MONITORING OF FOREST PRODUCTIVITY,

FUNCTIONALITY AND ECOSYSTEM SERVICES OF ITALIAN

FORESTS

Prof. Marco Marchetti

Research topics:• Forest and landscape ecology• Biodiversity conservation• Remote sensing • Carbon storage in forest ecosystems• Forest planning• Tree physiology• Dendrochronology• Wood technology

www.ecogeofor.unimol.it

…and the spin off, CSIG srl

www.ecogeofor.unimol.it

ONGOING

PROJECTS

forestlab.net Network

Monitoring of forest harvesting

Study area: central Italy (approximately 34,000 km2). A set of SPOT5 HRG multispectral images: years 2002–2007. Official administrative statistics of coppice clearcuts acquisition.

Monitoring of forest harvesting

More than 9500 clearcutsmapped and dated by on-screen interpretation.Various methods for semi-automatic clearcut mapping were tested by pixel- and object-oriented approaches.

Examples of comparison of SPOT5 HRG infrared images from different years evidencing clearcut areas (white delineated polygons with the

clearcut year). The images were acquired in late spring or summer 2003, 2006 and 2007. Both cases A and B are from Regione Molise.

Simulation of forest harvesting

LIFE ManForCBD: action ECo

OBJECTIVE: analyse and quantify the potential disturbances due to the forest management actions on forest landscape.

1

2

3

4

5

6

7

STUDY AREAS: seven sites in Italy and three in Slovenia (mainly beech forests).

METHOD: analysis of changes of SFM indicators related to forest spatial pattern

HOW: comparing spatial pattern under different forest management options 10 kmqareas for each site: not managed, traditional and innovative FM

TOOLS: passive satellite sensor, FLSM (forest landscape simulation model), MSPA (morphological spatial pattern analysis) and mapping tools

Simulation of forest harvesting

LIFE ManForCBD: action ECo

Site 1 – Cansiglio Segmentation and classification Forest types map

image classification

Simulation of forest harvesting

LIFE ManForCBD: action ECo

Forest types

Forest management

Forest Age

Stand IDHARVEST software

Forest types maps and other layers asinputs for forest harvesting simulation

Simulation of forest harvesting

LIFE ManForCBD: action ECo

Not harvested Traditional Innovative

ha % ha % ha %

Branch 4.10 0.41 1.50 0.15 1.90 0.19

Edge 28.63 2.86 16.63 1.66 16.54 1.65

Perforation 4.54 0.45 0.00 0.00 0.00 0.00

Islet 0.00 0.00 0.00 0.00 0.17 0.02

Core 760.94 76.09 196.37 19.64 192.76 19.28

Bridge 1.12 0.11 485.89 48.59 419.21 41.92

Loop 1.71 0.17 10.21 1.02 3.70 0.37

Non forest 197.75 19.77 288.18 28.82 364.51 36.45

a) b)

Scheme of resulting forest stand pattern following the two forest management criteria applied in two Cansiglio site plots.

Estimation of forest

parameters for wall-to-wall

mapping

Growing stock map of Molise Region

Well-known non-parametric k-Nearest Neighbours (k-NN) method is used for deriving a wall-to-wall growing stock volume map integrating optical remote sensing from IRS LISS-III (Indian Remote Sensing Satellite) image (July 2006) imagery and a field forest inventory.

Accepted: L’Italia Forestale e Montana 2013;

Estimation of forest

parameters for wall-

to-wall mapping

The growing stock volume map of Molise was converted in a forest age map on the basis of yield models applied for different groups of dominant even-aged tree species

Spatially explicit estimation of forest age integrating remotely sensed data and inverse yield modeling

techniques (submitted to i-Forest)

Ludovico Fratea, Maria Laura Carranzaa, Vittorio Garfib, Mirko Di Febbraroa, Daniela Tontic, Marco

Marchettic, Marco Ottavianoc, Giovanni Santopuolic, Gherardo Chiricib

a Envix Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy b Global Ecology Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy c Natural Resource & Environmental Planning Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone 86090 Pesche, IS, Italy

(Accepted to iForest)

Estimation of forest

parameters for wall-

to-wall mapping

Estimating and mapping forest structural diversity using AirborneLaser Scanning data

(submitted to Remote Sensing of Environment).

Mura M., McRoberts R. E., Fattorini L., Chirici G., Marchetti M.

