what remote sensing (lidar) can do and what not … · criteria for hqa species • als has...

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,


Werner Mücke, TUW-IPF

Beech forests


Growth categories √ Deadwood • Trees as habitats (very old trees,


Beech forests


Growth categories √ Deadwood • Trees as habitats (very old trees,


?

Hay meadows


• Spatial Vegetation Structure (Changes in sub-types on a fine scale ; small-scale Mosaic with other Grassland habitats)



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


• Spatial Vegetation Structure (Changes in sub-types on a fine scale; small-scale mosaic with other grassland habitats)



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


Diameter classes for trees Amount of dead wood • Habitat trees: size multiple trunks − “deformations“


Diameter classes for trees Amount of dead wood Habitat trees ? Species composition


Diameter classes for trees Amount of dead wood Habitat trees ? Species composition Information on management, e.g. missing shrub layer


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

what remote sensing (lidar) can do and what not … · criteria for hqa species • als has...

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