EVALUATION OF FOREST MACHINERY GROUND MOBILITY USING ALS DATA

Transcription

1 EVALUATION OF FOREST MACHINERY GROUND MOBILITY USING ALS DATA Marco Pellegrini, Stefano Grigolato, Raffaele Cavalli TESAF Departement University of Padova IUFRO 3.06 Conference - Forest Operations in Mountainous Terrain Honne (Norway) 3 June 2013

2 Products from ALS survey Increasing availability of data Development of novel methodologies for data analysis LAS DTM DSMs CHM Point metrics: classification, density, spatial distribution

3 ALS data use in forest operation planning DTM DSMs Evaluation of terrain morphology (micro and macro level) Identification of forest road network (under canopy) Precise calculation of forest road construction cost Detection of obstacle Analysis of forest structure (incl. undergrowth) Improved accuracy in forest accessibility evaluation CHM Location of single trees Quantification and qualification of forest growing stock Improved estimation of productivity EVALUATION OF THE OFF-ROAD MOBILITY

4 Elements affecting off-road mobility Flat areas 1. Ground properties (soil type and moisture content = bearing capacity) Alpine conditions 2. Terrain macro-morphology: up-hill and down-hill slope 3. Terrain micro-morphology: presence and number of obstacle (terrain roughness) 4. Presence of vegetation: standing trees, undergrowth 5. Performance of the machine: clearance (possibility to overpass obstacles) sideways stability (maximum lateral inclination) maximum slope (up and down-hill)

5 Layout of the work Input GIS-based Model

6 ALS product to support the analysis - DTM Classification of Ground points choose an appropriate iteration angle for forest environment. Traditional (4 ) More conservative (24 )

7 ALS product to support the analysis Roughness Map LASTool software: LAS ground + LASHeight 1. Extraction of the points below 1.5 m to the ground level. 2. Creation of a DSM using the LAS data subset. 3. Reclassification of the resulting raster

8 Single tree position and DBH layer MODEL BUILDER Local Maximum Detection algorithm using the schm CHM Tree height = 35,21 m Crown projection = 80 m 2 DBH = * canopy_proj * TreeHeight R = 75 %

9 Slope + Roughness + Trees = Trafficability map

10 Inclusion of the maximum lateral inclination backlink Moving direction in degree Slope aspect MTA α max = MTA / (cos(90 - HRMA)) HRMA is the difference between the direction of movement and the slope aspect HRMA = MovAng( ) - Aspect( )

11 Inclusion of limiting terrain slope Inclusion in the path distance tool as vertical cost factor Possibility to choose the maximum (up-hill direction) and minimum (down-hill direction) slope value.

12 GIS-Based model: least-cost path DTM Roughness Map Tree Map Source: Logging Area Destination: Landing Area Not Transitable Areas Limiting Slope Values Maximum Tilting Value Perpendicular movement : Cost_distance = [(Cost_Surface(a) + Cost_surface(b))] / 2 * Surface_distance(ab) * VerticalFactor(ab) Diagonal movement: Cost_distance = * [(Cost_Surface(a) + Cost_surface(b)) /2] * Surface_distance(ab) * VerticalFactor(ab)

13 Test Area NEWFOR Project (Alpine Space) Test-site: Altopiano di Asiago Average annual cutting of m 3 6 forwarder and 3 harvester in the area 330 m m m 3 ALS Survey June 2012 Point density: 11 p/m 2 full-waveform 3 forwarder harvesting site (HSM-208F 12t)

14 Model application LOW LIMITS Slope: -30% - +25% Angle: 4-41 % -57 % HIGH LIMITS Slope: -40% - +30% Angle: 4

15 Model results Linear Distance Extraction Route Err Dist Precision (Proximity) (m) (m) (%) 10m 20m Site 1 Real 550 Site 1 LOW % 58% Site 1 HIGH % 86% Site 2 Real 409 Site 2 LOW % 0% Site 2 HIGH % 97% Site 3 Real 779 Site 3 LOW % 48% Site 3 HIGH % 78% Site 4 Real 783 Site 4 LOW % 98% Site 4 HIGH % 98%

16 Discussion and conclusion ON-GOING DEVELOPMENT Improvement of forest operation planning through the use of ALS data, especially for the evaluation of the accessibility of the logging areas; Model gives a precise evaluation of extraction distance in relation to ground condition (useful to better evaluate working productivity); Limiting values are difficult to be set as often refer to the Human factor. Values higher than conventional limits proves to give better results; Limits of the model and further development: Uncertaintly of roughness map under dense forest canopy (low-density of point reaching ground): improvement using LiDAR metrics (ST-DEV, Full-waveform); Bended trees: wrong tree location; Test in areas where forest operation took place after ALS survey (Real-time GPS monitoring);

17 F***!! The model said FLAT!! Thank you for the attention More information: