurinn með verkefninu var að rannsaka hraunrennsli og landform er urðu til í eldgosinu norðan Vatnajökuls 2014-2015 og kennt við Holuhraun. Fjölþátta fjarkönnunartækni og gögn úr gervitunglum og flugvélum voru nýtt við úrvinnslu verkefnisins. Rannsóknin sneri að þremur megin þáttum: (1) greiningu á eðli varmaútstreymis frá Holuhrauni, út frá innrauðri varmageislun sem mæld er með gervitunglagögnum (2) aðgreining á mismunandi hraunyfirborði, út frá ofur-fjölrófs mælingum úr lofti, og (3) greiningu og flokkun á yfirborðshrjúfleika Holuhrauns út frá hæðarlíkani er aflað var með LiDAR settur upp í flugvél. Fyrsti þáttur beindist að eðli varmaútstreymis á meðan á eldgosi stóð. Stuðst var við gervitunglagögn og mælingar með FLIR tækni á meðan eldgosið stóð yfir. Afraksturinn er nýr hitastuðull fyrir Landsat 8 og greiningu á eldgosum, (TEI). Hitastuðullinn TEI er unninn út frá SWIR og TIR böndum Landsat 8 (bönd 6 og 10). Með TEI næst fram tvennt: (i) að greina þröskuld milli tveggja hitasviða; og (ii) að beita tvíbanda tækni til að greina hitastig innan hverrar myndeiningar (Th) af hrauninu. Hrjúfleiki hraunsins hefur áhrif á varmaútstreymi, og er gert ráð fyrir honum með því að reikna Hurst veldisstuðulinn (H) og eru reiknuð út frá radar endurkasti hraunyfirborðs. Hátt H einkennir flatt og mjúkt yfirborð og þunna skorpu á hrauninu, á meðan að lágt H einkennir úfið yfirborð, þykka skorpu og lága varmaútgeislun. Heildar varmaútgeislun með þessari aðferð er heldur vanmetin en ofmetin í samanburði við aðrar aðferðir. Hinsvegar er góð fylgni með mælingum í mörkinni og samanburðar aðferðum. Annar hluti rannsóknarinnar sneri að túlkun ofur-fjölrófsgreininga á yfirborði Holuhrauns. Flogið var yfir Holuhraun sumarið 2015 með ofur-fjölrófsmæli (AisaFENIX) um borð í flugvél frá NERC (Natural Environment Research Council Airborne Research Facility). Við greiningu á yfirborði innan hverrar myndeiningar var, (i) notast við aðferð runubundins hámarkshorns kúptrar keilu (SMACC) til að finna útmörk ofurrófs mælinganna, (ii) blönduð línulega rófgreining (LSMA) var nýtt til að greina styrk eða gnægð innan myndeiningar. SMACC og LSMA aðferðirnar bjóða upp á mjög hraða greiningu á yfirborði og útfellingum efna á yfirborðið. Hins vegar þarf að gera fleiri rófmælingar á staðnum, til þess að auka notkunnargetu aðferðarinnar í hraungosum framtíðarinnar. Þriðji þáttur rannsóknarinnar sneri að því að greina landfræðilega stöðuvísitölu (TPI) og einvíðan Hurst veldisvísi til að meta hrjúfleika á hinu endanlega yfirborði Holuhrauns. Við þessa greiningu var notast við LiDAR mælingu af hrauninu og hæðagrunn unninn út frá ljósmyndum. Hrjúfleikinn var metinn fyrir fjögur yfirborð sem einkenna hraunið: (1) broddahraun „spiny pāhoehoe lava“, (2) hrauntjörn „lava pond“, (3) klumpahraun „rubbly pāhoehoe lava“ og (4) upptjakkaða hrauntröð „inflated lava channel“. TPI fyrir yfirborð (1) og (4) gefur meðalgildi sem einkennist af litlum halla og flötu yfirborði. Hrauntjörnin einkennist af lágum og háum TPI gildum sem endurspegla bylgjukennt mynstur. Hinsvegar einkennast hrjúfustu yfirborðin (3) og hraunjaðrar af óreglulegu mynstri lágra og hárra TPI gilda. Hrjúfleika stuðull þessara yfirborða H, er á bilinu 0.30 ± 0.05 til 0.76 ± 0.04. Mestur er hrjúfleiki kubbahrauna og minnstur er hrjúfleiki þandar hrauntraðar. Hurts veldisvísir Holuhrauns er nærri 0.5, en það er í mjög góðu samræmi við niðurstöður fyrri rannsókna á jarðfræðilegum yfirborðum. Í heild gefur verkefnið mikilvæga sýn á notagildi fjarkönnunaraðferða við rauntímaeftirlit með hraungosum, m.a. með þróun stuðla sem munu nýtast við atburði framtíðar. Þá voru tengsl hraunmyndana við ýmsa eiginleika eldgosa skýrð, sem aftur getur gefið vísbendingar um eðli fyrri atburða

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

Illumination Invariant Deep Learning for Hyperspectral Data

Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth

reflectance spectra measurements of mt etna a comparison with multispectral hyperspectral satellite

We present a collection of eight reflectance spectra representative of Mt. Etna volcano lava flows. The reflectance spectra were measured with a FieldSpecPro from 350 nm to 2500 nm during a fieldwork in June 2007. The reflectance has been compared with reflectance obtained by multispectral Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) and by hyper spectral EO1-Hyperion satellites. Prior the comparison, reflectance spectra have been convolved with ASTER and EO1-Hyperion spectral functions. The results show percentage errors in accordance to those present in literature in the ASTER SWIR range. However, some differences have been confirmed for the ASTER reflectance product (ASTER_07) in visible channels. Regarding EO1-Hyperion, a good agreement of reflectance against field measurement has been found resulting in 5% percentage maximum error in the VIS and up 30% in SWIR spectral range. The capacity of reproducing spectral feature by EO1-Hyperion has been checked on bright pixels (ice-snow) in the acquired image

Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition : Performance Assessment and Results Analysis

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application

The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

Publisher's version (útgefin grein)The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.The first author was supported by the Indonesia Endowment Fund for Education (LPDP) Grant No. 20160222025516, European Network of Observatories and Research Infrastructures for Volcanology (EUROVOLC), The European Facility for Airborne Research (EUFAR) and Vinir Vatnajökuls during his Ph.D. project.Peer Reviewe

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase