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Mid-term Review2/Jul/15
Project SLOPE1
WP 1 Definition of requirements and system analysis
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Mid-term Review2/Jul/15
Project SLOPE2
T1.1 Users and System requirements
Brussels, July 2th, 2015
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Mid-term Review2/Jul/15
Overview
Status: Completed (100%) Length: 3 months (from M1 to M3) Involved Partners
Leader: ITENE Participants: GRAPHITECH, CNR, KESLA, COAST, MHG, BOKU,
FLY, GRE, TRE Aim: Gather information about user requirements to guide
future developments Output: Deliverable D1.01 (Submitted)
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Mid-term Review2/Jul/15
Procedure4
1. Identifying user groups
2. Developing functionalities
3. Creating relation Matrix
4. Developing questionnaires
5. Contact with End Users
6. Anlysis and conclusions
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Mid-term Review2/Jul/15
1. Identifying user groups5
1. Forest owners
2. Harvesting contractors
3. Transport companies
4. Mill companies
5. Biomass processing companies
6. Foresters / specialists
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2. Developing functionalities6
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3. Creating relation Matrix7
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4. Developing questionnaires8
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5. Contact with End Users9
Forestry related partners identified possible contacts
Phone / personal meeting were done to fill in questionnaires
Detailed info needed -> detailed questionnaires -> long time needed to answer them
Average time per contact in interviews: 30min - 1h
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6. Anlysis and conclusions10
Total: 23 questionnaires
Locations: Austria, Italy (Trento), Finland, Ireland
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6. Anlysis and conclusions11
PlanningFor selecting a harvesting area, the system should:
Consider cost and demand as a factor to select a harvesting areaDetermine the volume of timber available in the harvesting zoneAllow to know the age of treesMeasure trees heightDetermine slope and roughness of the terrainDetermine accessibility of the zone (road placement)
For marking a tree, the system should:Measure dimensions of treesDetermine quality of woodRegister specie and age of treesBe able of read all this information just before marking a treeIdentify trees unmistakably
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Deliverable Index D1.0112
All info and results gathered in D1.01
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Deliverable Annex13
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Mid-term Review 2/Jul/15
Contact info
Juan de Dios Daz (juan.diaz@itene.com)Emilio Gonzalez(egonzalez@itene.com)
Thank you for your attention
mailto:juan.diaz@itene.commailto:egonzalez@itene.com
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Mid-term Review2/Jul/15
Project SLOPE
T1.2 Hardware and equipment definition
Brussels, July 2th, 2015
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Mid-term Review2/Jul/15
Overview
Status: Completed (100%) Length: 3 Months (From M1 to M3) Involved Partners
Leader: Graphitech Participants: CNR, COAST, MHG, BOKU, FLY, GRE, ITENE
Aim: define the hardware and machinery specification on a system requirements basis
Output: D.1.04 Technical Requirements Report
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Mid-term Review2/Jul/15
GoalsThe Objective of the task was:
Define the Hardware and Software equipment including; Instruments and tools to collect forest information before
harvesting; Instrument and tools to collect timber information during the
harvesting; Instrument and tools for resources tracking;
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Mid-term Review2/Jul/15
Workflow
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Mid-term Review2/Jul/15
Forest SurveyRemote Sensing Information from: satellite, aerial, UAVs and Terrestrial laser Scanning: Multiresoultion forest survey
Satellite: Large scale (region), several repetion along time. Cost depending on spatial resolution
Airborne: Medium Scale (depending of altitude) not suitable for multiple repetion. High cost
UAVs: Small scale (plot) possible multiple repetion, high resolution. Medium Cost
TLS: Very small scale (portion of plot), Information not achieved from the above. Time consuming Medium cost.
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Satellite Images
Technical SpecificationsNumber of Satellites: 5Orbit Altitude: 630 km in Sun-synchronous orbitGlobal Revisit Time: 1 DayInclination: 97.8 degrees (solar-synchron)Ground sampling distance (nadir): 6,5 mPixel size (orthorectified): 5 mSwath Width: 77 km
Sensor Bands440 510 nm (Blue)520 590 nm (Green)630 685 nm (Red)690 730 nm (Red Edge)760 850 nm (Near IR)
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UAVVehicle 96cm wingspan Less 0.55kg dry-weight (0.68kg with RGB payload, 0.71kg with NIR payload) 45-50 minute flight time 40-90 km/h cruise speed Up to 45km/h or 12m/s wind resistance Up to 3Km radio link Up to 12sqkm coverage Linear landing Image resolution/pixels of 3-30cm Autopilot Payload Resolution 16MP 35 mm equivalent focal length 24mm Flight altitude (4cm/px GSD) 130 m
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Workflow
1
2
3
4
5
5
6
6
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Trees markingTagsUltra High Frequency RFID tags work at 868-902MHz. Standard for logistics and storageapplications.
Low cost (passive tags) and long reading range(4-5 meters).
Reader
Fully integrated handheld UHF RFID USB/Bluetooth reader
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Mid-term Review2/Jul/15
Cableway and Carriage
Check the weight of the timber Read the tags Check the presence of operators
below the line Open automatically the
electronic chockers Communicate with processor
head and with the server and the black box, transmitting the current situation, such asposition, working speed, fuelconsumption
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Processor Head Processor model ARBRO 1000 S
The factors determining the hydraulic demand (the resistance to advance) are the density/size of branches and the friction of the knives against the bark.
The lower productivity compared to roll processors is not influent, since the extraction of trees by cablecrane is relatively slow.
Relatively simple structure and electronics.
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Head Processor1. Machine control
system2. External control
system Compact rio + Externalindustrial pc
3. Actuators direcltycontrolled from machine control system.
4. External sensors.
1
2
3 4
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Head Processor
1) The new actuator bar for scanners scanning the cross section of log 2) Chain sawing module for sensing cutting forces and optimization of the cross-cut3) Feed power sensor4) Camera/3D vision sensor5) Colour camera(s) scanning side of the log 6) Ultrasound stress wavevelocity scanner 7) RFID reading system12
3
4
5
6
7
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Tracking system
Option 1 included manual RFID reader and tracking device in trucks,
Option 2 included fixed RFID reader with tracking device integrated in the truck.
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ConclusionsThe Achievements of the task are: Hardware and Software requirements have been defined; The identified resources will be the input for the future
developments during each specific tasks; The requirements and hardware sorted out from this deliverable
are the backbone of SLOPE system, however some future refinements can be needed.
The left of the responsible partner KESLA has caused a delay on deliverable submission, however all the adopted remedial actions have ensured that this had no effect on the other task activities.
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Mid-term Review2/Jul/15
Contact info
Federico Prandi: federico.prandi@graphitech.it
Thank you for your attention
mailto:federico.prandi@graphitech.it
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Mid-term Review2/Jul/15
Project SLOPE
T1.3 Human Machine Interface (HMI) definition
Brussels, July 2nd, 2015
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Mid-term Review2/Jul/15
Task Overview
Status: Completed (100%) Length: 3 Months (From M2 to M4) 5 Involved Partners
Leader: GraphiTech Participants: MHG, GRE, TRE, ITENE
Aim: define the user interface for the whole SLOPE system, including: User interface needs Web user interface requirements In-vehicle and on-field devices interfaces
Output: D.1.02 Human Machine Interface
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Process State of the art
User interfaces in forest production
Analysis of available user interfaces within consortium
Interface Requirements analysis Actors Use Cases
Interfaces definition Web client Mobile client In-vehicle client ERP
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User Interface Analysis
Human-Machine Interfaces can be seen as the parts, software or hardware handling the interaction between humans and machines[] Computer can have several different purposes ending in an open-ended dialog between users and computer.
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User Interface Analysis
Analysis of each available interface and classification against different types of HMI:
Direct manipulation interface Graphical user interface (GUI) Web User interfaces (WUI) Command Line Interfaces Touch User Interfaces Hardware User Interfaces Batch Interfaces Gesture interfaces
Intelligent User Interfaces Non-Command User interfaces Object Oriented User interfaces Tangible User Interfaces Task-Focused Interfaces Text based interfaces Zero Input Interfaces
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User Interface Analysis
Available interfaces Grap
hica
l use
r in
terf
ace
Web
-bas
ed
inte
rfac
e
Touc
h us
er
inte
rfac
e (M
obile
)
Hard
war
e In
terf
ace
Batc
h In
terf
ace
Touc
h U
ser
Inte
rfac
e (V
ehic
le)
Inte
llige
nt U
ser
inte
rfac
e
Dire
ct
Man
ipul
atio
n In
terf
ace
Task
focu
sed
inte
rfac
e
Ges
ture
Inte
rfac
e
Forestry Resource Planning System (MHG) V V V
Forest Analysis and Monitoring (TREE) V V V V V
Intelligent Harvesting Heads V V V
Cable Crane System (GRE) V V V
Geographical Information System for Environmental Planning (GRAPHITECH)
V V V V
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User Interface Requirements
From: User requirements reports (D.1.1) SLOPE reference scenario
Results: Requirements list Use cases by actor and interface
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Mid-term Review2/Jul/15
User Interface Requirements List
Selecting and planning harvesting area Provide trees information (height, age) Provide area information (available timber volume, ) Determine slope and roughness of the terrain Determine accessibility of the zone (road placement, road width, road slope, landing areas)
Tree marking Register specie and age of trees Be able of read all this information just before marking a tree
Cable Corridors Allow the estimation of total amount of timber to be harvested. Allow the selection of the intermediate support.
Cost Estimations Show harvesting costs based on users planning choices
Traceability Provide location of logs
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User Interface Requirements List
Harvesting monitoring/tree identification Show weather conditions and forecast. Estimate market demands. Obtain values of productivity and statistics of development of harvesting activities (related to the plan). Detect unmistakably each tree, accordingly to how it was marked. Show tree data before harvesting operation.
