Landscape‐scale patterns of canopy gaps in the old growth 

forest of Lom,Bosnia‐Herzegovina

Matteo GarbarinoEnrico Borgogno MondinoEmanuele LinguaTom NagelVojislav DukicZoran GovedarRenzo Motta

Torino

PadovaLjubljana

Banja Luka

Canopy gaps spatial pattern

Ecological importance of canopy gaps spatial pattern and distribution:‐they drive the gap‐phase regeneration of the canopy

‐they influence stand structure and biomass

‐they influence regeneration composition & dynamics

INTRODUCTION

Canopy gaps at landscape scaleINTRODUCTION

• Gaps at landscape scale are poorly studiedMany studies are based on field data collection (time consuming) 

• Fine resolution remote sensing data are now available to perform this kind of studies

‐ Satellite images (VHR < 1m resolution)

‐ LIDAR data (0.5m resolution)

Objectives

• Evaluating  the  potential of  fine  spatial resolution  data  to  detect  gaps  at  landscape scale

• Analyzing the spatial patterns of canopy gapsin an old‐growth forest

• Assessing  the  role  of  gap  geometry in conditioning the regeneration composition

INTRODUCTION

LOM forest reserve

LOMLOM

JanjJanj

PeruciPeruciççaa

CORE AREA

Established  in 1956: Klecovaca Mountains North Western Bosnia‐Herzegovina (44.482 N & 16.427 E)Reserve: 300 ha ; Core Area: 50 ha; Buffer Zone: 250 haElevation:1250‐1522  m  a.s.l.;  Soils:  brown;  Vegetation:  Piceo‐Abieti‐Fagetum illyricum (Maunaga, 2001)

BUFFER ZONE

METHODS

Image analysis: orthoprojection

KOMPSAT‐2

Korea Multi‐Purpose SATellite‐2Resolution: 1m Pan; 4m MSAcquisition date: 11/06/2009Type: Bundle 2A (UTM/WGS84)

ASTGTM 

ASTER Digital Elevation ModelResolution: 30m

NASA and METI (Slater, 2009)Absolute accuracy: 10m

15 Ground Control PointsCollected with a GeoXM 

GPS

GPS + +

ORTHOPROJECTION ORTHOPROJECTION 

Toutin rigorous model for Kompsat‐2 data

using PCI Geomatica 10.2

METHODS

Spatial distribution of planimetric error of 15 Ground Control Points 

collected with a GeoXM GPS

GCPs Accuracy: RMSE: 1.92 m

Planimetric precision: 1.00 – 3.20 m

ORTHOIMAGE Accuracy:RMSE x: 1.15 mRMSE y: 0.69 m

RMSE Tot.: 1.35 m

Image analysis: orthoprojectionMETHODS

GCP Residuals (m)

Image analysis: ClassificationMETHODS

• Image enhancement: atmospheric correction

(radiance to apparent reflectance)

• Unsupervised pixel based classification based on Neural network  trained by the photointerpretation of18 large (> 700 m²) gaps

• Land Cover Classes: Forests; Bare soils; Fields; Gaps; Soil‐meadow mosaic

The classification was performed with ENVI EX software

Landscape analysisMETHODS

Canopy gaps map derived as vector data from LC map adopting 32 m² as Minimum Mapping Unit

‐ Landscape metrics (size and shape of the gap)

‐ Gap size distribution

‐ Spatial pattern analysis (Ripley’s K)  multi‐distance spatial cluster analysis in ArcGis 9

RESERVERESERVE

BUFFER ZONEBUFFER ZONE CORE AREACORE AREA

Stand scale: field surveys

Data collected on the field: ‐ Gap fillers & Adjacent trees

‐ Seedlings & Saplings

‐ GPS position of the centroid

‐ CWD volume (gap makers)

Direct ordination analysis (RDA)Exploring  correlations  between  regeneration  composition  and 

gaps geometric characteristics A

DJA

CEN

T

GAP FILLERS

METHODS

Orthoimage

Land Cover map

RESULTS

Canopy Gaps MapRESULTS

Landscape scale

• Landscape metrics (gaps as patches)

METRICS Unit CORE BUFFER RESERVETotal Area ha 59.14 240.36 299.50Gap Number n 102 548 650Gap Density n/ha 1.72 2.28 2.17Gap size mean m² 62.59 81.16 78.24Gap size max m² 320 1776 1776Gap size SD m² 50.00 106.72 100.17Gap Fraction % 1.08 1.85 1.70

RESULTS

Landscape scaleRESULTS

LARGEST GAPS

Gaps spatial pattern (Ripley’s K)RESULTS

RESERVEClustered in the Reserve

Clustered in the buffer zone

BUFFER ZONECORE AREA

Random within the core area

Stand scale• Regeneration composition in relation to gap size and shape (Redundancy analysis)

RESULTS

RDA - I

Explained variability % 12

Correlation 1st axis 73.3

P‐ value (Monte Carlo test) 0.004

regeneration compositiongap characteristics

1 = seedlings (H < 1m)2 = saplings (DBH < 7.5cm)

Light demanding species positively correlated to large gaps

Beech saplings slightly associated to gap fillers

The  geometrical correction of the  Kompsat‐2 image allowed to reach a  good (1.35m)  RMS error.

This landscape approach proved to be sound  in detecting canopy gap > 32 m². Smaller gaps thatproved to be important for this kind of forestsmust be studied through a  field survey (local‐scale).

DISCUSSIONS

Image processing

DISCUSSIONS

Core area VS Buffer zone

The Core area of the reserve was dominated bysmall gaps that were randomly spatiallydistributed.

The  Buffer  zone,  more  disturbed by humanactivities (harvesting & grazing), was dominatedby larger and more clustered gaps. 

The  influence of geometric characteristics ofgaps on regeneration composition emerged as animportant factor

Light demanding species (maple, and  rowan) are more common in large gaps

Future research: compare Lom with other Balcanold‐growth forests

DISCUSSIONS

Regeneration

Aknowledgments

• All the people who helped us in the field data collection:  Fabio  Meloni,  Roberta  Berretti, Miroslav  Svoboda,  Tihomir  Rugani,  Dejan Firm, Alessandra Bottero, Daniele Castagneri, Beppe Dolce

• Planet  Action  project  for  providing  the satellite  images  http://www.planet‐action.org/