Project SLOPE Forest Monitoring

Project SLOPE
1
WP 2 – Forest information collection and
analysis

SLOPE WP 2 – Task 2.1
Andrea Masini, PhD
Remote sensing and multispectral analysis
Remote Sensing Department
Flyby S.r.l.

Task 2.1: participants
• CNR
• Coastway
• Flyby S.r.l. (Task Leader)
• TreeMetrics

Task 2.1: general description
1. Define a methodology to obtain a description of the scenarios using
available remote sensing data (From satellite, UAV and on ground
instrumentation)
2. Define how to realize a more complete forest inventory
AIMs:
Flyby
Define the approach
to monitor tree
growth and health in
mountainous
environment (E.g.
using different
vegetation indexes)
CoastWay/Treemetrics
Define the approach to
monitor the forest using
UAV and on ground
sensors
CNR/Flyby
Define the approach to fuse
heterogeneous information
(derived by satellites or other
instrumentations)
All task participants
Design of the architecture for the forest database
Participants Role

GANTT
01/2014 02/2014 03/2014 04/2014 05/2014 06/2014 07/2014 08/2014 09/2014 10/2014
START of Task 2.1 activities
1° Draft deliverable D2.01 to the
partner for contributions
Expected contributions from partners
2° Draft deliverable D2.01
DeliverableD2.01 ready
Before the task start
Satellite data acquired on a test area agreed with the task partners

Working on a case study
IRELAND
Rapideye Data available for SLOPE Partners

Task 2.1: expected output
• Deliverable D2.01 (month 8 – August 2014) :
Report on remote sensing data collected, on the
methodologies and the algorithm to extract needed
information and on the generated output

1° DRAFT D2.0.1 / index
1. General view on remote sensing
2. Remote sensing for forests study
3. Geological mapping and DEM extraction
4. The satellite sensors considered in SLOPE
5. The UAV platform considered and its sensors
6. On ground remote sensing sensor considered
7. METHODOLOGY
8. Preliminary results analysis : Ireland test case

Chapter 1 : General view on remote sensing
2° Meeting
1 General view on remote sensing
1.1 The electromagnetic spectrum
1.2 Sensors
1.2.1 Passive sensors
1.2.2 Active sensors
1.2.3 Earth Observation satellites

Chapter 2:
2 Remote sensing for forests study
2.1 Forest composition and vegetation behavior
2.1.1 Vegetation reflectance
2.1.2 Spectral vegetation indices
2.1.3 Biophysical parameters of forests
2.2 Data for forest inventories
2.3 Long-term time series of spectral vegetation indices
2.4 SMA Spectral Mixture Analysis

Other chapters are under costruction

METHODOLOGY
Define the type of
information
Define how to integrate all
available information
Define how to deliver
information

Andrea Masini, PhD
CTO
Flyby s.r.l.
Corso Ferrucci 77/9, 10138 Torino, Italy
Via Puini 97, 57128 Livorno, Italy
www.flyby.it
Tel: (+39) 0586-505016
Fax: (+39) 0586-502770
Mobile phone: (+39) 393-9976370
Thanks

Identification of Forest plantation on Google Earth

Flight Plan uploaded to Auto Pilot in accordance with
CAA / IAA / European Aviation Authority Regulations

Data Acquisition and Processing

DEM/ DTM / DCM /Crown Sizes / Animated views
Cross section created through the combined forest data
Software Used
• Faro Scane FLS Files
• Leica Cyclone PTS Files
• Cloud Compare LAS / PTS Files
• Post Flight Terra 3D

DEM/ DTM / DCM /Crown Sizes / Animated views
Faro Scene
(.fls)
Emotion 2
Cyclone (.pts)
Postflight Terra
3d
CloudCompare
(.LAS/Z Files)

Data Acquisition & Processing
Cross section through forest created using point tools software
Lidar Data combined with Aerial point cloud using Cloud Compare

