Derek Rogge

Identification of Distinct Functional Microstructural Domains Controlling C Storage in Soil

The physical, chemical, and biological processes forming the backbone of important soil functions (e.g., carbon sequestration, nutrient and contaminant storage, and water transport) take place at reactive interfaces of soil particles and pores. The accessibility of these interfaces is determined by the spatial arrangement of the solid mineral and organic soil components, and the resulting pore system. Despite the development and application of novel imaging techniques operating at the micrometer and even nanometer scale, the microstructure of soils is still considered as a random arrangement of mineral and organic components. Using nanoscale secondary ion mass spectroscopy (NanoSIMS) and a novel digital image processing routine adapted from remote sensing (consisting of image preprocessing, endmember extraction, and a supervised classification), we extensively analyzed the spatial distribution of secondary ions that are characteristic of mineral and organic soil components on the submicrometer scale in an intact soil aggregate (40 measurements, each covering an area of 30 μm × 30 μm with a lateral resolution of 100 nm × 100 nm). We were surprised that the 40 spatially independent measurements clustered in just two complementary types of micrometer-sized domains. Each domain is characterized by a microarchitecture built of a definite mineral assemblage with various organic matter forms and a specific pore system, each fulfilling different functions in soil. Our results demonstrate that these microarchitectures form due to self-organization of the manifold mineral and organic soil components to distinct mineral assemblages, which are in turn stabilized by biophysical feedback mechanisms acting through pore characteristics and microbial accessibility. These microdomains are the smallest units in soil that fulfill specific functionalities.

Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment

Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a 320 km2 area in the Albany Thicket Biome, South Africa, where land cover and land-use changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the chosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy.

A New Approach for Endmember Extraction and Clustering Addressing Inter- and Intra-Class Variability via Multiscaled-Band Partitioning

In this paper, a new method is introduced for detecting and clustering spectrally similar but physically distinct materials. The method exploits the spectral information by dividing the spectral domain into band subsets whose width varies from broad to narrower wavelength ranges. Multiple candidate endmembers containing intraclass spectral variability are extracted using a maximum volume-based endmember extraction method at each band subset. Spectral clustering of the extracted spectra is also accomplished by using a multiscaled-band partitioning approach. This allows for the generation of multiscaled clustering identification vectors that can be used to remove partial mixtures and also be used to derive the final set of endmember bundles which retain interclass endmember variability. The proposed method was evaluated using simulated and real hyperspectral data and in comparison with well-known methods for extracting a fixed set or multiple sets of endmembers. Results revealed the advantages of the multiscaled-band partitioning on both multiple endmember extraction and clustering with the latter being an independent module that can be applicable to endmember candidate libraries derived from other methods.

A novel approach for endmember bundle extraction using spectral space splitting

The traditional endmember extraction methods search for a fixed set of endmembers, each one assigned to a single material. However, in many real applications, the materials of interest may present spectral variability which is related to subtle absorption features crucial for their discrimination. Thus, extracting multiple spectra or bundles for different materials is considered a more effective approach for data analysis, accounting for intra-class spectral variability. In this work, a novel approach is introduced which aims at obtaining a full representation of materials in a given scene, specifically including those with low spectral contrast. The approach enables a traditional endmember extraction method, the N-FINDR, to extract image endmember bundles exploiting the original spectral bands through a spectral space splitting. Experiments were conducted using an airborne hyperspectral dataset for extracting endmembers of mafic and ultramafic lithological units and preliminary results show the potential usefulness of the new approach.

Small-scale distribution of copper in a Technosol horizon studied by nano-scale secondary ion mass spectrometry

RATIONALEnIn contaminated soil, copper (Cu) is commonly distributed among various very small particles. To enlighten the qualitative distribution of Cu in a contaminated Technosol (a soil developed from deposited technogenic material) on the sub-micron scale, we used nano-scale secondary ion mass spectrometry (NanoSIMS).nnnMETHODSnWe studied seven areas (up to 40xa0μmxa0×xa040xa0μm) on a thin section of a soil horizon by NanoSIMS, measuring 12 C- , 18 O- , 32 S- , 63 Cu- and 56 Fe16 O- . We evaluated the NanoSIMS measurements with a novel digital image processing tool to enlighten the composition of measured areas and thus the distribution of Cu at the sub-micron scale. Image processing comprised spatial and spectral smoothing, normalization, endmember extraction and supervised classification.nnnRESULTSnCopper was present in all areas studied on the thin section in hotspots. 63 Cu- was never the ion with the highest number of mean-normalized counts (MNCs). In classes indicating Cu accumulation, Fe or S had the highest MNCs with mostly small values for O, pointing to the presence of Cu in sulfides. Copper adsorbed on Fe oxides was also indicated. Direct interaction of Cu with organic matter was less important. Copper-containing minerals were rather adjacent to or surrounded by an organic matrix.nnnCONCLUSIONSnThe combination of NanoSIMS analyses with digital image processing gave us new insights into the distribution of Cu in contaminated soil. We suggest this combination as a new powerful tool for the identification of ionic contaminants in soil and other solid phases in the environment.

Effects of the multiscaled-band partitioning on the abundance estimation

Materials of interest comprised in a hyperspectral image often present intra-class spectral variability inherent to their natural compositional make-up. Obtaining the best spectral representations of such materials with respect to a given application is critical for both identification and spatial mapping. Recently, a multiscaled-band partitioning (MSBP) approach has been developed for detecting and clustering spectrally similar but physically distinct materials. In this work, it is examined 1) whether the endmember clusters of the multiscaled-band partitioning contribute to an improved abundance estimation compared to other endmember extraction methods and, 2) to what extent different unmixing strategies can retain the spectral variability of the extracted endmember clusters in the resulted abundance maps. Experiments were conducted using an airborne hyperspectral dataset highlighting the potential of MSBP for the unmixing process in case of materials with intra-class variability.