MONITORING OF FOREST PRODUCTIVITY, FUNCTIONALITY AND ECOSYSTEM SERVICES OF ITALIAN FORESTS. Prof. Marco Marchetti

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1 MONITORING OF FOREST PRODUCTIVITY, FUNCTIONALITY AND ECOSYSTEM SERVICES OF ITALIAN FORESTS Prof. Marco Marchetti

2 and the spin off, CSIG srl Research topics: Forest and landscape ecology Biodiversity conservation Remote sensing Carbon storage in forest ecosystems Forest planning Tree physiology Dendrochronology Wood technology

3 ONGOING PROJECTS forestlab.net Network

4 Monitoring of forest harvesting Study area: central Italy (approximately 34,000 km 2 ). A set of SPOT5 HRG multispectral images: years Official administrative statistics of coppice clearcuts acquisition.

5 Monitoring of forest harvesting More than 9500 clearcuts mapped and dated by onscreen interpretation. Various methods for semiautomatic 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 Both cases A and B are from Regione Molise.

6 Simulation of forest harvesting LIFE ManForCBD: action ECo 3 6 OBJECTIVE: analyse and quantify the potential disturbances due to the forest management actions on forest landscape. 1 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 kmq areas for each site: not managed, traditional and innovative FM 4 TOOLS: passive satellite sensor, FLSM (forest landscape simulation model), MSPA (morphological spatial pattern analysis) and mapping tools

7 Simulation of forest harvesting LIFE ManForCBD: action ECo image classification Site 1 Cansiglio Segmentation and classification Forest types map

8 Simulation of forest harvesting LIFE ManForCBD: action ECo Stand ID Forest types maps and other layers as inputs for forest harvesting simulation HARVEST software Forest Age Forest management Forest types

9 Simulation of forest harvesting LIFE ManForCBD: action ECo a) b) Scheme of resulting forest stand pattern following the two forest management criteria applied in two Cansiglio site plots. Not harvested Traditional Innovative ha % ha % ha % Branch Edge Perforation Islet Core Bridge Loop Non forest

10 Estimation of forest parameters for wall-to-wall mapping Accepted: L Italia Forestale e Montana 2013; 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. Growing stock map of Molise Region

11 Estimation of forest parameters for wallto-wall mapping Spatially explicit estimation of forest age integrating remotely sensed data and inverse yield modeling techniques (submitted (Acceptedto to i-forest) iforest) Ludovico Frate a, Maria Laura Carranza a, Vittorio Garfi b, Mirko Di Febbraro a, Daniela Tonti c, Marco Marchetti c, Marco Ottaviano c, Giovanni Santopuoli c, Gherardo Chirici b a Envix Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone Pesche, IS, Italy b Global Ecology Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone Pesche, IS, Italy c Natural Resource & Environmental Planning Lab, Dipartimento di Bioscienze e Territorio (DiBT), Università degli Studi del Molise, c.da Fonte Lappone Pesche, IS, Italy 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

12 Estimation of forest parameters for wallto-wall mapping Inference of average structural indexes values (DBH_STD and H_STD) by ALS and mapping Estimating and mapping forest structural diversity using Airborne Laser Scanning data (submitted to Remote Sensing of Environment). Mura M., McRoberts R. E., Fattorini L., Chirici G., Marchetti M. Comparison with estimation design-based Indice AVG design-based AVG model-assisted DBH_STD 6.56 ± ± 0.06 H_STD 2.90 ± ± 0.02

13 Estimation of forest parameters for wallto-wall mapping Predicting forest structural naturalness using k-nn and ALS data Selected diversity indexes: DBH_STD, H_STD, GS) SNI (0=min naturality; 1=max naturality): 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. 1 max y i1 Y 1 max + y i2 Y 2 max Y 1 Y 2 max n + + y max in Y n max Y n DBH_STD H_STD GS MEAN SE MEAN No. Feat. Var. Mean R 2 k t SSerr R 2 R 2 R 2 GS AVG SNI = ±

14 Estimation of forest parameters for wallto-wall mapping Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a modelassisted framework (submitted to Remote Sensing of Environment). Chirici, G., Mura, M., Fattorini, L., McRoberts, R., & Marchetti, M. Predictor variables Prediction technique Variables selected Total estimate (t) SE(RSE) estimate 95% confidence interval Echoes Linear 1,961, ,904 (10%) 1,558,314-2,365,458 k-nn 2,029, ,493 (10%) 1,618,954-2,440,166 CHM Linear 2,017, ,072 (10%) 1,611,271-2,422,993 k-nn 2,119, ,941 (10%) 1,709,628-2,528,676 Design-based - - 2,277, ,134 (11%) 1,766,793-2,787,329

15 Classification of LULC Object-oriented classification of ALS data and comparison with optical data in the LULC classification 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 Optical data IRS LISS III ALS data

16 Classificazione object-oriented di categorie di uso/copertura del suolo sulla base di dati ALS Classification of LULC G. Lopez, M. Mura, G. Chirici, M. Marchetti Atti XVIII Conferenza Nazionale ASITA 2014 OA = 80% OA = 63% OA = 54%

17 Monitoring functionality and ES LUCC ES TOF

18 Monitoring functionality and ES TREES OUTSIDE FOREST MAP MOLISE REGION SURFACE >50 Mt 2 WIDTH > 10 Mt LENGTH > 50 Mt WIDTH OF 1 TO 20 Mt

19 Monitoring functionality and ES The influence of Trees Outside Forest on the landscape connectivity of ecological networks: a case study in Molise Region. Second International Congress of Forestry - Florence 26/29 November Ottaviano M., Tonti D., Di Martino P., Chirici G., Marchetti M. TREES OUTSIDE FOREST (LANDSCAPE CONNECTIVITY) A B Study area dpc node dpc link Alto Molise forest Alto Molise forest and TOF Basso Molise forest Basso Molise forest and TOF 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). 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.

20 Monitoring functionality and ES TREES OUTSIDE FOREST (REMOTE SENSING ATTRIBUTES) Canopy Height Model from LiDAR data Available data: LiDAR 1x1 (2008) (at least 3 points per square meter) Orthophoto of an agricultural area with TOF polygons and lines

21 Testing of a detection system ALS on a Ultralight Air Vehicle 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: Scan angle (full range): 60 (100 max) Scan rate: 100 Hz Scan pattern: parallel Type of scanning mirror: rotating mirror

22 Testing of a detection system ALS on a Ultralight Air Vehicle points/m points/m 2 on soil

23 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)

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