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Forschergruppe (FOR) 1598: From Catchments as Organised Systems to Models based on Dynamic Functional Units (CAOS), Non-invasive geophysical and remote sensing methods to map and characterize relevant structures and processes

This project is a continuation of project F funded in the first phase of the DFG Research Group CAOS, where we evaluated the potential of different ground-based geophysical techniques for exploring hydrological systems regarding subsurface structures, characteristics, and processes. Building up on the results of this project, we now focus on further developing selected geophysical techniques (timelapse GPR imaging) for deepening our understanding of hydrological processes at the plot and hillslope scale. In addition, we propose to systematically evaluate modem remote sensing techniques because they cun-ently represent the only means to efficiently explore larger areas or entire catchments. Here, we focus on a combination of full-waveform laserscanning and hyperspectral imaging because they can provide detailed Information regarding geometrical and physical properties of earth's surface, respectively. To link remote sensing with point/plot/hillslope scale data as provided by geophysics and conventional hydrological field techniques, we believe that further methodological innovations are needed. For example, we plan to establish a unique field laboratory to better understand the responses of geophysical and remote sensing techniques to different natural and artificial hydrological events and to develop exploration strategies advancing the applicability of geophysics and remote sensing for hydrological applications at a variety of spatial scales.

HyperSax

Airborne hyperspectral remote sensing surveys over various areas in Saxony.

Landnutzungsanalyse mit Hilfe abbildender Spektroskopie

Mit einem flugzeuggetragenen abbildenden Spektrometer wird das vom Boden reflektierte Sonnenspektrum vermessen und die registrierten spektralen Signaturen Landnutzungsklassen zugeordnet. Bei dieser Zuordnung werden auch neuronale Netze eingesetzt. Neben der Verarbeitung spektral aufgeloester Daten wird augenblicklich an der Einbindung von Texturinformationen (mit neuronalen Netzen) gearbeitet.

Elemental pigment (hyperspectral imaging) data of the varved sediment record (HZM19) from Holzmaar, Germany

Hyperspectral image (HSI) scanning of the composite record from Holzmaar (HZM19) was measured using a Specim PFD-CL-65-V10 E line scan camera (University of Bern, Switzerland). Data were processed using the ENVI software following the workflow of Butz et al. (2015, doi10.1117/1.JRS.9.096031): data were white-corrected, masked for cracks in the sediment surface and Relative Absorption Band Depths (RABDs) were computed for 2mm wide subsets. RABD671 (band depths from 640 to 702 nm) for Total Chloropigments-a (TChl-a), RABD845 (790 - 900 nm) for Bacteriopheopigments-a (Bphe-a), and RABD620 (600 - 640 nm) for Phycocyanin (PhyCy). To translate HSI indices into absolute concentrations, a pigment extraction was performed at the University of Bern using 23 samples covering the full range of RABD671 and RABD845 index values. Ca 1 g of wet sediment was treated with 100 % acetone following the method of Lami et al. (1994, doi:10.1007/BF00684032) and extractions were measured using a Shimadzu UV-1800 spectrophotometer to obtain bulk concentrations of TChl-a and Bphe-a in µg/g dry sediment using a molar extinction coefficient for TChl-a and Bphe-a. A proxy-proxy calibration was carried out using an ordinary least square regression. After all, only 1.42 % and 0.77 % of datapoints are outside of the calibration ranges for Chl-a (calibration range: 12.75 – 1202.68 µg/g, intercept = -4799.52, slope= 4756,45, r² = 0.8, p-val = 0.00, RMSEP 10-fold = 169.03, RMSEP % = 14.05) and Bphe-a (calibration range 0.38 – 345.12 µg/g, intercept = -1295,8, slope= 1319,7, r² = 0.94, p-val = 0.00, RMSEP 10-fold = 25.26, RMSEP % = 7.32). Ages refer to Birlo et al. (2023) and the related dataset is Model D available via doi:10.1594/PANGAEA.949292.