Example of Data to Follow
Example of Survey Control Markers located on site

Coastway – UAV and Payloads
96cm wingspan
- less than 0.7kg take-off weight
- 16MP camera, electronically integrated
and controlled
- Lithium polymer battery
- 50 minutes of flight time
- 36-57km/h (10-16m/s) cruise speed
- Up to 45km/h (12m/s) wind resistance
- Up to 3km radio link
- Covers up to 1.5-10km2
- Linear landing
- Image resolution of 3-30cm/pixel
(depending on flight altitude)

Payloads
S110 NIR Standard
Example applications: biomass indication, growth monitoring, crop
discrimination, leaf area indexing.
This customised 12 MP camera is electronically integrated within
the eBee’s autopilot. The S110 NIR acquires image data in the near
infrared (NIR) band, the region where high plant reflectance occurs.
Its exposure parameters can be set manually and its RAW files are
fully supported by the eBee Ag’s software
The multiSPEC 4C is a cutting-edge sensor unit developed by
Airinov’s agronomy specialists and customised for the eBee Ag. It
contains four separate 1.2 megapixel sensors that are electronically
integrated within the eBee’s autopilot. These sensors acquire data
across four highly precise bands, plus each sensor features a global
shutter for sharp, undistorted images.
S110 RGB Optional
Example applications: real colour 2D and 3D visual rendering,
chlorophyll indication, drainage evaluation.
This customised 12 MP camera is electronically integrated within
the eBee’s autopilot. The S110 RGB acquires regular image data
in the visible spectrum, plus its exposure parameters can be set
manually and its RAW files are fully supported by the eBee Ag’s
software.

If you do one flight with a RGB camera, and then
another flight with a NIRGB (NearInfrared-
Green-Blue) camera, you can load both datasets
in the software and label them differently (e.g.
RGB and NIRGB) in the initial screen. The
software will do the initial calibration using
geometric information of both datasets, and
your results will be two orthomosaics matching
the band configuration of the original datasets:
one with an RGB bandset and one with NIRGB
bandset. To compute a vegetation index, you
would typically need to combine with a third
party software the first band of the NIRGB
mosaic together with the two last bands of the
RGB mosaic.

Development by UAV manufacturer for
Agricultural Mapping
Survey-grade aerial mapping
Collect aerial photography to produce
orthomosaics & 3D models with absolute
accuracy down to 3 cm - without Ground
Control Points. The eBee RTK features a
built-in L1/L2 GNSS receiver. This allows it
to receive correction data from most
leading brands of base station. Its 16 MP
camera can shoot imagery at a resolution of
down to 1.5 cm/pixel. These images can
then be transformed into orthomosaics &
3D models with absolute accuracy of down
to 3 cm / 5 cm – without the need for GCPs.

Overall Progress ofWP 2
•Equipment Purchased
•Flight Manual drafted and passed by the IAA & CAA
•Staff Trained and licences updated to allow flights outside of Ireland & UK (no combined regulation in
Europe yet)
•On board GPS tested against ground targets results +/- 100mm
•Combined tests carried out with Treemetrics at Gortahile Forest using Laser Scanning & Aerial imagery
•Flights carried out with different payloads RGB & NIR Cameras, Multi Spectral available for Trento
•Test site results will be uploaded to Slope dropbox, we need to agree who needs the data and format
•Test sites identified in Trento and Austria
•Written to ENAC – Italian Aviation Authority requesting permission to fly.

