Mapping urban surface materials using imaging spectroscopy data

Die Kartierung der städtische Oberflächenmaterialien ist aufgrund der komplexen räumlichen Muster eine Herausforderung. Daten von bildgebenden Spektrometern können hierbei durch die feine und kontinuierliche Abtastung des elektromagnetischen Spektrums detaillierte spektrale Merkmale von Oberflächenmaterialien erkennen, was mit multispektralen oder RGB-Bildern nicht mit der gleichen Genauigkeit erreicht werden kann. Bislang wurden in zahlreichen Studien zur Kartierung von städtischen Oberflächenmaterialien Daten von flugzeuggestützten abbildenden Spektrometern mit hoher räumlicher Auflösung verwendet, die ihr Potenzial unter Beweis stellen und gute Ergebnisse liefern. Im Vergleich zu diesen Sensoren haben weltraumgestützte abbildende Spektrometer eine regionale oder globale Abdeckung, eine hohe Wiederholbarkeit und vermeiden teure, zeit- und arbeitsaufwändige Flugkampagnen. Allerdings liegt die räumliche Auflösung der aktuellen weltraumgestützten abbildenden Spektroskopiedaten bei etwa 30 m, was zu einem Mischpixelproblem führt, welches mit herkömmlichen Kartierungsansätzen nur schwer zu bewältigen ist. Das Hauptziel dieser Studie ist die Kartierung städtischer Materialien mit bildgebenden Spektroskopiedaten in verschiedenen Maßstäben und die gleichzeitige Nutzung des Informationsgehalts dieser Daten, um die chemischen und physikalischen Eigenschaften von Oberflächenmaterialien zu erfassen sowie das Mischpixelproblem zu berücksichtigen. Konkret zielt diese Arbeit darauf ab, (1) photovoltaische Solarmodule mit Hilfe von luftgestützten bildgebenden Spektroskopiedaten auf der Grundlage ihrer spektralen Merkmale zu kartieren; (2) die Robustheit der Stichprobe von städtischen Materialgradienten zu untersuchen; (3) die Übertragbarkeit von städtischen Materialgradienten auf andere Gebiete zu analysieren.Mapping urban surface materials is challenging due to the complex spatial patterns. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients

Mapping urban Surface Materials Using Imaging Spectroscopy Data

Urban environment and its processes directly affect human life. Detailed and up-to-date urban surface material maps are of great importance to modelers studying meteorology, climatology and ecology, as well as to authorities seeking to understand the urban growth dynamics and spatial evolution. However, mapping urban surface materials is challenging due to the complex spatial patterns. An established source of up-to-date information is remote sensing, as demonstrated by the widespread usage of SAR, LiDAR and optical data. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients. To this end, we detected solar photovoltaics with an overall accuracy of about 80% to 90% by creating and combining spectral indices. This dissertation proved that the developed approach is suitable for accurate photovoltaic detection. We also demonstrated that the concept of urban surface material gradients is robust in sampling and transferable between similar urban areas. With these results, urban material gradients can be a generic technique for urban mapping with spaceborne imaging spectroscopy data. The methods developed invi the three parts of this dissertation improve the usefulness of imaging spectroscopy data for urban material detection from a classical method to the new concept of urban gradients, from airborne to spaceborne data, from pure pixel detection to solving the mixed pixel problem. By introducing and enhancing the gradient concept in urban mapping, the mixed pixel problem can be tackled, which is a promising approach for the analysis of imaging spectroscopy data from ongoing and upcoming spaceborne sensors. Overall, this thesis provides promising urban surface material mapping results by proposing a physical feature based approach as well as confirming and laying the foundation of the generic gradient concept in urban material studies. Further work can build on these results and could open a new field for the application of spaceborne imaging spectroscopy data

Solar Panels Area Estimation Using the Spaceborne Imaging Spectrometer DESIS: Outperforming Multispectral Sensors

Solar photovoltaic power plants are in rapid expansion throughout the world, with the total area occupied by panels being linked to the total electrical power produced. This paper considers this case as an instance of the generic problem of estimating the total area occupied by a class of interest in spaceborne hyperspectral images. As the spatial resolution characterizing these sensors is too coarse, spectral unmixing techniques identify the contribution of a specific material to the spectrum related to a single image element. Final results are obtained by summing all contributions in an area of interest, and favourably compared to pixel-based detection, also using higher resolution Sentinel-2 data. The data used in this paper are acquired by the currently operative DESIS sensor, mounted on the International Space Station, encouraging the use of spaceborne imaging spectrometers for such applications

Are urban material gradients transferable between areas?

