Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

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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

Assessing floristic composition with multispectral sensors - a comparison based on monotemporal and multiseasonal field spectra

none6siAssessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing approaches. In particular data from multispectral sensors are easy to obtain, globally accessible, and often provide a high temporal resolution. They hence offer a comprehensive basis for vegetation mapping. The potential of such sensors for vegetation mapping has, however, never been thoroughly investigated. In particular, a systematic test regarding the spectral capabilities of these data for an assessment of detailed floristic variation has not been implemented to date. We thus addressed in this study the question how the ability of optical sensors to map floristic variation is affected by their respective spectral coverage and number of bands. To answer this question, we simulated monotemporal and multiseasonal data of eleven multispectral sensors. These data were used to model gradual transitions in species composition (i.e., floristic gradients) within three types of spontaneous vegetation typical for Central Europe using Partial Least Squares regression. Comparison of the model fits (ranging up to R2 = 0.76 in cross-validation) illustrated the potential of multispectral data for detailed vegetation mapping. The results show that spectral coverage of the entire solar-reflective domain is the most important sensor characteristic for a successful assessment of floristic variation. Model and sensor performances as well as limitations are thoroughly discussed, and recommendations for sensor development are made based on the final conclusions of this study.mixedFeilhauer H.; Thonfeld F.; Faude U.; He K. S.; Rocchini D.; Schmidtlein S.Feilhauer H.; Thonfeld F.; Faude U.; He K. S.; Rocchini D.; Schmidtlein S

Assessing floristic composition with multispectral sensors: a comparison based on monotemporal and multiseasonal field spectra

62013 FEI 1reservedInternationalInternational coauthor/editorAssessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing approaches. In particular data from multispectral sensors are easy to obtain, globally accessible, and often provide a high temporal resolution. They hence offer a comprehensive basis for vegetation mapping. The potential of such sensors for vegetation mapping has, however, never been thoroughly investigated. In particular, a systematic test regarding the spectral capabilities of these data for an assessment of detailed floristic variation has not been implemented to date. We thus addressed in this study the question how the ability of optical sensors to map floristic variation is affected by their respective spectral coverage and number of bands. To answer this question, we simulated monotemporal and multiseasonal data of eleven multispectral sensors. These data were used to model gradual transitions of the species composition (i.e., floristic gradients) within three types of spontaneous vegetation typical for Central Europe using Partial Least Squares regression. Comparison of the model fits (ranging up to R2 = 0.76 in cross-validation) illustrated the potential of multispectral data for detailed vegetation mapping. The results show that spectral coverage of the entire solar-reflective domain is the most important sensor characteristic for a successful assessment of floristic variation. Model and sensor performances as well as limitations are thoroughly discussed, and recommendations for sensor development are made based on the final conclusions of this study.restrictedFeilhauer, H.; Thonfeld, F.; Faude, U.; He, K.S.; Rocchini, D.; Schmidtlein, S.Feilhauer, H.; Thonfeld, F.; Faude, U.; He, K.S.; Rocchini, D.; Schmidtlein, S

Classification des types de prairies et estimation de la diversité taxonomique à partir de séries temporelles d'images satellites

La télédétection offre de nombreuses possibilités pour caractériser la végétation aussi bien par sa composition que par sa structure. Si la capacité de cet outil à caractériser les milieux mono-spécifiques comme les grandes cultures a été montrée à de nombreuses reprises, plus de difficultés sont rencontrées lors de l’étude de milieux pluri-spécifiques comme les prairies. En effet, le mélange des espèces dans un milieu renvoie des valeurs radiométriques difficiles à interpréter. Dans ce contexte, l’objectif de ce stage est de caractériser le mode de gestion et la composition botanique des prairies à partir d’une série temporelle d’images satellites Formosat-2. Des relevés botaniques et des enquêtes sur le mode de conduite d’une cinquantaine de prairies ont été réalisés lors d’une campagne de terrain. Des typologies botaniques ont été construites avec une approche fonctionnelle de la composition en espèces, qui permet de rendre compte de la valeur d’usage agricole. Les prairies ont ainsi été distinguées selon leur précocité, leur potentiel de productivité, et la richesse en formes de vie (graminées, légumineuses, et diverses). Elles ont aussi été distinguées selon les différents modes de conduite qui ont été identifiés sur le terrain (prairies fauchées une fois, prairies fauchées deux fois, prairies pâturées et prairies mixtes). Des classifications supervisées ont été réalisées sur chacune de ces typologies et différents modèles linéaires ont été construits pour relié directement les taux de recouvrement des formes de vies avec les valeurs radiométriques enregistrées par les images. Les résultats indiquent qu’il est possible de distinguer les prairies fauchées des prairies mises en pâture avec une précision globale de plus de 80%. En revanche, la distinction des classes botaniques est difficile, notamment en raison du manque d’informations sur des paramètres non contrôlés, et d’un échantillonnage parfois irrégulier. De même, les modèles linéaires construits n’expliquent que très peu la composition botanique à partir des variables spectrales utilisées

The utility of new generation multispectral sensors in assessing aboveground biomass of Phragmites australis in wetlands areas in the City of Tshwane Metropolitan Municipality; South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Abstract available in PDF file

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