Earth Surface Materials Research Articles

A strontium isoscape of inland southeastern Australia

Abstract. The values and distribution patterns of the strontium (Sr) isotope ratio 87Sr/86Sr in Earth surface materials are of use in the geological, environmental, and social sciences. Ultimately, the 87Sr/86Sr ratios of soils and everything that lives in and on them are inherited from the rocks that are the parent materials of the soil's components. In Australia, there are few large-scale surveys of 87Sr/86Sr available, and here we report on a new, low-density dataset using 112 catchment outlet (floodplain) sediment samples covering 529 000 km2 of inland southeastern Australia (South Australia, New South Wales, Victoria). The coarse (<2 mm) fraction of bottom sediment samples (depth ∼ 0.6–0.8 m) from the National Geochemical Survey of Australia were milled and fully digested before Sr separation by chromatography and 87Sr/86Sr determination by multicollector-inductively coupled plasma mass spectrometry. The results show a wide range of 87Sr/86Sr values from a minimum of 0.7089 to a maximum of 0.7511 (range 0.0422). The median 87Sr/86Sr (± median absolute deviation) is 0.7199 (± 0.0071), and the mean (± standard deviation) is 0.7220 (± 0.0106). The spatial patterns of the Sr isoscape observed are described and attributed to various geological sources and processes. Of note are the elevated (radiogenic) values (≥∼ 0.7270; top quartile) contributed by (1) the Palaeozoic sedimentary country rock and (mostly felsic) igneous intrusions of the Lachlan geological region to the east of the study area; (2) the Palaeoproterozoic metamorphic rocks of the central Broken Hill region; both these sources contribute radiogenic material mainly by fluvial processes; and (3) the Proterozoic to Palaeozoic rocks of the Kanmantoo, Adelaide, Gawler, and Painter geological regions to the west of the area; these sources contribute radiogenic material mainly by aeolian processes. Regions of low 87Sr/86Sr (≤∼ 0.7130; bottom quartile) belong mainly to (1) a few central Murray Basin catchments; (2) some Darling Basin catchments in the northeast; and (3) a few Eromanga geological region-influenced catchments in the northwest of the study area; these sources contribute unradiogenic material mainly by fluvial processes. The new spatial Sr isotope dataset for the DCD (Darling–Curnamona–Delamerian) region is publicly available (de Caritat et al., 2022; https://dx.doi.org/10.26186/146397)​​​​​​​.

Hyper View: A new satellite picks out extreme detail of Earth's surface materials.

Hyper View: A new satellite picks out extreme detail of Earth's surface materials.

Characteristics and Spatial Distribution of Vs30 Based on Microtremor Inversion and MASW Data through Landform Unit in Yogyakarta, Indonesia

The Yogyakarta region is located in the earthquake-prone areas of Indonesia. An earthquake hazard can be assessed by identifying the site conditions from Vs30 to estimate the potential amplification of ground shaking. Site conditions are affected by the earth surface relief and material composition. The landform is characterized by differences in geomorphological structures and processes, relief/topography, and material. This research aims to analyze the characteristics and spatial distribution of Vs30 based on MASW and microtremor inversion for each landform unit in the Yogyakarta region. We collected 545 microtremor and MASW responses from field measurements. Data inversion was performed to obtain a 1D Vs30 model and estimate the Vs30 value. The results are presented in the Vs30 spatial distribution map and site classification. They show that the value of Vs30 is varied in a range of 140 – 1092 m/s. The site classification ranges from SE to SB. The Vs30 value tends to be similar to the USGS model, except in the karst landform in the Gunungkidul area. Here, the site class ranges from SC to SB, while the USGS model ranges from SD to SC. Based on the landform analysis, the higher average values of Vs30 are for karst, followed by structural, denudational, volcanic, fluvial, marine, and aeolian landforms. This difference indicates that local conditions significantly affect the Vs30. The higher the elevation and the steeper the slope, the larger the Vs30 value. Moreover, the higher values of Vs30 are determined by harder and solid/consolidated material.