Contingency plans Show possible failures or breakages
Online Purchases Register species of trees Develop a platform including mentioned characteristics and specifying provenance of logs
Inventory Show logs in different states (standing, ready to be harvested or harvested) Show accessibility of the zone (road placement) Show quality of wood
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Use cases
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Human Machine Interfaces Design
Based on principle of least astonishment human beings can only pay attention to one thing at one time exploit users' pre-existing knowledge as a way to minimize the learning
curve functionally similar or analogous programs with which your users are
likely to be familiar
Takes in account a conservative sector like Forestry
Takes in account already existing consortium platforms
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HMI Design - Desktop
Web based application (HTML5/WebGL Based) Easy integration into other systems Task based interface
Analytics Operation Forest
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HMI Design - Desktop
Main Functionalities: Analytics: set of tools to retrieve geometrical and geophysical (like slope and soil
components) information about the property and about the places of interest fordetermined operation or dataset
Terrain Providers, Imagery Providers, Measurement, Slope Analysis, Cadastral & Public Data,Points of Interest, Roads
Operation: tools to manage different operation related to harvesting and to plan themin determined temporal interval
Cableway Planning, Working Area Setup, Felling, Buildings & Terminals, Logistic, HarvestTracking, Weather Forecast
Forest: Tools to inspect the forestry inventory datasets and all the operation related toforest resource planning.
Area Selection, Trees Visualization, Virtual Marking, Stem Visualization Tool
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HMI Design Desktop - Analytics
View of the Ground lidar scan or images
Inspect datasheet and forestry operation chart s of ana area
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Mid-term Review2/Jul/15
HMI Design Desktop - Operation
Road construction and set property boundary
Insert in the scenario all the structure to plan the operation
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Mid-term Review2/Jul/15
HMI Design - Mobile
Main Functionalities: Tree and Forest Inventory: singe tree and area inspection. Logs and wood: to obtain the position on the map of all
the logs that have to be harvested. Machines: to visualize on the map the area of work and
the evolution of the harvesting and felling procedures Virtual Forest: show a simplified version of the forest
developed for the desktop platform Layers: Select additional layers to be applied to the map. Transportation: to show road and truck fleet movement
Subset of desktop functionalities Exploits mobile device capabilities (e.g. GPS, Camera) Tagging support for Forest Operators
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HMI Design In-Vehicle
Enrich already existing In-Vehicle systems Based on:
TRE RTFI: Harvest Production Monitoring & Control In-Vehicle Harvesting Head control system
PDA or Large screen tablet (10+ inches) 3 parts: Map, Function menu, Widget menu
Main Functionalities: Map & Function menu: similar to mobile Widget menu:
Quality Index: currently processed log quality Operation: to access scheduled operations Manage work time: set break intervals, optimize scheduling based on conditions Report issue: like failure/breakage, emergency call, etc. Work time clock: time left before break Climate and weather information: like weather forecast
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HMI Design In-Vehicle
Widget menuReal-time quality index
Function menu
Map
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HMI Design ERP Module
Separated interconnected views 1 unique portal
Management of log inventory Online purchasing/auctions For wood buyers/sellers and
sawmills One web interface with different
views and modules
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Conclusions
Guidelines for the definition of the SLOPE project interfaces Web Mobile In-vehicle Integrated ERP system
Based on: State of the art Use cases Explicit requirements
Subject to changes during the integration phase User requirements Integration testing feedbacks
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Contact info
Daniele Magliocchetti: daniele.magliocchetti@graphitech.itGiulio Panizzoni: giulio.panizzoni@graphitech.it
Thank you for your attention
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Mid-term Review2/Jul/15
Project SLOPE
T1.04 Mountainous Forest inventory data model definition
WP 1 - Definition of requirements and system analysis
Brussels, July 2th, 2015
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Mid-term Review2/Jul/15
Overview
Status: Completed (100%) Length: 4 Months (From M2 to M6) Involved Partners
Leader: CNR Participants: GRAPHITECH, COAST, MHG, BOKU, FLY, GRE, TRE
Aim: To define the required information for the FIS data population. Define data and metadata model of the FIS
Output: D.1.03 [M6]
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Mid-term Review2/Jul/15
Data formats and standards
Spatial Data
Standards for Openness and Technical Interoperability INSPIRE
Spectral data
Data collected by the harvesting machines
Sensor standards
Forestry related standards
Automatic Identification and data capture
Standards in Entity Identification
Geographic Standards
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Data formats and standardsSpatial Dataseveral typologies of spatial data anddifferent source of geographic information
Spectral Dataspectroscopy for the analysis of wood chemical-physical properties, hyperspectral imaging of wood, hyperspectral imaging of forest.
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Data formats and standardsData collected by the harvesting machinesRelevant variables, representing the characteristics of the harvesting system in the SLOPE scenario, will be measured with transducers/sensors. Some of the measured variables aim at monitoring machines parameters, enabling security, energy-saving, real-time control and automation functionalities. Some machines parameters will be also correlated to quality indices of the harvested material (e.g. cutting quality index).Another series of data are those collected by the sensors to determine parameters related to the wooden material characteristics (i.e. data from NIR and hyperspectral sensors, data from stress wave tests) or to measure geometrical features of the logs.
