Multiseasonal Remote Sensing of Vegetation with One-Class Classification – Possibilities and Limitations in Detecting Habitats of Nature Conservation Value

Mapping of habitats relevant for nature conservation often involves the identification of patches of target habitats in a complex mosaic of vegetation types extraneous for conservation planning. In field surveys, this is often a time-consuming and work-intensive task. Limiting the necessary ground reference to a small sample of target habitats and combining it with area-wide remote sensing data could greatly reduce and therefore support the field mapping effort. Conventional supervised classification methods need to be trained with a representative set of samples covering an exhaustive set of all classes. Acquiring such data is work intensive and hence inefficient in cases where only one or few classes are of interest. The usage of one-class classifiers (OCC) seems to be more suitable for this task – but has up until now neither been tested nor applied for large scale mapping and monitoring in programs such as those requested for the Natura 2000 European Habitat Directive or the High Nature Value (HNV) farmland Indicator. It is important to uncover the possibilities and mark the obstacles of this new approach since the usage of remote sensing for conservation purposes is currently controversially discussed in the ecology community as well as in the remote sensing community. Thus, the focal and innovative point of this thesis is to explore possibilities and limitations in the application of one-class classifiers for detecting habitats of nature conservation value with the help of multi-seasonal remote sensing and limited field data. The first study ascertains the usage of an OCC is suitable for mapping Natura 2000 habitat types. Applying the Maxent algorithm in combination with a low number of ground reference points of four habitat types and easily available multi-seasonal satellite imagery resulted in a combined habitat map with reasonable accuracy. There is potential in one-class classification for detecting rare habitats – however, differentiating habitats with very similar species composition remains challenging. Motivated by these positive results, the topic of the second study of this thesis is whether low and HNV grasslands can be differentiated with remotely-sensed reflectance data, field data and one-class classification. This approach could supplement existing field survey-based monitoring approaches such as for the HNV farmland Indicator. Three one-class classifiers together with multi-seasonal, multispectral remote sensing data in combination with sparse field data were analysed for their usage A) to classify HNV grassland against other areas and B) to differentiate between three quality classes of HNV grassland according to the current German HNV monitoring approach. Results indicated reasonable performances of the OCC to identify HNV grassland areas, but clearly showed that a separation into several HNV quality classes is not possible. In the third study the robustness and weak spots of an OCC were tested considering the effect of landscape composition and sample size on accuracy measurements. For this purpose artificial landscapes were generated to avoid the common problem of case-studies which usually can only make locally valid statements on the suitability of a tested approach. Whereas results concerning target sample size and the amount of similar classes in the background confirm conclusions of earlier studies from the field of species distribution modelling, results for background sample size and prevalence of target class give new insights and a basis for further studies and discussions. In conclusion the utilisation of an OCC together with reflectance and sparse field data for addressing rare vegetation types of conservation interest proves to be useful and has to be recommended for further research

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

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 sensing‐based 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

Modelling the distribution and compositional variation of plant communities at the continental scale

P. 978-990Aim: To investigate whether (1) environmental predictors allow to delineate the distri- bution of discrete community types at the continental scale and (2) how data complete- ness influences model generalization in relation to the compositional variation of the modelled entities.S

What explains inconsistencies in field-based ecosystem mapping?

Questions: Field-based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field-based ecosystem mapping are lacking. Here, we study how inconsistencies in field-based ecosystem maps depend on these factors. Location: Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods: In a balanced experiment, four sites were field-mapped wall-to- wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience-level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results: On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience-level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion: Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field-based mapping. classification, experience, field-based mapping, GIS, inter-observer variation, land-cover mapping, landscape metrics, ordination, scale, vegetation mappingpublishedVersio

Gradient-based assessment of habitat quality for spectral ecosystem monitoring

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. Models 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

Hyperspectral vs. Multispectral data: Comparison of the spectral differentiation capabilities of Natura 2000 non-forest habitats

Identification of the Natura 2000 habitats using remote sensing techniques is one of the most important challenges of nature conservation. In this study, the potential for differentiating non-forest Natura 2000 habitats from the other habitats was examined using hyperspectral data in the scope of VNIR (0.4–1 µm), SWIR (1–2.5 µm) and simulated multispectral data (Sentinel-2). The aim of the research was also to determine the most informative spectral ranges from the optical range. Five different Natura 2000 habitats common in Central Europe were analysed: heaths (code 4030), mires (code 7140), grasslands (code 6230) and meadows (codes 6410 and 6510). In order to guarantee the objectivity and transferability of the results each habitat was tested in two areas and in three campaigns (spring, summer, autumn). Hyperspectral data was acquired using HySpex VNIR-1800 and SWIR-384 scanners. The Sentinel-2 data was resampled based on HySpex spectral reflectance. The overflights were performed simultaneously with ground reference data – habitats and background polygons. The Linear Discriminant Analysis was performed in iterative mode based on spectral reflectance acquired from hyperspectral and multispectral data. This resulted in distribution of correctness rate values and information about the most differentiating spectral bands for each habitat. Based on the results of our experiments we conclude that: (i) hyperspectral data (both VNIR and SWIR) obtained from May to September was useful for differentiation of habitats from background with efficiency reaching over 90%, regardless of the area; (ii) the most useful spectral ranges are: in VNIR − 0.416–0.442 µm and 0.502–0.522 µm, in SWIR − 1.117–1.165 µm and 1.290–1.361 µm; (iii) the potential of multispectral data (Sentinel-2) in distinguishing Natura 2000 habitats from the background is diverse; higher for heaths and mires (comparable to hyperspectral data) lower for meadows (6410, 6510) and grasslands (6230); (iv) in case of meadows and grasslands, the correctness rate for the Sentinel-2 data was on average about 20% lower compared to the hyperspectral data

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

Mapping of habitats with relevance for nature conservation involves the identification of patches oftarget habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limitingthe necessary ground reference to a small sample of target habitats would greatly reduce and thereforesupport the field mapping effort. We thus aim to answer in this study the question: can semi-automatedremote sensing methods help to map such patches without the need of ground references from sites notrelevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiencyof large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map fourhabitat types across a patchy landscape of 1000 km2near Munich, Germany. This task was conductedusing the low number of 125 ground reference points only along with easily available multi-seasonalRapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectancedue to different phenological development across space, continuous transitions between the habitatsand the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simpleand affordable approach can thus be recommended for a first survey of previously unmapped areas, asa tool for identifying potential gaps in existing habitat inventories and as a first check for changes in thedistribution of habitats