Joint Characterization of Multiscale Information in High Dimensional Data

High dimensional data can contain multiple scales of variance. Analysis tools that preferentially operate at one scale can be ineffective at capturing all the information present in this cross-scale complexity. We propose a multiscale joint characterization approach designed to exploit synergies between global and local approaches to dimensionality reduction. We illustrate this approach using Principal Components Analysis (PCA) to characterize global variance structure and t-stochastic neighbor embedding (t-sne) to characterize local variance structure. Using both synthetic images and real-world imaging spectroscopy data, we show that joint characterization is capable of detecting and isolating signals which are not evident from either PCA or t-sne alone. Broadly, t-sne is effective at rendering a randomly oriented low-dimensional map of local clusters, and PCA renders this map interpretable by providing global, physically meaningful structure. This approach is illustrated using imaging spectroscopy data, and may prove particularly useful for other geospatial data given robust local variance structure due to spatial autocorrelation and physical interpretability of global variance structure due to spectral properties of Earth surface materials. However, the fundamental premise could easily be extended to other high dimensional datasets, including image time series and non-image data.

Material’s surface temperature variation by the statistical downscaling models in Jaipur, India

The satellite's inferred information has been important to distantly get to the temperature of Earth's surface materials. In this exploration, three MODIS satellite statistical downscaling models named (Resolution balanced downscaling (RM), Kriging Regression (KR), and Regression Tree (RT) have been created in mixed and homogeneous materials of Jaipur area, India to downscale MODIS MST information at 100 m spatial scale. The KR demonstrated the least error below and found superior working in mixed regions compared to RM and RT model. The regression-based material surface temperature (MST) downscaling methods has been reliant on the Earth's surface material, especially in the urban scene. The KR strategy would encourage the expectation of MST for various Earth surface materials for land surface-related researches.

An Interaction Methodology to Collect and Assess User-Driven Requirements to Define Potential Opportunities of Future Hyperspectral Imaging Sentinel Mission

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated.

Spatial-spectral classification of hyperspectral images based on multiple fractal-based features

Hyperspectral images are efficient tools for discriminating different types of earth's surface materials. Spectral features traditionally perform classification of hyperspectral images, but different studies have proved the efficiency of spatial features as complementary information in increasing the classification accuracy. The fractal geometry can be regarded as a potent tool for spatial data modeling. This study proposes a new classification method based on the integration of fractal and spectral features. For this purpose, three groups of fractal features, including mono-fractal, lacunarity and multi-fractal features are generated from the first few principal components of the hyperspectral image in different window sizes. These features are later stacked with spectral features and then fed to support vector machines classifier. The experiments are conducted on two real hyperspectral images Indian Pines, Pavia University. Final classification accuracy proved the superiority of the proposed classification method against the other competitive spatial-spectral methods.

Spatial variation of materials surface temperature by regression based downscaling model’s in Jaipur district, India

The satellite’s derived thermal data has been immensely supportive to remotely access the temperature of Earth's surface materials. The downscaling techniques have been used to predict material surface temperature (MST) at higher spatial resolutions. In this research, three MODIS satellite downscaling models named (Resolution adjusted regression downscaling (RAd), Kriging Regression (KR), and Regression Tree (RegTree)) have been developed and verified for urban and rural materials of Jaipur district, India. The Kriging regression downscaling model (KR) produced least RMSE (0.61 °C) followed by RAd (0.75 °C) and RegTree (0.82 °C) models at 100 m resolution. The KR model downscaled MODIS MST data at 100 m spatial resolution, showed error below (1 °C), for more than 60% of materials and less than (2 °C) error for 75% of the materials. KR based algorithm was most suitable for both rural and mixed landscape areas due to the reconstruction of residuals at each scale. The regression based material surface temperature (MST) downscaling techniques were precisely dependent on the Earth's surface material characteristics, particularly in urban landscape. The KR methodology would facilitate the prediction of MST for numerous Earth surface materials for lands surface and environmental related applications.

