regional forest maps by combination of sample surveys and satellite image interpretation tove vaaje...
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![Page 1: Regional forest maps by combination of sample surveys and satellite image interpretation Tove Vaaje Norwegian Institute of Land Inventory Norsk institutt](https://reader036.vdocument.in/reader036/viewer/2022081602/551bf8345503469e4f8b47a1/html5/thumbnails/1.jpg)
Regional forest maps by combination of sample surveys and satellite image interpretation
Tove Vaaje
Norwegian Institute of Land Inventory
Norsk institutt for jord- og skogkartlegging
NIJOS
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Regional forest maps
• Needed for smaller regions
• Useful for:– Area management– Resource management– Area analyses– Business purposes– Etc.
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Østfold Kommune
• A county southeast in Norway
•This area is used because of previous studies in the area
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Data sources
• DMK – digital land use maps
• DEM – digital elevation model
• NFI – National forest inventory
• Satellite images
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DMK
• Digital land use maps (digitalt markslagskart)
• Provides information about the land capability
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DEM
• Digital elevation model
• Can correct image values for the terrain effect
• Can stratify the NFI sample plots in altitude zones
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NFI
• National forest inventory– Based on sample plots laid out in a regular
grid with 3 kilometers distance between plots
– Each inventory cycle is five years– The permanent plots are supplied with
temporary plots
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Satellite Images
• Landsat TM images covering the reference area and the inventory area
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Method
• The method used is MSFI – Multi Source Forest Inventory
• Based on three components:– A defined neighbourhood for each pixel
– An algorithm that finds all the training pixels meeting the neighbourhood definition
– A method to calculate an estimate based on the training pixels in the neighbourhood
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MSFI
• A fundamental assumption is that spectral similarity implies similarity in forest condition the success of the method
relies on the correlation between the spectral and biotic variables
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Previous project
• The municipality of Hobøl northwest in Østfold county
• Analysed the use of MSFI using more than 1000 sample plots
• A program running MSFI was developed for the Norwegian forest
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Results of previous project
• 28 different forest attributes were estimated
• Satisfactory results were obtained for:– Dominant tree species– Top height– Number of conifers– Total number of trees– Mean height of young forest
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Use of data sources (1)
• Satellite images – Used for the spectral analysis– A deviated cloud mask is used to remove
NFI plots covered by clouds
• DMK– Forest mask– Production potential of forest
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Use of data sources (2)
• All the image files need to have the same number of rows and columns, and the same pixel size
• The pixels have to be adjusted to match each other. The satellite image is used as a snap grid
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The MSFI program
• The Norwegian MSFI program, developed by Arnt Kristian Gjertsen, is started with a run control file:
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Segmentation (1)
• Segmentation is performed to make more informative and usable maps
• Sequences from SkoGIS++, seg.exe and zone2vec.exe, are used
• A majority variable for each zone segment is selected
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Segmentation (2)
Not segmented: Segmented:
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Distribution
• To get more information about the data, a frequency commando can be used for the wanted attributes
Specific distribution of a certain maturity
class can easily be presented
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Distribution of maturity classes
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Results (1)
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Results (2)
• Comparison with the NFI statistics for some of the attributes:
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Results (3)
• Maturity classes, as presented, do not give a satisfying result. A new classification has to be introduced:
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Improvement of MSFI
• Issues which need to be solved in a new version of the MSFI-program:
» Areas covered with clouds are classfied in the inventory area. These pixels need to be marked as clouds, and not be classified
» NoData areas in the raster data have the value 0. This makes it possible to choose a NoData area as nearest neighbour
» Areas with high altitude differences is not corrected using the DEM data