regional forest maps by combination of sample surveys and satellite image interpretation

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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 by combination of sample surveys and satellite image interpretation. Tove Vaaje Norwegian Institute of Land Inventory Norsk institutt for jord- og skogkartlegging NIJOS. Regional forest maps. Needed for smaller regions Useful for: Area management Resource management - PowerPoint PPT Presentation

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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

Regional forest maps

• Needed for smaller regions

• Useful for:– Area management– Resource management– Area analyses– Business purposes– Etc.

Østfold Kommune

• A county southeast in Norway

•This area is used because of previous studies in the area

Data sources

• DMK – digital land use maps

• DEM – digital elevation model

• NFI – National forest inventory

• Satellite images

DMK

• Digital land use maps (digitalt markslagskart)

• Provides information about the land capability

DEM

• Digital elevation model

• Can correct image values for the terrain effect

• Can stratify the NFI sample plots in altitude zones

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

Satellite Images

• Landsat TM images covering the reference area and the inventory area

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

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

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

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

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

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

The MSFI program

• The Norwegian MSFI program, developed by Arnt Kristian Gjertsen, is started with a run control file:

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

Segmentation (2)

Not segmented: Segmented:

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

Distribution of maturity classes

Results (1)

Results (2)

• Comparison with the NFI statistics for some of the attributes:

Results (3)

• Maturity classes, as presented, do not give a satisfying result. A new classification has to be introduced:

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