Statistical analysis of materials surface temperature from regression models: A case study of Jaipur city, India

The satellite derived data is essential to remotely access the material surface temperature (MST) on Earth’s facade. The present study aims to predict MST at higher resolutions from MODIS satellite low resolution (1000 m) data. The vegetation proportion (VP) based regression models has been used in the case study of Jaipur city, India. Three fundamental VP parameters [Soil adjusted vegetation index (SAVI), Normalized difference vegetation index (NDVI), and fractional vegetation cover (fc)] has been analyzed by the linear and polynomial regression model connecting MST and VP. The MST prediction results showed that linear regression model achieved better accuracy as compared to polynomial models. The NDVI has shown the highest correlation coefficient (R2) and outperformed other VP, in all the seasons. The input data within pixel changeability coefficient (Cv) of (15% to 25%) had shown higher accuracy in MST prediction. The regression models were able to predict MST up to 200 m resolution from 1000 m spatial resolution data. The outcome of this research would facilitate the prediction of MST for numerous Earth surface materials.

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS).

Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

Is catchment geodiversity a useful surrogate of aquatic plant species richness?

AbstractAimConserving freshwater biodiversity in a rapidly changing world requires updated planning schemes and research efforts. Geodiversity – the diversity of Earth surface forms, materials and processes – and biodiversity are interlinked at a fundamental level. This relationship is being considered in a growing number of studies, yet research from freshwater environments is scarce. We used geodiversity (rock‐type, soil‐type and geomorphological richness), local and climatic variables to explore whether geodiversity can be used as a surrogate for aquatic plant species richness in lakes and rivers.LocationFinland.TaxonAquatic plants.MethodsWe compared geodiversity variables (measured within 1‐km2 grid cells) to well‐studied local (e.g. area, alkalinity) and climate (e.g. growing degree‐days) variables, and examined the patterns between habitat types (lakes and rivers) and among all taxa and major functional groups (helophytes and hydrophytes). We modelled lake (n = 145) and river (n = 146) plant species richness with generalized linear models, and further partitioned variation to measure the independent and shared contributions of the geodiversity, climate and local environmental variable groups. As a complementary analysis, and to identify single important variables explaining variation in aquatic plant species richness, we utilized boosted regression trees.ResultsWe found a positive relationship between aquatic plant species richness and catchment geodiversity variation with recurring patterns across two different freshwater habitat types and two aquatic plant functional groups. Higher variation in geodiversity (measured at landscape scale) supported higher freshwater biodiversity (measured at the local scale) of lakes and rivers.Main conclusionsGeodiversity can be a useful addition to biodiversity modelling, and it should be considered in conservation schemes and monitoring efforts, further supporting the principle of conserving nature's stage. Yet, differences between habitats and functional groups suggest that more habitat‐specific approaches and multiple biodiversity measures should be considered. Our study is an important signpost guiding further studies on the biodiversity–geodiversity relationship in freshwater ecosystems.

Theoretical Stable Hydraulic Section based on the Principle of Least Action

Despite decades of effort, stable hydraulic geometry for an open channel water flow has hardly been established because of too many unknown variables for too few rational relationships. This article derives the most efficient channel cross section using calculus of variations for the given flow area at the minimum wetting perimeter length, which is equivalent to the principle of least action. Analysis indicates that water can most efficiently flow in a semi-ellipse section channel with minimum friction and erosion. Anisotropy in channel erodibility was found to be necessary in the natural stable channel characterization because gravitation force and channel bank consolidation cannot be ignored in earth surface material. This channel cross section, based on the principle of least action, may be regarded as the theoretical stable hydraulic section for erodible bed, which was comparable to the observed river cross-sections during high flow periods.

