Hermann Heilmeier, Cici Alexander, Balázs Deák, Adam Kania, Werner Mücke, Anke Schroiff, Balázs Székely, Agnes Vári, András Zlinszky and Norbert Pfeifer
What Remote Sensing (LiDAR) Can Do and What Not for Habitat Mapping and Quality Assessment – Lessons from the
“ChangeHabitats2” Project
Habitat Assessment 3 Criteria (with (a lot of) Subcriteria)
- Habitat typical Structure
Habitat Assessment for Natura 2000
3 Criteria (with (a lot of) Subcriteria)
- Habitat typical Structure - Habitat typical (Plant-)Species - Anthropogenic Disturbances & Interferences total Assessment
(Beech) Forests
• Forest development stages / Spatial Structure /
Growth categories • Deadwood • Trees as habitats (very old trees,
trees with holes or nests or tinder fungus, bark fractures, living trees with fractured stems/crowns, upright root system of fallen trees)
Grasslands
• Stratification, internal structure
• Spatial Vegetation Structure (Changes in sub-types on a fine scale; small-scale mosaic with other grassland habitats)
• Structure of location and places
with special site conditions (Mosaic or uniform)
Remote sensing
• Airborne Laser Scanning • Hyperspectral Imaging
Beech forests
Forest development stages / Spatial Structure /
Growth categories • Deadwood • Trees as habitats (very old trees,
trees with holes or nests or tinder fungus, bark fractures, living trees with fractured stems/crowns, upright root system of fallen trees)
Beech forests
Forest development stages / Spatial Structure /
Growth categories • Deadwood • Trees as habitats (very old trees,
trees with holes or nests or tinder fungus, bark fractures, living trees with fractured stems/crowns, upright root system of fallen trees)
Werner Mücke, TUW-IPF
Werner Mücke, TUW-IPF
Beech forests
Forest development stages / Spatial Structure /
Growth categories √ Deadwood • Trees as habitats (very old trees,
trees with holes or nests or tinder fungus, bark fractures, living trees with fractured stems/crowns, upright root system of fallen trees)
Beech forests
Forest development stages / Spatial Structure /
Growth categories √ Deadwood • Trees as habitats (very old trees,
trees with holes or nests or tinder fungus, bark fractures, living trees with fractured stems/crowns, upright root system of fallen trees)
?
Hay meadows
• Stratification, internal structure
• Spatial Vegetation Structure (Changes in sub-types on a fine scale ; small-scale Mosaic with other Grassland habitats)
• Structure of location and places
with special site conditions (Mosaic or uniform)
Categorizing grassland vegetation in lowland hay meadows with full-waveform airborne LIDAR: a feasibility study for Natura 2000 András Zlinszky, Anke Schroiff, Adam Kania, Balázs Deák, Werner Mücke, Ágnes Vári, Balázs Székely and Norbert Pfeifer
Grasslands
• Stratification, internal structure
• Spatial Vegetation Structure (Changes in sub-types on a fine scale; small-scale mosaic with other grassland habitats)
• Structure of location and places
with special site conditions (Mosaic or uniform)
Pannonic grasslands
Modelling of soil properties in a NATURA 2000 habitat site in the Carpathian Basin Bernadett Gálya, Éva Bozsik, Nikolett Szőllősi, Péter Riczu, Lajos Blaskó, János Tamás, Balázs Deák, Katalin Bökfi and Hermann Heilmeier
Pannonic grasslands Pannonic salt steppes and marshes (1530)
Diverse habitat complex
Salt affected communities
Influenced by the ground-water level
Species poor vegetation
Pannonic loess steppic grasslands (6250) Situated on higher
elevations
Chernozemic soils
Short grasslands
Species rich
Indicators of vegetation types
Strong correlation between abiotic parameters and vegetation
Groundwater and soil salinity correlates with elevation
Correlation between vegetation type and micro-topography
Elevation profile
Beck m. Alop m. Alop m. Phol s. Achi s. Camp s. Pucc l. Camp s. Achi s. Loess p.
