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.
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.
Airborne hyperspectral remote sensing surveys over various areas in Saxony.
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.
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.
The dataset contains hyperspectral imagery acquired during airplane overflights on 8th September 2016 consisting of 242 spectral bands, ranging from VIS to SWIR (423 - 2438 nm) wavelength regions. It covers an area of about 78 km² which is dominated by beech and oak forests. The flight campaign was part of several flight campaigns within the EnMAP project and focused on hyperspectral analysis of plant physiology in deciduous and coniferous forests in the Gerolstein region in Rhineland-Palatinate, Germany.
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/).
The airborne hyperspectral image was acquired by the AVIRIS-Next Generation (AVIRIS-NG) instrument during the AVIRIS-NG Europe 2021 HyperSense campaign that has been conducted as a joint effort of ESA, NASA/JPL and the University of Zurich. Acquired was an agricultural area near Irlbach, Germany on May 30th, 2021. The data was preprocessed (radiometrically, geometrically and atmospherically corrected) to contain 419 bands in the 402 - 2495 nm spectral range. Metadata was acquired on the same day for the variables Leaf Area Index (LAI), Leaf Chlorophyll content, crop height and phenology. An overview of metadata acquisition and processing can be found in the HYPERedu YouTube videos on ground reference data acquisition in the field and ground reference data acquisition in the lab. More details on LAI and chlorophyll acquisition can be found in the field data guides assembled by the authors of this dataset via enmap.org (Danner et al., 2015; Süß et al., 2015). The dataset is made publically available within the massive open online course (MOOC) "Beyond the Visible - Introduction to Imaging Spectroscopy for Agricultural Applications", available from December 2022.
The dataset contains a spaceborne hyperspectral image acquired by EnMAP over Berlin, Germany, and surrounding areas on July 24th, 2022. The data was preprocessed to Level 1B format (systematically and radiometrically corrected) and is provided in separate BSQ files for the VNIR and SWIR sensor of the instrument, respectively. The Level 1B product is accompanied by a history file (xml), a metadata file (xml), six quality masks (cirrus, classes, cloud, cloud shadow, haze and snow) as well as quality test flags and pixel masks for the VNIR and SWIR files separately (all TIF format). In addition, this dataset comes with a digital elevation model, COP-DEM-GLO-30-R (ESA, Copernicus) and a Sentinel-2 scene (ESA, Copernicus) as references for geometric and atmospheric correction with the EnMAP processing tool (EnPT). Please note that the two datasets described above are NOT part of the same license as the EnMAP data. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - EnMAP data access and image preprocessing techniques", available from July 2023. Guidance on preprocessing hyperspectral imagery in general, access to EnMAP data and a hands-on tutorial on preprocessing of EnMAP data with EnPT in the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the MOOC course page and the EnPT documentation. More information about the EnMAP mission can be found on the mission website and in Guanter et al. (2016) and Storch et al. (2023).
Microplastic particles (MPP) occur in various environmental compartments all over the world. They have been frequently investigated in oceans, freshwaters, and sediments, but studying their distribution in space and time is somewhat limited by the time-consuming nature of the available accurate detection strategies. Here, we present an enhanced application of lab-based near-infrared imaging (NIR) spectroscopy to identify the total number of MPP, classify polymer types, and determine particle sizes while maintaining short measuring times. By adding a microscopic lens to the hyperspectral camera and a cross slide table to the setup, the overall detectable particle size has been decreased to 100 Ìm in diameter. To verify and highlight the capabilities of this enhanced, semi-automated detection strategy, it was applied to key areas of microplastic research, such as a lowland river, the adjacent groundwater wells, and marine beach sediments. Results showed mean microplastic concentrations of 0.65 MPP/L in the Havel River close to Berlin and 0.004 MPP/L in the adjacent groundwater. The majority of MPP detected in the river were PP and PE. In 8 out of 15 groundwater samples, no MPP was found. Considering only the samples with quantifiable MPP, then on average 0.01 MPP/L was present in the groundwater (98.5% removal during bank filtration). The most abundant polymers in groundwater were PE, followed by PVC, PET, and PS. Mean MPP concentrations at two beaches on the German Baltic Sea coast were 5.5~MPP/kg at the natural reserve Heiligensee and Hüttelmoor and 47.5 MPP/kg at the highly frequented Warnemünde beach. Quelle: link.springer.com
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