2nd Technical Meeting - WP2

Project SLOPE1

WP 2 Forest information collection and analysis

San Michele allAdige 19-21 Jan 2015

SLOPE WP 2 Task 2.1

Alberto Lupidi, PhD

Remote sensing and multispectral analysis

Remote Sensing DepartmentFlyby 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 approachto monitor tree growth and health in mountainous environment (Using different vegetation indexes)

CoastWay/TreemetricsDefine the approach to monitor the forest using UAV and on ground sensors

CNR/FlybyDefine the approach to fuse heterogeneous information (derived by satellites or other instrumentations)

Participants Role


Main Results

Definition of VI from sensed data

Estimation of biological parameters from VI

Analysis of parameter with growing level of

detail (Satellite -> UAVs -> Laser scanner)

Utilizability


Issues, delays and actions

Case study chosen, with a posterior inspection, revealed too

young/thin trees

The alternative (Gortahile forest) was not present in Sat data

(geographical mismatch)

Delays from due date (30 Aug 14) to achieve a suitable solution.

Action Taken: utilization of a combination of Ballyredmond and

Trentino data to show the effectiveness in VI utilization at different

scales and with different sensors


Working on a case study

IRELAND

MONTESOVER


RapidEye satellite imagery


Vegetation Indices

redNIRredNIRNDVI

+

=

redREredRENDRE

+

=

NDVINDRECCCI =

Normalized Difference Vegetation IndexUsed for vegetation/wood detection. Saturates in dense wood areas

Normalized Difference Red-Edge IndexCan be utilized to estimate chlorophyll levels /health status of trees.Unaffected by saturation

Canopy Chlorophyll Content IndexProposed as an alternative to measure variations in biomass, chlorophyll and nourishment (Nitrogen content)


NDVI / NDRE

NDVI

Oct - Jul

NDRE

Oct - Jul


CCCI

October 2012 July 2013


Chlorophyll estimation

October 2012 July 2013


NDVI Chl , UAV sensed data

Trentino dataset enhancement of detail and granularity


From forest database to stem characterization

Trunks analysis with terrestrial laser sensor


From data to information: the workflow


Conclusions Development of an integrated wood monitoring system

Definition of a chain processing for data from different platoforms

Relation between VI indices and biomass productivity index

Toward very detailed information to tree-level for woodcutting operators


Alberto Lupidi, PhDFlyby s.r.l.Corso Ferrucci 77/9, 10138 Torino, ItalyVia Puini 97, 57128 Livorno, Italywww.flyby.it

Thanks


http://www.flyby.it/

Species recognition by foresters

Contours added to create DSM

Post Processing can be time consuming as time is spent looking for the targets

New RTK unit will include all open areas in the DSM

The same GCP location identified in 6no. Photographs is difficult to tie dow


First Step - After Irish Tests

Identification of The Test Sites in Piscine & Montesover


Second Step Submit Flight Plans to ENAC

(Italian Aviation Authority)

WP2 Overall Progress of Tasks - Submitted and Pending deliverables

Task 2.1- Remote sensing and multispectral analysis FlyBy declared complete 14/10/2014

Task 2. 2 - UAV data acquisition and processing Coastway declared complete 14/01/2015

Task 2.3 - On-field digital survey systems Treemetrics declared complete 16/12/2014

Task 2.4 Harvest simulation tool based on 3D forest model Declaration required

Task 2.5 - Road and Logistic Planning Boku declaration required to be discussed

WP2 meeting required involving all Contributors San Michele allAdige 19-21 Jan 2015

WP2 - Submitted and Pending Deliverables

Task 2.1- Remote sensing and multispectral analysis FlyBy declared complete 14/10/2014Task 2.1- Remote Sensing and Multispectral analysis FlyBy Plan for Austrian Test

Task 2. 2 - UAV data Acquisition and Processing Coastway Revise document Plan for Austria

Task 2.3 - On-field Digital Survey Systems Treemetrics Revise Document Plan for Austria

Task 2.4 Harvest simulation tool based on 3D forest model deliverable required

Task 2.5 - Road and Logistic Planning Boku deliverable required

Questions to be asked in meeting;

Will the same reports be produced for the Austrian site?San Michele allAdige 19-21 Jan 2015

WP2 Criticalities

Prepare Flight Plan and Site Criticalities for WP2 Forest Information Collection and Analysis to be a success are

FlyBy, Coastway , Treemetrics, CNR have shared data this must continue.

