Digital Earth in Europe

Marconcini, Mattia; Esch, Thomas; Bachofer, Felix; Metz-Marconcini, Annekatrin

doi:10.1007/978-981-32-9915-3_20

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Therefore, there is an urgent need to build and contribute to standardized field vegetation databases at the national or European scales. Several projects are working on such databases, such as Information System on Nature and Landscapes (Jomier et al, 2019) or GeoNature (Corny et al, 2019) in France, and Copernicus inSitu (Marconcini et al, 2020) or the European Vegetation Archive (Chytrý et al, 2020) in Europe.…”

Section: Discussionmentioning

confidence: 99%

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

Rapinel

Panhelleux

Lalanne

et al. 2021

Progress in Physical Geography: Earth and Environment

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Grassland habitats provide many ecosystem services but are threatened by agricultural intensification and urbanization. While the lack of accurate and comprehensive inventories at the national scale makes them difficult to manage, advances in spatial modeling using open remote sensing data and open-source software, as well as the increasing use of ecological archives, provide new perspectives for mapping natural habitats. In this context, this study evaluated the contribution of spectral and environmental variables to discriminate and then map grassland habitats throughout France. To this end, 92 spectral variables derived from moderate-resolution imaging spectroradiometer data, 19 bioclimatic variables derived from WorldClim data, 4 topographic variables derived from the European Union Digital Elevation Model (DEM), and 8 soil variables derived from SoilGrids data were combined at a spatial resolution of 250 m. Reference plots that characterized 6 and 21 grassland ecosystems at European Nature Information System (EUNIS) levels 2 (broad habitats) and 3 (habitats), respectively, were collected from vegetation archives. We first performed descriptive analysis that included habitat description, ordination, and pairwise separability. We then performed predictive analysis of grassland habitats using a cross-validated random forest model that included a spatial constraint. While environmental and spectral variables characterized most grassland habitats well and consistently, some confusion occurred between habitats with similar abiotic conditions. The main grassland habitat types were correctly mapped at EUNIS level 2 ( F1 score = 0.68), but not at EUNIS level 3 ( F1 score = 0.52). In addition, the two variables that contributed most to the model were the near-infrared spectral band in spring and the minimum temperature of the coldest month. The model’s prediction at EUNIS level 2 for mainland France provides the map of grassland habitats at a new spatial scale.

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Section: Discussionmentioning

confidence: 99%

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

Rapinel

Panhelleux

Lalanne

et al. 2021

Progress in Physical Geography: Earth and Environment

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“…However, the cross-validation process was repeated 10 times to provide a reasonable estimate of modeling accuracy (Kuhn & Johnson, 2013), the low standard deviation values of the F1 score achieved in this study (Figure 4) stressing the wellfitting of the RF model. Nevertheless, this issue emphasizes the need to support and use vegetation-plot databases such as the European Vegetation Archive (Chytrý et al, 2016) or the Copernicus In Situ component (Marconcini et al, 2020) to better fit and validate species distribution models based on remotely sensed data.…”

Section: The Number Of Dates Required To Achieve Accurate Classificationmentioning

confidence: 99%

Contribution of SPOT-7 multi-temporal imagery for mapping wetland vegetation

Hubert‐Moy

Fabre

Rapinel

2020

European Journal of Remote Sensing

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Mapping the fine-grained pattern of vegetation is critical for assessing the functions and conservation status of wetlands. Although satellite time-series images can accurately model vegetation, the spatial resolution of these data is generally too coarse (> 6 m) to capture the fine-grained pattern of wetland vegetation. SPOT-7 satellite sensors address this issue since they acquire images at very high spatial resolution (1.5 m) with a potential high frequency revisit. While the ability of SPOT-7 images to discriminate wetland vegetation has yet to be assessed, this study investigates the contribution of SPOT-7 multi-temporal images for mapping the fine-grained pattern of 11 vegetation classes in a 470 ha fresh marsh (France). Random forest modeling, calibrated and validated using 170 vegetation plots, was conducted on four SPOT-7 pan-sharpened images collected from April-July 2017. The results highlight that (1) the wetland vegetation was accurately modeled (F1 score 0.88), (2) near-infrared spectral bands acquired in the spring are the most discriminating features, (3) the fine-grained pattern of vegetation plant communities is mapped well, and (4) model uncertainties reflect floristic transition, unconsidered classes or areas of shadow.

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“…In addition, thematic cloud providers have recently emerged addressing specific user groups. In the field of EO data analytics, these providers include international, publicly funded services such as the European Commission's cloud-based Data and Information Access Services (DIAS; e.g., CreoDIAS, Mundi; Marconcini et al 2020;European Commission 2022). Numerous initiatives at national and sub-national levels add to the landscape of EO cloud providers, which makes it increasingly difficult to maintain an overview (Hengl et al 2022).…”

Section: Introductionmentioning

confidence: 99%

A paradigm shift towards decentralized cloud-integrated spatial data infrastructures: Lessons learned and solutions provided for public authorities

Beyer¹,

Brandt²,

Schmidt³

et al. 2023

Preprint

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Digital transformation is a key to turn public authorities into organisations that make decisions based on data-driven insights. The use of big geodata can enable public authorities to tackle complex sustainability issues. However, the efficient management of large amounts of geodata through implementing viable data infrastructures represents a major challenge for public authorities. In this article, we propose a decentralized, cloud-integrated spatial data infrastructure (SDI) to meet the needs of public authorities mandated to provide data services based on earth observation (EO) imagery. We describe the SDI setup and integration of the EO cloud platform \cde, drawing on the specific SDI implementation at a federal, agricultural authority in Germany. Two practical applications are illustrated, underpinning the added value of a cloud-integrated SDI. We elaborate on lessons learned from SDI implementation by summarizing four key findings that may facilitate effective SDI establishment and use, namely i) the need for an organizational strategy, ii) identifying stakeholders, including their participatory roles, iii) long-term financial and human resource planning, and iv) the implementation of a data governance framework. The SDI proposed, serves as blueprint for public authorities aiding them on their path to become providers of data services, leveraging the potential of big geodata, including EO imagery.

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Digital Earth in Europe

Cited by 5 publications

References 10 publications

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

Contribution of SPOT-7 multi-temporal imagery for mapping wetland vegetation

A paradigm shift towards decentralized cloud-integrated spatial data infrastructures: Lessons learned and solutions provided for public authorities

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