Application of remote sensing method for geological interpretation of Sokoto Plain, Nigeria

Landsat-8 OLI imagery of Sokoto, Nigeria, was processed to emphasize the geology features and mineral potential of the area. Band ratios (4/2,5/6,6/7) were assigned to RGB. Band ratio 4/2 highlights ferric ion minerals, 5/6 emphasizes ferrous minerals, and 6/7 distinguishes iron oxide minerals from carbonate minerals. In a second technique, band ratio 6/7 was replaced with 7/5 in order to accentuate clay minerals with high reflectance within band 7. The last technique evaluated in this study used spectral information from minimum noise fraction image to map surface geology. Supervised classification training sites were selected using five classes (clay, ironstone, alteration zone, water and vegetation). The band ratio classification using maximum likelihood classification was fairly accurate and matched the geologic map of the area, also showing an alteration zone that coincided with the migmatite-quartz/mica schist contact. The classified image was finally passed through a filtering effect for generalization of the data. This filtering effect was helpful in discriminating the pixels of ironstone and those of the alteration zone on the classified map. This study shows the distribution of classified earth-surface materials in Sokoto plain with the aid of supervised classification of Landsat-8 multispectral bands interpreted to reflect in-situ features.Keywords: Landsat-8 imagery, Sokoto, mineral potential, supervised classification

Feature-Level Fusion of Landsat 8 Data and SAR Texture Images for Urban Land Cover Classification

Each of the urban land cover types has unique thermal pattern. Therefore, thermal remote sensing can be used over urban areas for indicating temperature differences and comparing the relationships between urban surface temperatures and land cover types. On the other hand, synthetic-aperture radar (SAR) sensors are playing an increasingly important role in land cover classification due to their ability to operate day and night through cloud cover, and capturing the structure and dielectric properties of the earth surface materials. In this research, a feature-level fusion of SAR image and all bands (optical and thermal) of Landsat 8 data is proposed in order to modify the accuracy of urban land cover classification. In the proposed object-based image analysis algorithm, segmented regions of both Landsat 8 and SAR images are utilized for performing knowledge-based classification based on the land surface temperatures, spectral relationships between thermal and optical bands, and SAR texture features measured in the gray-level co-occurrence matrix space. The evaluated results showed the improvements of about 2.48 and 0.06 for overall accuracy and kappa after performing feature-level fusion on Landsat 8 and SAR data.

Blue boron-bearing diamonds from Earth's lower mantle.

Geological pathways for the recycling of Earth's surface materials into the mantle are both driven and obscured by plate tectonics1-3. Gauging the extent of this recycling is difficult because subducted crustal components are often released at relatively shallow depths, below arc volcanoes4-7. The conspicuous existence of blue boron-bearing diamonds (typeIIb)8,9 reveals that boron, an element abundant in the continental and oceanic crust, is present in certain diamond-forming fluids at mantle depths. However, both the provenance of the boron and the geological setting of diamond crystallization were unknown. Here we show that boron-bearing diamonds carry previously unrecognized mineral assemblages whose high-pressure precursors were stable in metamorphosed oceanic lithospheric slabs at depths reaching the lower mantle. We propose that some of the boron in seawater-serpentinized oceanic lithosphere is subducted into the deep mantle, where it is released with hydrous fluids that enable diamond growth10. TypeIIb diamonds are thus among the deepest diamonds ever found and indicate a viable pathway for the deep-mantle recycling of crustal elements.

Anthropocene Landscape Change and the Legacy of Nineteenth- and Twentieth-Century Mining in the Fourmile Catchment, Colorado Front Range

Human impacts on earth surface processes and materials are fundamental to understanding the proposed Anthropocene epoch. This study examines the magnitude, distribution, and long-term context of nineteenth- and twentieth-century mining in the Fourmile Creek catchment, Colorado, coupling airborne LiDAR topographic analysis with historical documents and field studies of river banks exposed by 2013 flooding. Mining impacts represent the dominant Anthropocene landscape change for this basin. Mining activity, particularly placer operations, controls floodplain stratigraphy and waste rock piles related to mining cover >5% of hillslopes in the catchment. Total rates of surface disturbance on slopes from mining activities (prospecting, mining, and road building) exceed pre-nineteenth-century rates by at least fifty times. Recent flooding and the overprint of human impacts obscure the record of Holocene floodplain evolution. Stratigraphic relations indicate that the Fourmile valley floor was as much as two meters higher in the past 2,000 years and that placer reworking, lateral erosion, or minor downcutting dominated from the late Holocene to present. Concentrations of As and Au in the fine fraction of hillslope soil, mining-related deposits, and fluvial deposits serve as a geochemical marker of mining activity in the catchment; reducing As and Au values in floodplain sediment will take hundreds of years to millennia. Overall, the Fourmile Creek catchment provides a valuable example of Anthropocene landscape change for mountainous regions of the Western United States, where hillslope and floodplain markers of human activity vary, high rates of geomorphic processes affect mixing and preservation of marker deposits, and long-term impact varies by landscape location.

