@phdthesis{Bayer2013,
  author    = {Bayer, Anita},
  title     = {Methodological developments for mapping soil constituents using imaging spectroscopy},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-64399},
  school      = {Universit{\"a}t Potsdam},
  year      = {2013},
  abstract  = {Climatic variations and human activity now and increasingly in the future cause land cover changes and introduce perturbations in the terrestrial carbon reservoirs in vegetation, soil and detritus. Optical remote sensing and in particular Imaging Spectroscopy has shown the potential to quantify land surface parameters over large areas, which is accomplished by taking advantage of the characteristic interactions of incident radiation and the physico-chemical properties of a material. The objective of this thesis is to quantify key soil parameters, including soil organic carbon, using field and Imaging Spectroscopy. Organic carbon, iron oxides and clay content are selected to be analyzed to provide indicators for ecosystem function in relation to land degradation, and additionally to facilitate a quantification of carbon inventories in semiarid soils. The semiarid Albany Thicket Biome in the Eastern Cape Province of South Africa is chosen as study site. It provides a regional example for a semiarid ecosystem that currently undergoes land changes due to unadapted management practices and furthermore has to face climate change induced land changes in the future. The thesis is divided in three methodical steps. Based on reflectance spectra measured in the field and chemically determined constituents of the upper topsoil, physically based models are developed to quantify soil organic carbon, iron oxides and clay content. Taking account of the benefits limitations of existing methods, the approach is based on the direct application of known diagnostic spectral features and their combination with multivariate statistical approaches. It benefits from the collinearity of several diagnostic features and a number of their properties to reduce signal disturbances by influences of other spectral features. In a following step, the acquired hyperspectral image data are prepared for an analysis of soil constituents. The data show a large spatial heterogeneity that is caused by the patchiness of the natural vegetation in the study area that is inherent to most semiarid landscapes. Spectral mixture analysis is performed and used to deconvolve non-homogenous pixels into their constituent components. For soil dominated pixels, the subpixel information is used to remove the spectral influence of vegetation and to approximate the pure spectral signature coming from the soil. This step is an integral part when working in natural non-agricultural areas where pure bare soil pixels are rare. It is identified as the largest benefit within the multi-stage methodology, providing the basis for a successful and unbiased prediction of soil constituents from hyperspectral imagery. With the proposed approach it is possible (1) to significantly increase the spatial extent of derived information of soil constituents to areas with about 40 \% vegetation coverage and (2) to reduce the influence of materials such as vegetation on the quantification of soil constituents to a minimum. Subsequently, soil parameter quantities are predicted by the application of the feature-based soil prediction models to the maps of locally approximated soil signatures. Thematic maps showing the spatial distribution of the three considered soil parameters in October 2009 are produced for the Albany Thicket Biome of South Africa. The maps are evaluated for their potential to detect erosion affected areas as effects of land changes and to identify degradation hot spots in regard to support local restoration efforts. A regional validation, carried out using available ground truth sites, suggests remaining factors disturbing the correlation of spectral characteristics and chemical soil constituents. The approach is developed for semiarid areas in general and not adapted to specific conditions in the study area. All processing steps of the developed methodology are implemented in software modules, where crucial steps of the workflow are fully automated. The transferability of the methodology is shown for simulated data of the future EnMAP hyperspectral satellite. Soil parameters are successfully predicted from these data despite intense spectral mixing within the lower spatial resolution EnMAP pixels. This study shows an innovative approach to use Imaging Spectroscopy for mapping of key soil constituents, including soil organic carbon, for large areas in a non-agricultural ecosystem and under consideration of a partially vegetation coverage. It can contribute to a better assessment of soil constituents that describe ecosystem processes relevant to detect and monitor land changes. The maps further provide an assessment of the current carbon inventory in soils, valuable for carbon balances and carbon mitigation products.},
  language  = {en}
}
@phdthesis{Koerting2021,
  author    = {Koerting, Friederike Magdalena},
  title     = {Hybrid imaging spectroscopy approaches for open pit mining},
  doi       = {10.25932/publishup-49909},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-499091},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxix, 269},
  year      = {2021},
