using open source to analyze canada’s national forest inventory

Canada’s Natural Resources – Now and for the Future

Using Open Source to analyze Canada’s National Forest Inventory.

Chris West and Brian Low

Pacific Forestry Centre

Canadian Forest Service

Natural Resources Canada


Who we are (1)

Canada’s new National Forest Inventory (NFI) is designed to assess and monitor the extent, state and sustainable development of Canada’s forests.


Who we are (2)

Canada’s National Forest Information System (NFIS Canada) is an inter-governmental initiative that is funded jointly by the Federal, Provincial, and Territorial Governments of Canada.

Working together with its partners, NFIS provides authoritative information/data about sustainable forest management in Canada.


Canada’s Forest Sector Facts

Canada has 10% of the world’s forests. 41% of Canada’s surface area is forest or other

wooded lands. In 2004, the forest sector contributed $36.8

billion to the Canadian Economy (3.1% of Canada’s GDP)

(Facts taken from “Important Facts on Canada’s Natural Resources” 2005)


FOSS Technologies

Open Source Technologies utilized include: Mapserver / Proj4. Linux Apache PostgreSQL PostGIS. GDAL suite of tools. GeoTools / JTS Java / Eclipse


Project Overview

NFI employs a “plot-based design consisting of permanent observational units located on a national grid.”

Repeated measurements are conducted over a set time frame and changes in these plots are summarized and reported over time.

NFI is designed to “be able to provide quantitative information on Canada’s Forests and related resources.”


NFI Data

At least 25,000 2km by 2km plots with six layers/themes of data per plot consisting of: Land Cover photo interpreted (Vector Polygons). Land Use photo interpreted (Vector Polygons). Land Ownership photo interpreted (Vector

Polygons). Land Protected Status photo interpreted (Vector

Polygons). Canadian Terrestrial Ecozones (Vector WMS). Land Cover classified from satellite (Raster).


NFI Data Layers Visual


Spatial Analysis

Required for NFI reporting. Needs to support area queries at a national level

down to queries at a much finer level. Generally deal with spatial operations that

include the “by” or “within” (union or intersection) spatial operands operating on area.


A Sample of NFI Reporting Questions

What is the total forested area within a specified region?

What is the area of forested types within a specified region?

What is the area of forest type by age within a specified region?

What is the area of forest types by protection status within a specified region?


Issues to overcome

1. Spatial analysis requires thousands upon thousands of spatial overlays.

2. Mixed raster and vector data make spatial overlays difficult.

3. Large amounts of attribute data are stored in external databases.

4. Data collection is conducted over a long period of time, requiring spatial analysis to be done at regular intervals.


General Approach

1. Load vector NFI spatial data into a Spatial Database (PostGIS).

2. Resample 2km by 2km NFI plots to 5m by 5m cells (Mapserver, SLD, GDAL, custom code).

3. Load resampled data into a RDBMS (PostgreSQL).

4. Run simple SQL queries to generate NFI reports.


Step 1: Load Spatial Data

Load NFI vector plot data into PostGIS. UTM Zones were created to partition the data Mapserver used to publish data as raster images

(OGC WMS).


Resample plot data (1)

Mapserver is used as a “Vector to raster” engine to create raster images of an NFI plot layer and an NFI plot grid. Each NFI plot has a corresponding “grid file”

A 5m by 5m uniquely colored grid (160,000 cells) was created.

The grid files are aggregated into UTM zones using ogrtindex. The index files are referenced in a Mapfile.



Mapserver

Grid Image Plot Image

Plot Layer Spatial Data(PostGIS + SLD)Grid Data



The WMS getmap requests contains the bounding box of 2100 m by 2100 m with a width of 2100 pixels and a height of 2100 pixels.

The request returns a one metre resolution image.

Each 5m by 5m cell in the grid has 25 pixels.



A service was created to take the images and resample them.

This approach allowed spatial data to be stored in a text file.