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Integrated models
Multisource data
Multiscale data
Multitemporal data
The realization of forest inventories is strongly related to the harmonization of different data provided by different sources (different remote sensing or ground-based measurements) with different scales (different spatial and temporal resolutions) and different units. This process can be performed by means of dedicated elaborations and databases with geographical referencing functionalities (GIS).
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Overview of existing databases and services
EU forest datasets
Datasets available in the SLOPE pilot areas
ITAL
Y
Tren
to P
rovi
nce
AUST
RIA
Sal
zbur
g
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Required information to populate the FIS
to develop an interactive system for cableway positioning simulation (CwPT)
to assist tree marking forestry measurements estimations (TMT)
to define technology layers (harvest parameters) (TLT)
to support novel inventory data content (IDC)
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Annex A:
TABLES OF DATASETS FOR FIS POPULATION
TABLE A1: FOREST
FOREST
INFORMATION
(definition) [unit]
INPUT DATA
TYPE
[SCALE/spatial resolution]
{temporal resolution}
STANDARD
RELEVANCE
SOURCE/
SENSOR
High relevant
Relevant
Mod. relevant
Not relevant
TMT
TLT
CwPT
IDC
TERRAIN
Elevation
(height above ellipsoid -GPS- or height above geoid -Mean Sea Level -MSL)
[m]
Information directly derived from input data
Vector contour lines
DEM
raster elevation data
[Spatial Resolution: from 90 m to 10 cm]
shape files,
Geotif ,
DTED
Topographical data sources
SRTM elevation data
LiDAR data
Slope
(per cent change of elevation over a specific area)
[%]
Information derived from elevation
Vector contour lines
DEM
raster elevation data
[typical resolutions of 1 arc-second (approx. 30 meters) and 1/3 arc-second (approx. 10 meters), and in limited areas at 1/9 arc-second (approx. 3 meters)]
geotif image
Topographical data sources
SRTM elevation data
LiDAR data
ALS
Aspect
(horizontal direction to which a mountain slope faces
Aspect is the direction of the maximum rate of change in the z-value from each cell in a raster surface)
Information derived from slope
DEM
raster elevation data
geotif image
Topographical data sources
SRTM elevation data
LiDAR data
Topographic roughness (ruggedness)
(e.g. standard deviation of slope, standard deviation of elevation, slope convexity, variability of plan convexity)
Information derived from slope
DEM
raster elevation data
WCS
Topographical data sources
LiDAR data
ALS
Canopy height model (CHM)
(representation of the difference between the top canopy surface and the underlying ground topography)
Derived from DTM and DSM
(by filtering LiDAR point clouds to separate ground and canopy hits)
WCS
LiDAR data
ALS
FOREST STAND
Forest structural type
(Land-use and land-cover classification of forest and non-forest areas)
Raster maps
vector maps
Multispectral images
Land Cover datasets
FI
LiDAR data
Location
GIS coordinates
ETRS89
GRS 80
SRID
UTM
WGS84
StanForD
GPS
Ownership of the plot/s
(public or private ownership)
Cadastral raster maps/vector data
Alpha-numerical data
Land Parcel Identification System
StanForD
Land registry
Identification of the plot
[ID]
Alpha-numerical data
StanForD
FMP
Stand boundaries
Cadastral raster/vector data
Land Parcel Identification System
Land registry
FMP
Size of stand
[hectare]
Cadastral raster/vector data
Land Parcel Identification System
Land registry
Size of harvesting unit
[hectare]
Vector data
FMP
Stand density
(a quantitative measure of stocking expressed either absolutely in terms of number of trees, basal area, or volume -with or without bark- per unit area, or relative to some standard condition)
[number of trees/ha]
Alpha-numerical data
FMP
Harvest plan
Basal area
(cumulative area of the trees sections at breast height)
[m2/hectares]
Alpha-numerical data
FMP
Harvest plan
Standing volume
(the timber volume of all the standing trees in a forest stand. It can include the sole commercial timber volume -up to a minimum diameter-, all the tree volume -including non commercial stem volume, branches and tops- or present this data in a more comprehensive form -commercial volume, residual volume, total volume-)
[m3]
Alpha-numerical data
FMP
Harvest plan
Forest harvest intensity/removal rate
[%] percentange of the original standing volume
[n. marked trees/ha]
[m3]
Data derived from marked trees and original standing volume
Alpha-numerical data
FMP (as recommendation)
Harvest plan
Silvicultural practice
(silvicultural perscriptions at the stand )
Alpha-numerical data
FMP
TREE
Individual position of trees
Data derived from Laser scan data and UAV
GIS coordinates
Point cloud files
Shape files
Kml files
1 LiDAR data
2 ALS
3 TLS data
Tree DBH
(Average DBH value
Individual DBH value)
[cm over bark]
cm over bark
StanForD
4 Caliper measurement
5 TLS data
Tree Height