Graphitec to reveal how this data is being used

Boku to share the scientific data with all WP2 contributors

Equipment to be upgraded in line with current developments for Austrian test


WP2 Recommendations

Data to be discussed between the Survey Group and the Scientists

A larger area of the forest to be scanned to complete a full model

The current model to be revealed with a viewer for all parties


T2.3- TLS data and analysis

Kick-off Meeting 8-9/jan/2014

Delive- rableNumber61 Deliverable Title

Lead benefi-ciary number

Estimated indicative person- months Nature 62

Dissemi-nation level 63

Delivery date 64

D2.01 Remote Sensing data and analysis 7 14.00 R PU 8

D2.02 UAV data and analysis 4 11.00 R PU 8

D2.03 TLS data and analysis 9 13.00 R PU 10

D2.04 Harvest simulation tool based on 3D forest model 1 20.00 R PU 15

D2.05 Road and logistic simulation module 10 12.00 R PU 13

Total 70.00


D2.03 TLS data and analysis


D2.03) TLS data and analysis: This deliverable will contain a report on TLS data collected, the methodologies and algorithm to extract needed information and the generated output information. [Completed on time in month 10] (Updated following edits 16/12/2014)


Overview of Forestry Analysis


Typical Forest inventory workflow and monitoring


Types of Samples (plots and transects)


Sample Plots

Stratum sizes

Variability inside the strata

Inventory costs and accessibility that might vary between strata

Target accuracy

Inventory Planning is Essential in order to:1. Ensure efficient and effective data collection2. Minimise cost3. Optimise accuracy


Treemetrics Field Gathering App


The advantage of in field application:

Bluetooth recording with callipers

2 way communication between office and field using GPRS or Wifi.

Easy navigation in field for operator


Field Measurement


TLS Height recording hypsometer DBH recording


Field Data Analysis


Dendrometric study the measurement of the various dimensions of trees, such as their diameter, size, shape, overall volume, etc.

Using the TLS the following data is available:

Stem profile

Diameter at Breast Height (DBH)- DBH v Height, DBH v Crown Size

Tree Volume

Quality indicators (Straightness)


TLS Analysis for Plot 2


Stem 3D profile in Autostem



Demonstration

Overview of the Demo area

The first demonstration area is located in Piscine (Province of Trento, Italy). This demo area has 4.18 ha of mixed age forestry. It is composed mainly of Norway spruce (Picea abies) mixed intimately with other secondary species: Scots pine (Pinussylvestris), European larch (Larixdecidua), silver fir (Abies alba) and Pinus cembra.



Demonstration

A second demonstration area located in Montsover (Province of Trento, Italy), was initially proposed for study. However TLS sample has been not recorded in this site, due that the trees for extraction had not been marked in the field when field data collection was performed. Therefore, the combination of TLS and aerial/satellite remote sensing cannot be performed.


Spatial Analysis


Spatial information is essential in order to know how the forest parameters are spatially distributed in the forest stand. Therefore, the combination of forest parameters and remote sensing must be used to study how the dendrometric parameters are distributed.

Typically, the spatial analysis is based on the following steps: 1. Location of the forest area (Area of analysis).2. Pre-processing of the image3. Zoning for analysis.4. Delineation of the forest canopy (Forest productive area).5. Tree detection (if applicable).6. Forest stratification based on tree characteristics (if applicable).7. Inventory Plan (Samples plot location).8. Generalization based on field data (parameters distribution and final stratification).