Spectrophotometer data for calibration of HSI index of the varved sediment record (HZM19) from Holzmaar, Germany

To calibrate the hyperspectral imaging (HSI) index values from the sediments of Holzmaar (HZM19) to concentration, a spectrophotometrically measured pigment analysis (Butz et al., 2015; doi:10.1117/1.JRS.9.096031) was performed for 23 samples. These samples were selected to cover a wide range of pigment concentrations as documented by HSI scanning. Approximately 1 g of wet sediment was treated with 100 % acetone according to the method of Lami et al. (1994; doi:10.1007/BF00684032), and the extracts were measured with a Shimadzu UV-1800 spectrophotometer to obtain the mass concentration of Chl-a and Bphe-a in µg/g dry sediment using a mass extinction coefficient for Chl-a (Fiedor et al, 2002; doi:10.1562/0031-8655(2002)0760145POTBCS2.0.CO2) and for Bphe-a (Jeffrey and Humphrey, 1975; doi:10.1016/S0015-3796(17)30778-3).

Reflectance spectra and LIBS data of 16 iron-bearing surface samples from Rammelsberg – a non-ferrous metal mine in the Harz Mountains near Goslar, Germany

This data publication presents data from a solaroptical spectral investigation in the area of the Rammelsberg non-ferrous metal mine in the Harz Mountains near the city of Goslar. The investigation refers to the local communion stone quarry (“Kommunionssteinbruch”) above the former mining area. As this is a nature conservation zone, all measurements were carried out in-situ without any physical sampling action. The field measurements were carried out in June 2019 in cooperation with Bergbau Goslar GmbH and the German Research Centre for Geosciences (GFZ). The data were collected within the research project ReMon (Remote Monitoring of Tailings Using Satellites and Drones, https://www.gfz-potsdam.de/en/section/remote-sensing-and-geoinformatics/projects/remon/) which aims at developing a prototypical monitoring system for mine tailings by using different sensors scaling from satellite- to drone-based. The data were analysed in the unpublished B.Sc. thesis of Constantin Hildebrand (Hildebrand, 2019). Sixteen different surface materials were determined and examined on-site. Point and imaging hyperspectral data were acquired (with the spectroradiometer PSR+ 3500 operating in the range of 350 - 2500 nm and with the Cubert FireflEYEUHD-185 hyperspectral camera with a range of 450 - 950 nm, respectively), both data sets are presented as spectral libraries. Chemical analyses of the samples were performed by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS data were collected using a handheld LIBS analyzer, the SciAps Z-300. In this data publication the different in-situ measurements are presented for each of the sixteen samples. Detailed information about the analysed material, the area of spectral sampling and geochemical analyses are explained in this report and can also be found in the additional Excel® sheet provided with the data.

Global surface ocean HPLC phytoplankton pigments and hyperspectral remote sensing reflectance

This dataset is a global surface ocean compilation of high-performance liquid chromatography (HPLC) phytoplankton pigment concentrations and hyperspectral remote sensing reflectance (Rrs) data, with associated temperature and salinity measurements. The pigments measured include: total chlorophyll-a (Tchla), 19'-hexanoyloxyfucoxanthin (HexFuco), 19'-butanoyloxyfucoxanthin (ButFuco), alloxanthin (Allo), fucoxanthin (Fuco), peridinin (Perid), zeaxanthin (Zea), divinyl chlorophyll a (DVchla), monovinyl chlorophyll b (MVchlb), chlorophyll c1+c2 (Chlc12), chlorophyll c3 (Chlc3), neoxanthin (Neo), and violaxanthin (Viola). Rrs data are measured at 1 nm spectral resolution from 400-700 nm. The Rrs data from the ANT cruises were collected using a RAMSES hyperspectral radiometer, the Rrs data from the NAAMES, SABOR, Tara, RemSensPOC, BIOSOPE, and EXPORTS cruises were generated by a HyperPro (Satlantic, Inc.) hyperspectral radiometer. All samples presented in this dataset have previously been published and are publicly available, as referenced in the table: ANT: Bracher et al. (2015), https://doi.org/10.1594/PANGAEA.847820, NAAMES: Behrenfeld et al. (2014a), http://dx.doi.org/10.5067/SeaBASS/NAAMES/DATA001, Remote Sensing of POC: Cetinić (2013), http://dx.doi.org/10.5067/SeaBASS/REMSENSPOC/DATA001, SABOR: Behrenfeld et al. (2014b), http://dx.doi.org/10.5067/SeaBASS/SABOR/DATA001, Tara Oceans: Boss and Claustre (2009), http://dx.doi.org/10.5067/SeaBASS/TARA_OCEANS_EXPEDITION/DATA001, Tara Mediterranean: Boss and Claustre (2014), http://dx.doi.org/10.5067/SeaBASS/TARA_MEDITERRANEAN/DATA001, BIOSOPE: Claustre and Sciandra (2004), https://doi.org/10.17600/4010100 hosted at http://www.obs-vlfr.fr/proof/php/bio_open_access_data.php, EXPORTS: Behrenfeld et al. (2018), http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001. This compilation of these data is used in Kramer et al. (2021) to evaluate a model that reconstructs pigment concentrations from hyperspectral remote sensing reflectance.