Urban areas contain a complex mixture of surface materials resulting in mixed pixels that are challenging to handle with conventional mapping approaches. In particular, for spaceborne hyperspectral images (HSIs) with sufficient spectral resolution to differentiate urban surface materials, the spatial resolution of 30 m (e.g. EnMAP HSIs) makes it difficult to find the spectrally pure pixels required for detailed mapping of urban surface materials. Gradient analysis, which is commonly used in ecology to map natural vegetation consisting of a complex mixture of species, is therefore a promising and practical tool for pattern recognition of urban surface material mixtures. However, the gradients are determined in a data-driven manner, so analysis of their spatial transferability is urgently required. We selected two areas—the Ostbahnhof (Ost) area and the Nymphenburg (Nym) area in Munich, Germany—with simulated EnMAP HSIs and material maps, treating the Ost area as the target area and the Nym area as the well-known area. Three gradient analysis approaches were subsequently proposed for pattern recognition in the Ost area for the cases of (i) sufficient samples collected in the Ost area; (ii) some samples in the Ost area; and (iii) no samples in the Ost area. The Ost samples were used to generate an ordination space in case (i), while the Nym samples were used to create the ordination space to support the pattern recognition of the Ost area in cases (ii) and (iii). The Mantel statistical results show that the sample distributions in the two ordination spaces are similar, with high confidence (the Mantel statistics are 0.995 and 0.990, with a significance of 0.001 in 999 free permutations of the Ost and Nym samples). The results of the partial least square regression models and 10-fold cross-validation show a strong relationship (the calculation-validation R2 values on the first gradient among the three approaches are 0.898, 0.892; 0.760, 0.743; and 0.860, 0.836, and those on the second gradient are 0.433, 0.351; 0.698, 0.648; and 0.736, 0.646) between the ordination scores of the samples and their reflectance values. The mapping results of the Ost area from three approaches also show similar patterns (e.g. the distribution of vegetation, artificial materials, water, and ceremony area) and characteristics of urban structures (the intensity of buildings). Therefore, our findings can help assess the transferability of urban material gradients between similar urban areas

Sampling Robustness in Gradient Analysis of Urban Material Mixtures

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

FABIAN: A daily product of fractional austral-summer blue ice over Antarctica during 2000-2021 based on MODIS imagery using Google Earth Engine

Antarctic blue ice areas are exposed due to erosion and sublimation of snow. At the same time, surface melt can form surface types that are spectrally similar to blue ice, especially at low elevations. These are termed melt-induced blue ice areas. Both types of blue ice are sensitive indicators of climate change. Satellite remote sensing is a powerful technique to retrieve the spatial extent of blue ice areas and their variation in time. Yet, existing satellite-derived blue ice area products are either mono-temporal for the entire Antarctic ice sheet, or multi-temporal for a limited area. Here, we present FABIAN, a product of blue ice fraction over Antarctica, derived from the moderate resolution imaging spectroradiometer (MODIS) archive covering the period 2000–2021. A spectral mixture analysis (SMA) in Google Earth Engine, based on a careful selection of endmember spectra, accurately reconstructs the reflectance observed by MODIS in blue ice areas. Based on a validation with contemporaneous Sentinel-2 images, FABIAN has a root mean square error in blue ice fraction of approximately 10% ∼ 20% in wind-induced blue ice areas, and 20% ∼ 30% in melt-induced blue ice areas across six selected test sites in the coastal East Antarctic ice sheet. FABIAN is challenged in regions with shallow melt streams and lakes, since their spectral profiles are similar to those from blue ice areas in MODIS bands. For further analyses and applications, FABIAN holds the potential for (1) deriving annual blue ice area maps, (2) distinguishing between wind-and melt-induced blue ice types, (3) evaluating and correcting (regional) climate models, and (4) analyzing temporal variations in blue ice abundance and exposure

Obesity-Related Genetic Variants and Hyperuricemia Risk in Chinese Men

Objective: Obesity/metabolic syndrome and hyperuricemia are clinically associated; however, the association of obesity/metabolic syndrome-related genetic variants with hyperuricemia is not clear. Therefore, we assessed this association in Chinese men diagnosed with hyperuricemia in comparison to a non-hyperuricemia group.Methods: We genotyped 47 single nucleotide polymorphisms (SNPs) previously identified to be associated with obesity or metabolic syndrome in 474 adult males (aged ≥ 18 years) using multiplex polymerase chain reaction. Multivariate logistic regression was used to investigate the association between the genetic variations and hyperuricemia. Stratified analyses were applied to further assess the associations.Results: The obesity-related SNP in MSRA rs545854 significantly affected serum uric acid levels. In addition, the G-allele of rs545854 was positively associated with the risk of hyperuricemia [odds ratio (OR) = 2.80, 95% confidence interval (CI) = 1.19–6.64, P = 0.0188]. After adjusting the model for body mass index and central obesity, rs545854 was shown to be an independent factor increasing the risk of hyperuricemia (OR = 2.81, 95%CI = 1.18–6.70, P = 0.0196). Stratified analyses also showed a significant association between rs545854 and hyperuricemia among meat eaters (OR = 2.62, 95%CI = 1.09–6.26, P = 0.0308).Conclusion: The obesity-related SNP rs545854 was correlated with the serum uric acid level and risk of hyperuricemia in a male Chinese population. Therefore, men carrying this SNP could benefit from limiting their meat consumption to prevent hyperuricemia. These findings suggest an underlying genetic link between obesity and hyperuricemia worthy of further exploration