(Average height value - Individual height value of the vertical distance from ground level to the highest green point on the tree)
[m]
Information derived from the CHM
WFS
1. Relascope/ tape method measurements
2. Fertility class of the stand and DBH
3. TLS data
Tree Volume
(cubic measure of the amount of wood including stem, branches, stump and roots)
[m3]
Information derived from trees height, basal area, shape
TLS data
Tree Species
Percent species composition
Individual tree species
[species name]
Information derived from in field survey or determined from NDVI and RENDVI, or RGB images
Species Code
Norway Spruce = NS
Strata
StanForD
FI
Multispectral images
RGB areal photos
Age of trees
(Percent age composition)
FI
FMP
Statistical, historical data
Field sampling (coring)
Crown width
(average of four perpendicular crown radii)
[m]
Index number for each tree with XYZ Coordinate for each tree
WFS
1. LiDAR
2. ALS
Crown thickness
(Live crown depth)
[m]
RGB data photographic
1. Multispectra LiDAR data
2. ALS
Stem volume
(above ground volume production, without branches and stump)
[m3]
Information derived from trees height, basal area, shape
StanForD
1. LiDAR data
2. ALS
3. TLS data
Stem straightness
[cm/m]
Stem diameter at 10cm intervals up the tree. Each diameter has a XYZ for the center point of the tree
StanForD
4. TLS
Stem taper
Diamater measurement at 10cm intervals to 7cm min top diameter
StanForD
1. ALS
2. TLS
3. CTL-harvester measurements
Stem length
[m]
TLS
1. CTL-harvester measurements
Tree leaning
[%]
XYZ center point for each diameter interval
StanForD
2. ALS
3. TLS
Branchiness
[number of branches / lm]
Branch diameter in CM embedded in stem file
1. TLS
2. CTL-harvester measurements
Position of the lowest branch in the trunk
Embedded in stem file with XYZ position up the tree
1. TLS
Damaged trees
(Insect attacks
Fungi attacks
Failed trees)
Information derived from the NDIV
From in field inspection
1. ALS
2. Hyperspectral images
Marked trees
Virtual flag
FMP
RFID tags
Physical flag
RESTRICTIONS
Accessibility
(if any machine can reach the harvesting site)
Information derived from road network, slope and topographic roughness
Raster maps
Geospatial vector data
Topographical data sources
SRTM elevation data
Lidar data
ALS
FMP
Bearing capacity
(maximum average contact pressure between the harvesting machines and the soil which should not produce shear failure in the soil)
[kN/m2]
Site classification map
Snow cover
(minimum height of snow that hampers harvesting activities for a certain period and area)
[days]
Information derived from hydrological data
Hydrological datasets
Presence of protected animal species
Type of restrictions
Field survey
FMP
Presence of protected trees
GIS coordinates of protected trees
Type of restrictions
FMP
Protected reserves
Map and borders
Type of restrictions
FMP
FOREST
INFORMATION
(definition) [unit]
INPUT DATA
TYPE
[SCALE/spatial resolution]
{temporal resolution}
STANDARD
RELEVANCE
SOURCE/
SENSOR
High relevant
Relevant
Mod. relevant
Not relevant
TMT
TLT
CwPT
IDC
FOREST STAND
Forest structural type
(Land-use and land-cover classification of forest and non-forest areas)
Raster maps
vector maps
Multispectral images
Land Cover datasets
FI
LiDAR data
Location
GIS coordinates
ETRS89
GRS 80
SRID
UTM
WGS84
StanForD
GPS
Ownership of the plot/s
(public or private ownership)
Cadastral raster maps/vector data
Alpha-numerical data
Land Parcel Identification System
StanForD
Land registry
Identification of the plot
[ID]
Alpha-numerical data
StanForD
FMP
Stand boundaries
Cadastral raster/vector data
Land Parcel Identification System
Land registry
FMP
Size of stand
[hectare]
Cadastral raster/vector data
Land Parcel Identification System
Land registry
Size of harvesting unit
[hectare]
Vector data
FMP
Stand density
(a quantitative measure of stocking expressed either absolutely in terms of number of trees, basal area, or volume -with or without bark- per unit area, or relative to some standard condition)
[number of trees/ha]
Alpha-numerical data
FMP
Harvest plan
Basal area
(cumulative area of the trees sections at breast height)
[m2/hectares]
Alpha-numerical data
FMP
Harvest plan
Standing volume
(the timber volume of all the standing trees in a forest stand. It can include the sole commercial timber volume -up to a minimum diameter-, all the tree volume -including non commercial stem volume, branches and tops- or present this data in a more comprehensive form -commercial volume, residual volume, total volume-)
[m3]
Alpha-numerical data
FMP
Harvest plan
Forest harvest intensity/removal rate
[%] percentange of the original standing volume
[n. marked trees/ha]
[m3]
Data derived from marked trees and original standing volume
Alpha-numerical data
1. FMP (as recommendation)
2. Harvest plan
Silvicultural practice
(silvicultural perscriptions at the stand )
Alpha-numerical data
3. FMP
TREE
Individual position of trees
Data derived from Laser scan data and UAV
GIS coordinates
Point cloud files
