3rd technical meeting - wp2

Project SLOPE1

WP 2 – Forest information collection and analysis

Review Meeting 19 – 21 January Corcaigh

WP2 Slope Project

WP2.2 UAV Data Acquisition and Processing

Corcaigh

January 19th- 21st 2016

Partners: Coastway /CNR / Treemetrics / FLYby


WP2 Request for Permission - AustroControl


WP2.2 October 2015 weather conditions

PrecipitationThe probability that precipitation will be observed at this location varies throughout the month. Precipitation is most likely around October 31, occurring in 58% of days. Precipitation is least likely around October 5, occurring in 53% of days. Probability of Precipitation at Some Point in the Day in October

SnowThe likelihood of snow falling is highest around October 31, occurring in 12% of days.Probability of Snow Fall Being Reported in a Given Day in October

Moderate rain is the most severe precipitation observed during 64% of those days with precipitation. It is most likely around October 2, when it is observed during 37% of all days.

Light rain is the most severe precipitation observed during 11% of those days with precipitation. It is most likely around October 15, when it is observed during 6% of all days.


WP2.2 Permission Granted 17- September 2015


WP2.2 Permission Granted 17- September 2015

Licence to fly fitted to all equipment


Annaberg Austria – Test Site


WP2.2 Ground Markers

Installation of survey targets and static GPS observations over several days


WP2.2 Calibration of Cameras

Calibration of Multispectral Camera


WP2.2 flight Plan 120m above ground level


WP2.2 Image Log from UAV


WP2.2 Canopy Model on Lidar

Test

Test combine with scan data.

Test 3 Trento Flight all data sets recorded Satellite, UAV, TLS, Marking


WP2 Proposed Trials

View models using QT Software


“Beyond the State of the Art”

The Advantages of Multispectral data over RGB & IR

multiSPEC 4C - Ultra precise 4-band accuracy

The multiSPEC 4C provides image data across four highly precise bands - Green, Red, Red-edge and NIR - with no spectral overlap. In addition, its upward-facing irradiance sensor automatically compensates for sunlight variations, resulting in unparalleled reflectance measurement accuracy.

Technical features

Resolution 4 sensors of 1.2 MpGround resolution 10 cm/px (@100m)Sensor size 4.8 x 3.6 mm per sensorPixel pitch 3.75 umImage format RAW (Tiff)


WP2 – Coastway Involvement 23.5months

RGB Data enables you to determine the species and density of growth

Budget Man Months

Total ReturnedBal

RemainingWP 1 4 4.9 -0.9WP 2 11 11.67 -0.67WP 3 0 0 0WP 4 0 0.75 -0.75WP 5 2 1.75 0.25WP 6 0 0 0WP 7 4 3.4 0.6WP 8 2 1.36 0.64WP 9 0.5 0.63 -0.13

23.5 24.46

y WP Budgets f


WP2 Problems experienced & Solutions

Bureaucracy can be a big burden on accessing forestryWeather can be a problem such as high winds / snowAccess permits are required / permission fly varies from region to region IAA, CAA, ENAC , Austro Control & the Forest Wardens.

GPS quality can be poor the accuracy varies from 250mm to several meters but suffecient for forestry new developmemts of an RTK UAV does away with the need for GCP’s

Access to the mountain forest was restricted so a small clearing was located to enable launch of the UAV.


WP2 Beyond the state of the Art - Forestry Industry

Surveying Forestry with UAV’s opens up many fields which include.

Forestry Management for planting, thinning and harvesting

Disease identification

Forest Fire monitoring

Search and rescue

Landslide monitoring


WP2 Advantages of using Multispectral Camera

Growth and yield of the forestry crop is the result of its interaction with environmental factors, soil conditions and availability of water and nutrients in the soil.

With the availability of crop-health detecting sensors, it its now possible to obtain indicator kinetics such as NDVI, biomass, chlorophyll rate, leaf area index, water stress, flowering ,on a much smaller scale than before.

Multispectral imagery can determine the health, Species, of trees

The multispectral imagery can help determine haul routes by the reflection from different soil types.

The imagery can be used to determine growth rates over several seasons and help determine where thinning is required.

Coastway have received a lot of interest from Forestry companies in the UK & Ireland regarding forestry management.


