Classification of geodiversity in a natural tidal inlet system based on topobathymetric LiDAR data

Transcription

1 Classification of geodiversity in a natural tidal inlet system based on topobathymetric LiDAR data MSc Thesis, September 2015 Mikkel Skovgaard Andersen DANCORE, 2/ , Copenhagen

2 Content 1. Motivation of mapping in the shallow water zone 2. Introduction to geodiversity and topobathymetric LiDAR 3. Study area: Knudedyb, Danish Wadden Sea 4. LiDAR survey 5. Part 1: Processing a Digital Elevation Model (DEM) I. Method: Processing pipeline II. Results: DEM + accuracy assessment III. Discussion: Water surface detection and the dead zone 6. Part 2: Morphological classification I. Method: Two classification methods II. Results: Geomorphometric and morphological classifications III. Discussion: Method and application 7. Conclusions 8. Perspectives

3 Motivation: - why map the shallow water zones? Regulating permissions for fishing and dredging Navigation purposes Archaeological investigations Knowledge on natural coastal processes Predictions of future environmental changes EU aim: Retain good environmental status in EU marine waters by 2020

4 Enhedens navn Introduction: Geodiversity Definition: The natural range (diversity) of geological (rocks, minerals, fossils), geomorphological (land form, processes) and soil features. It includes their assemblages, relationships, properties, interpretations and systems. (Gray, M. (2004): Geodiversity valuing and conserving abiotic nature) Geosphere Biosphere Ecosystem management

5 Introduction: Topobathymetric LiDAR Nadir α air α water Snell s Law: sinα air sinα water = c air c water = n water n air

6 Introduction: Topobathymetric LiDAR Power Power Time Time Dead zone Water Land

7 Introduction: Aim Part 1: Processing a Digital Elevation Model (DEM) in the transition zone between land and water Part 2: Classifying morphological features based on the processed DEM

8 Enhedens navn Study Area Fanø Challenges: Changing water level (tides) Ponds on the tidal flat High turbidity Fanø Mandø 10 km

9 Enhedens navn Study Area Fanø A B A B C E F D C D E F

10 Surveys: LiDAR survey (May 2014) Department of Geosciences and Natural Resource Management

11 Enhedens navn Part 1 Processing a DEM

12 Methods Enhedens navn

13 Methods: Creating a topobathymetric DEM Raw data Strip adjustment Filtering Point cloud To DEM Refraction correction Water surface detection

14 Methods: Creating a topobathymetric DEM - Filtering Elevation (DVR 90) 0 m 4 m metres Water surface detection Refraction correction

15 Methods: Creating a topobathymetric DEM Water surface detection 1. LiDAR Point cloud 2. Shallow surface & Deep surface 3. Water surface elevation 4. Water surface

16 Methods: Creating a topobathymetric DEM Refraction correction α water = sin 1 sinα air n air n water n air = n water = 1.33 α air = 20 ± 1

17 Results Enhedens navn

18 Results: Topobathymetric DEM Elevation (m DVR90) >

19 Results: Topobathymetric DEM Elevation (m DVR90) >

20 Results: Elevation (m) 21 Topobathymetric DEM 10 Elevation (m DVR90) >

21 Results: Vertical accuracy and precision 2.5 x 1.25 x 0.8 m 0.92 x 0.92 x 0.30 m GCPs LiDAR points LiDAR points Accuracy = ±8.1cm Precision = ±7.6cm Precision = ±3.8cm

22 Discussion Enhedens navn

23 Discussion: Water surface detection method + Simple concept, easy to repeat + Water surface also covers the dead zone + Indications of reliable results Waves and sloping water surface are not modeled - Ponds are not modeled Involves manual processing

24 Discussion: The dead zone 25 cm Mean Low Water Mean Water Level Mean High Water

25 Discussion: The dead zone 6 cm 25 cm

26 Enhedens navn Part 2 Morphological classification

27 Enhedens navn Methods Classifying geomorphometry Classifying morphological features

28 Methods: Classifying geomorphometry Department of Geosciences and Natural Resource Management Benthic Terrain Modeler Bathymetric Positioning Index (BPI) Fine scale BPI Broad scale BPI

29 Methods: Classifying geomorphometry Department of Geosciences and Natural Resource Management DEM Classification classes: Fine scale BPI Broad scale BPI Small scale crest Large scale crest Depression Slope Flat Slope

30 Methods: Classifying morphological features

31 Methods: Classifying morphological features

32 Methods: Classifying morphological features

33 Methods: Classifying morphological features

34 Methods: Classifying morphological features

35 Methods: Classifying morphological features

36 Results Enhedens navn

37 Results: Geomorphometric classification Classification DEM

38 Results: Morphological classification Morphology

39 Discussion Enhedens navn

40 Enhedens navn Discussion: Morphological classification method + Successful classification of morphological features at different spatial scales + Straight forward concept Specifically developed to suit this study area Subjective scale determination

41 Enhedens navn Discussion: - what is it good for? Knowledge on morphology and processes in a challenging environment Knowledge on the physical foundation in ecosystem management Geosphere Biosphere

42 Conclusions Proposed method for processing raw topobathymetric LiDAR data New method for water surface detection A seamless topobathymetric DEM of 0.5 m resolution is created Maximum LiDAR penetration depth: 3 m Dead zone: 25 cm Vertical accuracy (95% confidence level) = ±8.1 cm Classification of 6 morphological classes: Swash bars, linear bars, beach dunes, intertidal flats, intertidal creeks and subtidal channels High-resolution topobathymetric LiDAR provides a promising tool for mapping morphological features at different spatial scales in the coastal zone

43 Enhedens navn Perspectives Full-waveform LiDAR processing Closing the gap in the dead zone Classification of materials Multi-temporal LiDAR surveying Improve point cloud processing pipeline More automatic workflow Modeling of inclined water surface (and waves)

44 Thank you Department of Geosciences and Natural Resource Management