Shape files
Kml files
1. LiDAR data
2. ALS
3. TLS data
Tree DBH
(Average DBH value
Individual DBH value)
[cm over bark]
cm over bark
StanForD
4. Caliper measurement
5. TLS data
Tree Height
(Average height value - Individual height value of the vertical distance from ground level to the highest green point on the tree)
[m]
Information derived from the CHM
WFS
1. Relascope/ tape method measurements
2. Fertility class of the stand and DBH
3. TLS data
Tree Volume
(cubic measure of the amount of wood including stem, branches, stump and roots)
[m3]
Information derived from trees height, basal area, shape
1. TLS data
Tree Species
Percent species composition
Individual tree species
[species name]
Information derived from in field survey or determined from NDVI and RENDVI, or RGB images
Species Code
Norway Spruce = NS
Strata
StanForD
2. FI
3. Multispectral images
4. RGB areal photos
Age of trees
(Percent age composition)
1. FI
2. FMP
3. Statistical, historical data
4. Field sampling (coring)
Crown width
(average of four perpendicular crown radii)
[m]
Index number for each tree with XYZ Coordinate for each tree
WFS
1. LiDAR
2. ALS
Crown thickness
(Live crown depth)
[m]
RGB data photographic
1. Multispectra LiDAR data
2. ALS
Stem volume
(above ground volume production, without branches and stump)
[m3]
Information derived from trees height, basal area, shape
StanForD
1. LiDAR data
2. ALS
3. TLS data
Stem straightness
[cm/m]
Stem diameter at 10cm intervals up the tree. Each diameter has a XYZ for the center point of the tree
StanForD
4. TLS
Stem taper
Diamater measurement at 10cm intervals to 7cm min top diameter
StanForD
1. ALS
2. TLS
3. CTL-harvester measurements
Stem length
[m]
5. TLS
1. CTL-harvester measurements
Tree leaning
[%]
XYZ center point for each diameter interval
StanForD
2. ALS
3. TLS
Branchiness
[number of branches / lm]
Branch diameter in CM embedded in stem file
1. TLS
2. CTL-harvester measurements
Position of the lowest branch in the trunk
Embedded in stem file with XYZ position up the tree
1. TLS
Damaged trees
(Insect attacks
Fungi attacks
Failed trees)
Information derived from the NDIV
From in field inspection
1. ALS
2. Hyperspectral images
Marked trees
Virtual flag
FMP
RFID tags
Physical flag
RESTRICTIONS
Accessibility
(if any machine can reach the harvesting site)
Information derived from road network, slope and topographic roughness
Raster maps
Geospatial vector data
Topographical data sources
SRTM elevation data
Lidar data
ALS
FMP
Bearing capacity
(maximum average contact pressure between the harvesting machines and the soil which should not produce shear failure in the soil)
[kN/m2]
Site classification map
Snow cover
(minimum height of snow that hampers harvesting activities for a certain period and area)
[days]
Information derived from hydrological data
Hydrological datasets
Presence of protected animal species
Type of restrictions
Field survey
FMP
Presence of protected trees
GIS coordinates of protected trees
Type of restrictions
FMP
Protected reserves
Map and borders
Type of restrictions
FMP
TREE
Individual position of trees
Data derived from Laser scan data and UAV
GIS coordinates
Point cloud files
Shape files
Kml files
1. LiDAR data
2. ALS
3. TLS data
Tree DBH
(Average DBH value
Individual DBH value)
[cm over bark]
cm over bark
StanForD
4. Caliper measurement
5. TLS data
Tree Height
(Average height value - Individual height value of the vertical distance from ground level to the highest green point on the tree)
[m]
Information derived from the CHM
WFS
1. Relascope/ tape method measurements
2. Fertility class of the stand and DBH
3. TLS data
Tree Volume
(cubic measure of the amount of wood including stem, branches, stump and roots)
[m3]
Information derived from trees height, basal area, shape
1. TLS data
Tree Species
Percent species composition
Individual tree species
[species name]
Information derived from in field survey or determined from NDVI and RENDVI, or RGB images
Species Code
Norway Spruce = NS
Strata
StanForD
2. FI
3. Multispectral images
4. RGB areal photos
Age of trees
(Percent age composition)
1. FI
2. FMP
3. Statistical, historical data
4. Field sampling (coring)
Crown width
(average of four perpendicular crown radii)
[m]
Index number for each tree with XYZ Coordinate for each tree
WFS
1. LiDAR
2. ALS
Crown thickness
(Live crown depth)
[m]
RGB data photographic
1. Multispectra LiDAR data
2. ALS
Stem volume
(above ground volume production, without branches and stump)
[m3]
Information derived from trees height, basal area, shape
StanForD
1. LiDAR data
2. ALS
3. TLS data
Stem straightness
[cm/m]
Stem diameter at 10cm intervals up the tree. Each diameter has a XYZ for the center point of the tree
StanForD
4. TLS
Stem taper
Diamater measurement at 10cm intervals to 7cm min top diameter
StanForD
1. ALS
2. TLS
3. CTL-harvester measurements
Stem length
[m]
5. TLS
1. CTL-harvester measurements
Tree leaning
[%]
XYZ center point for each diameter interval
StanForD
2. ALS
3. TLS