Karslruhe, Germany (2010) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on forest applications

This dataset accompanying the MOOC on forest applications contains an airborne hyperspectral HyMap image over the study site north of Karlsruhe in Southwest Germany which was recorded in August 2010. The surrounding area of Karlsruhe is characterized by its relatively warm climate due to the influence of the Upper-Rhine and its climate can be considered more continental than typical German conditions. Additionally it is characterized by its flat terrain. Here you can find a diversity of tree species growing in the mixed forests. These include coniferous trees such as Scots Pine, Douglas Fir, Norway Spruce, Silver Fir and Larch as well as deciduous tree species like European Beech, Oak and Red Oak. The image dataset is fully pre-processed –it was atmospherically and topographically corrected by the DLR using ATCOR4 and ORTH software – and provided in TIF format. In addition to the HyMap image, this dataset contains a point data shapefile with 250 sampling locations, which represents 5 tree species with 50 reference positions each. These reference positions were collected using visual interpretation of high-resolution images in combination with reference tree species maps provided by the local forest administration. These reference tree species maps are also provided as tif-files. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Forest Applications ", available from Summer 2025. Guidance on how to derive tree species classification maps using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the forest MOOC course pages and the regression workflow documentation. HYPERedu is an education initiative within the Environmental Mapping and Analysis Program (EnMAP), a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP serves to measure and model key dynamic processes of the Earth’s ecosystems by extracting geochemical, biochemical and biophysical variables, which provide information on the status and evolution of various terrestrial and aquatic ecosystems.

Demmin, Germany (2015) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on soil applications

This dataset accompanying the MOOC on soil applications contains an airborne hyperspectral HySpex image over the study site Demmin in Northern Germany which was recorded in October 2015. The surrounding area of Demmin is characterized by its glacial past and is largely used for agriculture. Here you can find relics of the ice age such as kettle holes - small, completely closed hollow shapes whose formation is attributed to the burial and subsequent thawing of an ice lens. Mostly overgrown nowadays by vegetation, SOC accumulates in these areas and higher contents are measured. The image dataset is fully pre-processed – all non-soil pixels are masked, the spectra were smoothed using a Savitzky-Golay Filter and transformed to first derivatives – and provided in BSQ format. In addition to the HySpex image, this dataset contains a point data shapefile with 27 sampling locations, as well as information on the soil organic carbon (SOC) contents [g/kg]. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Soil Applications ", available from Spring 2023. Guidance on how to derive quantitative soil maps (SOC content) using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the soil MOOC course pages and the regression workflow documentation.

Cabo de Gata-Nίjar Natural Park, Spain (2005) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on soil applications

The dataset contains a subset of an airborne hyperspectral HyMap image over the Cabo de Gata-Nίjar Natural Park in Spain from 15.06.2005, and soil wet chemistry data based on in-situ soil sampling. The Cabo de Gata-Nίjar Natural Park is a semi-arid mediterranean area in Southern Spain, sparsely populated and with a range of landscape patterns. The soils in this area are developed on volcanic and carbonatic bedrocks and are highly variable in their textural and mineralogical composition, offering interesting spectral variability. The airborne survey was acquired during a DLR / HyVista HyEurope campaign. The image dataset is fully processed for atmospheric and geometric correction with PARGE and ATCOR and is provided as orthorectified reflectance in BSQ format. Spatial resolution is 5 m and spectral coverage is 0.45-2.45 μm with 12-17 nm spectral sampling. In addition to the HyMap imagery, this dataset contains two soil reference datasets as CSV files, namely in-situ data for clay content and iron content. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Soil Applications ", available from Spring 2024. Guidance on how to derive semiquantitative and quantitative soil maps (clay and iron content) using the EnMAP-Box (QGIS plugin) EnSoMAP tool are provided as videos at the HYPERedu YouTube channel (https://www.youtube.com/@HYPERedu_GFZ/playlists) and the soil MOOC course pages (https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-soil-applications/).

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