Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Stenzel, Stefanie; Feilhauer, Hannes; Mack, Benjamin; Metz, Annekatrin; Schmidtlein, Sebastian

doi:10.1016/j.jag.2014.05.012

Cited by 56 publications

(56 citation statements)

References 40 publications

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“…2017, 9, 266 3 of 25 used to map N2000 forest and heathland habitat types, and evaluate broad aspects of habitat quality by mapping the presence of certain relevant vegetation and land cover classes present in the habitat [6]. More recently, a number of different approaches have also been considered: applying a one-class classifier to multi-seasonal RapidEye imagery to delineate and classify four N2000 habitat types, which were scattered across a complex landscape of different vegetation types [22]; an investigation of the potential of both IS and (simulated) multispectral images to model three N2000 mire habitat types, as well as their floristic composition [23]; the use of Airborne Laser Scanning to assess the conservation status of N2000 grassland habitats [24]; and combining multispectral and laser scanning data to assess the conservation status of Mediterranean forests [25].…”

Section: Introductionmentioning

confidence: 99%

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

et al. 2017

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Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in [2006][2007]24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82-0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.

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Section: Introductionmentioning

confidence: 99%

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

et al. 2017

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“…However, currently, only a few studies have implemented ecological knowledge in remote-sensing-based assessment systems for Natura 2000 monitoring [5][6][7]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…In the case of BSVM, zero or the separating hyperplane is considered as a "natural" threshold. We also consider the Equal Training Sensitivity and Specificity (ETSS) as a threshold [62] that can be derived easily for BSVM. Then the accuracies for the two thresholds were calculated by using independent test data, and the results of the best threshold were used.…”

Section: Biased Support Vector Machinementioning

confidence: 99%

“…comes with a default parameterization that has been proven to perform well in the mentioned studies [29,51,53,55,[58][59][60][61][62] and performs better compared with other OCC methods [53,58], especially when used for the detection of invasive species [36]. Moreover, Maxent directly provides variable importance together with the results, which makes it more convenient for exploring the relationships between predictor variables and detection targets, while a separate calculation has to be done with other OCC methods [60].…”

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confidence: 99%

“…Since the default parameters in Maxent were based on extensive empirical tuning studies and can produce a good performance [57,63], the complexity of the Maxent model was usually disregarded, especially when used for mapping real distributions based on remote sensing data [29,36,45,51,55,[58][59][60]62]. However, recent studies on species distribution models have demonstrated that it is necessary to evaluate model complexity, due to the fact that models with the Default Parameter Set (Maxent-DPS) tended to result in more complex models [63][64][65][66] and complex environmental response functions that are difficult to interpret from an ecological perspective [65][66][67].…”

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confidence: 99%

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Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

Liu

Gong

et al. 2017

Remote Sensing

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Spartina alterniflora has become the main invasive plant along the Chinese coast and now threatens the local ecological environment. Accurately monitoring the distribution of S. alterniflora is urgent and essential for developing cost-effective control strategies. In this study, we applied the One-Class Classification (OCC) methods of Maximum entropy (Maxent) and Biased Support Vector Machine (BSVM) based on Landsat time-series imagery to detect the species on the middle coast of Jiangsu in east China. We conducted four experimental setups (i.e., single-scene analysis, time-series analysis, Normalized Difference Vegetation Index (NDVI) time-series analysis and a compressed time-series analysis), using OCC methods to recognize the species. Then, we tested the performance of a compressed time-series model for S. alterniflora detection and evaluated the expansibility of this approach when it was applied to a larger region. Our principal findings are as follows: (1) Maxent and BSVM performed equally well, and Maxent appeared to have a more balanced performance over the summer months; (2) the Maxent model with the Default Parameter Set (Maxent-DPS) showed a slightly higher accuracy and more overfitting than Maxent with the Akaike Information Criterion corrected for small samples sizes (AICc)-selected parameter set model, but a t-test found no significant difference between these two settings; (3) April and December were deemed to be important periods for the detection of S. alterniflora; (4) a compressed time-series analysis model-including only three variables (December NDVI, March green and the third Principal Component in January, PC3)-yielded higher accuracy than single-scene analyses, which indicated that time-series analysis can better detect S. alterniflora than single-scene analyses; and (5) the Maxent model using the reconstructed optimal variables and 70 training samples over a larger region produced encouraging results with an overall accuracy of 90.88% and a Kappa of 0.78. The one-class classification method combined with a phenology-based detection strategy is therefore promising for the application of the long-term detection of S. alterniflora over extended areas.

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Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Cited by 56 publications

References 40 publications

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

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