Branchiness
[number of branches / lm]
Branch diameter in CM embedded in stem file
1. TLS
2. CTL-harvester measurements
Position of the lowest branch in the trunk
Embedded in stem file with XYZ position up the tree
1. TLS
Damaged trees
(Insect attacks
Fungi attacks
Failed trees)
Information derived from the NDIV
From in field inspection
1. ALS
2. Hyperspectral images
Marked trees
Virtual flag
FMP
RFID tags
Physical flag
RESTRICTIONS
Accessibility
(if any machine can reach the harvesting site)
Information derived from road network, slope and topographic roughness
Raster maps
Geospatial vector data
Topographical data sources
SRTM elevation data
Lidar data
ALS
FMP
Bearing capacity
(maximum average contact pressure between the harvesting machines and the soil which should not produce shear failure in the soil)
[kN/m2]
Site classification map
Snow cover
(minimum height of snow that hampers harvesting activities for a certain period and area)
[days]
Information derived from hydrological data
Hydrological datasets
Presence of protected animal species
Type of restrictions
Field survey
FMP
Presence of protected trees
GIS coordinates of protected trees
Type of restrictions
FMP
Protected reserves
Map and borders
Type of restrictions
FMP
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Mid-term Review2/Jul/15
Annex A:
TABLES OF DATASETS FOR FIS POPULATION
TABLE A 2: INFRASTRUCTURES AND BUILDINGS TABLE A 3: HYDROGRAPHY
TABLE A.5: RISK FACTORS
TABLE A.5: COMMUNICATION
INFRASTRUCTURE NETWORK
INFORMATION
INPUT DATA
(TYPE)[SCALE]
STANDARD
RELEVANCE
SOURCE
High relevant
relevant
Mod. relevant
Not relevant
TMT
TLT
CwPT
IDC
ROAD NETWORK
Primary public roads
Road graph
Road polygon
geospatial vector data
Functional Road Class (FRC)
Shapefile
kml files
Public road network databases
WIGeoStreet
Roads congestion
GPS mounted on the Truck
Web service
Secondary public roads
Road graph
Road polygon
geospatial vector data
Shapefile
kml files
Public road network databases
WIGeoStreet
Roads congestion
GPS mounted on the Truck
Web service
Forest roads
Road graph
Road polygon
geospatial vector data
Shapefile
kml files
FMP
WIGeoStreet
Public or private ownership
( catastrial classification)
Shapefile
kml files
Type of use
( e.g. exclusive use for forest activities/not exclusive use for forest activities)
alpha-numerical attribute on GIS layer
National Road codes
FMP
Forest road classes
(e.g. truck road, tractor road, etc.)
alpha-numerical attribute
National codes
Maps
FMP
Landing sites
Size of the landing site
(information derived by iterative computation by the forester or automatic detection/segmentation of areas)
LiDAR and ALS
Location of the landing site
(information derived by iterative computation by the forester or automatic detection/segmentation of areas)
Hystorical data
LiDAR and ALS
Stocking areas
Size [m2]
Hystorical data
LiDAR and ALS
Location [LAT, LONG]
BUILDINGS
buildings
Location
Maps
ALS
Overall dimensions
OVERHEAD POWER
TRANSMISSION LINES
Coodinates of Cables end points
GIS coordinates
Shapefile
kml files
Maps
ALS
GAS PIPELINES
Position of the pipe
Shapefile
kml files
Maps
WATER PIPELINES
Position of the pipe
Shapefile
kml files
HYDROGRAPHIC DATA
(surface water)
INFORMATION
INPUT DATA
(TYPE)[SCALE]
STANDARD
RELEVANCE
SOURCE
High relevant
Relevant
Mod. relevant
Not relevant
TMT
TLT
CwPT
IDC
drainage network with features such as rivers, streams, canals, lakes, ponds, coastline, dams, and streamgages.
Hydrography Dataset (e.g.)
drainage basins as enclosed areas (in size categories)
Watershed Boundary Dataset (e.g. )
COMMUNICATION
INFORMATION
INPUT DATA
(TYPE)[SCALE]
STANDARD
RELEVANCE
SOURCE
High relevant
relevant
Mod. relevant
Not relevant
TMT
TLT
CwPT
IDC
GPS satellite coverage
RTK Coverage
GPS Coordinates
ETRS89
GRS 80
SRID
UTM
WGS84
AT&T Map
local area telephone network
GPRS Telecoms Service provider coverage
GPS Coordinates
UMTS/3G Coverage Map
UMTS/3G Coverage Map
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Mid-term Review2/Jul/15
Annex B: TABLES OF DATA ON FOREST
PRODUCTION QUALITY AND AVAILABILITY
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Mid-term Review2/Jul/15
Annex C:
TABLES OF DATA DERIVED FROM THE FIS
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Mid-term Review2/Jul/15
ConclusionsReport D1.03 is a reference for the implementation of:
D2.01 Remote Sensing data and analysisD2.02 UAV data and analysis D2.03 TLS data and analysis
D2.04 the Harvest simulation toolD2.05 the Road and logistic simulation module
Data and metadata model defined in the D1.03 will be the base for the implementation of the mountainous forest information system database (T 5.01)
The report D1.03 defines also data acquired by means of non-destructive or semi-destructive testing techniques, for the multi-sensor characterization of the harvested material. A prerequisite for this is the definition of the technical characteristics of the hardware/sensors instrumenting the harvesting machines (Task 1.2 D1.04).