WP2 %Tasks Completed / Planning / Recommendations
• Trial in Ireland not listed but was critical to provide staff with training and familiarity with equipment
• Both data collection SME’s built a rapour and task force capable of the WP requirements
• Methodology is now in place and should run smoothly, I estimate T1.2 is 50% complete.
• Planning to carry out tests in Trento last week of July 2014
• Recommendations
• Agreement from the forest owners
• Permission from ENAC is critical
• Testing on the GPS & GPRS Service at the test sites is critical
• Agreement on the data sets, file types and deliverables critical prior to commencing

TrentoTest Site UAV Launch and Landing Sites

Presentation ofTasks Completed – Integration WP2.2
Separation of Point Cloud to aid creation of DEM by
classifying ground data from canopy data

Presentation ofTasks Completed – Integration WP2.2
Separate the DTM from the Point data enables modelling of the trees

Tasks Completed – Data collection
A combination of the Infrared,RGB, and Lidar point cloud data enables
the creation of a 3D model of the Forest

OngoingTasks
On going refinement of Methodology of data collection
Communications with Slope Partners
Communications with European Aviation Authorities
Logistics flight planning and team on the ground.
Refinement of canopy and forest modelling
Dissemination of data & reporting on achievements
Developing semi automated system, viewing trends in the industry
Viewing the market place and uses for the Slope product.

TreeMetrics
“PROVIDE MORE END PRODUCT FROM LESS
TREES”

The Products
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
• Taper Variation
• Straightness
• Branching
• Rot etc.

The Products: General Values
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp = €20 per M3
Large Sawlog = €60 per M3
Small Sawlog = €40 per M3

The Problem - “The Collision of Interests”
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?

Maximise Value
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?

Maximise Value: Sawlog Lengths
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
3.7mOption 1

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.3mOption 2

14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
7cm
14cm
16cm
7cm7cm
Pulp
7cm
PulpPulp M3?
Large Sawlog M3?
Small Sawlog M3?
4.9mOption 3

Log Quality: Straightness (Sweep), Taper,
Branching ,Rot,

Forest Mapper - First In The World – Online Forest
Mapping & Analysis - Data Management System

Forest Mapper: Automated net area calculation,
stratification and Location for ground sample plots
to be collected
Sample
Plots
Net Area
Stratification
(Inventory
Planning)

Supporting different field data collection tools, GPS,
Calipers, Vertex,

Terrestrial Laser Scanning Forest Measurement System
(AutoStem Forest)
Automated 3D Forest
Measurement System

Output From Field Survey
• XYZ Position of each tree
• Measurement Information
• Species
• Other information
– Defects
– etc

Mobile Field Survey App – Report Sharing -
Interconnectivity

Latest Development
• Online Market Place
• 15,000 forest owners
• Irish Farmers Association

Task 2.4 - 3D Modelling for
harvesting planning

• Objectives;
• Scheduling;
• Participants and roles;
• Overview and timeline;
Outlook

Objectives
Task 2.4 Goal: To generate and make accessible a detailed
interactive 3D model of the forest environment.
The WP’s purpose is to develop methodologies and tools to
fully describe terrain and stand characteristics, in order to
evaluate the accessibility for and efficiency of harvesting
technologies in mountain forests.

Scheduling
Start Month: 7
End Month: 15
Deliverable: Harvest simulation tool based on 3D forest model
Total MM: 20
Task leader: GRAPHITECH;
Participants: CNR, KESLA, COAST, BOKU, GRE, FLY, TRE

Participants role
GRAPHITECH(10): Task Leader. It has in charge the development of tool for representing
the virtual 3D environment of the mountain forest as well as the of the virtual system
on mobile and machine-mounted displays. Finally it will be involved into the
developmet of the solution for interactive cableway positioning.
CNR(1): Definition of the “technology layers” (i.e. harvest parameters) and
methodologies to coordinate tree marking with the subsequent harvesting operations.
KESLA(1): Acting as final user in order to simulate the behaivor of own machine into the
virtual system
COAST(2): Provide the input model for the virtual system combining the information of
task 2.1, 2.2 and 2.3

Participants role
BOKU(2): it will be involved into definition of the “technology layers” (i.e. harvest
parameters) then on the developmet of the solution for interactive cableway
positioning.
GRE(1): Acting as final user in order to simulate the behaivor of own machine into the
virtual system
FLY(1): Provide the input model for the virtual system combining the information of task
2.1, 2.2 and 2.3
TRE(2): Development of the Forest WarehouseTM for mountain forestry and support
the deployment of the virtual system.

