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

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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

*[email protected]

GINorden Conference, Turku, June 9, 2011

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Outline of the presentation

 Background

– General idea

– Different approaches in nature values

screening

 Our Approach

– Study areas, data and methods

– Preliminary results – Future work

Photo: Paula Kinanen

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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

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 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

(list is not exhaustive!)

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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-pepper- effect

 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

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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

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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

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Taka-Keljo-Isolahti (N 62˚1, E 25˚4)

Image: World View 2, 4.7.2010, ©Digital Globe2010

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Luopioinen (N 61˚2, E 24˚4)

Image: IMAGE2000, ©EC JRC

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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

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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

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Segmentation

©Digital Globe 2010

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Evaluation of segmentation

• Different methods and parameter choices tested

©Digital Globe 2010

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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)

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(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

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Classification results

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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

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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

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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

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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

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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