EnMAP Pre-Launch and Start Phase: Mission Update

Chabrillat, Sabine; Segl, Karl; Foerster, Saskia; Brell, Maximilian; Guanter, Luis; Schickling, Anke; Storch, Tobias; Honold, Hans‐Peter; Fischer, Sebastian

doi:10.1109/igarss46834.2022.9884773

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…All data are in the coordinate system WGS 84 UTM Zone 33 S (EPSG: 32,733). One of these spectrometers is the EnMAP mission, which is led by the Space Agency of the German Aerospace Centre (DLR) and successfully launched in 2022 (Müller et al, 2009;Stuffler et al, 2009;Chabrillat et al, 2022). Since March 2023, it has been possible to request and receive data on demand for the study area.…”

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

“…Since March 2023, it has been possible to request and receive data on demand for the study area. In general, EnMAP scenes typically have a geolocation error of 0.3-0.4 pixels following automatic co-registration (Chabrillat et al, 2022). However, the scene acquired in March 2023 demonstrates exceptional accuracy in terms of geolocation and displays no noticeable shift when compared to the Sentinel-2 scene.…”

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

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Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

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Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

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Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

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“…Recent technological developments and satellite missions such as the DLR's Earth Sensing Imaging Spectrometer (DESIS, [ 12 ]), the Hyperspectral Environmental Mapping and Analysis Program (EnMap, [ 13 ] or the first spaceborne GEDI Ecosystem Lidar [ 14 ]) are largely available free of charge to provide a deeper understanding of processes and accurate estimates of traits in vegetation and soil properties as a result of ecological pattern and their interactions. NASA's future Surface Biology and Geology (SBG) missions ( ) with the Hyperspectral Infrared Imager (HyspIRI) will be particularly important for the RS-based monitoring of vegetation- and geodiversity [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring vegetation- and geodiversity with remote sensing and traits

Lausch,

Selsam,

Pause

et al. 2024

Phil. Trans. R. Soc. A.

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Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ , close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

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“…In recent decades, remote sensing (RS) has opened up new and increasingly better opportunities for continuous and objective ecosystem monitoring to detect the status of and changes in their interactions with phylogenetic/genese, structure, taxonomy and function in vegetation diversity [13,14]; geodiversity [3,15]; geomorphodiversity [4,16]; and landscape intensification and urbanisation [17][18][19] from local to global scale. Recent technological advances in spaceborne RS such as the Hyperspectral Environmental Mapping and Analysis Program (EnMap, [20]), the DLR Earth Sensing Imaging Spectrometer (DESIS, [21]) or the GEDI Ecosystem LiDAR [22]), as well as future planned missions such as NASA's Surface Biology and Geology (SBG) (https://sbg.jpl.nasa.gov/ (accessed on 1 January 2024), [23]), are largely available free of charge to enable deeper process understanding and accurate estimates of ecological variables.…”

Section: Introductionmentioning

confidence: 99%

Ecosystem Integrity Remote Sensing—Modelling and Service Tool—ESIS/Imalys

Selsam,

Bumberger,

Wellmann

et al. 2024

Remote Sensing

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One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.

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EnMAP Pre-Launch and Start Phase: Mission Update

Cited by 9 publications

References 3 publications

Spectral imaging of grass species in arid ecosystems of Namibia

Spectral imaging of grass species in arid ecosystems of Namibia

Monitoring vegetation- and geodiversity with remote sensing and traits

Ecosystem Integrity Remote Sensing—Modelling and Service Tool—ESIS/Imalys

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