地球表層物質による微量元素とり込み過程とそのモデル化

Clay minerals, metal oxyhydroxides, and carbonates are important earth surface materials, which are secondary minerals formed from primary minerals with water and atmosphere under earth surface conditions. These minerals usually possess a high surface area and are highly reactive to foreign dissolved species; therefore, they play important roles as sorbents for trace elements in natural water. Predicting distributions of trace elements between solution and mineral phases is important for understanding migration and accumulation behaviors of toxic and useful elements on the earth surface. To predict distributions, it is required to elucidate the chemical processes governing the sorption of trace elements on or in minerals, and to model chemical processes quantitatively. The important chemical processes governing the sorption of trace elements by earth surface materials are adsorption, surface precipitation, ion exchange, mineralization, and coprecipitation. The current understanding of these chemical processes as revealed from laboratory experiments is reviewed, as well as current approaches to modeling.

Combining geodiversity with climate and topography to account for threatened species richness

Understanding threatened species diversity is important for long-term conservation planning. Geodiversity-the diversity of Earth surface materials, forms, and processes-may be a useful biodiversity surrogate for conservation and have conservation value itself. Geodiversity and species richness relationships have been demonstrated; establishing whether geodiversity relates to threatened species' diversity and distribution pattern is a logical next step for conservation. We used 4 geodiversity variables (rock-type and soil-type richness, geomorphological diversity, and hydrological feature diversity) and 4 climatic and topographic variables to model threatened species diversity across 31 of Finland's national parks. We also analyzed rarity-weighted richness (a measure of site complementarity) of threatened vascular plants, fungi, bryophytes, and all species combined. Our 1-km2 resolution data set included 271 threatened species from 16 major taxa. We modeled threatened species richness (raw and rarity weighted) with boosted regression trees. Climatic variables, especially the annual temperature sum above 5 °C, dominated our models, which is consistent with the critical role of temperature in this boreal environment. Geodiversity added significant explanatory power. High geodiversity values were consistently associated with high threatened species richness across taxa. The combined effect of geodiversity variables was even more pronounced in the rarity-weighted richness analyses (except for fungi) than in those for species richness. Geodiversity measures correlated most strongly with species richness (raw and rarity weighted) of threatened vascular plants and bryophytes and were weakest for molluscs, lichens, and mammals. Although simple measures of topography improve biodiversity modeling, our results suggest that geodiversity data relating to geology, landforms, and hydrology are also worth including. This reinforces recent arguments that conserving nature's stage is an important principle in conservation.

MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA

Abstract. The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4–2.5 μm range has also been proved significant in the field of spaceborne mineral potential mapping. Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.

Water monitoring with hyperspectral techniques

1 - The poor of the world depends directly on water and other natural resources for their livelihoods. Water resources must therefore be managed in a sustainable manner in order to maintain the economic, social and environmental functions and to contribute to the livelihoods of people. 2 - Advancements in sensor technologies and processing algorithms have resulted in technical capabilities that can record and identify Earth surface materials based on the interaction of electromagnetic energy with the molecular structure of the material being sensed. 3 - Non-destructive and operative methodologies (NIR and Raman) will be tested through field surveys and laboratory analysis using Aquaphotomics approach. This approach requires precise measuring and mapping capabilities at field level of key data at a sufficient level of accuracy depending on the availability of equipment that must be also operated at a cost-effective way.