LG – loess grasslands LP – Loess pastures AS – Alkali steppes AR – Artemisia steppes AC – Achillea steppes OA – Open alkali grasslands CA – Camphorosma swards PL – Plantago swards PH – Pholiurus swards AM – Alkali meadows AM – Alopecurus meadows BM – Beckmannia meadows
Classified maps
Kappa Statistic Producer’s Accuracy User’s Accuracy F1 Score
Level 4 55.3 57.9 59.2 55.8
Level 3 64.9 68.9 69.3 66.2
Adding Topographic Position and Topographic Wetness Index to DTM
Adding Topographic Position and Topographic Wetness Index to DTM
1 2 3 4 5 6 7 8 Producer’s Accuracy (%)
Cynodonti-Poëtum (1) 18 544 369 0 0 0 0 0 1.93
Achilleo-Festucetum (2) 5 8929 58 0 3 137 874 0 89.24
Artemisio-Festucetum (3) 0 511 0 0 65 35 0 0 0.00
Camphorosmetum annue (4) 0 0 62 315 3 361 0 0 42.51
Pholiuro-Plantaginetum (5) 0 52 0 484 761 568 0 0 40.80
Puccinellion limosae (6) 0 8 0 97 29 612 0 0 82.04
Agrostio-Alopecuretum (7) 0 256 0 0 137 0 8430 0 95.55
Agrostio-Beckmannietum (8) 0 0 0 0 0 0 86 870 91.00
User’s Accuracy (%) 78.26 86.69 0.00 35.16 76.25 35.73 89.78 100.00 Cohen’s kappa coefficient κ: 0.72
HQI – Forests
Trees with very straight trunks Trees with multiple trunks Canopy gaps (natural) − Standing dead trees − Toppled trees with roots Dense shrub layer ? Very high herb vegetation
HQI – Grasslands
Over-, undergrazed, mown and abandoned grasslands ? Trampled areas Bare alkali soil surfaces Erosion channels Tussocks
Mücke (2014)
“Key factors“ for assessing a N2000 habitat
Diameter classes for trees Amount of dead wood
“Key factors“ for assessing a N2000 habitat
Diameter classes for trees Amount of dead wood • Habitat trees: size multiple trunks − “deformations“
“Key factors“ for assessing a N2000 habitat
Diameter classes for trees Amount of dead wood Habitat trees ? Species composition
“Key factors“ for assessing a N2000 habitat
Diameter classes for trees Amount of dead wood Habitat trees ? Species composition Information on management, e.g. missing shrub layer
“Key factors“ for assessing a N2000 habitat
Diameter classes for trees Amount of dead wood Habitat trees ? Species composition Information on management Patchiness of shrub layer − Indicator species for disturbances, e.g.
stinging nettle, elder Amount of canopy gaps
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Lacunarity calculation
Figures: Turcotte 1997 p.109, p.111
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Various methods, popular: Allain & Cloitre (1991), 1D & 2D
Székely et al. | Voxelized LIDAR data and lacunarity: testing the idea | FORDISMAN 2014 | Aug 11-14, 2014, Tartu, Estonia
Animation: Karperien et al. 2013
1D window moves around (r varies)
2D window moves around (window size varies)
Point cloud visualization: A. Zlinszky
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Visualization of voxel slices / lacunarity curves
Voxel slices (increasing elevation) Lacunarity curves
Székely et al. | Voxelized LIDAR data and lacunarity: testing the idea | FORDISMAN 2014 | Aug 11-14, 2014, Tartu, Estonia
A thin moving window
Test area: a hilly area in Hungary
ALS: commercial FWF scanning
Forest type: Sessile oak (Quercus petraea)
dominated
What Airborne Laser Scanning Can Do Habitat mapping and monitoring of large areas
within a short time; Estimate processes across the landscape which is
not possible with field work • Using multiple neighbourhood sizes • Using multiple resolutions
Estimation of vertical structure in forests difficult through field work;
Less subjective; Identify areas for field-based assessment.
Scale Higher resolution (smaller pixel size) of input
variables does not always mean better accuracy of classification
• “Salt-and-pepper” effects • Does not relate to the size of the studied feature
Different scales for computing variables may relate
to different ecological factors and processes • Depth of water table • Soil moisture • Under-storey light conditions
Criteria for HQA Species
• ALS has limitations in classifying species • Complementary remote sensing data such as
Hyperspectral data may be useful Structure
• ALS has an advantage over other remote sensing data
Disturbance • Very important for assessing HQ • Artificial linear features can be detected using ALS
data • Classification of invasive species in the shrub layer
may be difficult Indicators for invasiveness of a community
Criteria for HQA Currently used criteria were developed for field-
based assessment. May be necessary to modify and develop new
criteria, if required, for assessment based on remotely sensed data.
A few of the criteria, related to the structure of forests, can be directly obtained from ALS data.
• Canopy cover • Lying deadwood
It may be necessary to identify indicators for others Grass cover/tall herbaceous vegetation in forests
Generally: RS should be used for HQ features which are not possible to estimate in the field, e.g. Quantification of forest canopy structure Factors affecting HQ at large scales