A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations

Fractional vegetation cover (FVC) is an important measure for the conservation, restoration and maintenance of biodiverse environments, giving the spatial patterns and distributions of photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation as well as bare soil (BS) in a given region. Using hyperspectral remote sensing observations from DESIS and EnMAP (Environmental Mapping and Analysis Program), we derive FVC for Camarena, Spain, a semi-arid region southwest of Madrid and an important test site for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and compare the results from both sensors. DESIS and EnMAP are both hyperspectral remote sensing instruments with spatial resolutions of 30 m but they differ in other key aspects. DESIS has a spectral range of 400-1000 nm and a maximum spectral resolution of 2.55 nm whilst EnMAP has a range of 400-2500 nm and a resolution of 6.5-10 nm. The SWIR bands of EnMAP make it far more useful for the derivation of FVC than DESIS due to characteristic absorption features above 1500 nm which help to disentangle NPV and BS spectra. Nevertheless, abundances can still be derived from the FVC processing, accepting that the RMSEs are higher for the DESIS results (13% for PV, 18% for NPV, 9% for BS) than for the EnMAP results (12% for PV, 14% for NPV, 4% for BS). The FVC processing of the DESIS and EnMAP images consists of three steps. After some pre-processing (band removal and smoothing), pure spectra are retrieved from the image using the spatial-spectral endmember extraction method developed by Rogge. This method creates a global set of endmembers from the image after the masking of pixels which are not vegetation or soil. Secondly, the extracted endmembers are classified with a Logistic Regression (for DESIS) or a Random Forest (for EnMAP) classifier which were trained from a spectral library containing 631 samples. Three classes are used for the classification: PV, NPV amd BS. Unmixing is the final stage which uses a MESMA approach where each pixel is considered to be a linear combination of one PV spectrum, one NPV spectrum and one BS spectrum from the labelled endmember library. The class abundance are the weights found in the linear unmixing and an extra shade component is considered. In this work, we will present FVC maps derived from EnMAP and DESIS of Camarena which is a semi-arid region covering approximately 75 km2 in the Province of Toledo, Spain, where the land is mainly used for rainfed agriculture. It has an undulating topography with vegetation growing on sloping areas that were either not considered good enough for farming or later abandoned. Since June 2019, 60 cloud free images were acquired by DESIS over the region and EnMAP has so far acquired 8 cloud free images in this area since launch in April 2022. Several EnMAP images in July-August 2022 coincide closely with a DESIS observation which will enable quantifiable comparisons to be made between the two sensors and allow for an evaluation of the results considering the different wavelength ranges of each sensor

The Spaceborne Imaging Spectrometer DESIS: Data Access and Scientific Applications

The DLR Earth Sensing Imaging Spectrometer (DESIS) is a space-based instrument installed and operated on the International Space Station (ISS). This space mission is the achievement of the collaboration between the German Aerospace Center (DLR) and the US company Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operation and data tasking

Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

From Springer Nature via Jisc Publications RouterHistory: received 2021-07-08, accepted 2021-10-18, registration 2021-10-25, pub-electronic 2021-11-05, online 2021-11-05, collection 2021-12Publication status: PublishedFunder: Society for Sedimentary Geology Foundation; Grant(s): Student research grantFunder: International Association of Sedimentologists; doi: http://dx.doi.org/10.13039/501100007463; Grant(s): Postgraduate research grantFunder: British Sedimentological Research Group; doi: http://dx.doi.org/10.13039/100011045; Grant(s): Trevor Elliot memorial grantFunder: American Association of Petroleum Geologists Foundation; doi: http://dx.doi.org/10.13039/100013604; Grant(s): Classen Family grantFunder: Canadian Foundation for Innovation; Grant(s): John R. Evans Leaders Fund - Funding for research infrastructure (project 22222)Funder: National Science and Engineering Research Council of Canada; Grant(s): Discovery grantAbstract: Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol

Mapping urban surface materials with imaging spectroscopy data on different spatial scales