Inference of average structural indexes values (DBH_STD and H_STD) by ALS and mapping

Indice AVG design-based AVG model-assisted

DBH_STD 6.56 ± 0.58 6.36 ± 0.06

H_STD 2.90 ± 0.17 2.93 ± 0.02

Comparison with estimation design-based

Estimation of forest

parameters for wall-

to-wall mapping

Predicting forest structural naturalness using k-NN and ALS dataSelected diversity indexes: DBH_STD, H_STD, GS)

SNI (0=min naturality; 1=max naturality):

1 −

yi1 − Y1max

Y1max +

yi2 − Y2max

Y2max + ⋯+

yin − Ynmax

Ynmax

n

DBH_STD H_STD GSMEAN SE MEAN

No. Feat. Var. Mean R2 k t SSerr R2 R2 R2 GS

5 0.595 6 -1.71 620.88 0.503 0.619 0.663

AVG SNI = 0.7379 ± 0.0118

Predicting forest structural naturalness using k-Nearest Neighbors and Airborne Laser Scanning data

(submitted to Canadian Journal of Forest Research).

Mura M., McRoberts R. E., Chirici G., Marchetti M.

Estimation of forest

parameters for wall-

to-wall mapping

Comparing echo-based and canopy height model-based metrics forenhancing estimation of forest aboveground biomass in a model-assisted framework (submitted to Remote Sensing of Environment).

Chirici, G., Mura, M., Fattorini, L., McRoberts, R., & Marchetti, M.

Predictor variablesPrediction

technique

Variables

selectedTotal estimate (t) SE(RSE) estimate 95% confidence interval

EchoesLinear 1,961,886 205,904 (10%) 1,558,314-2,365,458

k-NN 2,029,560 209,493 (10%) 1,618,954-2,440,166

CHMLinear 2,017,132 207,072 (10%) 1,611,271-2,422,993

k-NN 2,119,152 208,941 (10%) 1,709,628-2,528,676

Design-based - - 2,277,061 255,134 (11%) 1,766,793-2,787,329

Classification of LULC

Classificazione object-oriented di categorie di uso/copertura del suolo sulla base di dati ALS

G. Lopez, M. Mura, G. Chirici, M. Marchetti

Atti XVIII Conferenza Nazionale ASITA 2014

Object-oriented classification of ALS data and comparison with optical data in the LULC classification

Optical dataIRS LISS III

ALS data

Classification of LULC

Classificazione object-oriented di categorie di uso/copertura del suolo sulla base di dati ALS

G. Lopez, M. Mura, G. Chirici, M. MarchettiAtti XVIII Conferenza Nazionale ASITA 2014

OA = 80%

OA = 54%OA = 63%

• LUCC• ES• TOF

Monitoring functionality and ES

Monitoring functionality and ES

TREES OUTSIDE FOREST MAPMOLISE REGION

WIDTH > 10 Mt

WIDTH OF 1 TO 20 Mt

SURFACE >50 Mt2

LENGTH > 50 Mt

Maximum percentage values of the probability of

connectivity for node and link for both study

areas and for both landscape spatial pattern

(without and with TOF inclusion).

Study area dPCnode dPClink

Alto Molise forest 5.86551 4.25171

Alto Molise forest and TOF 7.31495 4.44020

Basso Molise forest 0.0113006 25.4541

Basso Molise forest and TOF 0.342345 25.1031

Examples of maps of the 10 links connecting the 5 largest components (A)

without TOF; (B) with inclusion of TOF. Number identify the components.

The shortest and direct pathway of connectivity for (A) landscape is from

Component 10 to Component 33. For (B) landscape is from Component 10

to Component 66.

The influence of Trees Outside Forest on the landscapeconnectivity of ecological networks: a case study in MoliseRegion.Second International Congress of Forestry - Florence 26/29November 2014.

Ottaviano M., Tonti D., Di Martino P., Chirici G., Marchetti M.

TREES OUTSIDE FOREST (LANDSCAPECONNECTIVITY)

A B

Monitoring functionality and ES

Monitoring functionality and ES

Orthophoto of an agricultural area with TOF polygons and lines

Canopy Height Model from LiDAR data

Available data: LiDAR 1x1 (2008)(at least 3 points per square meter)

TREES OUTSIDE FOREST

(REMOTE SENSING ATTRIBUTES)

LiDAR Yellowscan (L’Avion Jaune)

Laser:

Wavelength: 905 nm

Sender: Pulsed laser diode

Pulse repetition rate: 36 kHz

Pulse energy: ≤ 375 nJ

Pulse width: 4.5 ns

Returns per pulse: 3

Beam divergence: 28.6 mrad x 1.43 mrad

Angular resolution: 0.125°Scan angle (full range): 60° (100°max)

Scan rate: 100 Hz

Scan pattern: parallel

Type of scanning mirror: rotating mirror

Testing of a detection system ALS on a Ultralight

Air Vehicle

•40-50 points/m2

•1-10 points/m2 on soil

Testing of a detection system ALS on a Ultralight

Air Vehicle

Advantages and disadvantages

• Versatility/Logistics

• Autonomy – Coverage area

• Manageability - Low Flying

• Manual flight : irregular

trajectories (direction, altitude)

Testing of a detection system ALS on a Ultralight

Air Vehicle