Using Geochemical Fingerprints for Assessing Sediment Source Apportionment in an Agricultural Catchment in Central Argentina

In the hilly semi-arid region of central Argentina, where the agricultural frontier expands at the expense of natural ecosystems, soil erosion is one of the most alarming environmental problems. Thus, obtaining knowledge about the dynamics of erosive processes and identifying erosion hotspots constitutes a primary scientific objective. This investigation is focused on estimating the apportionments of main sources of sediments, at the mouth of a small catchment called Durazno del Medio, located in the province of San Luis, Argentina. Elemental Analysis, measured by Energy Dispersive X-ray Fluorescence (EDXRF), was used to select potential geochemical fingerprints of sediment. The unmixing model MixSIAR was applied to approximate the contribution of each identified source in the sediment accumulation areas at the mouth of the catchment. Potential sediment sources were selected using two criteria: (i) a hierarchical approach to identify the main geomorphological units (GUs) and (ii) the main land uses (LU), recognized by examining satellite images and field recognitions. The selected geochemical tracers were able to distinguish sources located in the Crystalline basement hills with loess-patched (CBH) as the main sediment contributors.

Double Regression-Based Sparse Unmixing for Hyperspectral Images

Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression-based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression-based sparse unmixing via K -means ( DRSU M K − means ) is proposed. The improved K -means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l 2 , 0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row-hard-threshold function to solve the l 2 , 0 norm directly. Then, DRSU M K − means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSU M K − means .

A Combined Unmixing Framework for Impervious Surface Mapping on Medium-Resolution Images with Visible Shadows

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.

From field to stream: Tracing streambed organic carbon origins at a catchment scale

<p>Quantifying organic carbon (OC) levels and the processes altering them is key in unlocking soils potential as a mediator of climate change through sequestration of atmospheric CO<sub>2</sub>. In areas of high soil erosion increased fluxes of OC across the terrestrial-aquatic interface are likely and understanding these fluxes is crucial in integrating lateral OC fluxes within the carbon cycle. For this study of a small UK catchment, OC mapping and Revised Universal Soil Loss Equation (RUSLE) based erosion modelling provided estimates of proportional soil OC loss coming from each land use. Sediment fingerprinting using <em>n</em>-alkane biomarkers and a Bayesian unmixing model provided a comparison of streambed OC proportions by land use to assess which processes were dominating OC input to streams. Results showed that RUSLE-based soil OC loss proportions exhibited disconnect with sediment fingerprinting OC composition and the river corridor and riparian environment were key zones in regulating terrestrial to aquatic fluxes of OC.</p>

Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability

Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

An Alternative Approach to Mapping Pigments in Paintings With Hyperspectral Reflectance Image Cubes Using Artificial Intelligence

Spectral imaging modalities, including reflectance and X-ray fluorescence, play an important role in conservation science. In reflectance hyperspectral imaging, the data are classified into areas having similar spectra and turned into labeled pigment maps using spectral features and fusing with other information. Direct classification and labeling remain challenging because many paints are intimate pigment mixtures that require a non-linear unmixing model for a robust solution. Neural networks have been successful in modeling non-linear mixtures in remote sensing with large training datasets. For paintings, however, existing spectral databases are small and do not encompass the diversity encountered. Given that painting practices are relatively consistent within schools of artistic practices, we tested the suitability of using reflectance spectra from a subgroup of well-characterized paintings to build a large database to train a one-dimensional (spectral) convolutional neural network. The labeled pigment maps produced were found to be robust within similar styles of paintings.

Phenology-Based Mapping of an Alien Invasive Species Using Time Series of Multispectral Satellite Data: A Case-Study with Glossy Buckthorn in Québec, Canada

Glossy buckthorn (Frangula alnus Mill.) is an alien species in Canada that is invading many forested areas. Glossy buckthorn has impacts on the biodiversity and productivity of invaded forests. Currently, we do not know much about the species’ ecology and no thorough study of its distribution in temperate forests has been performed yet. As is often the case with invasive plant species, the phenology of glossy buckthorn differs from that of other indigenous plant species found in invaded communities. In the forests of eastern Canada, the main phenological difference is a delay in the shedding of glossy buckthorn leaves, which occurs later in the fall than for other indigenous tree species found in that region. Therefore, our objective was to use that phenological characteristic to map the spatial distribution of glossy buckthorn over a portion of southern Québec, Canada, using remote sensing-based approaches. We achieved this by applying a linear temporal unmixing model to a time series of the normalized difference vegetation index (NDVI) derived from Landsat 8 Operational Land Imager (OLI) images to create a map of the probability of the occurrence of glossy buckthorn for the study area. The map resulting from the temporal unmixing model shows an agreement of 69% with field estimates of glossy buckthorn occurrence measured in 121 plots distributed over the study area. Glossy buckthorn mapping accuracy was limited by evergreen species and by the spectral and spatial resolution of the Landsat 8 OLI.

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.