Functions
• Forestry measurements estimations;
The platform will allow the combination of accurate tree profile information
with up to date remote sensing data.
• Interactive system for cableway positioning simulation.
• Definition of the “technology layers” (i.e. harvest parameters);
Technological layers show technical limitations of machines and
equipment on different forest areas.
• Deployment of the virtual system on mobile and machine-mounted displays.

Two levels of abstraction
1St Level: 2D map accessing
of forest and logistic
information inlcuding:
Cadastral, Volume of timber,
accessibility.
Where available, the system
allow access to the SLOPE
information system

Two levels of abstraction
2nd Level: 3D map accessing
of forest tree by tree features
allowing interaction and
simulation of cable crane
positioning

Timeline
Defining the first version of the 3D forest model, Partner involved (TRE, COAST, FLY);
Interface to access to the FIS database, including OGC services, both for 2D and 3D (Task
5.1+BOKU);
Cable Crane simulation tool (GRE);
Final version

Platform Core
Using the remote data (Satellite, UAVs orthophotos and digital surface model) combined
with on field information (TLS), each single tree feature will be segmented including its
deducted geometric properties.
Task 2.1
Task 2.2
Task 2.3
3D forest model
Virtual 3D
environment

Platform Core
Canopy surface model Laser scanner point cloud Aerial & satellite images

3D Modelling for harvesting planning
What Technologiy for 3D forest modelling?
Realistic rendering Parametric model Point cloud visualization

3DVisualizationTechnologies
Approaches
• Desktop Visualization Platform
with Mobile Porting
• Web-Client Visualization
Platform
Desktop Platform
• Open-Source Library for 3d
visualization (OpenInventor, Vtk,
Openscenegraph)
• 3d Engine ( UdK, Irrichlicht
Engine, Unity 3d)
Technologies
Web Client
• WebGL : implementation of
OpenGL ES 2.0 for web,
programmable in JavaScript
• Java Applet based on Opensource
Globe Nasa World wind, Cesioum

Actions
- Parallel session on WP2 tomorrow;
- bi-weekly Skype/webex session;
- Dedicated folder on consortium dropbox to share documentation;
- Ftp area to exchange large testing datasets.

Thank you for your attention
DR. FEDERICO PRANDI
Federico.prandi@graphitech.it
Fondazione Graphitech
Via Alla Cascata 56C
38123 Trento (ITALY)
Phone: +39 0461.283394
Fax: +39 0461.283398

Project SLOPE
79
T 2.5 – Road and Logistic planning
Mikkeli, 2nd-4th July, 2014

1.Task objectives
80
 Task objectives:
 Build and validate and Optimization model to decide on optimal logistic network
in a given forest area. This means to calculate locations for buffer areas, mills and
processing plants, routes and flows between nodes, according to a forecast
demand
 Build and validate a Model to estimate traffic on individual sections for road
maintenance and construction purposes in this forest area according to a
forecasted demand
Grumes in Trento, has been chosen as forest area for testing the models
 To be developed from M8 (August 14) to M13 (January 15)
 Includes development of “D2.05 Road and logistic simulation module”
 Due to Month 13.
 Partners involved
 ITENE (leader), GRAPHITECH, CNR, BOKU, FLY

2. Approaches for sites location and flow
allocation decisions
81
 The goal is to determine an optimal (minimum cost) forest logistic network to
respond future demands
 The approach should determine:
 Location of facilities (normally from a set of posible sites)
 Size and capacity of facilities (storage areas and processing sites)
 Volume to harvest in every landing and stand area
 Volume of timber to transport from landings to facilities (it gives a first
estimation of road traffic for road planning)
 Routes to connect nodes

82
 The model should consider inputs like:
 Forecast of future demand of timber
 Geographic characteristics of the area (Map, distances, slopes, available
areas, sizes, coordinates, …)
 Actual roads from forest to mills (forest accessibility). Map, type, …
 Amount and quality of available timber
 Possible location of mills/biomass areas and distance to the forest
(coordinates, size)
 Dimension of the logs needed
 Individual costs related to transport, infrastructures costs and others like
clearing meadows or watersides, artificial anchors, locking public roads.