Demonstration

EO Photo UAV Photo

EO image UAV image

Source Bing Maps UAV

Resolution 0.5 m 0.1 m

Bands RGB RGB-Nir



Demonstration

DTM Slope

DTM generated from LIDAR was available for the demo area. This has a resolution of 1m. From the DTM the slope has been calculated in order to have a better understanding of the demo area.



Demonstration

Digital Height Model



Demonstration

Productive Area

EO DATA UAV DATA with DTM

DHM from UAV data allows to detect scrub areas



Demonstration

Stratification

EO DATA UAV DATA with DTM



Demonstration

Tree Counting

EO DATA UAV DATA with DTMDHM from UAV data allows to detect scrub areas



DemonstrationTree Counting EO data

PlotManual counting

Trees Detected(matches)

False detections

Over-segmented

Double crowns

MatchingValidation

rate1 20 15 1 9 2 75%2 24 20 3 4 2 83%3 21 14 0 2 3 67%4 24 15 5 2 4 63%5 20 16 1 0 2 80%

Total 109 80 10 17 13 73%



DemonstrationTree Counting - UAV with DTM

PlotManual counting

Trees Detected(matches)

False detections

Over-segmented

Double crowns

MatchingValidation

rate1 20 20 1 2 0 100%2 24 21 2 4 0 88%3 21 20 1 1 0 95%4 24 21 1 3 0 88%5 20 20 0 0 0 100%

Total 109 102 5 10 0 94%



Demonstrationy = 0.3573x2 - 2.2107x + 5.5136

R2 = 0.0335

0

1

2

3

4

5

6

0 1 2 3 4 5

Crown diameter (m)

Volume(m3)

y = 137.46x2 - 11.411x + 2.239R2 = 0.0372

0

1

2

3

4

5

6

0.0 0.1 0.1 0.2

ExRG index

Volume(m)

No correlation with volume using EO data (Vegetation index and Crown Diameter)



Demonstration

Good correlation with UAV DHM (DBH prediction from Crown Diameter) (Volume prediction from Height*Basal Area)

y = -0.0069x2 + 0.4969x - 0.0193R2 = 0.7064

0

1

2

3

4

5

6

0 5 10 15

Predicted Height * Basal Area (m3)

Volume(m3)

y = 0.618x - 1.3349R2 = 0.6215

0

1

2

3

4

5

6

0 2 4 6 8 10Crown diameter (m)

DBH(m)



Demonstration


Technical Meeting 19-20-21/jan/2015

Task 2.4 - 3D Modelling for harvesting planning



PART 1- Task goals, timeline and partecipants

PART 2 - Alpha Version of the 3d Harvesting Planner: integrating the data coming from

task 2.1,2.2,2.3;- first interactive planning tools; - first version of cable crane setup.

PART 3- Live Demo of the 3D Forest Model

Outlook



Aims

Deliverable 2.4: Harvest Simulation Tool based on 3D forest model / Software platform to visualize harvest processing

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.



TimeLine

Start Month: 7 August 2014End Month: 15 April 2015Deliverable: Harvest simulation tool based on 3D forestmodelTotal MM: 20Task leader: GRAPHITECH;Participants: CNR, COAST, BOKU, GRE, FLY, TRE



Participants role

GRAPHITECH (10MM): Task Leader. It has in charge the development ofsoftware tool for representing the virtual 3D environment of the mountain forestas well as the of the virtual system on mobile and machine-mounted displays.Finally it will be involved into the development of the solution for interactivecableway positioning simulation.

CNR (1MM): Definition of the technology layers and methodologies tocoordinate tree marking with the subsequent harvesting operations.

COASTWAY (2MM): Provide the input model taken from UAV survey datacombined with the information of task 2.1 and 2.3



Participants role

BOKU (2MM): it will be involved into definition of the technology layers (i.e.harvest parameters) for cableway positioning.

GREIFENBERG (1MM): Acting as final user in order to simulate the behaviour ofits own machines into the virtual system.