  abstract  = {This work develops hybrid methods of imaging spectroscopy for open pit mining and examines their feasibility compared with state-of-the-art. The material distribution within a mine face differs in the small scale and within daily assigned extraction segments. These changes can be relevant to subsequent processing steps but are not always visually identifiable prior to the extraction. Misclassifications that cause false allocations of extracted material need to be minimized in order to reduce energy-intensive material re-handling. The use of imaging spectroscopy aspires to the allocation of relevant deposit-specific materials before extraction, and allows for efficient material handling after extraction. The aim of this work is the parameterization of imaging spectroscopy for pit mining applications and the development and evaluation of a workflow for a mine face, ground- based, spectral characterization. In this work, an application-based sensor adaptation is proposed. The sensor complexity is reduced by down-sampling the spectral resolution of the system based on the samples' spectral characteristics. This was achieved by the evaluation of existing hyperspectral outcrop analysis approaches based on laboratory sample scans from the iron quadrangle in Minas Gerais, Brazil and by the development of a spectral mine face monitoring workflow which was tested for both an operating and an inactive open pit copper mine in the Republic of Cyprus. The workflow presented here is applied to three regional data sets: 1) Iron ore samples from Brazil, (laboratory); 2) Samples and hyperspectral mine face imagery from the copper-gold-pyrite mine Apliki, Republic of Cyprus (laboratory and mine face data); and 3) Samples and hyperspectral mine face imagery from the copper-gold-pyrite deposit Three Hills, Republic of Cyprus (laboratory and mine face data). The hyperspectral laboratory dataset of fifteen Brazilian iron ore samples was used to evaluate different analysis methods and different sensor models. Nineteen commonly used methods to analyze and map hyperspectral data were compared regarding the methods' resulting data products and the accuracy of the mapping and the analysis computation time. Four of the evaluated methods were determined for subsequent analyses to determine the best-performing algorithms: The spectral angle mapper (SAM), a support vector machine algorithm (SVM), the binary feature fitting algorithm (BFF) and the EnMap geological mapper (EnGeoMap). Next, commercially available imaging spectroscopy sensors were evaluated for their usability in open pit mining conditions. Step-wise downsampling of the data - the reduction of the number of bands with an increase of each band's bandwidth - was performed to investigate the possible simplification and ruggedization of a sensor without a quality fall-off of the mapping results. The impact of the atmosphere visible in the spectrum between 1300-2010nm was reduced by excluding the spectral range from the data for mapping. This tested the feasibility of the method under realistic open pit data conditions. Thirteen datasets based on the different, downsampled sensors were analyzed with the four predetermined methods. The optimum sensor for spectral mine face material distinction was determined as a VNIR-SWIR sensor with 40nm bandwidths in the VNIR and 15nm bandwidths in the SWIR spectral range and excluding the atmospherically impacted bands. The Apliki mine sample dataset was used for the application of the found optimal analyses and sensors. Thirty-six samples were analyzed geochemically and mineralogically. The sample spectra were compiled to two spectral libraries, both distinguishing between seven different geochemical-spectral clusters. The reflectance dataset was downsampled to five different sensors. The five different datasets were mapped with the SAM, BFF and SVM method achieving mapping accuracies of 85-72\%, 85-76\% and 57-46\% respectively. One mine face scan of Apliki was used for the application of the developed workflow. The mapping results were validated against the geochemistry and mineralogy of thirty-six documented field sampling points and a zonation map of the mine face which is based on sixty-six samples and field mapping. The mine face was analyzed with SAM and BFF. The analysis maps were visualized on top of a Structure-from-Motion derived 3D model of the open pit. The mapped geological units and zones correlate well with the expected zonation of the mine face. The third set of hyperspectral imagery from Three Hills was available for applying the fully-developed workflow. Geochemical sample analyses and laboratory spectral data of fifteen different samples from the Three Hills mine, Republic of Cyprus, were used to analyse a downsampled mine face scan of the open pit. Here, areas of low, medium and high ore content were identified. The developed workflow is successfully applied to the open pit mines Apliki and Three Hills and the spectral maps reflect the prevailing geological conditions. This work leads through the acquisition, preparation and processing of imaging spectroscopy data, the optimum choice of analysis methodology, and the utilization of simplified, robust sensors that meet the requirements of open pit mining conditions. It accentuates the importance of a site-specific and deposit-specific spectral library for the mine face analysis and underlines the need for geological and spectral analysis experts to successfully implement imaging spectroscopy in the field of open pit mining.},
  language  = {en}
}