83
 Stand
Cable
ways
forest
lanes

84
minor
road
main
road
land
land
land
stand
stand
stand

85
Solution flow
Possible flow
lands in forest storage and facilities (saw,
mills, biomass)

86
 Location of a single facility by center-of-
gravity method
 Output: XY coordinates for the facility
 Optimization based only on distances
 Binary model (source-sink)
 Useful for a first estimation of a facility location
to be supplied from specific lands

87
 Location of selected number of facilities
by the exact center-of-gravity method
 Output: XY coordinates of a selected number
of facilities
 Optimization based only on distances
 Useful for a first estimation of 2 or more
facility locations to be supplied from specific
lands

88
 P-median multiple facility location
 Output: selected facilities from a list of
candidate sites receiving flows from other sites
 Optimization based on transport costs and fix
costs, but lack of capacity constrains and other
inventory costs
 Useful for a first estimation of 2 or more facility
locations to be supplied from specific lands

89
 Mixed integer linear programming
problem
 Output: selected facilities and optimal flows
between nodes
 Optimization based on transport costs and fix
costs, capacity constrains and inventory costs
 Three stages model
 More appropriate approach for a network with
more than 2 node types
lands in forest storage and facilities
(saw, mills, biomass)

90
 Dynamic linear programming
 Consider changing demand
 Output:
 Selected facilities
 Size an capacity of facilities (storage and processing sites)
 Volume of harvest in every landing and stand área
 Volume to transport:
 Timber from landings to facilities
 Product from facilities to demand sites
 Decision to expand production capacity in a specific
period in the planning horizon
 Minimize total costs for timber supply and
transport, investment and operational costs,
product transport cost to demand sites, fixed
cost for capacity expansion
-
200
400
600
800
1.000
1.200
1 2 3 4 5 6 7
Period Demand Volume
lands in forest storage and facilities
(saw, mills, biomass)

91
 Previous Work
Facilities Location Models: An Application for the Forest Production
and Logistics
JUAN TRONCOSO T. 1, RODRIGO GARRIDO H. 2, XIMENA IBACACHE J. 3
July 2002
1 Departamento de Ciencias Forestales, Pontificia Universidad Católica de Chile, Casilla 305,
Correo 22, Santiago, Chile. E-mail: jtroncot@puc.cl
2 Departamento de Ingeniería de Transporte, Pontificia Universidad Católica de Chile.
3 Escuela de Ingeniería Forestal, Universidad Mayor.

92
 INPUTS
 Demands of product per each period and type of quality from demand site
 DATA COLLECTION FOR THE MODEL
 Positions of stands, lands, storage areas, processing sites (saw, paper mills and
biomass heating and power plants), demand sites
 Volume available to harvest in every stand per quality of timber and destination (saw,
mill or energy)
 Position for stand respect existing roads
 Slope or grade of difficulty to access
 Capacity of ground to support specific machinery
 Size and availability of skyline deployment sites
 Capacity and location of storage areas and buffers, and processing sites
 Characteristics of processing sites and conversion facilities
 Distances between different nodes

93
 COST FACTORS
 supply and transport operational costs
 final product transport cost to demand sites
 fixed cost for capacity expansion during the planning horizon
 investment associated to construction of a new site
 OUTPUT
 Selected facilities
 Size an capacity of facilities (storage and processing sites)
 Volume of harvest in every landing and stand área
 Volume to transport
 Timber from landings to facilities
 Product from facilities to demand sites
 Decision to expand production capacity in a specific period in the planning horizon