FLYBY (1MM): Provide the input model for the virtual system combining theinformation of task 2.1, 2.2 and 2.3.

TREEMETRICS (2MM): Development of the Forest Warehouse for mountainforestry and support the deployment of the virtual system. Provide input datataken form terrestrial laser scanning analysis.



Functionalities

Forestry measurements estimations:The platform will allow the combination of accurate tree profile

information with up to date remote sensing data.

Interactive tools 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-embedded displays.



Two levels of abstraction1St Level: 2D mapaccessing of forest and logistic information including:Cadastal dat, Volume of timber, accessibility of area.Where available, the system allow access to the SLOPE forest information system.2nd Level: 3D map

accessing of forest with information and visualization of the single tree and stems profile. Cable Crane positioningand tools to planharvesting area



Platform CoreCanopy surface model Laser scanner point cloud Aerial & satellite images



Integrating Data

Using the remote data (Satellite, UAVs orthophotos and digital surface model) combinedwith on field information (TLS), each single tree feature will be segmented including itsdeducted geometric properties.

Task 2.1

Task 2.2

Task 2.3

3D forest modelVirtual 3D

environment



Input DataTEST CASE- PILOT A Monte SoverRound-up of the data coming from deliverables 2.1.,2.2,2.3

Flyby

Coastway

Treemetrics



3D Harvesting Planner

Alpha version of the 3d model of forest

WebGL Based : HTML5 canvas no need for plugin, run in almost any modernbrowser

CesiumJS: extend an Open-Source library for GEO-Visualization on virtual globe (measuring tools, point clouds, cabke crane, vehicle tracking, etc.)

Serving spatial-data through Tile Map Service and Web Map Service compliant server




Visualization of Digital Surface Model of the test case pilot area

PISCINE AND SOVER:

DSM derived from UAV processed images




Visualization of Orthophoto RGB and NIR aerial images of the test case pilot area

PISCINE AND SOVER:

This data are derived from UAV processed images




Single Tree visualization and stems profile

Visualization of the trees segmentation and single tree stem profile. The data are coming form Treemetrics surveys with terrestrial laser scanner




Planning Tools for measurements

User could draw a polygonal area ora a line that will be used to take measurements useful to setup a landing zone or any other operationalarea




Cableway positioning simulation and cable crane following Greifenbergguidelines and suggestion




Cableway positioning simulation and cable crane following Greifenbergguidelines and suggestion

Typology of Cableway used by Greifenberg in alpine/mountainuousscenario

Operational lane of carriage/lifting

Catenary Simulation

Constrains on real operational scenario for cableway construction




Cable Crane Setup alpha-phase : Catenary Simulation

Setup a new cable crane by set the points of start and arrives of the cable



Future Roadmap

- Improve cable crane setup and configuration- Dynamic load of external layers- GPS vehicle tracking- Show properties for each tree- Terrain analysis tool- User interface as described within D.1.2 Human Machine Interfaces Definition

- Vegetation Index and time series analisys visualization for deeperforest/biomass analysis

- Complete Integration with the Forest Warehouse devolped by Treemetrics- Integration with the Forest Information System developed by MHG- Technology layers definitions on cable harvesting.- Integration with Road and Logistic Planning- Virtual Tree Marking



Open Discussion



Thank you for your attention

GIULIO PANIZZONI

[email protected]

Fondazione GraphitechVia Alla Cascata 56C38123 Trento (ITALY)

Phone: +39 0461.283393Fax: +39 0461.283398


mailto:[email protected]

Technical Meeting2015

Project SLOPE1

T 2.5 Road and Logistic planning

Trento, 19th-21st January, 2015

19-21/jan/

Index2

Task objectives

Work done so farSLOPE logistic modelData model

Next steps

Contact info

1. Task objectives3

Task objectives:Identify and analyze logistics elements within the forest and theircharacteristics for site locations and flow allocation decisionsIntegration of the data with the global forest modelBuild and validate an Optimization model to allocate landings and transportwood to mills and processing plants

To be developed from M8 to M13

Includes development of D2.05 Road and logistic simulation moduleDue to Month 13.

