Quantifying structure of Natura 2000 heathland habitats using spectral mixture analysis and segmentation techniques on hyperspectral imagery

Mücher, C.A.; Kooistra, Lammert; Vermeulen, Marleen; Borre, Jeroen Vanden; Haest, Birgen; Haveman, R.

doi:10.1016/j.ecolind.2012.09.013

Cited by 31 publications

(28 citation statements)

References 21 publications

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“…Thereby, an image pixel is linked to the structure of the site-specific ordination space that holds information such as the direction of habitat succession, the distribution of plant species, or, indirectly, about the abiotic gradients. Commonly, this information has to be defined before mapping and the conservation status assessment is based exactly on these defined categories [12,14]. The functional aggregation technique coupled with probability, pressure strength, and assessment predictions also allow a continuous interpolation of ordinated plot information.…”

Section: Conservation Status Assessmentmentioning

confidence: 99%

“…There is still a considerable gap in knowledge transfer between remote-sensing specialists and ecologists in conjunction with the application demands of legal authorities [5,8,9]. The first steps in combining ecological knowledge with Natura 2000 habitat management are usually carried out using indicator species mapping [10][11][12], whereby habitat types and indicator species for habitat-status assessment are modeled separately or on the basis of object classes describing habitat quality and quantity in aggregate forms as habitat units [13][14][15][16]. Such approaches start from the premise that vegetation and habitat structures exist in a discrete pattern that can be classified a priori into categories [17].…”

Section: Introductionmentioning

confidence: 99%

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Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Neumann

Weiss²,

Schmidtlein

et al. 2015

Remote Sensing

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Abstract:The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species' habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. OPEN ACCESSRemote Sens. 2015, 7 2872Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R 2 = 0.79-0.85), whereas second axis of dry heaths (R 2 = 0.13) and first axis for pioneer grasslands (R 2 = 0.49) are more difficult to describe.

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Section: Conservation Status Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Neumann

Weiss²,

Schmidtlein

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…There exist studies comparing PCA, MNF, and ICA with new methodologies [9,[13][14][15], as well as other studies that analyze their behavior or improve these techniques [1,2,5,11,[16][17][18], without making a comparison with the existing methods. Additionally, previous studies [8,19,20] have used a small number of components to obtain adequate classification maps. However, in such studies, the most common approach used to select components is to determine them through eigenvalues, which are the measure of the variance explained by the components obtained from the dimensionality reduction techniques.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Component Selection Strategies in Hyperspectral Imagery

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Entropy

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Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.

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“…Using a priori assumption of ecological relevancy to overcome lacks of understanding may mislead in identifying significant habitat parameter in complex ecosystem dynamics. In contrast, hyper spectral sensors offer finer spectral measurements in hundreds of spectral bands that allow for a detailed representation of habitat characteristics (Mücher et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2013) show that habitat modeling based on remote sensing data has already proven to be a valuable instrument for environmental modeling and monitoring. There is an increasing use of remote sensing in ecological research at species level worldwide (Wang 2012;Mücher et al 2013). Especially in North America, there are several promising monitoring approaches as, e.g., the ''NPScape'' project by the US National Park Service and NASA as well as adjacent projects in Canada (by Parks Canada Agency and the Canadian Space Agency) aiming to enhance the use of remote sensing data for facilitating monitoring at National Parks (see Gross et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Digital and real-habitat modeling of Hipparchia statilinus based on hyper spectral remote sensing data

Luft

Neumann

Itzerott

et al. 2015

Int. J. Environ. Sci. Technol.

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The abandonment of military areas leads to succession processes affecting valuable open-land habitats and is considered to be a major threat for European butterflies. We assessed the ability of hyper spectral remote sensing data to spatially predict the occurrence of one of the most endangered butterfly species (Hipparchia statilinus) in Brandenburg (Germany) on the basis of habitat characteristics at a former military training area. Presenceabsence data were sampled on a total area of 36 km 2 , and N = 65 adult individuals of Hipparchia statilinus could be detected. The floristic composition within the study area was modeled in a three-dimensional ordination space. Occurrence probabilities for the target species were predicted as niches between ordinated floristic gradients by using Regression Kriging of Indicators. Habitat variance could be explained by up to 81 % with spectral variables at a spatial resolution of 2 9 2 m by transferring PLSR models to imagery. Ordinated ecological niche of Hipparchia statilinus was tested against environmental predictor variables. N = 6 variables could be detected to be significantly correlated with habitat preferences of Hipparchia statilinus. They show that Hipparchia statilinus can serve as a valuable indicator for the evaluation of the conservation status of Natura 2000 habitat type 2330 (inland dunes with open Corynephorus and Agrostis grasslands) protected by the Habitat Directive (Council Directive 92/43/EEC). The authors of this approach, conducted in August 2013 at Döberitzer Heide Germany, aim to increase the value of remote sensing as an important tool for questions of biodiversity research and conservation.

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Quantifying structure of Natura 2000 heathland habitats using spectral mixture analysis and segmentation techniques on hyperspectral imagery

Cited by 31 publications

References 21 publications

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Assessment of Component Selection Strategies in Hyperspectral Imagery

Digital and real-habitat modeling of Hipparchia statilinus based on hyper spectral remote sensing data

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