3. Approaches to estimate traffic in existing roads
94
 Once the different sites and locations have been selected, and flows between
sites have been determined for each future period,
 A Logistics Resource Planning Model will be used to determine the volume to
harvest in every period in every land, processing and transport means, and a
more precise estimation of traffic in every individual sections of road in terms of
number of trip per vehicles type (size, weight) in each period
 This traffic estimation will allow to define plans for road maintenance and
construction in the forest area, taking into account the capability of roads to
accept trucks and cranes of different weights and sizes

95
 Similarities to DRP method
Land 1
SITE: Saw Plant
X
City 1
Product demandHarvest orders
Land 2 City 2

96
SITE: Saw Plant X
Minumum Batch (harvest) (m3/period) 500
Lead time (number of periods) 1
Safety stock (m3) 200
Period 1 2 3 4 5 6 7
Demand Volume (m3) 400 500 600 1.000 500 600 1.000
Available Stock (m3) 700 300 300 200 200 200 100 100
Harvest recepcion (m3) - 500 500 1.000 500 500 1.000
Harvest order launch (m3) 500 500 1.000 500 500 1.000
Land 1
To harvest (m3) 500 500 1.000
Available m3 in land 1 2.000 1.500 1.000 -
Size of vehicle (m3) 10
Number of vehicle trips size 10m3 50 50 100
land 2
To harvest (m3) - - - 500 500 1.000 -
Available m3 in land 1 3.000 2.500 2.000 1.000 1.000
Size of vehicle (m3) 10
Number of vehicle trips size 10m3 50 50 100 -

97
4.Work done so far
 1st virtual meeting (webex conference)– 16.06.2014
 Attendants: Daniele and Giulio (GRAPHITEC), Gianni (CNR), Marco (FLYBY), Martin
(BOKU), Patricia, Emilio and Loli (ITENE)
 Agenda:
 Task 2.5 objectives, description of subtasks and partner roles
 Decision on forest area as test scenario (Grumes, Trento has been decided as test
forest area)
 Next steps and dates

98
4.Work done so far
 Discussion tomorrow in the T2.5 technical session:
 Collect general info of Grumes forest area: Map with locations and
roads, available characteristics, facilities, actors (owners), …
 Review planning models used in the literature
 Identify and organize detailed Grumes forest data collection for
models

5.Work plan
99
 Choose a test scenario. Done. (Grumes, Trento)
 Collect general info of Grumes forest area: Map with locations and roads, available characteristics,
facilities, owners willing to show interest, give data, demand scenario, … (GRAPHITEC & CNR)
DEADLINE: 15 JULY 2014
 Review network opt models (BOKU) and traffic estimation models (CNR) used in the literature (CNR,
BOKU,ITENE). Conclusion Report. DEADLINE: 15 AUGUST 2014
 Formulate/design a Network optimization model for logistics site location and flow allocation decisions
(BOKU) DEADLINE: 30 SEPTEMBER 2014
 Formulate/design model to estimate traffic in existing roads (CNR) DEADLINE: 30 SEPTEMBER 2014
 Collect detailed Grumes forest data for models: Costs, model elements, etc. (ITENE, GRAPHITEC, CNR,
FLYBY). DEADLINE: 31ST OCTOBER 2014
 Data Elements integration with the global forest model (ITENE) DEADLINE: 31ST OCTOBER 2014
 Program the Optimization model to allocate landings with the mills and plants, and traffic calculation
on individual sections (BOKU) DEADLINE: 14TH NOVEMBER 2014
 Program the model for road planning based on the amount of timber to be transported and
identification of traffic on existing forest infrastructure (BOKU) DEADLINE: 28TH NOVEMBER 2014
 Validate models and run a scenario simulation with demand data (BOKU) DEADLINE: 19TH DECEMBER
2014

6. Contact info
100
 Emilio Gonzalez
 egonzalez@itene.com
 Patricia Bellver
 pbellver@itene.com

Project SLOPE Forest Monitoring

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