Partners involved: allITENE (leader), GRAPHITECH, CNR, BOKU, FLY

Technical Meeting19-21/jan/2015

1. Task objectives4

LOGISTIC OPTIMIZATION MODEL:

The goal is to determine an optimal forest logisticnetwork to respond future demands.

The approach would determine:Allocation of each buffer to the customer considering lowestsupply costsWhich storage yards to open and how much timber will flowvia these yards during damage events, i.e. when too muchtimber is produced within a certain period


1. Task objectives5

The forest supplynetwork consists ofseveral nodes:

Roadside stocks(buffers, B).Central storageyards (S).Customers (C),

representing asaw mill, papermill or biomassplant.


2. Work done so far6

Task activitiesby partner: ITENE

(Task leader)

- Understanding the forestry supply chain and logistic processes.- Identifying relevant logistic locations- Defining characteristics of logistics elements- Extensive bibliographic research- Coordinate the task activities- Integration with the global model- Elaboration of Data Model for the Task integration- Choosing a real scenario with real data

BOKU

- Participate in the common meetings and issues- Understanding the forestry supply chain and logistic processes.- Providing feedback of deliverables and other documents.- Extensive bibliographic research- Developing the logistic model- Programming the logistic model- Choosing a real scenario with real data- Validating model in real scenario

CNR

- Participating in the common issues- Identifying relevant logistic locations- Providing feedback of deliverables and other documents- Extensive bibliographic research- Choosing a real scenario with real data

GRAPHITECH - Participating in the common meetings and issues- Providing feedback of deliverables and other documentsFLYBY - Participating in the common meetings and issues

DoneIn progress

Future task

Technical Meeting 19-21/jan/2015



Templates to collect some data and bibliographic information



2 possible scenarios to test the model:

Italy - Alps area (maybe in Trento) CNR(Still in contact. TBC)

Austria - Austrian State Forest BOKU(Still in contact. TBC)


2.1. SLOPE logistic model9

DECISION VARIABLESxijk Volume to be transported from i to j for assortment kyi 1, if storage yard i should be openedzik 1, if a buffer i is available for delivery for assortment k

DATA VARIABLESsik Volume of wood at i for assortment kcijk Transport costs from i to j for assortment kchik Harvesting costs at i for assortment kcsmik Costs for manipulating of assortment k at storage yard idjk Demand at j for assortment kfi Fixed costs for opening storage yard i

Minimum turnover of storage yard i for assortment k

Maximum capacity of storage yard i for assortment k



OBJECTIVE FUNCTION:To supply timber at the lowest supply costs (harvesting, transportationand fixed and variable costs at storage yards):

Harvesting costs for(chi) differ because oreasons, like amoun

each bufferf several

t of timber,tree volume, extraction distanceetc.

The sum of transport costs are calculated as theca lat

volume flow multiplied by costs for each assortmentmultipli(k) and each used route.

If storage yards are opened (yi=1),fixed costs (fi) per month occur.

The sum of variable processingcosts at storage yards are

lcu ed the volume flowed by costs for

manipulating for each assortment(k).



CONSTRAINTS:

The demand of assortments k at the customer destinations j hasto be satisfied within a specific range.

On one hand the resource limits at the harvesting plots (buffers)must be kept, but on the other hand we do not want left overs ofassortments like saw logs or pulp wood.

For storage yards the wood flow must be balanced.

For considering a safety stock, a minimum turnover via storageyards can be determined.

Depending on the capacity of the storage yard, a maximumturnover per month can be defined.

The decision variable for the wood flows has to be nonnegative.

The decision variables if a storage yard (yi) will be opened and ifdelivery should be executed from the buffer (zik) can only takethe value 0 or 1.



