A Natura 2000 Monitoring Framework Using Plant Species Gradients for Spectral Habitat Assessment

Transcription

1 A Natura 2000 Monitoring Framework Using Plant Species Gradients for Spectral Habitat Assessment Carsten Neumann, Gabriele Weiß, Sibylle Itzerott Department 1 Section 1.4

2 Döberitzer Heide ASD spectroradiometer measurements x1 m & 2x2 m measurement area 81 areas for moist 72 for dry habitats floristic field surveys 1x1, 5x5, 30x30, 150x150, 60x240 m areas - species cover Braun-Blanquet - relevant parameters for nature conservation - structural plant- and population parameter Study Area & Sampling Design

3 Monitoring Framework

4 Non-metric multidimensional scaling -> projecting samples x species matrix into 3D environmental space -> similarities (Bray-Curtis distance) are compared and differences between original & ordination are minimized -> an interpretation scheme is designed (colors for RGB space or Isosurfaces) Corynephorus canescenz Arrhenatherum elatius Calamagrostis epigejos Calluna vulgaris Carex agg. Molinia caerulea Environmental Space Modeling

5 variable aggregation within ordination space to derive FFH assessment parameter & habitat types -> dominance of character species -> species of disturbance -> developmental stage -> scrub/gras encroachment Is there a spatial variance structure that can predict species diversity within ordination space? Corynephorus canescenz (LRT 2330) Calluna vulgaris (LRT 4030) Festuca ovina agg. (LRT 6120) ordinated point cloud anisotropic correlation of a spatial stochastic variable Kriging Isosurface spatial correlation length spatial variance structure Habitat Parameter Aggregation

6 Definition of LRT specific Habitat Type functions -> functional relationship for quantitative determination of habitat types -> variable aggregation through adjustment of variables and parameter -> standardized linear combination translated to Occurrence Probabilities OP = [ a species A + b species B + ] - [ a disturb A + b disturb B + ] LRT 2330 open pioneer grassland Corynephorus canescenz 0.5 Bare ground 0.2 Cladonia spec. -> transition between probabilities < 50 % -> normalized inter habitat type transition strength Habitat Parameter Aggregation

7 Definition of Intra Habitat Disturbance Species Complexes -> Habitat type specific disturbance functions are defined on the basis of known indicator species -> Disturbance strength is described by min-max normalized species cover for probabilities > 30 % LRT 2330 open pioneer grassland habitat encroachment a) 1.00 Cladonia spec Polytrichum piliferum b) 1.00 Polytrichum piliferum 0.99 Rubus caesius et fructicosus agg Rumex acetosella c) 1.00 Rumex acetosella 0.92 Agrostis capillaris 0.44 Calamagrostis epigejos -> Natura 2000 habitat assessment categories -> subtracting encroachment complexes from habitat type probabilities Habitat Parameter Aggregation

8 PLS Regression between field spectra and score values of habitat type plots spectral model NMS1 spectral model NMS2 R² RMSE [%] n_c n_pred R² RMSE [%] n_c n_pred 0, , , , , , external validation with terrestrial habitat type mapping and assessment occurrence probability assessment categories cor RMSE [%] cor RMSE [%] LRT LRT LRT external validation with plant species assemblages on transect plots Spectral Modeling & Spatial Prediction

9 Example for predicted FFH-habitat types and assessment categories on a open dryland at the Döberitzer Heide Spatial Prediction

10 Indicator species: Hipparchia statilinus Habitat characteristics -xerothermic areas -sun exposed -mosaic complexes with silver grass (corynephorus canescens), sandy soils and dry moss communities -interleaving with calluna-heath - Interleaving with Koeleria macrantha Species of Interest

11 -> August experts, students, interested people participated to map ~ 4000ha -> presence/absence data were collected in 13 sub-areas divided in 100 m² raster -> 65 Hipparchia statilinus & 242 Hipparchia semele were mapped Sampling Design

12 Spectral models RGB Composite Spectral models transferred to hyperspectral image signatures Spectral Modeling & Spatial Prediction

13 Habitat models in the environmental space -> presence/absence coordinates projected to ordination -> 3D Indicator Kriging -> Occurrence probabilities based on floristic composition 0 < 0.1 & 0.9 < 1 0 < 0.2 & 0.75 < 1 0 < 0.35 & 0.6 < 1 0 < 0.45 & 0.55 < 1 Occurrence Aggregation

14 1 2 3 RGB Composite 3D Kriging Cloud Spectral models transferred to hyperspectral image signatures Spectral Modeling & Spatial Prediction

15 Habitat parameter correlation and significance Habitat parameter visualization Habitat Parameter

16 Thank you for your attention! Department 1 Section 1.4