This work focuses on the development of methods for mapping urban surface materials by means of imaging spectroscopy data with different spatial resolution. General findings from this work represent a sensor- and site-independent framework for the automated extraction of spectrally pure pixels using an urban image spectral library while coping with its potential incompleteness. The extraction of spectrally pure pixels serves as a basic prerequisite for the subsequent use of image analysis methods to obtain detailed urban surface material maps. These material maps enabled the determination of gradual material transitions that were finally related to complex spectral mixtures resulting from 30 m spatial resolution imaging spectroscopy data to analyse typical material compositions within certain administrative units. The findings demonstrate the great potential of using upcoming spaceborne imaging spectroscopy data for a regular area-wide mapping of surface materials in urban areas. Im Fokus dieser Arbeit stand die Entwicklung von Methoden zur Kartierung urbaner Oberflächenmaterialien mittels abbildender Spektroskopiedaten unterschiedlicher räumlicher Auflösung. Das vorgestellte Konzept zur automatisierten sensor- und ortsunabhängigen Extraktion spektral reiner Pixel aus flugzeuggetragenen Fernerkundungsdaten berücksichtigt dabei die mögliche Unvollständigkeit einer urbanen Bildspektralbibliothek. Die Extraktion spektral reiner Pixel dient als Grundvoraussetzung für den späteren Einsatz von Bildanalyseverfahren zur Gewinnung detaillierter Kartierungen urbaner Oberflächenmaterialien. Aus diesen sind Materialgradienten ableitbar, die mit den komplexen Spektralmischungen aus Hyperspektraldaten mit 30 m räumlicher Auflösung in Verbindung gebracht wurden. Die Analyse typischer Materialzusammensetzungen innerhalb städtischer Verwaltungseinheiten zeigt das enorme Potential zukünftiger Hyperspektralsatelliten für die Erfassung des Materialvorkommens von Städten

Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping

The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on both the new data and new methodology in the field of hyperspectral imaging. First, the application of the future hyperspectral satellite EnMAP in impervious surface area (ISA) mapping was studied. During the search for the appropriate ISA mapping procedure for the new data, the subpixel classification based on nonnegative matrix factorization (NMF) achieved the best success. The simulated EnMAP image shows great potential in urban ISA mapping with over 85% accuracy. Unfortunately, the NMF based on the linear algebra only considers the spectral information and neglects the spatial information in the original image. The recent wide interest of applying the multilinear algebra in computer vision sheds light on this problem and raised the idea of nonnegative tensor factorization (NTF). This thesis found that the NTF has more advantages over the NMF when work with medium- rather than the high-spatial-resolution hyperspectral image. Furthermore, this thesis proposed to equip the NTF-based subpixel classification methods with the variations adopted from the NMF. By adopting the variations from the NMF, the urban ISA mapping results from the NTF were improved by ~2%. Lastly, the problem known as the curse of dimensionality is an obstacle in hyperspectral image applications. The majority of current dimension reduction (DR) methods are restricted to using only the spectral information, when the spatial information is neglected. To overcome this defect, two spectral-spatial methods: patch-based and tensor-patch-based, were thoroughly studied and compared in this thesis. To date, the popularity of the two solutions remains in computer vision studies and their applications in hyperspectral DR are limited. The patch-based and tensor-patch-based variations greatly improved the quality of dimension-reduced hyperspectral images, which then improved the land cover mapping results from them. In addition, this thesis proposed to use an improved method to produce an important intermediate result in the patch-based and tensor-patch-based DR process, which further improved the land cover mapping results

Spatial Sub-Sampling Using Local Endmembers for Adapting OSP and SSEE for Large-Scale Hyperspectral Surveys

Airborne and satellite hyperspectral sensors can collect significant quantities of data, commonly terabits in size. Thus, there is a need for the development of computational cost effective algorithms that speed up processing, yet retain essential data quality and information. Endmember extraction is critical in the processing chain of hyperspectral data. Existing methods have primarily been demonstrated on small data sets so their usefulness with large data sets has not been fully explored. The objective of this paper is to adapt the Orthogonal Subspace Projection (OSP) and Spatial Spectral Endmember Extraction (SSEE) algorithms, such that they run efficiently on large data sets without the loss of global image endmember quality. This is demonstrated using a simulated and two real hyperspectral images, the last of which is a multi- flight-line survey. The two adapted methods (OSP SS and SSEE SS) make use of spatial sub-sampling via local endmember extraction to reduce the size of the original data set. This paper will demonstrate that this type of spatial sub-sampling retains the full volume of the data, and thus, the vertices of the simplex that represent the global image endmembers. The results also show that computational time is reduce by more than half using the adapted methods. For the large multi- flight-line survey SSEE SS was able to better retain endmembers of natural materials compared with OSP SS. These results illustrate the potential of spatial sub-sampling for other endmembe