INVENTORY? - MEASUREMENTS & - BASIC STATISTICS ARE IMPORTANT FOR PRUDENT FOREST MANAGEMENT

Transcription

1 INVENTORY? - MEASUREMENTS & - BASIC STATISTICS ARE IMPORTANT FOR PRUDENT FOREST MANAGEMENT BY BALOZI AND JARNO

2 Contents What is ArboLiDAR? Input data Sampling and field work Automatic stand segmentation Inventory model Result calculation

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7 ArboLiDAR

8 ArboLiDAR What is ArboLiDAR? Forest inventory process and software developed by Arbonaut Ltd Combines remote sensing data, field data and mathematical modelling Goal is to produce reliable and accurate inventory information Contains both stand segmentation and forest inventory

9 ArboLiDAR is a tool package which runs in ESRI ArcInfo, ArcEditor and ArcView environments Open source (GDAL) version Sampling tools ArboLiDAR Tools

10 Input Data

11 Input Data Laser Scanning Light Detection And Ranging Three components: Distance measurement by laser Inertial measurement GPS The result is 3D-point cloud (x,y,z coordinates) Number of observations varies 3D description of vegetation

12 Removal of overlapping flightlines Point classification vegetation ground error Digital terrain model (ground points) Z-values to heights from ground level LiDAR data processing

13 Field reference plots

14 Field reference plots Sampling Sampling and measurements are done either by customer or Arbonaut Sampling SRS Clusters Weighted sample with LiDAR Field reference data is required for each inventory area Usually plots If there is a lot of variation in the project area, more plots are required

15 Field reference plots Coverage analysis Does the sample represent the target area?

16 LiDAR vegetation height Field reference plots Coverage analysis LiDAR vegetation density

17 Field reference plots Measurements Usually 9 m fixed radius plots (In Finland) The GPS coordinates of the center point are carefully recorded and afterwards differentially corrected Diameter + species of all trees Height from sample trees

18 Field reference plots Calculations and checking Has the field data been collected at the same time as the remote sensing data? Tree level calculations Height models Volume models Tree level data plot level data Average height and diameter Volume, basal area and stem count /ha Checking the plot level calculations with lidar data Measurement / location errors Clearing / thinning operations

19 Automatic segmentation of forest stands

20 Automatic segmentation Background Forest stands are the basic units in forest management (criteria: timber size, species composition) Traditionally done with manual editing using aerial photographs Automatic forest stand segmentation is based on information derived from LiDAR size of trees Aerial photographs (optional) tree species composition

21 Automatic segmentation Additional processing Automatic smoothing Property boundaries Roads, powerlines.. Automatic and manual editing Final operational units are combined by the customer

22 Inventory model & result calculation

23 Inventory model Totals and species-specific estimates Seedling and mature forest results are usually produced as a separate process Also class variables (thinning, clearing) Non-parametric methods K-MSN the values are calculated as weighted averages of k neighbours Sparse bayes Linear regression models for each forest attribute results forced to logical with optimization rules

24 Estimated volume Inventory model How good is it? Accuracy of estimates on plot level Independent test data Control measurements in the field Multiple plots / stand Measured volume

25 Inventory result calculation Results (stand level forest attributes) are calculated to Stands Grid Data is usually divided based on lidar height No inventory Seedling Mature stands

26 Inventory result calculation Stands are divided to small estimation units Unit size = sample plot size Lidar variables and other variables are calculated to estimation units Result calculation Automatic aggregation of stand level results Also an estimate of the reliability

27 Thank you!