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Mid-term Review2/Jul/15
Contact info
Thank you for your attention
Mariapaola Riggio: riggio@ivalsa.cnr.it
mailto:riggio@ivalsa.cnr.it
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Mid-term Review2/Jul/15
Project SLOPE
WP1 T1.5 - System Architecture
Brussels, 2nd July, 2015
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Mid-term Review2/Jul/15
Overview
Status: Completed (100%) Length: 11 Months (From M02 to M12) Involved Partners
Leader: MHG Participants: GRAPHITECH, FLY, TRE, ITENE
Aim: Design the technology specification of system architecture Output: D.1.05 System architecture specifications
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Mid-term Review2/Jul/15
Objectives
Design the technology specification of the system architecture
Specify applications and technologies to be used Specify design principles Design model and interfaces for application
integrations in different integration levels Design deployment platform
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Mid-term Review2/Jul/15
Deliverable in brief
Specify existing applications and technologies Describes each partners applications and
technologies What current applications/systems can do What technologies they use How we can integrate them to the SLOPE
platform
-> Architecture should support many different kind of technologies
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Mid-term Review2/Jul/15
Deliverable in brief
Specify architecture design model to be used Specifies design principles are used in the SLOPE
platform architecture Service oriented architecture With SOA we can loosely integrate very
different systems together Goal is to make integrations with minimum
modifications to exsisting codebases Architecture diagrams
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Mid-term Review2/Jul/15
Deliverable in brief
Specify integration technologies Specifies integration technologies and components
to be used on this platform Liferay -> Presentation level integration
(different ways to integrate) Web Services (SOAP/REST) for service level
integration GeoServer -> spatial data from SLOPE FIS
database
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Mid-term Review2/Jul/15
Deliverable in brief
Specify deployment platform for the SLOPE Describes the deployment platform
Use neutral, scalable cloud service for deployment (not inside any partners secure infrastructure)
This helps to open access for every partner that needs
Jelastic PaaS-platform for deployment
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Mid-term Review2/Jul/15
System Architecture Overview
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Mid-term Review2/Jul/15
Component Diagram
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Mid-term Review2/Jul/15
Summary
Deliverable can be found from SLOPE Dropbox folder Feedback and communication delays affected to the delivery time. This didnt affect
to execution of another tasks. All objectives were reached that are in the DOW. Deliverable specifies technologies, architecture model, partner applications,
integration patterns and slope deployment platform. System architecture specification can be updated during project Specified system architecture brings good guidelines/framework for SLOPE FIS
development
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Mid-term Review2/Jul/15
Mid-term Review 2/Jul/2015
Contact info
Veli-Matti Plosila veli-matti.plosila@mhgsystems.com
Seppo Huurinainen seppo.huurinainen@mhgsystems.com
Thank you for your attention
mailto:veli-matti.plosila@mhgsystems.commailto:Seppo.huurinainen@mhgsystems.com
Project SLOPEProject SLOPEOverviewProcedure1. Identifying user groups2. Developing functionalities3. Creating relation Matrix4. Developing questionnaires5. Contact with End Users6. Anlysis and conclusions6. Anlysis and conclusionsDeliverable Index D1.01Deliverable AnnexContact infoProject SLOPEOverviewGoalsWorkflowForest SurveySatellite ImagesUAVWorkflowTrees markingCableway and CarriageProcessor Head Head ProcessorHead ProcessorTracking systemConclusionsContact infoProject SLOPETask OverviewProcessUser Interface AnalysisUser Interface AnalysisUser Interface AnalysisUser Interface RequirementsUser Interface Requirements ListUser Interface Requirements ListUse cases Human Machine Interfaces DesignHMI Design - DesktopHMI Design - DesktopHMI Design Desktop - AnalyticsHMI Design Desktop - OperationHMI Design - MobileHMI Design In-VehicleHMI Design In-VehicleHMI Design ERP ModuleConclusionsContact infoProject SLOPEOverviewData formats and standardsData formats and standardsData formats and standardsIntegrated modelsOverview of existing databases and servicesRequired information to populate the FISAnnex A:TABLES OF DATASETS FOR FIS POPULATION Annex A:TABLES OF DATASETS FOR FIS POPULATION Annex B: TABLES OF DATA ON FOREST PRODUCTION QUALITY AND AVAILABILITY Annex C:TABLES OF DATA DERIVED FROM THE FIS ConclusionsContact infoProject SLOPEOverviewObjectivesDeliverable in briefDeliverable in briefDeliverable in briefDeliverable in briefSystem Architecture OverviewComponent DiagramSummaryContact info
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