SUPPLEMENTARY CALCULATIONS:

Harvesting costs:

Road transport costs:

Cijk Supply costs from origin (i) to destination (j) [EUR/m3]tijk

nik

sik

Drive time from origin (i) to destination (j) for assortment k [h]Loading time for assortment k [h]Unloading time for assortment k [h]Operational delay time for assortment k [h]

Cost of transport vehicle per hour per road category r [EUR/h]

Road charge from i to j [EUR]

Calculated number of trips needed to transport the total volumefrom i for assortment kVolume of wood at i for assortment kCosts for construction and maintenance of forest roads per roadcategory r [EUR/m]

CotherOther costs e.g. shifting costs for trucks, pre-concentration of rawmaterial [EUR/m3]

Chik Harvesting costs at i for assortment k [EUR/m3]

Csaw

Cyarder

Cprocessor

tvik

Cost of felling with chain saw per hour [EUR/h]Cost of extracting with tower yarder per hour [EUR/h] Costof processing with processor head per hour [EUR/h]

Average tree volume at i for assortment k [m]

distik Average extraction distance at i for assortment k [m]slik

Cmount

Cdmount

Average slope at i for assortment k [%]Costs for mounting the cable line [EUR/m3]Costs for dismounting the cable line [EUR/m3]


2.2. Data model13

BUFFER (B)

STORAGE YARDS (S)

CUSTOMERS (C)

B/C

B/S

S/K

ASSORTMENTS (K)tlktuktdk

B/S/K

B/C/K

S/C/K

B/K

C/K

S/C

Technical Meeting 19-21/jan/2015

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3. Next steps14

Implementation of the basic optimization model and test with theoretical data

Confirmation of real scenario and stakeholders involved

Collect additional requirements to the optimization model coming from realscenario stakeholders, other inputs (e.g. MHG)

Get real scenario input data for the model

Model tested with real data from real scenario

Data model to be integrated to SLOPE data model

Draft of deliverable to be sent to all partners

Feedback integration and final deliverable

Parallel sessionTo discussdeadlines andoptions to geta real scenario


4. Contact info15

Emilio [email protected]

Loli [email protected]


mailto:[email protected]:[email protected]

eting2015

16

Thanks for your attention!!!


Project SLOPESLOPE WP 2 Task 2.1Task 2.1: participantsTask 2.1: general descriptionMain ResultsIssues, delays and actionsWorking on a case studyRapidEye satellite imageryVegetation IndicesNDVI / NDRECCCIChlorophyll estimationNDVI Chl , UAV sensed dataFrom forest database to stem characterizationFrom data to information: the workflowConclusionsSlide Number 17Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28First Step - After Irish Tests Identification of The Test Sites in Piscine & MontesoverSecond Step Submit Flight Plans to ENAC (Italian Aviation Authority)WP2 Overall Progress of Tasks - Submitted and Pending deliverablesWP2 - Submitted and Pending DeliverablesWP2 Criticalities WP2 RecommendationsT2.3- TLS data and analysisD2.03 TLS data and analysisOverview of Forestry AnalysisTypes of Samples (plots and transects)Treemetrics Field Gathering AppField MeasurementField Data AnalysisTLS Analysis for Plot 2DemonstrationDemonstrationSpatial AnalysisDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationSlide Number 57OutlookAimsTimeLineParticipants roleParticipants roleFunctionalitiesTwo levels of abstractionPlatform Core Integrating DataInput Data3D Harvesting Planner 3D Harvesting Planner3D Harvesting Planner3D Harvesting Planner3D Harvesting Planner3D Harvesting Planner3D Harvesting Planner3D Harvesting PlannerFuture RoadmapOpen DiscussionThank you for your attentionProject SLOPEIndex1. Task objectives1. Task objectives1. Task objectives2. Work done so far2. Work done so far2. Work done so far2.1. SLOPE logistic model2.1. SLOPE logistic model2.1. SLOPE logistic model2.1. SLOPE logistic model2.2. Data model3. Next steps4. Contact infoSlide Number 94

Education

2nd Technical Meeting - WP2