Classification of natural areas in northern Finland using remote sensing images and ancillary data

Hatunen, Suvi; Härmä, Pekka; Kallio, Manne; Törmä, Markus

doi:10.1117/12.800170

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Thus information also on ground vegetation and soils are needed in order to define CLC classes. These data were interpreted using decision tree approach (See5 by RuleQuest24) with the aid of IMAGE2006, biotope maps and national GIS data 14 . One of the benefits of this kind of classifier is that variables can be continuous like satellite images or estimation results, or categorical like map layers or previous classification results.…”

Section: Classification Of Northern Finlandmentioning

confidence: 99%

Change detection for Finnish CORINE land cover classification

et al. 2011

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This paper describes the ideas, data and methods to produce Finnish Corine Land Cover 2006 (CLC2006) classification. This version is based on use of existing national GIS data and satellite images and their automated processing, instead of visual interpretation of satellite images. The main idea is that land use information is based on GIS datasets and land cover information interpretation of satellite images. Because Finland participated to CLC2000-project, also changes between years 2000 and 2006 are determined. Finnish approach is good example how national GIS data is used to produce data fulfilling European needs in bottom-up fashion.

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Section: Classification Of Northern Finlandmentioning

confidence: 99%

Change detection for Finnish CORINE land cover classification

et al. 2011

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“…On the basis of multi-temporal Landsat images and a fusion of SPOT panchromatic data with the Landsat, several vegetation classes (including different forest types, maquis classes differentiated by their average heights, pseudo-steppe and scrub vegetation) were recognized (GRIGNETTI et al 1997). A decision tree classifier incorporating, optical and microwave remote sensing data as well as DEM and soil information was developed for the classification of broad habitat classes in Finland (HATUNEN et al 2008). The classified habitats were pine, spruce, deciduous forests, two classes of mountain birch, open bog, grasslands, heathlands and open rocks.…”

Section: Broad and Detailed Habitat Mapping Using Remote Sensing Techmentioning

confidence: 99%

Earth Observation for Habitat and Biodiversity Monitoring

2013

GI_Forum 2013 – Creating the GISociety

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The special workshop "Earth observation for ecosystem and biodiversity monitoring -best practices in Europe and globally, at GI_Forum 2013, focused on the outcomes of the EUfunded projects MS.MONINA and BIO_SOS and related activities that highlight the potential of Earth observation data and technologies in support of biodiversity and ecosystem monitoring. In Europe, nature conservation rests upon a strong, yet ambitious policy framework with legally binding directives. Thus, geospatial information products are required at all levels of implementation. With advances in Earth observation data availability and the forthcoming of powerful data analysis tools we enter a new dimension of satellitebased services. Recent achievements of such endeavours were showcased and challenges discussed, using best practice examples from both inside and outside Europe. This article summarizes the state-of-the-art of satellite-based habitat mapping and accommodates the paper contributions in the current scientific discourse.

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“…Images were resampled to 20 meter pixel using cubic convolution interpolation. Geometric correction was quite successful; the mean residuals of the average residuals of individual images were 7.9 and 7.8 meters in X and Y-direction (Hatunen et al, 2008). Clouds and their shadows were detected visually and masked out.…”

Section: Satellite Images and Gis Datamentioning

confidence: 99%

“…Topographic correction was made using the statisticalempirical correction (Itten and Meyer, 1993) where the effect of topographic variations is determined by computing illumination image using DEM, then computing regression line between image channels and illumination image and correcting image by subtracting the product of illumination image and slope of regression line from original image. Shadow areas were also determined during topographic correction (Hatunen et al, 2008). Figure 1 presents the mosaic covering vegetation zones 4c and 4d.…”

Section: Satellite Images and Gis Datamentioning

confidence: 99%