WP2.2 Multi spectral data - NDVI - Normalized Difference

Vegetation Index - Annaberg


Questions

Thank you for your attention

[email protected]

Kick-off Meeting 8-9/jan/2014

T2.03 TLS data and analysis Introduction

Work Package Number 2 Start date M4

Work Package Title Forest information collection and analysis

Activity Type RTD

Participant Name(only contributing partners) TreeMetrics

Person-monthsp-m RP2 July-October 2015 2,90

p-m total planned in DoW 10,00

10,00 person-months has been spent in this workplace since the start of the projectThe participation of Treemetrics in this work package is now completed.


TLS data and analysisThis deliverable contains a report on TLS data collected, the

methodologies and algorithm to extract needed information and the generated output information.

T2.03 TLS data and analysis Introduction


-Montsover (Province of Trento, Italy)- Norway spruce (Picea abies) mixed with European larch (Larix decidua) and silver fir (Abies alba)

T2.03 TLS data and analysis Demostration area

Overview of the Demo area


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).

T2.03 TLS data and analysis Spatial analysis


UAV Photo

UAV image

Source UAVResolution 0.1 mBands RGB-Nir



DTM

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.


UAV DSM


Digital Height Model


Similar type of trees and tree height was represented with differences up to 20m.

22m aprox.

45 m aprox.


UAV Digital Surface Model


•In very steep terrain additional ground points are required

•Digital Surface model was not used in this forest.


Tree Counting



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

T2.03 TLS data and analysis Inventory plan

Where the samples must be taken?

•Near cable crane

•All species trees types must be represented (stratification)

•Inventory costs and accessibility

•Variability of the forest stand

•Target accuracy

•Number of tree market in the TLS plot

How many samples?

Cable crane


The advantage of in field application:

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

-Pre-loaded sample area location

- Easy navigation in field for operator

- Bluetooth recording with callipers

- Stem defects recording

T2.03 TLS data and analysis Field measurement


TLS Height recording hypsometer DBH recording

T2.03 TLS data and analysis Field measurement


T2.03 TLS data and analysis Field Measurement


Tree detection and branches removal (Autostem)

T2.03 TLS data and analysis TLS 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

•Stem defects (Straightness)

T2.03 TLS data and analysis TLS analysis


T2.03 TLS data and analysis Data inference

Tree level estimation

High res.image

Tree level regression

Tree detectionTree detection TLS samples

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

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Predicted Height * Basal Area (m3)

Volume(m3)

Regression analysis



y = 15.595Ln(x) - 3.3124R2 = 0.5709

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Tree Crown (m2)

DBH (cm)

y = 0.2277x + 23.766R2 = 0.3594

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Tree Crown (m2)

Height (m)

Only the tree crown size can be used for the estimation of the tree parameters


Predicted DBH= F(crown size) Predicted Height = F(DBH)Predicted Volume = Taper equations (DBH, height)


y = -0.0023x2 + 0.5775x + 8.3554R2 = 0.7706

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Height (m)

DBH (cm)


T2.03 TLS data and analysis Known limitations

1.- Tree level inference•High resolution (lower than 30 cm) required for tree detection (e.g. UAV)•Hidden trees under forest canopy can be height (63%) in mixed stand. Ideally to

be used in even-age plantations

•Field tree locations (GPS) do not match correctly tree crown detected by UAV

(Ideally to be used in area with good GPS signal and use corrections when

required)

•Limited species detection (ideally to be used in pure stands)

•Relative good correlation between Volume and Crown/Height (around 70%)


T2.03 TLS data and analysis Implementation

AutoStemTM

TLS

Cutting simulator

SLOPEField App

TM DBManual

measurement

Field Terminal

SD card

Bluetooth

Internet

Field Site Treemetrics Service Centre

Update Slope DB SLOPE

DB

SLOPEService Centre

Treemetrics Intranet

TMProcess Control

Forest Information System

Office Site •Information display•Reports•Updates

Update API


T2.03 TLS data and analysis Implementation

•The use of TLS in forest inventory improves significantly the estimation of tree volume

•The use of UAV image (0.2cm) or medium resolution EO (0.5m) can improve significantly the field inventory planning.

•The combination of TLS and UAV image can improve significantly the estimation and location of the timber in the forest.

•The analysis of allometric relationships at tree level has result successful using UAV stereo analysis and very limited using EO data .

•The estimation of the timber in the forest need to take into the trees hidden trees

3rd technical meeting - wp2

Education