An ontological system for interoperable spatial generalisation in biodiversity monitoring

Nieland, Simon; Moran, Niklas; Kleinschmit, Birgit; Förster, Michael

doi:10.1016/j.cageo.2015.08.006

Cited by 5 publications

(7 citation statements)

References 54 publications

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“…They stated that up to 65% of 'change' could potentially be caused by observer error. This error is even enhanced within NATURA 2000 areas, because local experts tend to overestimate locally relevant species and underestimate locally abundant species (Förster et al, 2008;and further discussed by Nieland et al, 2015). Foody (2008) concludes that the reliability of a map should always be judged within its context in order to reduce inappropriate criticism and false perceptions of remote sensing-based results.…”

Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 95%

“…An accuracy assessment via this matrix should be interpreted as a comparison between two maps (a field-derived and a remote sensing-derived) which both contain a level of uncertainty, rather than a comparison between the remote sensing map and a true reference (Foody, 2008). The later method can either produce smoothing effects by using too coarse resolution or result in intra-class variations if the spatial resolution of the data is very high (Nieland et al, 2015). In this context Hearn et al (2011) reported inconsistencies in repeated vegetation mapping efforts.…”

Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 98%

“…Some parameters for the evaluation, e.g., coverage of open soil or lichens, should rather be considered based on mapping units. Here, object-based analysis (Mücher et al, 2013) or a kernel-based approach (Nieland et al, 2015) could be solutions for finding units of homogeneous vegetation. An alternative could be an adaptation of the guidelines to take account of the capabilities of remote sensing.…”

Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 99%

“…The differentiation of the mainly addressed habitat type European dry heaths (H4030) from similar habitats in the study area (H2030 Dry sand heaths with Calluna and Genista) could be accomplished by adapting the species index and by an additional information layer representing the amount of sand. However, these two habitats are difficult to distinguish via remote sensing and were merged in other studies as well (e.g., Nieland et al, 2015). Moreover, the latter habitat is of minor importance in the study area.…”

Section: Applicability Of the Approachmentioning

confidence: 98%

See 3 more Smart Citations

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Schmidt

Fassnacht

Neff

et al. 2017

International Journal of Applied Earth Observation and Geoinfor

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Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 95%

Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 98%

Section: Comparing the Remote Sensing Results And Field Derived Classimentioning

confidence: 99%

Section: Applicability Of the Approachmentioning

confidence: 98%

See 2 more Smart Citations

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Schmidt

Fassnacht

Neff

et al. 2017

International Journal of Applied Earth Observation and Geoinfor

Self Cite

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“…According to Nieland et al. (), the reasons in mismatching field‐based data and remote sensing data (besides methodological uncertainties and field‐based mapping errors) are given in an improper scale‐match between field data and the resolution of the remotely sensed data (Small ). This can either produce smoothing effects or a high intra‐class variation.…”

Section: Discussionmentioning

confidence: 99%

Synergetic use of Sentinel‐1 and Sentinel‐2 for assessments of heathland conservation status

Schmidt

Fassnacht

Förster

et al. 2017

Remote Sens Ecol Conserv

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Habitat quality assessments often demand wall-to-wall information about the state of vegetation. Remote sensing can provide this information by capturing optical and structural attributes of plant communities. Although active and passive remote sensing approaches are considered as complementary techniques, they have been rarely combined for conservation mapping. Here, we combined spaceborne multispectral Sentinel-2 and Sentinel-1 SAR data for a remote sensing-based habitat quality assessment of dwarf shrub heathland, which was inspired by nature conservation field guidelines. Therefore, three earlier proposed quality layers representing (1) the coverage of the key dwarf shrub species, (2) stand structural diversity and (3) an index reflecting co-occurring vegetation were mapped via linking in situ data and remote sensing imagery. These layers were combined in an RGB-representation depicting varying stand attributes, which afterwards allowed for a rule-based derivation of pixel-wise habitat quality classes. The links between field observations and remote sensing data reached correlations between 0.70 and 0.94 for modeling the single quality layers. The spatial patterns shown in the quality layers and the map of discrete quality classes were in line with the field observations. The remote sensingbased mapping of heathland conservation status showed an overall agreement of 76% with field data. Transferring the approach in time (applying a second set of Sentinel-1 and -2 data) caused a decrease in accuracy to 73%. Our findings suggest that Sentinel-1 SAR contains information about vegetation structure that is complimentary to optical data and therefore relevant for nature conservation. While we think that rule-based approaches for quality assessments offer the possibility for gaining acceptance in both communities applied conservation and remote sensing, there is still need for developing more robust and transferable methods.

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A Semantic-Based Approach for Landscape Identification

Toulet

Roux

Laques

et al. 2019

Advances in Knowledge Discovery and Management

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Here we present an original method for the automation of landscape identification in a satellite image. There are two major challenges in this process. The first lies in the ability to take all expert knowledge into account for the full time it takes to analyze the image. The second is successfully structuring and persisting this knowledge so that it becomes interoperable and usable in the Semantic Web context. In this paper, we explain how the combination of several strategies associating image processing, the calculation of specific characteristics and inductive logic programming (ILP) can feed into the automation process, and how the integration of knowledge via the construction of dedicated ontologies can meet these challenges.

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An ontological system for interoperable spatial generalisation in biodiversity monitoring

Cited by 5 publications

References 54 publications

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

Synergetic use of Sentinel‐1 and Sentinel‐2 for assessments of heathland conservation status

A Semantic-Based Approach for Landscape Identification

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