Simple Overlay example with two four pixel images:

Image 1 Image 2

Overlay1,1

0,0 0,0 1,0

1,1

1,0

0,10,1

Output file

red, green, 2blue, black, 1blue, yellow, 1



The output CSV file is resampled to find the highest number of pixels for each grid cell. The resulting document contains 160,000 rows (one for each 5m by 5m cell) consisting of each resampled colour pair.

The colour representing the plot layer in each record is replaced by its unique polygon identifier.


Example of Resampling

5m

5m

Grid Image (5X5 pixels) Land cover Image (5X5 pixels)

Raster Overlay

Output file

green,blue,10

green,yellow,7

green,pink,8


Resampled 5X5 Cell

green,blue,10

green,yellow,7

green,pink,8

Raster Overlay Output

Resampling Process

green,blue


Load Resampled data intoPostgreSQL

Resampled data gets loaded into one table in PostgreSQL.


SQL Queries to Generate NFIReports

Because the spatial data and the attribute data are both located in a RDBMS, SQL queries can be written to handle the “by” and “within” spatial operands.


Example Cell SummariesGeneration Query

The SQL statement adds the following records in the CELL_SUMMARIES table:

SELECT DISTINCT 'plot1_pelo' as TILE_THEME_ID, c1.theme_value c2.theme_value c3.theme_value c4.theme_value as THEME_VALUE, count(*) as VALUE_COUNT

FROM cell_values c1, cell_values c2, cell_values c3, cell_values c4 WHERE c1.tile_theme_id = 'plot1_landcover' AND c2.tile_theme_id = 'plot1_landuse' AND c3.tile_theme_id = 'plot1_ownership' AND c4.tile_theme_id = 'plot1_protectedstatus' AND c1.cell_num = c2.cell_num AND c2.cell_num = c3.cell_num AND c3.cell_num = c4.cell_num GROUP BY

c1.theme_value,c2.theme_value,c3.theme_value,c4.theme_value;


Sample Output

Sample output from query.

tile_theme_id landcover_value land_use_value ownership_value protected_status_value value_countplot1_pelo 1162986_00040556 1162986_00000038 1162986_00000462 1162986_00000469 445plot1_pelo 1162986_00040559 1162986_00000038 1162986_00000462 1162986_00000469 326plot1_pelo 1162986_00040560 1162986_00000038 1162986_00000462 1162986_00000469 619plot1_pelo 1162986_00040561 1162986_00000038 1162986_00000462 1162986_00000469 683plot1_pelo 1162986_00040577 1162986_00000038 1162986_00000462 1162986_00000469 396plot1_pelo 1162986_00040578 1162986_00000038 1162986_00000462 1162986_00000469 300plot1_pelo 1162986_00040579 1162986_00000038 1162986_00000462 1162986_00000469 780plot1_pelo 1162986_00040580 1162986_00000038 1162986_00000462 1162986_00000469 1033plot1_pelo 1162986_00040581 1162986_00000038 1162986_00000462 1162986_00000469 1314


Why this Approach

Allows the combination of vector and raster data to be spatially analyzed.

Allows user to do spatial queries using SQL. Utilizes the power of a RDBMS. Allows spatial data to be easily joined (SQL) to

attribute data. Easily scalable because open source software is

used.


Acknowledgments

Canadian Council of Forest Ministers (CCFM) Canadian Forest Service, Natural Resources

Canada (CFS, NRCan) National Forest Inventory staff including:

Mark Gillis Katja Power Alex Song

Past Contributors: Robin Quenet Rick Morrison


References

Low , B. (2005). ExPAND Expanding Primary Attributes for National Decision Making, M.Sc. Thesis University of Victoria.

Gillis et al. (2004). About NFI. <https://nfi.nfis.org/about_nfi/index_e.shtml>

Natural Resources Canada (2005). Important Facts on Canada’s Resources.


Questions???

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