Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data

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1 Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data Aleksi Räsänen*, Anssi Lensu, Markku Kuitunen Environmental Science and Technology Dept. of Biological and Environmental Science University of Jyväskylä GINorden,

2 Background Outline of the presentation General idea Different approaches in nature values screening Our Approach Study areas, data and methods Preliminary results Future work Photo: Paula Kinanen 2

3 Why to screen nature values? Currently, biodiversity impact assessments are usually done late in land use planning processes Little changes can be made to plans Nature values are mostly assessed with rigorous field work which is expensive and time consuming Nature values can be screened with GIS methods and remote sensing data Provides preliminary information about nature Photo: Paula Kinanen 3

4 How to study biodiversity/nature values (list is not exhaustive!) Habitats or species as surrogates for biodiversity Habitat/biotope classification & ranking Manual RS interpretation Automated classification Habitats ranked, e.g. by the potential number and rarity of species Habitat modelling for different species Known habitat preferences E.g. with LiDAR & other RS data Known existence of species -> modelling & extrapolation Biodiversity / plant-species richness assessment with RS data E.g. NDVI, spectral heterogeneity 4

5 Nomenclature: VHR and OBIA Very high spatial resolution (VHR) remote sensing (RS) data Pixel size less than 10 m e.g. aerial photos, new satellite images (Quickbird, WorldView), laser scanning data Pixel based classification would result in salt-and-peppereffect Object based image analysis (OBIA) Objects instead of pixels are analyzed and classified Objects are distinguished with segmentation Should mimic human perception of objects Object oriented and object based at the same time 5

6 Our approach Based on habitat ranking (Rossi & Kuitunen 1996) Goal is to update the method to GIS & RS era and to improve it Two goals Automated habitat classification & ranking Ranked by potential number (& rarity) of species Mapping valuable nature spots Possible locations of spots May predict more spots than actually exist Pulmonaria obscura, photo: Tuomo Kuitunen 6

7 Study areas Taka-Keljo-Isolahti in Jyväskylä and Muurame municipalities, Central Finland 70 km 2 Diverse, includes old forest conservation areas Luopioinen, Eastern part of Pälkäne municipality, Tampere Region 400 km 2 Diverse area with many herb rich forest patches, lakes and calcareous rocks Presence/absence data of 700 plant species from 1 km 2 quadrats collected by Tuomo Kuitunen 7

8 Taka-Keljo-Isolahti (N 62 1, E 25 4) Image: World View 2, , Digital Globe2010 8

9 Luopioinen (N 61 2, E 24 4) Image: IMAGE2000, EC JRC 9

10 Data Remote sensing data WorldView2 satellite imagery (Taka-Keljo only) 8 bands, resolution 2 m, year 2010 Aerial images (TK & part of Luopioinen) 4 bands, resolution 20 cm(tk) or 50 cm (L), 2007 and 2010 LiDAR (TK & part of L) Landsat & Image2000 (25 m resolution) from early 2000 GIS datasets NLS Topographic Database Soil and bedrock maps CLC 2006, CLC 2000 National forest inventory data Ground water maps Data for supervised classification & accuracy assessment Forest inventory & compartment data Own field work data of habitat classes 10

11 Methods / analysis steps 1. Segmentation step Both region growing & watershed delineation tested WV2 / aerial image data used 2. Evaluation of segmentation 3. Classification Several variables derived from data Different supervised methods tested: classification trees, automated analysis (e.g. random forests) 4. Uncertainty assessment Not in strict order, e.g. some classification can be done before segmentation 11

12 Segmentation Digital Globe

13 Evaluation of segmentation Different methods and parameter choices tested Digital Globe

14 Classification: A rough list of variables used in classification Satellite & aerial imagery Spectral variables (e.g. mean reflectance, standard deviation, NDVI) Textural (GLCM) variables (e.g. entropy, contrast) Frequency variables (e.g. local Fourier transform, wavelets) LiDAR (Micro)topography/geomorphometry (e.g. slope, aspect) Tree stand structural characteristics (e.g. height) GIS data Soil, bedrock NLS topographic database classification (NFI classification) 14

15 Classification: List of habitats (approx. 30 classes) Forests Herb-rich, mesic/moist, dry; all in 4 successional stages Springs, rocky areas Mires / peatlands Open, pine, spruce; all drained / not drained Meadows Mesic/wet, dry Riparian habitats, flooded areas, beaches Waters Oligotrophic lakes, eutrophic lakes, streams and rivers Fields, roadsides, gardens 15

16 Classification results Digital Globe

17 Uncertainty assessment Variation between and within classes assessed Gives information about uncertainty and classification accuracy The class of some segments is certain and for some not so certain Classification accuracy evaluated also with the help of field work Part of the field work data left to this purpose In image: Tone: class Dark areas: more certain Light areas: less certain 17

18 Preliminary results: habitat ranking Highest rank is given to habitats with high diversity or high number of rare species In image: red=low value, green=high Connectivity and complementarity are taken into account in future work Digital Globe

19 Preliminary results: Single valuable nature spots Different possibilities, e.g. Small segments classified to different habitat than surrounding segments Segments that have some distinct features Only probable locations Must be verified with the help of field work Jyväskylä municipality

20 Problems and issues to consider How to classify forest habitats in Finland in RS era? Cajanderian classification is based on field layer Classification only roughly to pine, spruce & deciduous forest? Ancillary data is valuable in more precise classification How to weigh current value vs. potential value? Problems with successional stage and human impact Automated analysis vs. map evaluation by an expert Mapping on several scales needed Both large areas and ecological networks as well as small patches are valuable How to compare the value of large areas vs. small hot spots? Some field work or at least general knowledge of the area is always needed! Automated analysis can reduce the amount of field work or can be used in screening/scoping phase 20

21 Issues to consider in future work Landscape ecological knowledge Landscape should be considered as a whole instead of looking into separate valuable spots Connectivity analysis, landscape pattern analysis What are nature values after all and why to conserve nature? Ecosystem services, biodiversity and rarity, naturalness, scenic values etc. Partly contradictory goals, different areas can be valuable due to different reasons Linking work to practical and real world land use planning problems Societal, ecological, economical aspects 21

22 Thank you! Acknowledgements Maj and Tor Nessling Foundation University of Jyväskylä Finnish Doctoral Program in Environmental Science and Technology (EnSTe) photo: Tuomo Kuitunen 22

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